Method of treating an inflammatory disease by administering an agent which binds a surface receptor on a tuft cell that induces an ILC class 2 inflammatory response

An atlas of intestinal epithelial cells, intestinal epithelial stem cells and intestinal immune cells identifies new cell populations, markers, networks, and responses to stimuli. Intestinal epithelial cell sub-types are also found in the trachea. Accordingly, disclosed are methods of modulating epithelial cell differentiation, maintenance and/or function, related methods for the treatment of disease, including IBD and asthma. Also disclosed are methods and kits for identifying cell types, their differentiation, homeostasis and activation.

1. 11. A method for treating an inflammatory disease in a subject, comprising: (i) detecting the presence of epithelial tuft cells by expression of a group of genes or polypeptides selected from the groups consisting of: a) Lrmp, Dclk1, Cd24a, Tas1r3, Ffar3, Sucnr1, Gabbr1, Drd3, Etv1, Gfi1b, Hmx2, Hmx3, Runx1, Jarid2, Nfatc1, Zp710, Zbtb41, Spib, Foxe1, Sox9, Pou2f3, Ascl2, Fhf Tcf4, Gprc5c, Sucnr1, Ccrl1, CGprc5a, Opn3, Vmnn2r26 and Tas1r3; b) Cd24a, Tas1r3, Ffar3, Sucnr1, Gabbr1 and Drd3: c) Etv1, Gfi1b, Hmx2, Hmx3, Runx1, Jarid2, Nfatc1, Zfp710, Zbtb41, Spib, Foxe1, Sox9, Pou2f3, Ascl2, Ehf and Tcf4; d) Etv1, Hmx2, Spib, Foxe1, Sox9, Pou2/3, Ascl2, Ehf and ift4; e) Ffar3, Gprc5c, Sucnr1, Ccrl1, Gprc5a, Opn3, Vmn2r26 and Tas1r3; f) Etv1, Hnx2, Spib, Foxe1, Pou2f3, Sox9, Ascl2, Hoxa5, Hivep3, Ehf, Tcf4, Mxd4, Hmx3, Hoxa3 and Nfatc1; g) Lrmp, Gnat3, Gnb3, Plac8, Trpm5, Gng13, Ltc4s, Rgs13, Hck, Alox5ap, Avil, Alox5, PRtpn6, Atp2a3 and Plk2; and h) Rgs13, Rpl41, Pps26, Zmiz1, Gpx3, Suox, Tslp and Socs1; (ii) contacting the epithelial tuft cells by administering to the subject a tuft cell modulating agent selected from a therapeutic antibody antagonist or a fragment thereof capable of binding to a surface receptor on the tuft cell in an amount sufficient to reduce the inflammatory response of epithelial tuft cells, wherein the surface receptor is capable of inducing an ILC class 2 inflammatory response.
2. 22. The method of claim 1, wherein the tuft cell modulating agent comprises an agent capable of modulating the expression or activity of a transcription factor selected from the group consisting of Etv1, Hmx2, Spib, Foxe1, Pou2f3, Sox9, Ascl2, Hoxa5, Hivep3, Ehf, Tcf4, Mxd4, Hmx3, Hoxa3 and Nfatc1.
3. 33. The method of claim 1, wherein the agent is administered to a mucosal surface.
4. 44. The method of claim 1, wherein the epithelial tuft cells are detected using a technique selected from the group consisting of RT-PCR, RNA-seq, single cell RNA-seq, western blot, ELISA, flow cytometry, mass cytometry, fluorescence activated cell sorting, fluorescence microscopy, affinity separation, magnetic cell separation, microfluidic separation, and combinations thereof.
5. 55. The method of claim 1, wherein the epithelial tuft cells comprise: a respiratory tuft cell, a gastrointestinal tuft cell, a subset of gastrointestinal tuft cells, or a subset of respiratory tuft cells.

CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a national phase of International Application No. PCT/US2018/027388, filed Apr. 12, 2018, which claims the benefit of U.S. Provisional Application No. 62/484,746, filed Apr. 12, 2017. The entire contents of the above-identified applications are hereby fully incorporated herein by reference.


STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH
This invention was made with government support under Grant Nos. OD020839, DK114784, DK043351 and DK097485 awarded by the National Institutes of Health. The government has certain rights in the invention.


TECHNICAL FIELD
The subject matter disclosed herein is generally directed to compositions and methods for modulating, controlling or otherwise influencing epithelial cell differentiation, homeostasis and activation in the gut and respiratory system. This invention also relates generally to identifying and exploiting target genes and/or target gene products that modulate, control or otherwise influence epithelial cell differentiation, homeostasis and activation in a variety of therapeutic and/or diagnostic indications. This invention also relates generally to a gut and trachea atlas identifying novel cell types and markers for detecting, quantitating and isolating said cell types.
BACKGROUND
The functional balance between the epithelium and the constituents within the lumen plays a central role in both maintaining the normal mucosa and in disease. Intestinal epithelial cells (IECs) of the small intestinal epithelium comprise two major lineages—absorptive and secretory1—reflecting its dual roles to absorb nutrients and form a flexible barrier, monitoring and titrating responses to a variety of noxious substances or pathogens2. Enterocytes of the absorptive lineage comprise approximately 80% of the epithelium and are specialized for digestion and transport of nutrients3. The secretory lineage comprises five further terminally differentiated types of IECs: goblet, Paneth, enteroendocrine, tuft and microfold (M) cells4-6—each with distinct and specialized sensory and effector functions.
The epithelium is organized in a repeating structure of villi, which project toward the lumen, and nearby crypts (FIG. 1a). The crypts of the small intestine are the proliferative part of the epithelium, in which intestinal stem cells (ISCs) and progenitors, termed transit-amplifying cells (TAs), reside6,7. In contrast, only fully differentiated cells are found on the villi2,7. The crypt also contains Paneth cells, which secrete anti-microbial peptides (AMPs), such as defensins and lysozyme, into the lumen to keep the microbiota in check8,9. The highly proliferative TA cells migrate along the crypt-villus axis and differentiate into functionally distinct epithelial cell types that subsequently reach the tip of the villus, where mature cells undergo apoptosis and shed to the lumen1.
Epithelial tissue turns over rapidly (˜5 days)8, allowing it to dynamically shift its composition in response to stress or pathogens. For example, parasitic infection typically induces hyperplasia of goblet cells, which produce and secrete mucins to prevent pathogen attachment, strengthening the epithelial barrier and facilitating parasite expulsion10. Rare (0.5-1%) enteroendocrine cells (EECs) secrete over 20 individual hormones and are key mediators of intestinal response to nutrients11,12 by directly detecting fluctuations in luminal nutrient concentrations via G-protein-coupled receptors (GPCRs)11. Finally, IECs communicate with immune cells to initiate either inflammatory responses or tolerance in response to lumen signals2,13. Tuft cells5, a rare IEC population, promote type-2 immunity in response to intestinal parasites by expressing interleukin-25 (Il25), which in turn mediates the recruitment of group 2 of innate lymphoid cells (ILC2s) that initiate the expansion of T-helper type 2 (Th2) cells upon parasite infection14-16. M cells, which reside exclusively in follicle-associated epithelia (FAE)17, play an important role in immune sensing by transporting luminal content to immune cells found directly below them18 in Peyer's patches, gut associated lymphoid follicles. Disruption in any of the major innate immune sensors and proximity effector functions of IECs may result in increased antigenic load through weakening of the epithelial barrier, and may lead to the onset of acute or chronic inflammation.
Despite this extensive knowledge, given the complexity of the epithelial cellular ecosystem, many questions remain open. First, do we know all the discrete epithelial cell types of the gut, or are there additional types, or new sub-types that have eluded previous studies. Second, what are the molecular characteristics of each type. For example, mapping the GPCRs and hormones expressed by EECs has important therapeutic applications; charting known and new specific cell surface markers would provide handles for specific cell isolation, and help assess the validity of legacy ones; and finding differentially expressed transcription factors (TFs) will open the way to study the molecular processes that accompany the differentiation of IECs, such as tuft or enteroendocrine cells. Third, we still know little about the response of individual cell populations to pathogenic insult, both in terms of changes in cellular proportions and cell-intrinsic responses.
A systematic atlas of single-cell RNA profiles can help address these questions, as the gene-expression program of a given cell closely reflects both its identity and function19,20 Most previous studies have examined the gene-expression profiles of IECs, but relied on known markers to purify cell populations6, 15, 21, 22, which may isolate either a mixed population if marker expression is more promiscuous than assumed, or a subset of a larger group if overly specific. They may further fail to detect rare cellular populations or intermediate, transient states on a continuum. A recent study23 attempted to overcome these limitations using single-cell RNAseq (scRNA-seq), but analyzed only several hundred single cells, which may be insufficient to address the diversity of IECs, especially for subtypes that occur at a frequency of less than 0.1%11,12 Additional, studies53, 30, 145 also attempted to overcome these limitations using single-cell RNAseq (scRNA-seq). All of these studies have not yet extensively characterized intestinal epithelial cellular diversity.
The intestinal mucosa maintains a functional equilibrium with the complex luminal milieu, which is dominated by a spectrum of gut microbial species and their products. The functional balance between the epithelium and the lumen plays a central role in maintaining the normal mucosa and in the pathophysiology of many gastrointestinal disorders2. To maintain barrier integrity and tissue homeostasis in response to immune signals and luminal contents2, the gut epithelium constantly regenerates by rapid proliferation and differentiation149. This process is initiated by intestinal stem cells (ISCs), which give rise to committed progenitors that in turn differentiate to specific IEC types103,39.
ISC differentiation depends on external signals from an ecosystem of non-epithelial cells in the gut niche. In particular, canonical signal transduction pathways, such as Wnt and Notch113,114, are essential to ISC maintenance and differentiation, and rely on signals from stromal cells115,150. The intestinal tract is also densely populated by innate and adaptive immune cells, which maintain the balance between immune activation and tolerance2,151. However, it is unknown if and how immune cells and the adjacent ISCs interact.
Several studies suggest an important role for immune cells in tissue homeostasis. Tissue-resident innate immune cells, such as macrophages and type 3 innate lymphoid cells (ITLC3s), can play a role in regeneration of the gut115,116 and other tissues117,119. Among adaptive immune cells, recent studies have implicated T regulatory cells (Tregs) in regeneration within muscles, lungs, and the central nervous system118, 152, 153 Skin-resident Tregs were very recently shown to be involved in maintaining hair follicle stem cell (HFSC) renewal through Jagged1-mediated Notch signaling154. In the gut, mouse models of intestinal infection, T cell depletion, and inflammatory bowel disease (IBD) all display aberrant epithelial cell composition, such as goblet cell hypoplasia or tuft cell expansion13, 14, 155. These phenotypes have been primarily interpreted as reflecting intestinal epithelial cell dysfunction and changes in gut microbial populations13, 151, 156, 157.
The small intestinal mucosa is a complex system. The mucosa comprises multiple cell types involved in absorption, defense, secretion and more. These cell types are rapidly renewed from intestinal stem cells. The types of cells, their differentiation, and signals controlling differentiation and activation are poorly understood. The small intestinal mucosa also possesses a large and active immune system, poised to detect antigens and bacteria at the mucosal surface and to drive appropriate responses of tolerance or an active immune response. Finally, there is complex luminal milieu which comprises a combination of diverse microbial species and their products as well as derivative products of the diet. It is increasingly clear that a functional balance between the epithelium and the constituents within the lumen plays a central role in both maintaining the normal mucosa and the pathophysiology of many gastrointestinal disorders. Many disorders, such as irritable bowel disease, Crohn's disease, and food allergies, have proven difficult to treat. The manner in which these multiple factors interact remains unclear. Furthermore, studying the small intestinal mucosa can provide insight into the mucosa of the respiratory system. Airways conduct gases to the distal lung and are the sites of disease in asthma and cystic fibrosis.
SUMMARY
In one aspect, the present invention provides for an isolated tuft cell characterized in that the tuft cell comprises expression of any one or more genes or polypeptides selected from the group consisting of: a) Lrmp, Dclk1, Cd24a, Tas1r3, Ffar3, Sucnr1, Gabbr1, Drd3, Etv1, Gfi1b, Hmx2, Hmx3, Runx1, Jarid2, Nfatc1, Zfp710, Zbtb41, Spib, Foxe1, Sox9, Pou2f3, Ascl2, Ehf Tcf4, Gprc5c, Sucnr1, Ccrl1, Gprc5a, Opn3, Vmn2r26 and Tas1r3; or b) Cd24a, Tas1r3, Ffar3, Sucnr1, Gabbr1 and Drd3; or c) Etv1, Gfi1b, Hmx2, Hmx3, Runx1, Jarid2, Nfatc1, Zfp710, Zbtb41, Spib, Foxe1, Sox9, Pou2f3, Ascl2, Ehf and Tcf4; or d) Etv1, Hmx2, Spib, Foxe1, Sox9, Pou2f3, Ascl2, Ehf and Tcf4; or e) Ffar3, Gprc5c, Sucnr1, Ccrl1, Gprc5a, Opn3, Vmn2r26 and Tas1r3; or f) Etv1, Hmx2, Spib, Foxe1, Pou2f3, Sox9, Ascl2, Hoxa5, Hivep3, Ehf Tcf4, Mxd4, Hmx3, Hoxa3 and Nfatc1; g) or Lrmp, Gnat3, Gnb3, Plac8, Trpm5, Gng13, Ltc4s, Rgs13, Hck, Alox5ap, Avil, Alox5, Ptpn6, Atp2a3 and Plk2; or h) Rgs13, Rpl41, Rps26, Zmiz1, Gpx3, Suox, Tslp and Socs1; or i) tuft cell marker genes in any of Tables 3-6 or 15A.
In one embodiment, the tuft cell may be an immune-like tuft cell and the cell may further comprise expression of any one or more genes or polypeptides selected from the group consisting of: a) Ptprc (CD45) and Tslp; or b) Siglec5, Rac2, Ptprc, Sf6galnac6, Tm4sf4, Smpx, Ptgs1, C2, Gde1, Cpv1, S100a1, Fcna, Fbxl21, Ceacam2, Sucnr1, Spa17, Kcnj16, AA467197, Cd300lf Trim38, Vmn2r26, Gcnt1, Irf7, Plk2, Glyctk and Tslp; or c) Lyn, Rhog, Il17rb, Irf7 and Rac2; or d) tuft-2 cell marker genes in Table 8.
In one embodiment, the tuft cell may be a neuronal-like tuft cell and the cell may further comprise expression of any one or more genes or polypeptides selected from the group consisting of: a) Nrep, Nradd, Ninj1, and Plekhg5; or b) Nradd, Endod1, Gga2, Rbm38, Sic44a2, Cbr3, Ninj1, Mblac2, Usp11, Sphk2, Atp4a, Uspl1, Mical1, Mta2, Inpp5j, Svil, Kcnn4, Dnahc8, Anxa11, Zjhx3, Lnpp5b, Tip3, Jup, and St5; or c) tuft-1 cell marker genes in Table 8.
The tuft cell may be a gastrointestinal tuft cell or subset of a gastrointestinal tuft cell, or a respiratory tuft cell or a subset of respiratory tuft cells. The tuft cell may be a respiratory or digestive system tuft cell. The digestive system tuft cell may comprise an esophageal epithelial cell, a stomach epithelial cell, or an intestinal epithelial cell. The respiratory tuft cell may comprise a laryngeal epithelial cell, a tracheal epithelial cell, a bronchial epithelial cell, or a submucosal gland cell.
In another aspect, the present invention provides for a method of detecting tuft cells in a biological sample, comprising determining the expression or activity of any one or more genes or polypeptides selected from the group consisting of: a) Lrmp, Dclk1, Cd24a, Tas1r3, Ffar3, Sucnr1, Gabbr1, Drd3, Etv1, Gfi1b, Hmx2, Hmx3, Runx1, Jarid2, Nfatc1, Zfp710, Zbtb41, Spib, Foxe1, Sox9, Pou2f3, Ascl2, Ehf Tcf4, Gprc5c, Sucnr1, Ccrl1, Gprc5a, Opn3, Vmn2r26 and Tas1r3; or b) Cd24a, Tas1r3, Ffar3, Sucnr1, Gabbr1 and Drd3; or c) Etv1, Gfi1b, Hmx2, Hmx3, Runx1, Jarid2, Nfatc1, Zfp710, Zbtb41, Spib, Foxe1, Sox9, Pou2f3, Ascl2, Ehf and Tcf4; or d) Etv1, Hmx2, Spib, Foxe1, Sox9, Pou2f3, Ascl2, Ehf and Tcf4; or e) Ffar3, Gprc5c, Sucnr1, Ccrl1, Gprc5a, Opn3, Vmn2r26 and Tas1r3; or f) Etv1, Hmx2, Spib, Foxe1, Pou2f3, Sox9, Ascl2, Hoxa5, Hivep3, Ehf Tcf4, Mxd4, Hmx3, Hoxa3 and Nfatc1; or g) Lrmp, Gnat3, Gnb3, Plac8, Trpm5, Gng13, Ltc4s, Rgs13, Hck, Alox5ap, Avil, Alox5, Ptpn6, Atp2a3 and Plk2; or h) Rgs13, Rpl41, Rps26, Zmiz1, Gpx3, Suox, Tslp and Socs1; or i) tuft cell marker genes in any of Tables 3-6 or 15A, whereby said expression indicates tuft cells. The tuft cells may be immune-like tuft cells and the method may further comprise detecting the expression of any one or more genes or polypeptides selected from the group consisting of: a) Ptprc (CD45) and Tslp; or b) Siglec5, Rac2, Ptprc, Sf6galnac6, Tm4sf4, Smpx, Ptgs1, C2, Gde1, Cpv1, S100a1, Fcna, Fbxl21, Ceacam2, Sucnr1, Spa17, Kcnj16, AA467197, Cd300lf Trim38, Vmn2r26, Gcnt1, Irf7, Plk2, Glyctk and Tslp; or c) Lyn, Rhog, Il17rb, Irf7 and Rac2; or d) tuft-2 cell marker genes in Table 8. The tuft cells may be neuronal-like tuft cells and the method may further comprise detecting the expression of any one or more genes or polypeptides selected from the group consisting of: a) Nrep, Nradd, Ninj1, and Plekhg5; or b) Nradd, Endod1, Gga2, Rbm38, Sic44a2, Cbr3, Nmj1, Mblac2, Usp11, Sphk2, Atp4a, Uspl1, Mical1, Mta2, Inpp5j, Svil, Kcnn4, Dnahc8, Anxa11, Zjhx3, Lnpp5b, Tip3, Jup, and St5; or c) tuft-1 cell marker genes in Table 8. The tuft cell may be a gastrointestinal tuft cell or subset of a gastrointestinal tuft cell, or a respiratory tuft cell or subset of respiratory tuft cells. The tuft cell may be a respiratory or digestive system tuft cell. The digestive system tuft cell may comprise an esophageal epithelial cell, a stomach epithelial cell, or an intestinal epithelial cell. The respiratory tuft cell may comprise a laryngeal epithelial cell, a tracheal epithelial cell, or a bronchial epithelial cell. The expression or activity of one or more genes or polypeptides may be detected in a bulk sample, whereby a gene signature is detected by deconvolution of the bulk data.
In another aspect, the present invention provides for a method for detecting or quantifying tuft cells in a biological sample of a subject, the method comprising: a) providing a biological sample of a subject; and b) detecting or quantifying in the biological sample tuft cells as defined herein. The biological sample may be a biopsy sample and wherein quantifying may comprise staining for one or more tuft cell genes or polypeptides.
In another aspect, the present invention provides for a method for isolating tuft cells from a biological sample of a subject, the method comprising: a) providing a biological sample of a subject; and b) isolating from the biological sample tuft cells as defined herein. The isolating may comprise labeling one or more surface markers and sorting cells in the biological sample. The sorting may be by FACS. The isolating may comprise binding an affinity reagent to one or more surface markers expressed on cells in the biological sample. The affinity reagent may be an antibody coated magnetic bead.
In another aspect, the present invention provides for a method for modulating epithelial cell proliferation, differentiation, maintenance, and/or function, the method comprising contacting an epithelial tuft cell or a population of epithelial tuft cells as defined herein with a tuft cell modulating agent in an amount sufficient to modify proliferation, differentiation, maintenance, and/or function of the epithelial tuft cell or population of epithelial tuft cells. The tuft cell may be an immune-like tuft cell. The tuft cell may be a neuronal-like tuft cell. Not being bound by a theory, the present invention allows for these previously unknown tuft cells to be targeted specifically. The epithelial tuft cell may be a laryngeal epithelial cell, a tracheal epithelial cell, a bronchial epithelial cell, a submucosal gland cell, a gut epithelial cell, an intestinal epithelial cell, or an esophageal epithelial cell. The modulating of the epithelial cell proliferation, differentiation, maintenance, and/or function may comprise modulating inflammation. The inflammation may comprise an ILC2 inflammatory response. The modulating may be for the treatment of asthma (e.g., allergic asthma, therapy resistant-asthma, steroid-resistant severe allergic airway inflammation, systemic steroid-dependent severe eosinophilic asthma, chronic rhino-sinusitis (CRS)), bronchitis, cystic fibrosis, infection (e.g., pneumonia or tuberculosis), emphysema, lung cancer, pulmonary hypertension, chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, α-1-anti-trypsin deficiency, congestive heart failure, atopic dermatitis, food allergy, chronic airway inflammation, or primary eosinophilic gastrointestinal disorder (EGID) (e.g., eosinophilic esophagitis (EoE), eosinophilic gastritis, eosinophilic gastroenteritis, and eosinophilic colitis) in a subject in need thereof. The modulating may comprise inhibiting the activity of a tuft cell.
The tuft cell modulating agent may comprise a therapeutic antibody, antibody fragment, antibody-like protein scaffold, aptamer, protein, genetic modifying agent or small molecule. The tuft cell modulating agent may comprise an agent capable of binding to a surface receptor on the tuft cell. The agent may block activation of the surface receptor. The agent may block binding of a ligand to the surface receptor. The agent may be a blocking antibody. The tuft cell modulating agent may comprise an agent capable of modulating the expression or activity of a transcription factor selected from the group consisting of Etv1, Hmx2, Spib, Foxe1, Pou2f3, Sox9, Ascl2, Hoxa5, Hivep3, Ehf, Tcf4, Mxd4, Hmx3, Hoxa3 and Nfatc1. The agent may be administered to a mucosal surface. The agent may be administered to the lung, nasal passage, trachea, gut, intestine, or esophagus. The agent may be administered by aerosol inhalation. The agent may be administered by swallowing.
In another aspect, the present invention provides for a kit comprising reagents to detect at least one tuft cell gene or polypeptide as described herein. The kit may comprise at least one antibody, antibody fragment, or aptamer. The kit may comprise primers and/or probes for quantitative RT-PCR or fluorescently bar-coded oligonucleotide probes for hybridization to RNA (see e.g., Geiss G K, et al., Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat Biotechnol. 2008 March; 26(3):317-25).
The ability to identify cell types, metabolic state, cycling state and the like has many utilities—for example, identifying the source of a cancer cell type; identifying disease states; screening for drug effects; and applied and basic research.
In another embodiment provided is a method for identifying tuft cells in a sample, comprising detecting expression of any one or more of Cd24a, Tas1r3, Ffar3, Sucnr1, Gabbr1 or Drd3 protein or mRNA, wherein the expression indicates tuft cells. Such a method may further comprise detecting expression of any one or more of Ptprc or Tslp protein or mRNA, wherein the expression indicates a subset of tuft cells, and may further comprise detecting expression of any one or more of Nrep, Nradd, Ninj1, and Plekhg5 protein or mRNA, wherein the expression indicates a subset of tuft cells.
In another embodiment provided is an isolated gastrointestinal tract cell characterized by expression of one or markers for a cell type selected from any of Tables 3 to 10 or 15 A to D.
In another embodiment provided is a method for detecting or quantifying gastrointestinal tract cells in a biological sample of a subject, the method comprising detecting or quantifying in the biological sample gastrointestinal tract cells as defined in herein. The gastrointestinal tract cell may be detected or quantified using one or more markers for a cell type selected from any of Tables 3 to 10 or 15 A to D.
In another embodiment provided is a method of isolating a gastrointestinal tract cell from a biological sample of a subject, the method comprising isolating from the biological sample gastrointestinal tract cells as defined herein. The gastrointestinal tract cell may be isolated using one or more surface markers for a cell type selected from any of Tables 3 to 10 or 15 A to D.
The gastrointestinal tract cells may be isolated, detected or quantified using a technique selected from the group consisting of RT-PCR, RNA-seq, single cell RNA-seq, western blot, ELISA, flow cytometry, mass cytometry, fluorescence activated cell sorting, fluorescence microscopy, affinity separation, magnetic cell separation, microfluidic separation, and combinations thereof.
The ability to identify cell types, metabolic state, cycling state and the like has many utilities—for example, identifying the source of a cancer cell type; identifying disease states; screening for drug effects; and applied and basic research.
These and other aspects, objects, features, and advantages of the example embodiments will become apparent to those having ordinary skill in the art upon consideration of the following detailed description of illustrated example embodiments.



BRIEF DESCRIPTION OF THE DRAWINGS
FIGS. 1A-G—A single-cell expression atlas of intestinal epithelial cells. FIG. 1A shows a schematic overview. Two complementary scRNA-seq methods used to create a high-resolution atlas of the mouse small intestinal epithelium. FIG. 1B shows cell type clusters. t-distributed stochastic nearest-neighbor embedding (tSNE) visualization of 7,216 single cells. Individual points correspond to single cells shaded by their assignment to clusters using a k-nearest neighbor (kNN) graph-based algorithm (see Methods). Although EECs are classified as a single group by clustering, the tSNE embedding separates out the enterochromaffin subset (small left-hand cluster, top of figure). This heterogeneity is fully characterized in (FIG. 3). Legend shows the cluster post-hoc annotation to cell types. FIG. 1C shows cell type-specific signatures. Heatmap shows the relative expression level (row-wise Z-score of log2(TPM+1) expression values, (bar) of genes (rows) in high confidence cell-type-specific signatures based on both full-length and 3′ scRNA-seq data, across the individual post-mitotic IECs (columns). Shading code marks the cell types and their associated signatures. FIGS. 1D-E show Mptx2 is a novel Paneth cell marker. (D) Shown is combined single-molecule fluorescence in situ hybridization (smFISH) with immunofluorescence assay (IFA) of FFPE sections of Mptx2 co-stained with the canonical Paneth cell Lyz1 protein marker. Scale bar, 20 μm. (E) In situ hybridization (ISH) of Mptx2 at lower magnification. Scale bar, 50 μm. FIGS. 1F-G show cell type-specific transcription factors (TFs) and G protein-coupled receptors (GPCRs). Heatmaps depict the average relative expression (Z-score of mean log2(TPM+1), bar) of the top 10 TFs (F) and GPCRs (G) (columns) that are specifically expressed in the cells of each IEC type (rows) based on the higher depth, full-length scRNA-seq data.
FIGS. 2A-F—Differentiation from stem cells to mature enterocytes. FIG. 2A shows gene signature-based embedding of the IEC lineage. Shown are 7,216 single IECs (see main text and Methods) positioned by signature scores for key cell types: the difference between the signature scores for tuft and enteroendocrine cells (x-axis); between enterocyte and goblet cell scores (y-axis), and the stem cell score (z-axis). Each signature score was computed using 50 genes (Methods). Cells are shaded by expression levels of the stem cell marker Lgr5 (left), cell-cycle gene set (center), and the enterocyte marker Alpi (right). FIGS. 2B-E show diffusion-map embedding of 5,282 cells progressing through stages of enterocyte differentiation (Methods). (B-C) Cells are shaded by their cluster assignment (FIG. 1B). Diffusion component 1 and 3 (DC-1 and DC-3) are associated with the transition from stem cells to progenitors (B), while DC-2 distinguishes between proximal and distal enterocyte fate commitment (C). (D-E) Cells are shaded by the expression (log2(TPM+1), bar) of known and novel TFs associated with stages of differentiation (D), or with proximal or distal enterocyte differentiation (E). FIG. 2F shows the top 10 markers for absorptive and secretory IECs. Heatmap shows the mean expression level (bar, Log2(TPM+1)) for genes (rows) in cells in the two subsets (columns).
FIGS. 3A-F—Novel classification of rare enteroendocrine subtypes. FIG. 3A shows type discovery by unsupervised clustering. Shown is a tSNE embedding of the 533 enteroendocrine cells (EECs) from the droplet-based dataset. Cells are numbered and shaded based on the 12 clusters determined through kNN-graph based clustering (Methods), and labeled by post-hoc analysis based on known genes (B-C). FIG. 3B shows EEC subtype signatures. Heatmap of the relative expression level (row-wise Z-scores, bar) of the most specific (FDR<0.01, log2(fold change) >0.1) genes (rows) for the cells (columns) in each of the 12 detected clusters (coded as in A). FIG. 3C shows marker based classification of EECs. Violin plots show the distribution of expression (log2(TPM+1)) of genes (columns) encoding major EEC TFs, markers genes, and hormones in the cells (dots) from each of the 12 subtype clusters (rows), coded as in A. Grey bars indicate traditional nomenclature for EEC subtypes based on hormone expression (S, I, L, K, A). FIG. 3D shows smFISH of the co-expression of gut hormones Cck (“I”), Ghrl (“A”) and Gcg (“L”) by individual EECs. Scale bar, 50 μm. Inset (×5) of triple positive SILA cell FIG. 3E shows distribution of EEC subtypes in different SI regions. Proportion (y axis) of each EEC subset in cells sampled from each of three regions of the small intestine, duodenum, jejunum and ileum (legend) in each mouse (dots, n=2 mice per region). Error bars: standard error of the mean (SEM). (*FDR<0.25, **FDR<0.1, ***FDR<0.01, χ2 test, Methods) FIG. 3F shows combined smFISH and IFA of enterochromaffin cells with Reg4 (left) and Tph1 (middle) co-stained with ChgA antibody (right). Scale bar, 20 μm.
FIGS. 4A-H—A CD45-positive subset of tuft cells expresses the epithelial cytokine TSLP. FIG. 4A shows tuft cell subsets. tSNE embedding of 166 tuft cells from the droplet-based dataset (FIG. 1B). Cells are shaded by their subtype assignment based on kNN-graph-clustering (Methods), and annotatedpost-hoc (legend, top right). FIG. 4B shows gene signatures for Tuft-1 and Tuft-2 cells. Heatmap shows the relative expression (row-wise Z-scores, −bar) of the consensus marker genes for Tuft-1 and Tuft-2 cells (rows) across single cells from the droplet-based dataset (columns) assigned to Tuft-1 and Tuft-2 cell clusters. The top 25 genes are shown, all FDR<0.01 and log 2 fold change >0.1 in both plate- and droplet-based datasets). FIG. 4C shows TSLP expression in Tuft-2 cells. Violin plots show the distribution of expression of epithelial cytokines (1125, left; 1133, middle; TSLP: right) in the cells (dots) in enterocytes, Tuft-1- and Tuft-2 subsets, in full-length scRNA-seq data. Both tuft cell subsets express 1125, but TSLP is enriched in the Tuft-2 subset. (*FDR<0.1, ***FDR<0.0001, Mann-Whitney U-test). FIGS. 4D-E shows validation of high TSLP expression by Tuft-2 cells. (D) Combined smFISH and IFA of TSLP co-stained with DCLK1, scale bar 10 μm. (E) qPCR (y axis, relative quantification compared to Tuft-2 group) of Alpi (enterocyte marker), TSLP and Dclk1 (tuft cell markers) from cells defined as Tuft-1, Tuft-2 or randomly selected single cells from processed plates of the full-length scRNA-seq data (16 cells per group). (*p<0.05, **p<0.005, t-test). FIG. 4F shows high expression of Ptprc (CD45) by Tuft-2 cells. Violin plots show the distribution of expression of Cd14 (top-left), EpCAMf (top-right), Dclk1 (bottom-left) and Ptprc (CD45; bottom-right) in the cells (dots) of enterocyte, Tuft-1 and Tuft-2 subsets as well as monocytes based on the deeper-coverage full-length scRNA-seq data. FIG. 4G shows validation of CD45 expression by tuft cells. Top left: smFISH imaging ofPtprc (encoding CD45) co-stained with DCLK1 antibody. Scale bar 50 μm. Top right: Distribution of CD45 protein levels within Gfi1b-GFP labeled cells, compared to background (light grey) and monocytes (dark grey) based on FACS. Bottom: IFA co-staining of DCLK1, Gfi1b-GFP and CD45 within the same tuft cell. Scale bar 15 μm. FIG. 4H shows isolation of Tuft-2 cells using FACS based on CD45 expression. Proportion (y axis) of detected Tuft-1 and Tuft-2 cells (shaded as in a-f) in 3′ droplet scRNAseq data (n=3 pooled mice) from cells sorted using EpCAM alone (left) or using EpCAM and CD45 (right) (*p<0.05, *** p<0.0005, hypergeometric test).
FIGS. 5A-F—Microfold (M) cell-specific gene signatures. FIG. 5A shows Tuft-2 cells express a higher level of known M cell genes. tSNE embedding of 101 tuft cells (squares: Tuft-1; circles: Tuft-2) extracted from full-length scRNA-seq data (FIG. 8A). Cells are shaded by their relative score (bar, Methods) for the expression of 20 known M cell genes17. FIGS. 5B-C show RANKL-mediated in-vitro differentiation of M cells. (B) tSNE embedding of 5,434 epithelial cells profiled from intestinal organoids with and without treatment of RANKL. Blue: 384 differentiated M cells, identified by unsupervised clustering (FIG. 14E). (C) Shown are the proportions of epithelial cells (y axis) in each cell subset (x axis; subsets identified by graph-clustering and labeled post-hoc; Methods) from organoids grown under control conditions (white bars) or treated with RANKL for 3 days (light shaded bars) or 6 days (dark shaded bars). FIGS. 5D-F show M cells from follicular-associated epithelium (FAE) in vivo. (D) M cell cluster. Heatmap shows the Pearson correlation coefficient (bar) between expression profiles from each pair of cells (rows, columns), for 4,700 FAE derived epithelial cells (n=5 mice). Cells are ordered by unsupervised clustering (Methods), with large clusters down-sampled to a maximum of 250 cells for visualization only. Arrow marks a group of 18 M cells. (E-F). Gene signatures of in vivo M cells. Heat maps show the mean expression (−bar) in each FAE cell type cluster (columns) of genes (rows) for known (grey bars) or novel (black bars) cell surface markers (E) or transcription factors (F), identified as specific (FDR<0.05, Mann-Whitney U-test) to M cells in vivo.
FIGS. 6A-I—Tailored remodeling of the proportion and transcriptional programs of intestinal epithelial cells in response to different infections. FIG. 6A shows functional changes in IEC transcriptional programs in Salmonella infection. Shown are the significance (−log10(q), x axis) for the top 10 enriched GO terms among genes in Salmonella-treated IECs compared to control IECs. FIG. 6B shows up-regulation of Reg3b and Reg3b expression in both enterocytes and other epithelial cells during Salmonella infection. Violin plots show the distribution of expression levels (log2(TPM+1), y axis) of antimicrobial C-type lectins Reg3 g (top left) and Reg3b (top right), and interferon inducible and regulatory proteins Zbp1 (bottom left) and Igtp (bottom right) in control and Salmonella-treated enterocytes and all other cells (grey). FIGS. 6C-D show changes in cell composition during Salmonella and helminth infection. (C) tSNE visualization of IECs subsets (numbered and shaded according to their assignment to cell-type clusters using unsupervised clustering; legend) in controls (left; n=4 mice), Salmonella infected mice (n=2, center left), and mice infected with the intestinal parasite H. polygyrus for 3 (n=2, center right) or 10 (n=2, right) days. FIG. 6D shows frequencies (y axis) of cells of each subtype (as in c) in each mouse (dots) under each infection condition (*FDR<1×10−5; **FDR<1×10−10, Wald test). Error bars: standard error of the mean (SEM). FIG. 6E shows cell-intrinsic changes in enterocyte transcriptional programs following Salmonella infection. Heatmap shows the relative expression (row-wise Z-scores, bar) of 104 genes (left panel, rows) of which 58 (right panel) are specific to Salmonella infection (Methods), significantly up-regulated (FDR<0.05, Mann-Whitney U-test, log 2 fold-change >0.1) in individual enterocytes (columns) from the Salmonella infected mice compared to controls (grey). Enterocytes from H. polygyrus-treated mice (3 days; 10 days) are shown (right panel) for comparison. Labels indicate 10 representative up-regulated genes. FIG. 6F shows shifts in composition of tuft cell subsets in response to H. polygyrus infection. Frequencies (y axis) of cells in each subset (FIG. 16B-C) after 3 (left) and 10 (days) of infection in each mouse (dots, n=2 mice). Error bars: standard error of the mean (SEM). (*FDR<0.25; **FDR<0.05, Wald test). FIG. 6G shows up-regulation of anti-parasitic genes by goblet cells in response to H. polygyrus infection. Violin plots show the distribution of expression levels (log2(TPM+1), y axis) of three genes, previously implicated in anti-parasitic immunity70, which are up-regulated by goblet cells from control mice (grey) and mice infected by H. polygyrus for 3 and 10 days (light and dark, respectively) (FDR<0.05, Mann-Whitney U-test, 3′ scRNA-seq dataset). FIG. 6H shows cell intrinsic changes in enterocyte transcriptional programs following Salmonella infection. Heatmap shows the relative expression (row-wise Z-scores, bar) of 104 (left) genes (rows) of which 58 are specific to Salmonella infection (right, Methods) significantly up-regulated (FDR<0.05, Mann-Whitney U-test, Log2 fold-change >0.1) in individual enterocytes (columns) from the Salmonella infected mice compared to controls (grey). Enterocytes from H. polygyrus-treated mice (3 days; 10 days) are shown (right) for comparison, labels indicate 10 representative up-regulated genes. FIG. 6I shows cell intrinsic changes in goblet cell transcriptional programs following helminth infection. Heatmap shows the relative expression (row-wise Z-scores, bar) of 20 genes (left panel, rows) of which 14 are specific to H. polygyrus infection (right panel, Methods) significantly up-regulated in individual goblet cells (columns, FDR<0.05, Mann-Whitney U-test, Log2 fold-change >0.1) from H. polygyrus infected mice (3 days; 10 days) compared to control (grey). Goblet cells from Salmonella-treated mice are shown (right) for comparison, labels indicate 10 representative up-regulated genes.
FIGS. 7A-G—Identifying intestinal epithelial cell-types in scRNA-seq data by unsupervised clustering, related to FIG. 1. FIGS. 7A-B show quality metrics for scRNA-seq data. Shown are distributions of the number of reads per cell (left), the number of genes detected with non-zero transcript counts per cell (center) and the fraction of reads mapping to the mm10 mouse transcriptome per cell (right) in the droplet-based 3′ scRNA-seq data (A) and the plate-based full-length scRNA-Seq data (B). FIG. 7C-F show agreement across batches. (C) Contribution of batches to each cluster. Each pie chart shows the batch composition (legend) of each detected cluster (post-hoc annotation and number of cells are marked on top) in the droplet-based 3′ scRNA-seq dataset. All 10 biological replicates contribute to all clusters, and no major batch effect is observed. (n=6 mice). (D) Contribution of each mouse to each cluster. Shown is the proportion of detected cells (y axis) in each major cell type (x axis) in the droplet-based 3′ scRNA-seq dataset in each of six mice (dots). Grey bar: mean; error bars: standard error of the mean (SEM). (E) Agreement in expression profiles across mice. Box and whisker plot shows the Pearson correlation coefficients (x axis) in average expression profiles (average log2(TPM+1)) for cells in each cluster (y axis), across all pairs of mice. Black bar indicates median value, box edges correspond to the 25th and 75th percentiles, while whiskers indicate a further 1.5*IQR where IQR is the interquartile range. Note that clusters with additional sub-types (e.g., Tuft, enteroendocrine cells) show more variation, as expected. (F) Scatter plots compare the average log2(TPM+1) gene expression values between two scRNA-seq experiments from the droplet-based 3′ scRNA-seq dataset (top, x and y axis), two scRNA-seq experiments from the plate-based full length scRNA-seq dataset (center, x and y axis), or between the average of a plate-based full-length scRNA-seq (x axis) and a population control (y axis) (bottom). Pearson correlation is marked top left. FIG. 7G shows additional QC metrics and post-hoc cluster annotation by the expression of known cell-type markers. tSNE visualization of 7,216 single cells, where individual points correspond to single cells. Top left corner to bottom right corner, in order: Cells are shaded by their assignment to clusters (top left, identical to FIG. 1B), mean expression (log2(TPM+1), bar) of several known marker genes for a particular cell type or state (indicated on top), the mouse from which they originate (legend), the number of reads per cell (bar), the number of genes detected per cell (bar) and the number of transcripts as measured by unique molecular identifiers (UMIs) per cell.
FIGS. 8A-F—Identification and characterization of intestinal epithelial cell-types in plate-based full-length scRNA-seq data by unsupervised clustering, related to FIG. 1. FIG. 8A shows QC metrics and post-hoc cluster annotation by the expression of known cell-type markers. tSNE visualization of 1,522 single cells where individual points correspond to single cells. Top left corner to bottom right corner, in order: Cells are numbered and shaded by their assignment to clusters, using a k-nearest neighbor (kNN) graph-based algorithm (Methods; Legend shows the cluster post-hoc annotation to cell types); mean expression (log2(TPM+1), bar) of several known marker genes for a particular cell type or state (indicated on top; same as in FIG. 7G); the mouse from which they originate (legend) and its genotype, the FACS gate used to sort them (legend), the number of reads per cell (bar) and the number of genes detected per cell (bar). FIG. 8B shows cell-type-specific signatures. Heatmap shows the relative expression level (row-wise Z-scores, bar) of genes (rows) in consensus cell-type-specific signatures (same genes as FIG. 1C, with the exception of enterocytes), across the individual post-mitotic IECs (columns) in the full-length scRNA-seq data. Shading marks the cell types and their associated signatures. FIG. 8C shows Mptx2, a novel Paneth cell marker. tSNE of the cells from the droplet-based 3′ scRNA-seq (left, as in FIG. 1B) and plate-based full-length scRNA-seq (right, as in A) datasets, shaded by expression (log2(TPM+1), bar) of the mucosal pentraxin Mptx2. FIG. 8D shows cell-type-enriched GPCRs. Heatmap shows the relative expression (row-wise Z-scores, bar) of genes encoding GPCRs (rows) that are significantly (FDR<0.001, Mann-Whitney U-test) up- or down-regulated in the cells (columns) in a given cell-type (top, coded as in A) compared to all other cells, in the plate-based full-length scRNA-seq data. FIG. 8E shows cell type specific Leucine-rich repeat (LRR) proteins. Heatmap depicts the mean relative expression (column-wise Z-score of mean log2(TPM+1) values, bar) of genes (columns) encoding LRR proteins that are significantly (FDR<0.001, Mann-Whitney U-test) up- or down-regulated in a given cell-type (rows) compared to all other cells, in the plate-based full length scRNA-seq data. FIG. 8F shows violin plots displaying the distribution of expression levels of Selm, Fxyd3, Hepacam2, Cd24a and Tm4sf4 across intestinal epithelial cell types.
FIGS. 9A-E—Mapping of differentiation processes using low-dimensional embedding, related to FIG. 2 FIG. 9A shows principal components analysis (PCA) of IECs. Shown are the first two PCs (x and y axis) of a PCA of 7,216 IECs. Cells (points) are shaded by the signature scores of enterocytes (left), cell-cycle (middle) and secretory cells (right). The secretory signature score is the sum of the Paneth, goblet, enteroendocrine and tuft signature scores (Methods). FIG. 9B shows gene signature-based embedding of the IEC lineage. Shown are 7,216 single IECs positioned by signature scores for key cell types: the difference between the signature scores for enterocyte and enteroendocrine cells (x axis); the difference between goblet and tuft cell scores (y axis), and the stem cell score (z axis) (as in FIG. 2B). Each signature score was computed using 50 genes (Methods). Cells are shaded by log2(TPM+1) expression (bar) of the goblet cell markerMuc2 (left), the tuft cell marker Dclk1 (middle), and the enteroendocrine marker Chgb (right). FIGS. 9C-E show that DC-3 reflects the distinction between stem cells and enterocyte progenitors. Diffusion-map embedding of 5,282 cells progressing through stages of enterocyte differentiation (see also FIG. 2C). Shown are DC-1 (x axis) and DC-3 (y axis) with cells (points) shaded by the score (bar, Methods) for gene signatures of the cell-cycle (C), stem cells (D), and enterocytes (E).
FIGS. 10A-Q—Enterocyte differentiation toward proximal and distal fates, related to FIG. 2. FIGS. 10A-F show DC-1 is driven by enterocyte differentiation and DC-2 distinguished proximal and distal enterocytes. Diffusion-map embedding of 5,282 cells through stages of enterocyte differentiation. Shown are DC-1 (x axis) and DC-2 (y axis) with cells (points) shaded by the score (bar, Methods) for gene signatures of the cell cycle (A), stem cells (B), enterocytes (C), or by the expression levels (log2(TPM+1), bar) of the proximal enterocyte marker Lct (D), and the distal markers Mep1a (E) and Fabp6(F). FIG. 10G shows TF genes differentially expressed between proximal and distal cell fate. Heatmap shows the mean expression level (bar) of 44 TFs differentially expressed between the proximal and distal (legend) enterocyte clusters of FIG. 1B (FDR<0.05, Mann-Whitney U-test). FIG. 10H shows single-cell profiles from regional sites of the small intestine. tSNE embedding of 11,665 single cells extracted from three regions of the small intestine (duodenum, jejunum and ileum), shaded by the region of origin (top, legend) or their assignment to cell-type by unsupervised clustering (bottom, legend). n=2 mice. FIG. 10I shows validation of predicted regional markers. Heatmap shows the expression level (row-wise Z-score, bar) in each of the 1,041 enterocytes (columns) analyzed from three regions of the small intestine (duodenum, jejunum and ileum; bar, top) of 108 genes (rows) predicted to be markers of proximal (light grey) and distal (dark grey) enterocytes (bar, left) using unsupervised cluster analysis (FIG. 1B,C). FIG. 10J shows validation of proximal and distal enterocyte markers. smFISH of Lct and Fabp6 (white) in the duodenum (proximal small intestine, top) and the ileum (distal small intestine, bottom). Dotted line indicates the boundary between the crypt region (below) and the villi (above). Scale bar, 50 μm. FIG. 10K shows regional enterocyte signatures. Relative expression of genes (rows) across cells (columns), sorted by region. FIG. 10L shows regional differences in [SC differentiation. Diffusion-map embedding of 8,988 cells shaded by region (left), cluster (center left), or expression of novel regional markers of ISCs (Gkn3, Bex]) or enterocytes (Fabp1, Fabp6). FIGS. 10M-P show regional variation in Paneth cell sub-types and stem cell markers. FIGS. 10M-N show Paneth cell subsets. (M) tSNE of 10,396 single cells (points) obtained using a large cell-enriched protocol (Methods), numbered and shaded by clusters annotated post-hoc. n=2 mice. FIGS. 10N-0 show Paneth cell subset markers. (N) Expression (row-wise Z-score, bar) of genes specific (FDR<0.05, Mann-Whitney U-test, log2 fold-change >0.5) to each of the two Paneth cell subsets (average of 724.5 cells per subtype, down-sampled to 500 for visualization) shown in (M). FIG. 10O shows two Paneth subsets reflect regional diversity. Expression of the same genes (rows) as in (N) but in Paneth cells from each of three small intestinal regions (176.3 cells obtained per each of the regions on average, columns; FIG. 10H); 11 of 11 Paneth-1 markers are enriched in the ileal Paneth cells, while 7/10 Paneth-2 markers are enriched in duodenal or jejunal Paneth cells (FDR<0.05, Mann-Whitney U-test). FIG. 10P shows regional variation of intestinal stem cells. Expression (row-wise Z-score) of genes specific to stem cells from each intestinal region (FDR<0.05, Mann-Whitney U-test, log 2 fold-change >0.5). There are 1,226.3 obtained cells per each of the three regions on average, down-sampled to 500 for visualization. FIG. 10Q shows novel regional stem cell markers (P) identify distinct populations in diffusion map space. Close-up of stem-cell region of diffusion space (FIG. 2C) shaded by expression level (log2(TPM+1), bars) pan-ISC marker Lgr5 (left), proximal ISC marker Gkn3 (center) and distal ISC marker (Bex1). Dashed line is a visual guide.
FIGS. 11A-E—Heterogeneity within EECs, related to FIG. 3. FIG. 11A shows EEC subset discovery and spatial location. Shown is a tSNE embedding of the 533 enteroendocrine cells (EECs) identified from the droplet-based datasets for whole SI and regional samples (Methods). FIG. 11B shows agreement in hormone detection rates between 3′ droplet and full-length scRNA-seq. Scatter plot shows the detection rate (fraction of cells with non-zero expression of a given transcript) for a set of known EEC hormones, TFs and marker genes (legend) in EECs from the full-length dataset (x axis), and from the 3′ droplet-based dataset (y axis). Linear fit (dashed line) and 95% confidence interval (shaded) are shown. FIG. 11C shows expression of key genes across subset clusters. tSNE plot shows cells numbered and shaded by either by their assignment to 12 clusters (top left plot; identical to FIG. 3A) or by the expression (log2(TPM+1), bar) of genes encoding either gut hormones (Sct, Sst, Cck, Gcg, Ghrl, GIP, Nts), or markers of immature EECs (Neurog3), mature EECs (Chgb) or enterochromaffin cells (Tac1, Reg4). FIG. 11D shows co-expression of GI hormones by individual cells. Left: Heatmap shows the expression (bar) of canonical gut hormone genes (rows) in each of 533 individual EECs (columns), ordered by their assignment to the clusters in a (bar, top). Right: Heatmap shows for each cluster (columns) the percentage of cells (bar, inset text) in which the transcript for each hormone (rows) is detected. FIG. 11E shows potential markers for the enteroendocrine (EEC) lineage. Shown is a Volcano plot of the differential expression of each gene (dot) between 310 of the EECs and 6,906 remaining IECs (x axis), and the significance (−log10(Q value)) of each such test (y axis). Genes (points) are shaded by their expression level (Log2(TPM+1), bar)). The names of known lineage TFs and of gut hormone genes are indicated.
FIGS. 12A-F—Classification and specificity of enteroendocrine subsets related to FIG. 3. FIG. 12A shows relationships between EEC subsets. Dendrogram shows the relationship between EEC clusters as defined by hierarchical clustering of mean expression profiles of all the cells in a subset (Methods). Estimates for the significance of each split are derived from 100,000 bootstrap iterations using the R package pvclust (*p<0.05; ** p<0.01, p<0.001, χ2 test). Heat map (B) shows cell-cell Pearson correlations (r, bar) between the scores across 11 significant PCs (p<0.05, Methods) across the 533 EECs (rows, columns). Rows and columns are ordered using cluster labels obtained using unsupervised clustering (Methods). FIG. 12C shows subset specificity of gut hormones and related genes. Scatter plot shows for each gene its specificity to its marked cell subset (y axis; defined as the proportion of cells not in a given subset which do not express a given gene) and its sensitivity in that subset (defined as the fraction of cells of a given type which do express the gene, Methods). Subsets are coded as in the legend. Genes are assigned to the subset where they are most highly expressed on average. Genes were chosen based on their known annotation as gut hormones (Cck, Gal, Gcg, Ghrl, GIP, Iapp, Nucb2, Nts, Pyy, Sct, Sst), enterochromaffin markers (Tph1, Tac1) and canonical EEC markers (Chga, Chgb). FIG. 12D shows the enteroendocrine marker Reg4 is substantially expressed in enteroendocrine, goblet and Paneth cells. Violin plots show the distribution of expression (log2(TPM+1), y axis) of Reg4 in each of the IEC subsets (x axis). FIG. 12E shows mapping the in vivo-identified EEC subsets to EEC subsets in organoid53. Heatmap shows the Pearson correlation (bar) between average expression profiles of the cells of each of 12 subsets in the study (columns), and seven recently reported clusters (rows) from organoids53. Cluster-pairs that are maximal across both a row and a column are highlighted (white border). FIG. 12F shows GPCRs enriched in different EEC subtypes. Heatmap shows the expression levels (row-wise Z-score, bar) averaged across the cells in each of the EEC sub-types (columns) of 11 GPCR-encoding genes (rows) that are differentially expressed (FDR<0.25, Mann-Whitney U-test) in one of the EEC subtype clusters.
FIGS. 13A-F—Characterization of tuft cell heterogeneity and identification of hematopoietic lineage marker Ptprc (CD45) in a subset of tuft cells, related to FIG. 4. FIG. 13A shows Tuft-1 and Tuft-2 cells. tSNE visualization of 102 tuft cells (points) from the plate-based full-length scRNA-seq dataset (FIG. 7F), labeled by their sub-clustering into Tuft-1 and Tuft-2 subtypes. FIG. 13B shows gene signatures for Tuft-1 and Tuft-2 cells. Heatmap shows the relative expression (row-wise Z-scores, bar) of the consensus Tuft-1 and Tuft-2 marker genes (rows), across single cells from the plate-based dataset (columns) assigned to Tuft-1 and Tuft-2 cell clusters. Top 25 genes shown for each subtype (all FDR<0.01 and log 2 fold change >0.1 in both plate- and droplet-based datasets). FIG. 13C shows Tuft-2 signature genes are enriched in immune functions. Shown are the significantly enriched (Methods, FDR<0.1, −log10(Q-value), x axis) GO terms (y axis) in the gene signature for the Tuft-2 subset. FIG. 13D shows expression of neuron- and immune-related genes in Tuft-1 and Tuft-2 subsets, respectively. Plot shows for each gene (y axis) its differential expression (x axis) between Tuft-1 and Tuft-2 cells. Bar indicates Bayesian bootstrap74 estimates of log 2 (fold change), and hinges and whiskers indicate 25% and 95% confidence intervals, respectively. FIG. 13E shows validation of CD45 expression in some Tuft cells. IFA showing co-expression of a specific tuft cell marker, DCLK1 and CD45 (white). Scale bar, 200 μm. FIG. 13F shows isolation of Tuft-2 cells using FACS based on CD45 expression. tSNE embedding of 332 EpCAM+/CD45+ FACS-sorted single cells (points, n=3 pooled mice), shaded by unsupervised clustering (top left), the expression of the Tuft cell marker Dclk1 (top right), or the signature scores for Tuft-1 and Tuft-2 cells (bottom left and right, respectively).
FIGS. 14A-I—Microfold (M) cells from RANKL-treated intestinal organoids and in vivo, related to FIG. 5. FIG. 14A shows previously reported17 M cell signature genes expressed in Tuft-2 cells. Heat map shows the mean expression level (log2(TPM+1), bar) of M cell signature genes17 (rows) in cells from the Tuft-1 and Tuft-2 subsets (columns) and in mature enterocytes, shown for comparison, based on the high-coverage full-length scRNA-seq data. Cells in the Tuft-2 subset express a significantly higher level of these genes on average (p<1×10−5, Mann-Whitney U-test). FIGS. 14B-E show scRNA-seq identifies M cells in RANKL treated organoids. tSNE embedding of 5,434 single cells (dots) from organoids, highlighting (B) those from control (left) or RANKL-treated (middle, right) intestinal organoids; or coloring each cell (C-D) by the expression (log2(TPM+1), bar) of the canonical M cell markers TNF-alpha induced protein 2 (Tnfaip2, M-sec, C) and glycoprotein 2 (Gp2, D). FIG. 14E shows expression of M cell marker genes17, 58, 75 in each of the organoid cell clusters. Violin plots show the distribution of expression levels (log2(TPM+1)) for each of 10 previously reported M cell marker genes58 (columns), in the cells (dots) in each of 13 clusters identified by k-NN clustering of the 5,434 scRNA-seq profiles from organoids. FIGS. 14F-G show M cell gene signature in vitro. Heat maps show for each cell type cluster of organoid-derived intestinal epithelial cells (columns) the mean expression (bar) of genes (rows) for known (grey bars) or novel (black bars) M cell markers (F) or transcription factors (G), identified as specific (FDR<0.05, Mann-Whitney U-test) to M cells both in vitro and in vivo (Methods). FIG. 14H shows congruence of in vitro and in vivo-derived M cell gene signatures. Violin plot shows the distribution of the mean expression of the in vitro-derived signature genes (y-axis) across the in vivo M cells (blue) and all other cells derived from the FAE (grey). FIG. 14I shows in vivo expression of the M cell signature genes from organoids. Heatmaps show the mean expression level (Log2(TPM+1), bar) each of the genes specific to M cells (FDR<0.05, Mann-Whitney U-test, Log2 fold change >0.5) in the organoid data (rows), in the cells from each of the cell type clusters (columns) from the organoids (left) or from in vivo IECs (right). Known and novel M cell markers are marked (left). Genes that are specific to M cells in vitro but expressed by IECs in vivo (grey) are filtered out, and a refined set of 18 specific M cell markers (black) that are not expressed by in vivo IECs is retained.
FIGS. 15A-E—Intestinal epithelial cell response to pathogenic stress, related to FIG. 6. FIG. 15A shows generalized and pathogen-specific response genes. Volcano plots show for each gene (dot) the differential expression (DE, x axis), and its associated significance (y axis; (−log10(Q value); Likelihood-ratio test) in response to either Salmonella (top) or H. polygyrus (bottom). Genes strongly up-regulated in Salmonella (FDR<10−6) or H. polygyrus (FDR<5×10−3) are highlighted by shading, respectively. (All highlighted genes were significantly differentially expressed (FDR<0.05) in both the 3′ scRNA-seq and the higher depth full-length scRNA-seq datasets.) Left panels: all genes differentially expressed in the noted parasite infection vs. uninfected controls; middle panels: the subset differentially expressed in both parasites vs. control; right panels: the subset differentially expressed only in the noted parasite but not the other (Methods). FIG. 15B shows global induction of enterocyte-specific genes across cells during Salmonella infection. tSNE embedding of 9,842 single IECs from control wild-type mice (left) and mice infected with Salmonella (right). Cells are shaded by the expression of the indicated genes, all specific to enterocytes in control mice (Tables 3-5) and strongly up-regulated by infection (FDR<10−10 in both the 3′ scRNA-seq datasets and in the higher depth full length scRNA-seq dataset). FIG. 15C shows up-regulation of pro-inflammatory apolipoproteins Serum Amyloid A 1 and 2 (Saa1 and Saa2) in distal enterocytes under Salmonella infection. Violin plot shows log2(TPM+1) expression level (y axis) of Saa1 (top) and Saa2 (bottom) across all post-mitotic cell-types from control and Salmonella-treated mice (n=4 mice, sample identity shown by legend) (*FDR<0.01; **FDR<0.0001, Mann-Whitney U-test). FIG. 15D shows up-regulation of antimicrobial peptides by Paneth cells following Salmonella infection. Violin plots show log 2 (TPM+1) expression levels (y axis) of genes encoding antimicrobial peptides (panels, marked on top left) and the mucosal pentraxin Mptx2 (bottom right) in the cells (dots) from control and Salmonella-infected mice (n=4 mice, sample identity shown by legend) (*FDR<0.1; **FDR<0.01, **FDR<0.0001, Mann-Whitney U-test). FIG. 15E shows paneth cell numbers detected (using graph-clustering, Methods) after Salmonella. Frequencies (y-axis) of Paneth cells in each mouse (dots) under each condition (legend). Error bars: standard error of the mean (SEM). (**FDR<0.01, Wald test).
FIGS. 16A-D—Goblet and tuft cell responses to H. polygyrus show a unique defense mechanism, related to FIG. 6. FIG. 16A shows genes significantly induced in response to H. polygyrus infection in a non-cell-type specific manner. tSNE visualization of 9,842 single IECs (dots) from control wild-type mice (left) and mice infected with H. polygyrus for three (middle) or ten (right) days. Cells are shaded by the expression (log2(TPM+1), bar) of the indicated genes. Genes were selected as significantly differentially expressed in response to infection in a non-cell-type specific manner (FDR<0.001 in both the 3′ scRNA-seq and full-length scRNA-seq datasets). Ifitm3 is specific to H. polygyrus infection, while others are up-regulated in both pathogenic infections. FIGS. 16B-C show expression of the Tuft-1 signature (left), Tuft-2 signature (middle) and Dclk1 (right) in the combined dataset of control, Salmonella and H. polygyrus infected cells in tuft cell subgroups defined by cluster analysis. (B) Violin plots of the distribution of the respective signature scores (left and middle) and the expression of Dclk1 (right, log 2 (TPM+1, y axis) in cells (dots) in each of the tuft subsets (x axis). (C) tSNE mapping of the 409 tuft progenitor, Tuft-1 and Tuft-2 cells, shaded by the scores for each signature (bar, left and middle) and their assignment to subtype clusters via kNN-graph clustering (right). FIG. 16D shows anti-parasitic protein secretion by goblet cells during H. polygyrus infection. Immunofluorescence assay (IFA) of FFPE sections of RELMb (top-left), E-cadherin (Bottom left) and their merged view (right) after 10 days of helminth infection. White arrow: sections of H. polygyrus. Scale bar, 200 μm.
FIG. 17—illustrates that epithelial cells in healthy cells partition by cell type in tSNE plots.
FIG. 18—illustrates that the atlas uncovers almost all cell types and subtypes in the colon.
FIG. 19—illustrates that the atlas uncovers almost all cell types and subtypes in the colon.
FIG. 20—illustrates the cell-of-origin for key IBD GWAS genes.
FIG. 21—illustrates the cell-of-origin for key IBD GWAS G-protein coupled receptor (GPCR) genes.
FIG. 22—illustrates the cell-of-origin for key IBD GWAS cell-cell interaction genes.
FIG. 23—illustrates the cell-of-origin for key IBD GWAS genes expressed in epithelial cells.
FIG. 24—illustrates that the atlas can be used to determine the cell-of-origin for GWAS genes for other indications.
FIG. 25—illustrates that the atlas can be used to determine cell-cell interaction mechanisms.
FIG. 26—illustrates that the atlas can be used to determine fibroblasts that support the stem cell niche.
FIG. 27—Cell types in trachea—sets forth clustering of single cells based on tSNE analysis.
FIG. 28—Cell type clusters—sets forth a heatmap showing clusters of cells in the trachea.
FIG. 29—Cell type signatures—sets forth a heat map showing cell type specific gene signatures.
FIG. 30—Transcription factors—sets forth cell type specific transcription factor expression in the trachea.
FIG. 31—shows the Tuft Cell is dynamically maintained by the Stem Cell lineage.
FIG. 32—shows the Tuft Cell is dynamically maintained by the Stem Cell lineage.
FIG. 33—shows the Tuft Cell is dynamically maintained by the Stem Cell lineage.
FIG. 34—shows the Respiratory Tuft Cell produces ILC2-modulating IL-25.
FIG. 35—shows tuft cell markers—sets forth violin plots showing tuft cell specific expression in the trachea and gut.
FIG. 36—shows tuft cell markers in gut and trachea—sets forth a heat map showing differential expression in the gut and trachea.
FIGS. 37A-E—A single-cell expression atlas of tracheal epithelial cells. FIG. 37A shows a schematic overview. Two complementary scRNA-seq methods used to create an atlas of the mouse tracheal epithelium. FIG. 37B shows cell type clusters. t-distributed stochastic nearest-neighbor embedding (tSNE) visualization of 7,193 3′ scRNA-seq profiles. Single cells (points) are shaded by their assignment to clusters (Methods; tSNE plot used for visualization only) and annotated post hoc (legend). Dashed circle: ionocyte cluster. FIG. 37C shows cell type clusters. Left: Pearson correlation coefficients (r, bar) between every pair of 7,193 cells (rows and columns) ordered by cluster assignment (bar, rows and columns). Inset (right): zoom of 288 cells from the rare types (black border on left). FIG. 37D shows gene signatures. Relative expression level (row-wise Z-score of log2(TPM+1) expression values, bar) of cell type-specific genes (rows) in each epithelial cell (columns). Large clusters (basal, club) are down-sampled to 500 cells. FIG. 37E shows cluster-specific transcription factors (TFs). Mean relative expression (row-wise Z-score of mean log2(TPM+1), bar) of the top TFs (rows) that are enriched (FDR<0.01, likelihood-ratio test) in cells (columns) of each cluster.
FIGS. 38A-I—Krt13+ club cell progenitors exhibit rapid turnover and are found in hillocks. FIGS. 38A-B show alternative putative developmental paths to club cells. Diffusion map embedding of 6,905 cells inferred to differentiate from basal to club to ciliated cells (Methods), shaded by either cluster assignment (left) or expression (Log2(TPM+1), bar) of specific genes (all other panels). FIG. 38B shows cell fate trajectories. Schematic of the number of individual cells associated with each cell fate trajectory (Methods). Krt13+ cells occur in hillock structures. FIG. 38C shows whole-mount stain of Krt13 (magenta) and ciliated cell marker Acetylated tubulin (AcTub) shows the distribution of hillocks (which lack ciliated cells) throughout the trachea. FIG. 38D shows immunofluorescence stainings of Krt13 and either basal (Trp63+, solid white line top panel), suprabasal (Trp63+, dashed white line top panel) or luminal (Scgb1a1+, solid white line, bottom panel) markers (magenta, both panels), showing distinct strata of basal Trp63+Krt13+ cells and luminal Scgb1a1+Krt13+ cells. FIG. 38E shows Hillocks are proliferative. Co-stain of EdU (magenta) and Krt13. FIG. 38F shows a schematic of hillocks within pseudostratified ciliated epithelium. FIGS. 38G-I show proximal vs. distal specific club cell expression. Relative expression level (row-wise Z-score, bar) for genes (rows) enriched in proximal and distal tracheal club cells (FDR<0.05, likelihood-ratio test) in the full-length scRNA-seq data. FIGS. 38H-I show ucous metaplasia in distally-derived epithelia. FIG. 38H shows Muc5ac (goblet cell stain) and AcTub (ciliated cell stain) levels in cultured epithelia from proximal (top) or distal (bottom) trachea stimulated with recombinant IL-13 (rIL-13, 25 ng/mL, right) vs. control (left). FIG. 38I shows goblet cell quantification (ln(Muc5ac+/GFP+ ciliated cells, y-axis) in Foxj1-GFP mice (n=6, dots) in each of four conditions in (h) (x-axis). **p<0.01, ***p<0.001, Tukey's HSD test, black bars: mean, error bars: 95% CI.
FIGS. 39A-G—Pulse-Seq reveals novel lineage paths and records cell dynamics with single-cell resolution. FIG. 39A shows Pulse-Seq. Tmx: tamoxifen, mT: tdTomato, mG: mGFP. FIG. 39B-C show cell type clusters and lineage labeling. tSNE visualization of 66,265 scRNA-seq profiles from Pulse-Seq. Cells shaded by assignment to clusters (B, Methods), or by the presence of a lineage label (C). FIG. 39D shows lineage tracing of each tracheal epithelial cell type. Estimated fraction (%, y-axis, Methods) of cells of each type that are positive for the fluorescent lineage label (by FACS) from n=3 mice per time-point (x-axis). Points: individual mice. *p<0.1, *p<0.05, **p<0.01, ***p<0.001, likelihood-ratio test (Methods), error bars: 95% CI. FIG. 39E shows ciliated and goblet cells are produced later than club and rare epithelial cell types. Estimated daily rate of new lineage labeled cells (%, y-axis, Methods, FIG. 42C) for each type (x-axis). *p<0.05, **p<0.01, rank test (Methods), error bars: 95% CI. FIG. 39F shows conventional lineage trace of Gnat3+ tuft cells confirms they are generated by basal cells. Left: Representative images and basal cell lineage labeling quantification (bar plot, right) of Gnat3+ tuft cells at Day 4 (0%, n=2 mice, dots) and Day 30 (22.9%, 95% CI [0.17, 0.30], n=3 mice) post-labeling. Dashed white lines: unlabeled tuft cells; solid white lines: labeled tuft cells. ***p<0.001, likelihood-ratio test. Error bars: 95% CI. FIG. 39G shows cell types, lineage, and cellular dynamics inferred using Pulse-Seq.
FIGS. 40A-H—Tuft and goblet cell subtypes display unique functional gene expression programs. FIG. 40A shows tuft-1 and tuft-2 sub-clusters. tSNE visualization of 892 tuft cells (points) shaded either by their cluster assignment (left, legend), or by the expression level (log2(TPM+1), bar, remaining panels) of marker genes for mature tuft cells (Trpm5), tuft-1 (Gng13), tuft-2 (Alox5ap) subsets. FIG. 40B-D show gene signatures for tuft-1 and tuft-2 subsets. FIG. 40B shows distribution of expression levels (y-axis, log2(TPM+1)) of the top markers for each subset (x-axis). NS: FDR>0.05, ****FDR<10−10, likelihood-ratio test. FIG. 40C shows relative expression level (row-wise Z-scores, bar) of genes (rows) differentially expressed (FDR<0.25, likelihood-ratio test) in tuft cells (columns) of each sub-cluster (bar, top). FIG. 40D shows validation of tuft-1 and tuft-2 markers in vivo. Immunofluorescence staining of expression of the respective tuft-1 and tuft-2 cell markers Gng13 and Alox5ap (magenta) by distinct tuft cells (solid white lines), along with pan-tuft marker Trpm5 (blue) and DAPI (grey). FIG. 40E shows tuft-1 and tuft-2 subtypes are each generated from basal cell parents. Estimated fraction (%, y-axis, Methods) of cells of each type that are positive for the basal-cell lineage label (by FACS) from n=3 mice (points) per time-point (x-axis) in the Pulse-Seq experiment. ***p<0.001, likelihood-ratio test (Methods), error bars: 95% CI. FIGS. 40F-G show tuft-1 and tuft-2 respectively express chemosensory and inflammatory gene modules. Differential expression between tuft subtypes for all genes (F, left), those involved in leukotriene synthesis (F, center left), taste transduction (F, right), and transcription factors (G). Labeled genes are differently expressed in the tuft cell subsets (FDR<0.01, likelihood-ratio test). FIG. 40H shows validation of goblet cell subtype markers. Immunofluorescence staining of the goblet-1 (Tff2, magenta) and goblet-2 Lipf markers along with DAPI (blue) in distinct cells (solid white lines).
FIGS. 41A-K—The pulmonary ionocyte is a novel mouse and human airway epithelial cell type that specifically expresses CFTR. FIG. 41A shows mouse ionocyte markers. Expression level (mean log2(TPM+1), bar) of ionocyte markers (columns, FDR<0.05 in both 3′ and full-length scRNA-seq datasets, likelihood-ratio test and Supplementary Table 3) in the 3′ scRNA-seq dataset of each airway epithelial cell type (rows). Dot size: proportion of cells with non-zero expression. intensity: mean expression in those cells with non-zero expression. FIG. 41B shows ionocytes specifically express V-ATPase and Cftr. Immunofluorescent co-labeling of EGFP (Foxi1+) ionocytes and a V-ATPase subunit (Atp6v0d2, top left, solid white line) and Cftr (bottom left, solid white line). FIG. 41C shows tSNE visualization shaded by expression level of ionocyte markers Foxi1 (left) and Cftr (right) across all 66,265 trachea epithelial cells from the Pulse-Seq experiment and in the subset of 276 ionocytes (inset). FIG. 41D shows qRT-PCR confirms ionocyte enrichment of Cftr relative to ciliated cells and EpCAM+ populations. Expression (ΔΔCT, y-axis) of ionocyte (Cftr, Foxi1) and ciliated cell (Foxj1) markers (x-axis) detected using qRT-PCR of prospectively isolated populations of ionocytes and ciliated cells from Foxi1−(n=4, dots) and Foxj1-GFP mice (n=3), respectively. All values normalized relative to EpCAM+ populations from wild type mice (n=6; 7.30 ΔΔCT, 95% CI [±0.66]), ***p<0.001, Dunn's Method, error bars: 95% CI. FIG. 41E shows Foxi1-KO displays loss of expression of ionocyte TFs and Cftr in ALI cultured epithelia. Expression (ΔΔCT, y-axis) of ionocyte (Cftr: −2.77 ΔΔCT, 95% CI [±0.28], Foxi1: −9.46 ΔΔCT, 95% CI [±3.32], Asc3: −4.77 ΔΔCT, 95% CI [±0.57]) and basal (Trp63), club (Scgb1a1), or ciliated (Foxj1) markers (x-axis) in hetero- and homozygous KO (legend), normalized to wild type littermates. The mean of independent probes (p1 and p2) was used for Cftr. Heterozygous KO: n=4; homozygous KO: n=6, wild type: n=8, *p<0.05, **p<0.01, Dunn's Method, error bars: 95% CI. FIG. 41F shows ionocyte depletion via Foxi1-KO disrupts mucosal homeostasis in ALI cultured epithelia. Effective viscosity (cP, left) and ciliary beat frequency (Hz, right) from optical coherence tomography (OCT) in homozygous Foxi1-KO (n=9, dots) vs. wild type littermates (x-axis, n=3 mice). ***p<0.001, ****p<0.0001, Mann-Whitney U-test. g-h. shows Foxi1 transcriptional activation (Foxi1-TA) in ferret increases Cftr expression and chloride transport. FIG. 41G shows qRT-PCR expression quantification (ΔΔCT, y-axis) of ionocyte markers (x-axis) in ferret Foxi1-TA ALI (n=4) normalized to mock transfection (Cftr: −1.39 ΔΔCT, 95% CI [±0.44], Foxi1: −5.37 ΔΔCT, 95% CI [±0.91], Methods), error bars: 95% CI. FIG. 41H shows Foxi1 activation in ferret cell cultures results in a CFTR inhibitor-sensitive short-circuit current (ΔIsc). Representative trace of short-circuit current (Isc, y-axis) tracings from Foxi1-TA ferret ALI after sgRNA reverse transfection (n=4, light blue) vs. mock transfection (n=4, black). FIG. 41I shows ionocytes are sparsely distributed in human bronchial epithelium. In situ hybridization shows cells co-labeled for Foxi1 and Cftr (20 double Z probe pairs spanning 960 nucleotides including the only documented CFTR splice site). FIGS. 41J-K show human pulmonary ionocytes are the major source of Cftr in the bronchial epithelium. FIG. 41J shows tSNE of 765 human pulmonary ionocytes (points) identified using clustering of 78,217 3′ droplet scRNA-seq profiles (grey points) from human bronchial epithelium (n=1 patient). FIG. 41K shows Difference in fraction of cells in which transcript is detected (x axis) and log2 fold-change (y-axis) between human ionocytes and all other bronchial epithelial cells. All labeled genes are differentially expressed (log2 fold-change >0.25 and FDR<<10−10, Mann-Whitney U-test). Shading: consensus ionocyte markers in mouse (log 2 fold-change >0.25, FDR<10−5, likelihood-ratio test) and human.
FIG. 42—Shows that a new lineage hierarchy of the airway epithelium reframes our understanding of the cellular basis of airways disease. Specific cells are associated with novel cell-type markers and disease-relevant genes.
FIGS. 43A-D—Identifying tracheal epithelial cell types in 3′ scRNA-seq. FIG. 43A shows quality metrics for the initial droplet-based 3′ scRNA-seq data. Distributions (y axis) of the number of reads per cell (x-axis, left), the number of the genes detected with non-zero transcript counts per cell (x-axis, center), and the fraction of reads mapping to the mm10 transcriptome per cell (x-axis, right). Dashed and blue lines: median value and kernel density estimate, respectively. FIG. 43B shows cell type clusters are composed of cells from multiple biological replicates. Fraction of cells in each cluster that originate from a given biological replicate (legend, bottom right, n=6 mice); post hoc annotation and number of cells are indicated above each pie chart. All biological replicates contribute to all clusters (except for WT mouse 1 which did not contain any of the very rare ionocytes), and no significant batch effect was observed. FIG. 43C shows reproducibility between biological replicates. Average gene expression values (log2(TPM+1), x and y axes) across all cells of two representative 3′ scRNA-seq replicate experiments (Pearson correlation coefficient, top left), blue shading: gene (point) density. FIG. 43D shows Post hoc cluster interpretation based on the expression of known cell type markers4. tSNE of 7,193 scRNA-seq profiles (points), shaded by cluster assignment (Methods, top left) or by the expression (log2(TPM+1), bar) of a single marker genes or the mean expression of several marker genes4 for a particular cell type.
FIGS. 44A-D—Identifying tracheal epithelial cell types in full-length scRNA-seq. FIG. 44A shows quality metrics for full-length, plate-based scRNA-seq data. Distributions (y axis) of the number of reads per cell (x-axis, left), the number of the genes detected with non-zero transcript counts per cell (x-axis, center), and the fraction of reads mapping to the mm10 transcriptome per cell (x-axis, right). FIG. 44B-C show high reproducibility between plate-based scRNA-seq data from biological replicates of tracheal epithelial cells. Average expression values (x and y axes; log2(TPM+1)) in two representative full-length scRNA-seq replicate experiments (left panel, x and y axes) and in the average of a full-length scRNA-seq dataset (right panel, x axis) and a population control (right panel, y axis) for cells extracted from proximal (B) and distal (C) mouse trachea. Blue shading: density of genes (points); r-Pearson correlation coefficient. FIG. 44D shows Post hoc cluster annotation by the expression of known cell-type markers. tSNE of 301 scRNA-seq profiles (points) shaded by region of origin (top left panel), cluster assignment (top second panel, Methods), or, for the remaining plots, the expression level (log2(TPM+1), −bar) of a single marker genes or the mean expression of several marker genes4 for a particular cell type. All clusters are populated by cells from both proximal and distal epithelium except rare NE cells, which were only detected in proximal experiments (top left panel).
FIGS. 45A-E—High-confidence consensus cell type markers, and cell type-specific expression of asthma-associated genes. FIG. 45A shows cell type clusters in full-length plate-based scRNA-seq data. Cell-cell Pearson correlation coefficient (r, bar), between all 301 cells (individual rows and columns) ordered by cluster assignment (bar, as in FIG. 38d). Right: zoomed in view of 17 cells (black border on left) from the rare types. FIG. 45B shows high confidence consensus markers. Relative expression level (row-wise Z-score of mean log2(TPM+1), bar at bottom) of consensus marker genes (rows, FDR<0.01 in both 3′-droplet and full-length plate-based scRNA-seq datasets, likelihood-ratio test) for each cell type (flanking bar) across 7,193 cells in the 3′ droplet data (columns, left) and the 301 cells in the plate-based dataset (columns, right). FIG. 45C-E show cell type-specific expression of genes associated with asthma by GWAS. c. Relative expression (Z-score of mean log2(TPM+1), bar bottom right) of genes (rows) that are associated with asthma in GWAS and enriched (FDR<0.01, likelihood-ratio test) for cell type (columns) specific expression in our 3′ scRNA-seq data. FIG. 45D for each gene from (c) shown is the significance (−log10(FDR), Fisher's combined p-value, likelihood-ratio test, y axis) and effect size (point size, mean log2(fold-change)) of cell type specific expression in the relevant cell (legend) and its genetic association strength from GWAS15 (x axis). FIG. 45E shows distribution of expression levels (y axis, log2(TPM+1)) in the cells in each cluster (x axis, legend) for two asthma GWAS genes: Cdhr3 (left; specific to ciliated cells) and Rgs13 (right; specific to tuft cells). **FDR<0.0001, likelihood-ratio test.
FIGS. 46A-E—Krt13+ progenitors express a unique set of markers distinct from mature club cells. FIG. 46A shows Krt8 does not distinguish pseudostratified club cell development from hillock-associated club cell development. Diffusion map embedding of 6,905 cells (as in FIG. 38A) shaded either by their Krt13+ hillock membership (top left), or by expression (Log2(TPM+1), shaded bar) of specific genes (all other panels). FIG. 46B shows Hillocks are more proliferative. Fraction of EdU+ epithelial cells (%, y-axis; representative image in FIG. 38E) in hillocks and non-hillock areas (x axis). ***p<0.001, likelihood-ratio test, black bar: mean, error bars: 95% CI. shows Krt13+ hillock cells are turned over rapidly. Fraction of Krt13+ cells that are club cell lineage labeled (%, y axis) at day 5 (10.2%, 95% CI [0.07, 0.16]) and its dilution at day 80 (5.2%, 95% CI [0.03, 0.08]). Error bars: 95% confidence interval, n=3 mice (dots). *p<0.05, likelihood-ratio test. shows Genes and processes associated with Krt13+ cells. FIG. 46D shows the differential expression (x axis, log2(fold-change)) and its associated significance (y axis, log10(FDR)) for each gene (dot) that is differentially expressed in Krt13+ cells (identified using clustering in diffusion map space, Methods) as compared to all cells (FDR<0.05, likelihood-ratio test). Shaded: cell type with highest expression (genes whose highest expression is in Krt13+ cells). Dots show all the genes differentially expressed (FDR<0.05) between Krt13+ hillock cells and other cells. Those genes with absolute effect sizes greater than log2(fold-change)>1 are marked with large points, while others are identified as small points (grey). FIG. 46E shows Krt13+ cell type-enriched pathways. Representative MSigDB78 gene sets (rows) that are significantly enriched (x axis and bar, −log10(FDR), hypergeometric test) in Krt13+ cells.
FIGS. 47A-H—Genes associated with cell fate transitions. FIGS. 47A-H show Relative mean expression (loess-smoothed row-wise Z-score of mean log2(TPM+1), bar at bottom) of significantly (p<0.001, permutation test) varying genes (A-D) and TFs (E-H) (rows) across subsets of 6,905 (columns) basal, club and ciliated cells. Cells are pseudotemporally ordered (x axis, all plots) using diffusion maps (FIG. 38A). Each cell was assigned to a cell fate transition if it was within d<0.1 of an edge of the convex hull of all points (where dis the Euclidean distance in diffusion-space) is assigned to that edge (Methods).
FIGS. 48A-F—Lineage tracing using Pulse-Seq. FIG. 48A shows Post hoc cluster annotation by known cell type markers4. tSNE of 66,265 scRNA-seq profiles (points) from Pulse-Seq, shaded by the expression (log2(TPM+1), bar) of single marker genes for a particular cell type or cell-cycle score79 (bottom right) FIG. 48B shows Labeled fraction of basal cells is unchanged during Pulse-Seq time course, as expected. Estimated fraction (%, y-axis, Methods) of cells of each type that are positive for the fluorescent lineage label (by FACS) in each of n=3 mice (points) per time-point (x axis). NS: p>0.1, likelihood-ratio test (Methods), error bars: 95% CI. FIG. 48C shows Pulse-Seq lineage-labeled fraction of various cell populations over time. Linear quantile regression fits (trendline, Methods) to the fraction of lineage-labeled cells of each type (n=3 mice per time point, dots, y-axis) as a function of the number of days post tamoxifen-induced labeling (x-axis). β: estimated regression coefficient, interpreted as daily rate of new lineage-labeled cells, p: p-value for the significance of the relationship, Wald test (Methods). As expected, goblet and ciliated cells are labeled more slowly than club cells (FIG. 39E). FIGS. 48D-F show Conventional Scgb1a1 (CC10) lineage trace of rare epithelial types shows minimal contribution to rare cell lineages. Fraction of Scb1a1 labeled (club cell trace) cells (y axis, %) of Gnat3+ tuft cells (D) at day 4 (0.6%, 95% CI [0.00, 0.04]) and day 30 (6.3%, 95% CI [0.04, 0.11]), Foxi1-GFP+ ionocytes at day 30 (2.9%, 95% CI [0.01, 0.11]) (E), and Chga+ neuroendocrine (NE) cells at day 4 (2.5%, 95% CI [0.01, 0.08]) and day 30 (2.6%, 95% CI [0.01, 0.08]) (F) after club cell lineage labeling. ** p<0.01, likelihood-ratio test. Error bars: 95% confidence interval. Each time point cell type combination has at least n=2 mice.
FIGS. 49A-E—Club cell heterogeneity and lineage tracing hillock-associated club cells using Pulse-Seq. FIGS. 49A-B show PC-1 and PC-2 are associated with basal to club differentiation and both proximodistal heterogeneity and hillock gene modules respectively. FIG. 49A shows PC-1 (x-axis) vs. PC-2 (y-axis) for a PCA of 17,700 scRNA-seq profiles of club cells (points) in the Pulse-Seq dataset, shaded by signature scores (legends, Methods) for basal (left), proximal club cells (center left), distal club cells (center right), the Krt13+/Krt4+ hillock (right), or their cluster assignment (inset, right). FIG. 49B shows bar plots show the extent (normalized enrichment score, y-axis, Methods) and significance of association of PC-1 (left) and PC-2 (right) for gene sets associated with different airway epithelial types (x-axis), or gene modules associated with proximodistal heterogeneity (FIG. 2g). Heatmaps shows the relative expression level (row-wise Z-score of log2(TPM+1) expression values, bar) of the 20 genes (rows) with the highest and lowest loadings on PC-1 (left) and PC-2 (right) in each club cell (columns, down-sampled to 1,000 cells for visualization only). NS p>0.05, *p<0.05, **p<0.01, ***p<0.001, permutation test (Methods). FIG. 49C shows lineage tracing of hillock-associated cells. Estimated fraction (%, y-axis, Methods) of cells of each type that are positive for the fluorescent lineage label (by FACS) from n=3 mice (points) per time-point (x axis). ***p<0.001, likelihood-ratio test (Methods), error bars: 95% CI. FIG. 49D shows Hillock-associated club cells are produced at a greater rate than all club cells. Estimated rate (%, y-axis) based on the slope of quantile regression fits (Methods) to the fraction of lineage-labeled cells of each type (x-axis). **p<0.01, rank test (Methods), error bars: 95% CI. FIG. 49E shows schematic of the more rapid turnover of basal to club cells inside (top) and outside (bottom) hillocks.
FIGS. 50A-I—Heterogeneity of rare tracheal epithelial cell types. FIG. 50A shows cell type-enriched GPCRs. Relative expression (Z-score of mean log2(TPM+1), bar) of the GPCRs (columns) that are most enriched (FDR<0.001, likelihood-ratio test) in the cells of each trachea epithelial cell type (rows) based on full-length scRNA-seq data. FIG. 50B shows tuft cell-specific expression of Type I and Type II taste receptors. Expression level (mean log2(TPM+1), bar) of tuft-cell enriched (FDR<0.05, likelihood-ratio test) taste receptor genes (columns) in each trachea epithelial cell type (rows, labeled as in e) based on full-length scRNA-seq data. FIG. 50C shows tuft cell-specific expression of the Type-2 immunity-associated alarmins Il25 and Tslp. Mean expression level (y-axis, log2(TPM+1)), of Il-25 (left) and Tslp (right) in each cell type (x axis). ***FDR<10−10, likelihood-ratio test. FIG. 50D shows morphological features of tuft cells. Immunofluorescence staining of the tuft-cell marker Gnat3 along with DAPI. Arrowhead: “tuft”, arrows: cytoplasmic extension. FIGS. 50E-F show mature and immature subsets are identified using marker gene expression. The distribution of expression of scores (y-axis, using top 20 marker genes, Supplementary Table 1, Methods) for tuft (e) goblet (f), basal and club cells (label on top) in each cell subset (x axis) (basal and club cells downsampled to 1,000 cells). *p<0.05, ***p<0.001, Mann-Whitney U-test. FIGS. 50G-H show gene signatures for goblet-1 and goblet-2 subsets. The distribution (G) and relative expression level (H, row-wise Z-scores, bar) of marker genes that distinguish (log 2 fold-change >0.1, FDR<0.001, likelihood-ratio test) cells in the goblet-1 and goblet-2 sub-clusters (bar, top and left) from the combined 3′ scRNA-seq datasets. FIG. 50I shows immunofluorescence staining of the goblet-1 marker Tff2 (magenta), the known goblet cell marker Muc5ac, and DAPI (blue). Solid white line: boundary of a goblet-1 cell.
FIGS. 51A-H—Ionocyte characterization. FIG. 51A shows Immunofluorescent characterization of ionocytes. Ionocytes visualized with EGFP(Foxi1) mouse. EGFP appropriately marks Foxi1 antibody-positive cells (left panel, solid white line). EGFP+ cells express canonical airway markers Ttfl (Nkx2-1) and Sox2 (solid white lines). EGFP(Foxi1)+ cells do not label with basal (Trp63), club (Scgb1a1), ciliated (Foxj1), tuft (Gnat3), neuroendocrine (NE) (Chga), or goblet (Tff2) cell markers (dashed white lines). FIG. 51B shows ionocytes are sparsely distributed in the surface epithelium. Representative whole mount confocal image of ionocytes EGFP(Foxi1) and ciliated cells (AcTub). FIG. 51C shows GFP(Foxi1)+ ionocytes extend cytoplasmic appendages (arrows). FIG. 51F shows immunofluorescent labeling of GFP(Foxi1)+ cells in the submucosal gland. Dotted line separates surface epithelium (SA) from submucosal gland (SMG). FIG. 51E shows Ascl3-KO moderately decreases ionocyte TFs and Cftr in ALI cultured epithelia. Expression quantification (ΔΔCT, y-axis) of ionocyte (Cftr: −0.82 ΔΔCT, 95% CI [±0.20], Foxi1: −0.75 ΔΔCT, 95% CI [±0.28], Ascl3: −10.28 ΔΔCT, 95% CI [±1.85]) and basal (Trp63), club (Scgb1a1), or ciliated (Foxj1) markers (x-axis) in hetero- and homozygous KO (legend) are normalized to wild type littermates. The mean of independent probes (p1 and p2) was used for Cftr. n=10 and 5 hetero- and homozygous KO, respectively and n=4 wild type mice. *p<0.05, **p<0.01, Dunn's Method, error bars: 95% CI. FIG. 51F shows increased depth of airway surface liquid (ASL) in Foxi1-KO ALI culture compared to WT. Representative OCT image of ASL. bar: airway surface liquid and mucous layer depth. Scale bar (white): 10 m. FIGS. 51G-H show increased forskolin ΔIeq in heterozygous and KO epithelia. ΔIeq (y axis) in ALI cultures of wild type (WT), heterozygous (HET) and Foxi1 knock-out (KO) mice (n=5 WT, n=4 HET, n=6 KO, dots) that were characterized for their forskolin-inducible equivalent currents (G, Ieq) and for currents sensitive to CFTRinh-172 (H). The inhibitor-sensitive ΔIegs reported may be somewhat underestimating the true inhibitor-sensitive current, since not for all filters the inhibitor response reached a steady plateau on the time scale of the experiment.
FIGS. 52A-C—Ionocyte characterization. FIGS. 52A-B show ionocyte depletion or disruption via Foxi1-KO disrupts mucosal homeostasis in ALI cultured epithelia. ASL depth determined via OCT (A) and pH (B) in homozygous Foxi1-KO (n=9, dots) vs. wild type littermates (x-axis, n=3 mice). p values: Mann-Whitney U-test. FIG. 52C shows Foxi1-TA results in increased Cftr short-circuit current (ΔIsc, y-axis) in ferret ALI vs. mock transfected controls (Methods). n=5, *p<0.05, t-test, error bars: 95% CI.



DETAILED DESCRIPTION OF THE EXAMPLE EMBODIMENTS
General Definitions
Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. Definitions of common terms and techniques in molecular biology may be found in Molecular Cloning: A Laboratory Manual, 2nd edition (1989) (Sambrook, Fritsch, and Maniatis); Molecular Cloning: A Laboratory Manual, 4th edition (2012) (Green and Sambrook); Current Protocols in Molecular Biology (1987) (F. M. Ausubel et al. eds.); the series Methods in Enzymology (Academic Press, Inc.): PCR 2: A Practical Approach (1995) (M. J. MacPherson, B. D. Hames, and G. R. Taylor eds.): Antibodies, A Laboratory Manual (1988) (Harlow and Lane, eds.): Antibodies A Laboratory Manual, 2nd edition 2013 (E. A. Greenfield ed.); Animal Cell Culture (1987) (R. I. Freshney, ed.); Benjamin Lewin, Genes IX, published by Jones and Bartlet, 2008 (ISBN 0763752223); Kendrew et al. (eds.), The Encyclopedia of Molecular Biology, published by Blackwell Science Ltd., 1994 (ISBN 0632021829); Robert A. Meyers (ed.), Molecular Biology and Biotechnology: a Comprehensive Desk Reference, published by VCH Publishers, Inc., 1995 (ISBN 9780471185710); Singleton et al., Dictionary of Microbiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York, N.Y. 1994), March, Advanced Organic Chemistry Reactions, Mechanisms and Structure 4th ed., John Wiley & Sons (New York, N.Y. 1992); and Marten H. Hofker and Jan van Deursen, Transgenic Mouse Methods and Protocols, 2nd edition (2011)
As used herein, the singular forms “a” “an”, and “the” include both singular and plural referents unless the context clearly dictates otherwise.
The term “optional” or “optionally” means that the subsequent described event, circumstance or substituent may or may not occur, and that the description includes instances where the event or circumstance occurs and instances where it does not.
The recitation of numerical ranges by endpoints includes all numbers and fractions subsumed within the respective ranges, as well as the recited endpoints.
The terms “about” or “approximately” as used herein when referring to a measurable value such as a parameter, an amount, a temporal duration, and the like, are meant to encompass variations of and from the specified value, such as variations of +/−10% or less, +/−5% or less, +/−1% or less, and +/−0.1% or less of and from the specified value, insofar such variations are appropriate to perform in the disclosed invention. It is to be understood that the value to which the modifier “about” or “approximately” refers is itself also specifically, and preferably, disclosed.
Reference throughout this specification to “one embodiment”, “an embodiment,” “an example embodiment,” means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrases “in one embodiment,” “in an embodiment,” or “an example embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment, but may. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner, as would be apparent to a person skilled in the art from this disclosure, in one or more embodiments. Furthermore, while some embodiments described herein include some but not other features included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention. For example, in the appended claims, any of the claimed embodiments can be used in any combination.
Whereas the terms “one or more” or “at least one”, such as one or more members or at least one member of a group of members, is clear per se, by means of further exemplification, the term encompasses inter alia a reference to any one of the members, or to any two or more of the members, such as, e.g., any ≥3, ≥4, ≥5, ≥6 or ≥7 etc. of the members, and up to all members. In another example, “one or more” or “at least one” may refer to 1, 2, 3, 4, 5, 6, 7 or more.
The term “optional” or “optionally” means that the subsequent described event, circumstance or substituent may or may not occur, and that the description includes instances where the event or circumstance occurs and instances where it does not.
The term “isolated” as used throughout this specification with reference to a particular component generally denotes that such component exists in separation from—for example, has been separated from or prepared and/or maintained in separation from—one or more other components of its natural environment. More particularly, the term “isolated” as used herein in relation to a cell or cell population denotes that such cell or cell population does not form part of an animal or human body.
The terms “subject”, “individual” or “patient” are used interchangeably throughout this specification, and typically and preferably denote humans, but may also encompass reference to non-human animals, preferably warm-blooded animals, even more preferably mammals, such as, e.g., non-human primates, rodents, canines, felines, equines, ovines, porcines, and the like. The term “non-human animals” includes all vertebrates, e.g., mammals, such as non-human primates, (particularly higher primates), sheep, dog, rodent (e.g. mouse or rat), guinea pig, goat, pig, cat, rabbits, cows, and non-mammals such as chickens, amphibians, reptiles etc. In one embodiment, the subject is a non-human mammal. In another embodiment, the subject is human. In another embodiment, the subject is an experimental animal or animal substitute as a disease model. The term does not denote a particular age or sex. Thus, adult and newborn subjects, as well as fetuses, whether male or female, are intended to be covered. Examples of subjects include humans, dogs, cats, cows, goats, and mice. The term subject is further intended to include transgenic species.
The terms “sample” or “biological sample” as used throughout this specification include any biological specimen obtained from a subject. Particularly preferred are samples from the intestinal tissue, but may also include samples from intestinal lumen, faeces, or blood. The term “tissue” as used throughout this specification refers to any animal tissue types, but particularly preferred is intestinal tissue. The tissue may be healthy or affected by pathological alterations. The tissue may be from a living subject or may be cadaveric tissue. The tissue may be autologous tissue or syngeneic tissue or may be allograft or xenograft tissue.
Reference is made to U.S. Provisional application Ser. No. 62/533,653, filed Jul. 17, 2017 and International application serial number PCT/US2017/060469, filed Nov. 7, 2017.
All publications, published patent documents, and patent applications cited herein are hereby incorporated by reference to the same extent as though each individual publication, published patent document, or patent application was specifically and individually indicated as being incorporated by reference.
OVERVIEW
The inventors have identified novel markers capable of identifying new subpopulations of cells, have developed an atlas of the cells in the small intestine and trachea. Additionally, the inventors have validated the population of tuft cells in the trachea (i.e., a subset of epithelial cells). The present invention provides isolated and modified cells, including therapeutic compositions thereof, as well as methods for identifying said cells types and method for modulating said cells for the treatment of gastrointestinal disorders, such as irritable bowel disease, Crohn's disease, and food allergies and respiratory disorders, such as asthma.
As disclosed further herein, Applicants combined single-cell RNA-sequencing (scRNA-Seq) and in vivo lineage tracing to study the cellular composition and hierarchy of the murine tracheal epithelium. Applicants identified new tuft cell types. Applicants revised the cellular hierarchy of the epithelium and demonstrated that tuft cells, neuroendocrine cells, and ionocytes are all direct descendants of basal cells and that they continually turn over. Applicants further discovered a novel cell population that resides in “hillocks”, previously unrecognized epithelial structures. Applicants found that club cells functionally vary based on their location within the respiratory tree and identify disease-relevant subsets of tuft and goblet cells. By associating cell type-specific gene expression programs with key disease genes, Applicants establish a new cellular narrative for airways disease.
Embodiments disclosed herein provide markers and gene signatures for identifying, isolating and modulating cells for the treatment of diseases and disorders associated with the gut and the respiratory system. Understanding the development, differentiation and function of an organ, such as the intestine, requires the identification and characterization of all of its component cell types. In the small bowel, intestinal epithelial cells (IECs) sense and respond to microbial stimuli and noxious substances, provide crucial barrier function and participate in the coordination of immune responses. Here, Applicants profiled 53,193 individual IECs from mouse small intestine and intestinal organoid cultures. Using unsupervised clustering, Applicants defined specific gene signatures for major IEC lineages, including the identification of Mptx2, a mucosal pentraxin, as a novel Paneth cell marker. In addition, Applicants identified unexpected diversity of hormone-secreting enteroendocrine populations, revealing co-expression programs of gut hormone genes, previously thought to represent different enteroendocrine subtypes, and constructed a novel hierarchical taxonomy of these cells. Applicants also distinguished two subtypes of Dclk1-positive tuft cells, one of which (Tuft-2) expresses both the epithelial cytokine Tslp and the pan-immune cell marker Ptprc (CD45), which has not been previously associated with any non-hematopoietic cell type. Finally, Applicants characterized how the intrinsic states and proportions of these cell types are reshaped in response to infections, e.g. Salmonella enterica and Heligmosomoides polygyrus infections. Salmonella infection led to an increased number of Paneth cells and enterocytes, and Paneth cell-specific up-regulation of both defensins and Mptx2. In addition, an absorptive enterocyte-specific antimicrobial program was broadly activated across all IEC types, demonstrating previously uncharacterized cellular response to pathogens. In contrast, H. polygyrus led to expansion of goblet and tuft cell populations, with a particular expansion of the Cd45+ Tuft-2 group. The high-resolution atlas highlights new markers and transcriptional programs, novel allocation of sensory molecules to cell types and organizational principles of gut homeostasis and physiology.
Here, Applicants use scRNA-seq to chart a comprehensive atlas of the epithelial cells of the small intestine. Applicants identified gene signatures, key TFs and specific GPCRs for each of the major small intestinal differentiated cell types, and traced their differentiation from ISCs. Applicants identified and characterized cellular heterogeneity within specific cell-types, and validated individual genes and signatures in situ. Applicants found a transcriptional signature distinguishing proximal and distal enterocytes, established a novel classification of the different subtypes of the enteroendocrine cells and their differential deployment at different locations, and identified a previously unrecognized separation of tuft cells to two sub-types, one with a neuron-like and one with an immune-like gene signature, expressing Ptprc (CD45) and TSLP, a pan-immune cell marker and epithelial cytokine, respectively. Finally, Applicants demonstrated how these cell types and states change dynamically as the small intestine adapts to infection by distinct classes of pathogens. The high resolution cell atlas better defines the composition of the gut, highlights novel key molecules, TFs and GPCRs that can impact gut function and shows how changes in gut composition can play a key role in maintaining homeostasis in response to pathogens.
Airways conduct gases to the distal lung and are the sites of disease in asthma and cystic fibrosis. Here, Applicants further combined single-cell RNA-sequencing (scRNA-Seq) and in vivo lineage tracing to study the cellular composition and hierarchy of the murine tracheal epithelium. Applicants identified a new rare cell, the pulmonary ionocyte. Applicants revised the cellular hierarchy of the epithelium and demonstrated that tuft cells, neuroendocrine cells, and ionocytes are all direct descendants of basal cells and that they continually turn over. Applicants further discovered a novel cell population that resides in “hillocks”, previously unrecognized epithelial structures. Applicants found that club cells functionally vary based on their location within the respiratory tree and identify disease-relevant subsets of tuft and goblet cells. Remarkably, Applicants found that the cystic fibrosis gene, CFTR, is predominantly expressed in pulmonary ionocytes in both mouse and human. Loss of ionocytes in mouse epithelia results in the loss of Cftr expression, abnormal surface fluid, and increased mucus viscosity, all of which are altered in cystic fibrosis. By associating cell type-specific gene expression programs with key disease genes, Applicants establish a new cellular narrative for airways disease.
Isolated Cells, Markers, and Gene Signatures
The small intestinal mucosa is at equipoise with a complex luminal milieu which comprises a combination of diverse microbial species and their products as well as derivative products of the diet. It is increasingly clear that the functional balance between the epithelium and the constituents within the lumen plays a central role in both maintaining the normal mucosa and the pathophysiology of many gastrointestinal disorders. The barrier function is part fulfilled by anatomic features that partly impede penetration of macromolecules and diverse set of specialized cells that monitor and titrate responses to a variety of noxious substances or pathogens (Peterson and Artis, 2014). The underlying mucosal immune system is poised to detect antigens and bacteria at the mucosal surface and to drive appropriate responses of tolerance or an active immune response.
IECs of the small intestinal epithelium comprise two major lineages—absorptive and secretory (Clevers, 2006)—reflecting its dual roles. Enterocytes of the absorptive lineage comprise approximately 80% of the epithelium and are specialized for digestion and transport of nutrients (Ferraris et al., 1992). The secretory lineage comprises five further terminally differentiated types of IECs: goblet, Paneth, enteroendocrine, tuft and microfold (M) cells (Barker et al., 2007; Gerbe et al., 2012; Sato et al., 2009)—each with distinct and specialized sensory and effector functions.
The epithelium is organized in a repeating structure of villi, which project toward the lumen, and nearby crypts (FIG. 1a). The crypts of the small intestine are the proliferative part of the epithelium, in which intestinal stem cells (ISCs) and progenitors, termed transit-amplifying cells (TAs), reside (Barker et al., 2007; Barker et al., 2012; Miyoshi and Stappenbeck, 2013). In contrast, only fully differentiated cells are found on the villi (Barker, 2014; Clevers, 2013; Peterson and Artis, 2014). The crypt also contains Paneth cells, which secrete anti-microbial peptides (AMPs), such as defensins and lysozyme, into the lumen to keep the microbiota in check (Cheng and Leblond, 1974b; Clevers, 2013; Salzman et al., 2003). The highly proliferative TA cells migrate along the crypt-villus axis and differentiate into functionally distinct epithelial cell types that subsequently reach the tip of the villus, where mature cells undergo apoptosis and shed to the lumen (Clevers, 2006). Epithelial tissue turns over rapidly (˜5 days) (Barker, 2014; Clevers, 2013; van der Flier et al., 2009), allowing it to dynamically shift its composition in response to stress or pathogens.
For example, parasitic infection typically induces hyperplasia of goblet cells, which produce and secrete mucins to prevent pathogen attachment, strengthening the epithelial barrier and facilitating parasite expulsion (Pelaseyed et al., 2014). Rare (0.5-1%) enteroendocrine cells (EECs) secrete over 20 individual hormones and are key mediators of intestinal response to nutrients (Furness et al., 2013; Gribble and Reimann, 2016) by directly detecting fluctuations in luminal nutrient concentrations via G-protein-coupled receptors (GPCRs)(Gribble and Reimann, 2016). Mapping these GPCRs and hormones has important therapeutic applications. Finally, IECs communicate with immune cells to initiate either inflammatory responses or tolerance in response to lumen signals (Biton et al., 2011; Peterson and Artis, 2014).
A rare IEC population, tuft cells (Gerbe et al., 2012) promote type-2 immunity in response to intestinal parasites by expressing interleukin-25 (1125), which in turn mediates the recruitment of group 2 of innate lymphoid cells (ILC2s) that initiate the expansion of T-helper type 2 cells upon parasite infection (Gerbe et al., 2016; Howitt et al., 2016; von Moltke et al., 2016). Furthermore, M cells reside exclusively in follicle-associated epithelia found only above Peyer's patches, which are gut associated lymphoid follicles (de Lau et al., 2012). M cells play an important role in immune sensing by transporting luminal content to immune cells found directly below them (Mabbott et al., 2013). Disruption in any of the major innate immune sensors and proximity effector functions of IECs may result in increased antigenic load through weakening of the epithelial barrier, and may lead to the onset of acute or chronic inflammation. Despite this extensive knowledge, given the complexity of the epithelial cellular ecosystem, many questions remain open.
The present invention identifies discrete epithelial cell types of the gut and respiratory tract, additional types, or new sub-types that have eluded previous studies. The present invention also provides a molecular characterization of each type. For example, mapping the GPCRs and hormones expressed by EECs has important therapeutic applications; charting known and new specific cell surface markers can provide handles for specific cell isolation, and help assess the validity of legacy ones; and finding differentially expressed transcription factors (TFs) can open the way to study the molecular processes that accompany the differentiation of IECs, such as tuft or enteroendocrine cells. The present invention also identifies targets and pathways involved in the response of individual cell populations to pathogenic insult, both in terms of changes in cellular proportions and cell-intrinsic responses.
Tuft Cells: Tuft cells, sometimes referred to as brush cells, are chemosensory cells in the epithelial lining of the intestines and respiratory tract. The names “tuft” and “brush” refer to the microvilli projecting from the cells. Ordinarily there are very few tuft cells present but they have been shown to greatly increase at times of a infection, including parasitic infection. Several studies have proposed a role for tuft cells in defense against parasitic infection. In the intestine, tuft cells are the sole source of secreted interleukin 25 (IL-25), a cytokine involved in type 2 immunity (Harris, Science (2016) Vol. 351, Issue 6279, pp. 1264-1265; and Howitt and Lavoie (2016) Science. 351: 1329-33). Applicants have discovered for the first time signature genes specific for tuft cells in the gut and respiratory tract that can be used to isolate, detect and target tuft cells, specifically novel subtypes of tuft cells (e.g., neuronal, immune). Prior to the present invention, the specific subtypes of tuft cells could not be detected or modulated.
Type 2 innate lymphoid cells (ILC2s) regulate the initiation of allergic tissue inflammation at mucosal surfaces, in large part due to their ability to rapidly produce effector cytokines such as IL-5 and IL-13 (Neill, D. R. et al. Nature 464, 1367-1370, (2010); and Moro, K. et al. Nature 463, 540-544, (2010)). ILCs are also vital in maintaining tissue homeostasis by promoting epithelial cell proliferation, survival, and barrier integrity (Monticelli, L. A. et al. Nature immunology 12, 1045-1054, (2011)). Alarmin cytokines, such as IL-25 and IL-33, activate ILC2s to promote tissue homeostasis in the face of epithelial injury, but also play critical roles in initiating allergic inflammatory responses (Huang, Y. et al. Nature immunology 16, 161-169, (2015); Cheng, D. et al. American journal of respiratory and critical care medicine 190, 639-648, (2014); and Gudbjartsson, D. F. et al. Nature genetics 41, 342-347, (2009)).
Accordingly, one aspect, embodiment disclosed herein provide isolated cells, in particular isolated tuft cells. The isolated tuft cell may be a gastrointestinal tuft cell or subset of a gastrointestinal tuft cell, or a respiratory tuft cell or a subset of respiratory tuft cells. The tuft cell may be a respiratory or digestive system tuft cell. The digestive system tuft cell may comprise an esophageal epithelial cell, a stomach epithelial cell, or an intestinal epithelial cell. The respiratory tuft cell may comprise a laryngeal epithelial cell, a tracheal epithelial cell, a bronchial epithelial cell, or a submucosal gland cell.
The isolated cells disclosed herein may be defined by the presence of certain markers or gene signatures unique to that isolated cell type or sub-type, or a particular cell state of said cell type or sub-type. As used herein a “cell state” refers to a particular functional state, for example the cell state of a tuft cell in homeostasis, may be differentiated from the cell state of a tuft cell after exposure to certain external stimuli such exposure to certain cytokines after an infection, based on the presence of certain markers or gene signatures.
Markers: The term “marker” is widespread in the art and commonly broadly denotes a biological molecule, more particularly an endogenous biological molecule, and/or a detectable portion thereof, whose qualitative and/or quantitative evaluation in a tested object (e.g., in or on a cell, cell population, tissue, organ, or organism, e.g., in a biological sample of a subject) is predictive or informative with respect to one or more aspects of the tested object's phenotype and/or genotype. The terms “marker” and “biomarker” may be used interchangeably throughout this specification.
Preferably, markers as intended herein may be peptide-, polypeptide- and/or protein-based, or may be nucleic acid-based. For example, a marker may be comprised of peptide(s), polypeptide(s) and/or protein(s) encoded by a given gene, or of detectable portions thereof. Further, whereas the term “nucleic acid” generally encompasses DNA, RNA and DNA/RNA hybrid molecules, in the context of markers the term may typically refer to heterogeneous nuclear RNA (hnRNA), pre-mRNA, messenger RNA (mRNA), or copy DNA (cDNA), or detectable portions thereof. Such nucleic acid species are particularly useful as markers, since they contain qualitative and/or quantitative information about the expression of the gene. Particularly preferably, a nucleic acid-based marker may encompass mRNA of a given gene, or cDNA made of the mRNA, or detectable portions thereof. Any such nucleic acid(s), peptide(s), polypeptide(s) and/or protein(s) encoded by or produced from a given gene are encompassed by the term “gene product(s)”.
Preferably, markers as intended herein may be extracellular or cell surface markers, as methods to measure extracellular or cell surface marker(s) need not disturb the integrity of the cell membrane and may not require fixation/permeabilization of the cells.
The term “protein” as used throughout this specification generally encompasses macromolecules comprising one or more polypeptide chains, i.e., polymeric chains of amino acid residues linked by peptide bonds. The term may encompass naturally, recombinantly, semi-synthetically or synthetically produced proteins. The term also encompasses proteins that carry one or more co- or post-expression-type modifications of the polypeptide chain(s), such as, without limitation, glycosylation, acetylation, phosphorylation, sulfonation, methylation, ubiquitination, signal peptide removal, N-terminal Met removal, conversion of pro-enzymes or pre-hormones into active forms, etc. The term further also includes protein variants or mutants which carry amino acid sequence variations vis-à-vis corresponding native proteins, such as, e.g., amino acid deletions, additions and/or substitutions. The term contemplates both full-length proteins and protein parts or fragments, e.g., naturally-occurring protein parts that ensue from processing of such full-length proteins.
The term “polypeptide” as used throughout this specification generally encompasses polymeric chains of amino acid residues linked by peptide bonds. Hence, insofar a protein is only composed of a single polypeptide chain, the terms “protein” and “polypeptide” may be used interchangeably herein to denote such a protein. The term is not limited to any minimum length of the polypeptide chain. The term may encompass naturally, recombinantly, semi-synthetically or synthetically produced polypeptides. The term also encompasses polypeptides that carry one or more co- or post-expression-type modifications of the polypeptide chain, such as, without limitation, glycosylation, acetylation, phosphorylation, sulfonation, methylation, ubiquitination, signal peptide removal, N-terminal Met removal, conversion of pro-enzymes or pre-hormones into active forms, etc. The term further also includes polypeptide variants or mutants which carry amino acid sequence variations vis-à-vis a corresponding native polypeptide, such as, e.g., amino acid deletions, additions and/or substitutions. The term contemplates both full-length polypeptides and polypeptide parts or fragments, e.g., naturally-occurring polypeptide parts that ensue from processing of such full-length polypeptides.
The term “peptide” as used throughout this specification preferably refers to a polypeptide as used herein consisting essentially of 50 amino acids or less, e.g., 45 amino acids or less, preferably 40 amino acids or less, e.g., 35 amino acids or less, more preferably 30 amino acids or less, e.g., 25 or less, 20 or less, 15 or less, 10 or less or 5 or less amino acids.
The term “nucleic acid” as used throughout this specification typically refers to a polymer (preferably a linear polymer) of any length composed essentially of nucleoside units. A nucleoside unit commonly includes a heterocyclic base and a sugar group. Heterocyclic bases may include inter alia purine and pyrimidine bases such as adenine (A), guanine (G), cytosine (C), thymine (T) and uracil (U) which are widespread in naturally-occurring nucleic acids, other naturally-occurring bases (e.g., xanthine, inosine, hypoxanthine) as well as chemically or biochemically modified (e.g., methylated), non-natural or derivatised bases. Exemplary modified nucleobases include without limitation 5-substituted pyrimidines, 6-azapyrimidines and N-2, N-6 and O-6 substituted purines, including 2-aminopropyladenine, 5-propynyluracil and 5-propynylcytosine. In particular, 5-methylcytosine substitutions have been shown to increase nucleic acid duplex stability and may be preferred base substitutions in for example antisense agents, even more particularly when combined with 2′-O-methoxyethyl sugar modifications. Sugar groups may include inter alia pentose (pentofuranose) groups such as preferably ribose and/or 2-deoxyribose common in naturally-occurring nucleic acids, or arabinose, 2-deoxyarabinose, threose or hexose sugar groups, as well as modified or substituted sugar groups (such as without limitation 2′-O-alkylated, e.g., 2′-O-methylated or 2′-O-ethylated sugars such as ribose; 2′-O-alkyloxyalkylated, e.g., 2′-O-methoxyethylated sugars such as ribose; or 2′-O,4′-C-alkylene-linked, e.g., 2′-O,4′-C-methylene-linked or 2′-O,4′-C-ethylene-linked sugars such as ribose; 2′-fluoro-arabinose, etc.).
Nucleoside units may be linked to one another by any one of numerous known inter-nucleoside linkages, including inter alia phosphodiester linkages common in naturally-occurring nucleic acids, and further modified phosphate- or phosphonate-based linkages such as phosphorothioate, alkyl phosphorothioate such as methyl phosphorothioate, phosphorodithioate, alkylphosphonate such as methylphosphonate, alkylphosphonothioate, phosphotriester such as alkylphosphotriester, phosphoramidate, phosphoropiperazidate, phosphoromorpholidate, bridged phosphoramidate, bridged methylene phosphonate, bridged phosphorothioate; and further siloxane, carbonate, sulfamate, carboalkoxy, acetamidate, carbamate such as 3′-N-carbamate, morpholino, borano, thioether, 3′-thioacetal, and sulfone internucleoside linkages. Preferably, inter-nucleoside linkages may be phosphate-based linkages including modified phosphate-based linkages, such as more preferably phosphodiester, phosphorothioate or phosphorodithioate linkages or combinations thereof. The term “nucleic acid” also encompasses any other nucleobase containing polymers such as nucleic acid mimetics, including, without limitation, peptide nucleic acids (PNA), peptide nucleic acids with phosphate groups (PHONA), locked nucleic acids (LNA), morpholino phosphorodiamidate-backbone nucleic acids (PMO), cyclohexene nucleic acids (CeNA), tricyclo-DNA (tcDNA), and nucleic acids having backbone sections with alkyl linkers or amino linkers (see, e.g., Kurreck 2003 (Eur J Biochem 270: 1628-1644)). “Alkyl” as used herein particularly encompasses lower hydrocarbon moieties, e.g., C1-C4 linear or branched, saturated or unsaturated hydrocarbon, such as methyl, ethyl, ethenyl, propyl, 1-propenyl, 2-propenyl, and isopropyl. Nucleic acids as intended herein may include naturally occurring nucleosides, modified nucleosides or mixtures thereof.
A modified nucleoside may include a modified heterocyclic base, a modified sugar moiety, a modified inter-nucleoside linkage or a combination thereof. The term “nucleic acid” further preferably encompasses DNA, RNA and DNA/RNA hybrid molecules, specifically including hnRNA, pre-mRNA, mRNA, cDNA, genomic DNA, amplification products, oligonucleotides, and synthetic (e.g., chemically synthesised) DNA, RNA or DNA/RNA hybrids. A nucleic acid can be naturally occurring, e.g., present in or isolated from nature, can be recombinant, i.e., produced by recombinant DNA technology, and/or can be, partly or entirely, chemically or biochemically synthesised. A “nucleic acid” can be double-stranded, partly double stranded, or single-stranded. Where single-stranded, the nucleic acid can be the sense strand or the antisense strand. In addition, nucleic acid can be circular or linear.
Unless otherwise apparent from the context, reference herein to any marker, such as a peptide, polypeptide, protein, or nucleic acid, may generally also encompass modified forms of the marker, such as bearing post-expression modifications including, for example, phosphorylation, glycosylation, lipidation, methylation, cysteinylation, sulphonation, glutathionylation, acetylation, oxidation of methionine to methionine sulphoxide or methionine sulphone, and the like.
The reference to any marker, including any peptide, polypeptide, protein, or nucleic acid, corresponds to the marker commonly known under the respective designations in the art. The terms encompass such markers of any organism where found, and particularly of animals, preferably warm-blooded animals, more preferably vertebrates, yet more preferably mammals, including humans and non-human mammals, still more preferably of humans.
The terms particularly encompass such markers, including any peptides, polypeptides, proteins, or nucleic acids, with a native sequence, i.e., ones of which the primary sequence is the same as that of the markers found in or derived from nature. A skilled person understands that native sequences may differ between different species due to genetic divergence between such species. Moreover, native sequences may differ between or within different individuals of the same species due to normal genetic diversity (variation) within a given species. Also, native sequences may differ between or even within different individuals of the same species due to somatic mutations, or post-transcriptional or post-translational modifications. Any such variants or isoforms of markers are intended herein. Accordingly, all sequences of markers found in or derived from nature are considered “native”. The terms encompass the markers when forming a part of a living organism, organ, tissue or cell, when forming a part of a biological sample, as well as when at least partly isolated from such sources. The terms also encompass markers when produced by recombinant or synthetic means.
In certain embodiments, markers, including any peptides, polypeptides, proteins, or nucleic acids, may be human, i.e., their primary sequence may be the same as a corresponding primary sequence of or present in a naturally occurring human markers. Hence, the qualifier “human” in this connection relates to the primary sequence of the respective markers, rather than to their origin or source. For example, such markers may be present in or isolated from samples of human subjects or may be obtained by other means (e.g., by recombinant expression, cell-free transcription or translation, or non-biological nucleic acid or peptide synthesis).
The reference herein to any marker, including any peptide, polypeptide, protein, or nucleic acid, also encompasses fragments thereof. Hence, the reference herein to measuring (or measuring the quantity of) any one marker may encompass measuring the marker and/or measuring one or more fragments thereof.
For example, any marker and/or one or more fragments thereof may be measured collectively, such that the measured quantity corresponds to the sum amounts of the collectively measured species. In another example, any marker and/or one or more fragments thereof may be measured each individually.
The term “fragment” as used throughout this specification with reference to a peptide, polypeptide, or protein generally denotes a portion of the peptide, polypeptide, or protein, such as typically an N- and/or C-terminally truncated form of the peptide, polypeptide, or protein. Preferably, a fragment may comprise at least about 30%, e.g., at least about 50% or at least about 70%, preferably at least about 80%, e.g., at least about 85%, more preferably at least about 90%, and yet more preferably at least about 95% or even about 99% of the amino acid sequence length of the peptide, polypeptide, or protein. For example, insofar not exceeding the length of the full-length peptide, polypeptide, or protein, a fragment may include a sequence of ≥5 consecutive amino acids, or ≥10 consecutive amino acids, or ≥20 consecutive amino acids, or ≥30 consecutive amino acids, e.g., ≥40 consecutive amino acids, such as for example ≥50 consecutive amino acids, e.g., ≥60, ≥70, ≥80, ≥90, ≥100, ≥200, ≥300, ≥400, ≥500 or ≥600 consecutive amino acids of the corresponding full-length peptide, polypeptide, or protein.
The term “fragment” with reference to a nucleic acid (polynucleotide) generally denotes a 5′- and/or 3′-truncated form of a nucleic acid. Preferably, a fragment may comprise at least about 30%, e.g., at least about 50% or at least about 70%, preferably at least about 80%, e.g., at least about 85%, more preferably at least about 90%, and yet more preferably at least about 95% or even about 99% of the nucleic acid sequence length of the nucleic acid. For example, insofar not exceeding the length of the full-length nucleic acid, a fragment may include a sequence of ≥5 consecutive nucleotides, or ≥10 consecutive nucleotides, or ≥20 consecutive nucleotides, or ≥30 consecutive nucleotides, e.g., ≥40 consecutive nucleotides, such as for example ≥50 consecutive nucleotides, e.g., ≥60, ≥70, ≥80, ≥90, ≥100, ≥200, ≥300, ≥400, ≥500 or ≥600 consecutive nucleotides of the corresponding full-length nucleic acid.
The terms encompass fragments arising by any mechanism, in vivo and/or in vitro, such as, without limitation, by alternative transcription or translation, exo- and/or endo-proteolysis, exo- and/or endo-nucleolysis, or degradation of the peptide, polypeptide, protein, or nucleic acid, such as, for example, by physical, chemical and/or enzymatic proteolysis or nucleolysis. The phrase “gene or gene product signature” as intended throughout this specification refers to a set, group or collection of one or more, preferably two or more markers, such as genes or gene products, the expression status or profile of which is associated with or identifies a specific cell type, cell subtype, or cell state of a specific cell type or subtype. Such gene or gene product signatures can be used for example to indicate the presence of a specific cell type, cell subtype, or cell state of a specific cell type or subtype in a population of cells, and/or the overall cell type composition or status of an entire cell population. Such gene or gene product signatures may be indicative of cells within a population of cells in vivo. Preferably, a reference herein to a gene or gene product signature comprising or consisting of one or more genes or gene products from a discrete list of genes or gene products may denote that the genes or gene products said to be comprised by or constituting the signature are expressed in a specific cell type, cell subtype, or cell state of a specific cell type or subtype, i.e., that cells of the specific cell type, cell subtype, or cell state of the specific cell type or subtype are positive for the genes or gene products comprised by the signature.
Gene Signatures: Typically, a gene signature may comprise or consist of two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, 15 or more, 20 or more, 25 or more, 30 or more, 35 or more, 40 or more, 45 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, or 100 or more, or 200 or more, or 300 or more, or 400 or more, or 500 or more genes or gene products. Where the present specification refers to a signature as comprising or consisting of one or more genes set forth in a given Table, the signature may comprise of consist of, by means of example and without limitation, one, or two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, 15 or more, 20 or more, 25 or more, 30 or more, 35 or more, 40 or more, 45 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, or 100 or more (provided that the recited number does not exceed the number of genes or gene products listed in the Table) or substantially all or all genes or gene products as set forth in the Table. In certain embodiments, the signature may comprise or consist of at least 1%, at least 2%, at least 3%, at least 4%, at least 5%, at least 6%, at least 7%, at least 8%, at least 9%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, or at least 90%, or at least 95%, e.g., 96%, 97%, 98%, 99%, or up to 100% (by number) of the genes or gene products set forth in the Table (rounded up or down as conventional to the closest integer).
As used herein a signature may encompass any gene or genes, or protein or proteins, whose expression profile or whose occurrence is associated with a specific cell type, subtype, or cell state of a specific cell type or subtype within a population of cells. Increased or decreased expression or activity or prevalence may be compared between different cells in order to characterize or identify for instance specific cell (sub)populations. A gene signature as used herein, may thus refer to any set of up- and down-regulated genes between different cells or cell (sub)populations derived from a gene-expression profile. For example, a gene signature may comprise a list of genes differentially expressed in a distinction of interest. It is to be understood that also when referring to proteins (e.g. differentially expressed proteins), such may fall within the definition of “gene” signature.
The signatures as defined herein (be it a gene signature, protein signature or other genetic signature) can be used to indicate the presence of a cell type, a subtype of the cell type, the state of the microenvironment of a population of cells, a particular cell type population or subpopulation, and/or the overall status of the entire cell (sub)population. Furthermore, the signature may be indicative of cells within a population of cells in vivo. The signature may also be used to suggest for instance particular therapies, or to follow up treatment, or to suggest ways to further modulate intestinal epithelial cells. The signatures of the present invention may be discovered by analysis of expression profiles of single-cells within a population of cells from isolated samples (e.g. biopsy), thus allowing the discovery of novel cell subtypes or cell states that were previously invisible or unrecognized.
The presence of subtypes or cell states may be determined by subtype specific or cell state specific signatures. The presence of these specific cell (sub)types or cell states may be determined by applying the signature genes to bulk sequencing data in a sample. Not being bound by a theory, a combination of cell subtypes having a particular signature may indicate an outcome. Not being bound by a theory, the signatures can be used to deconvolute the network of cells present in a particular pathological condition. Not being bound by a theory the presence of specific cells and cell subtypes are indicative of a particular response to treatment, such as including increased or decreased susceptibility to treatment. The signature may indicate the presence of one particular cell type. In one embodiment, the novel signatures are used to detect multiple cell states or hierarchies that occur in subpopulations of cells that are linked to particular pathological condition (e.g. cancer), or linked to a particular outcome or progression of the disease, or linked to a particular response to treatment of the disease.
The signature according to certain embodiments of the present invention may comprise or consist of one or more genes and/or proteins, such as for instance 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more. In certain embodiments, the signature may comprise or consist of two or more genes and/or proteins, such as for instance 2, 3, 4, 5, 6, 7, 8, 9, 10 or more. In certain embodiments, the signature may comprise or consist of three or more genes and/or proteins, such as for instance 3, 4, 5, 6, 7, 8, 9, 10 or more. In certain embodiments, the signature may comprise or consist of four or more genes and/or proteins, such as for instance 4, 5, 6, 7, 8, 9, 10 or more. In certain embodiments, the signature may comprise or consist of five or more genes and/or proteins, such as for instance 5, 6, 7, 8, 9, 10 or more. In certain embodiments, the signature may comprise or consist of six or more genes and/or proteins, such as for instance 6, 7, 8, 9, 10 or more. In certain embodiments, the signature may comprise or consist of seven or more genes and/or proteins, such as for instance 7, 8, 9, 10 or more. In certain embodiments, the signature may comprise or consist of eight or more genes and/or proteins, such as for instance 8, 9, 10 or more. In certain embodiments, the signature may comprise or consist of nine or more genes and/or proteins, such as for instance 9, 10 or more. In certain embodiments, the signature may comprise or consist of ten or more genes and/or proteins, such as for instance 10, 11, 12, 13, 14, 15, or more. It is to be understood that a signature according to the invention may for instance also include a combination of genes or proteins.
It is to be understood that “differentially expressed” genes/proteins include genes/proteins which are up- or down-regulated as well as genes/proteins which are turned on or off. When referring to up- or down-regulation, in certain embodiments, such up- or down-regulation is preferably at least two-fold, such as two-fold, three-fold, four-fold, five-fold, or more, such as for instance at least ten-fold, at least 20-fold, at least 30-fold, at least 40-fold, at least 50-fold, or more. Alternatively, or in addition, differential expression may be determined based on common statistical tests, as is known in the art.
As discussed herein, differentially expressed genes/proteins may be differentially expressed on a single cell level, or may be differentially expressed on a cell population level. Preferably, the differentially expressed genes/proteins as discussed herein, such as constituting the gene signatures as discussed herein, when as to the cell population level, refer to genes that are differentially expressed in all or substantially all cells of the population (such as at least 80%, preferably at least 90%, such as at least 95% of the individual cells). This allows one to define a particular subpopulation of cells. As referred to herein, a “subpopulation” of cells preferably refers to a particular subset of cells of a particular cell type which can be distinguished or are uniquely identifiable and set apart from other cells of this cell type. The cell subpopulation may be phenotypically characterized, and is preferably characterized by the signature as discussed herein. A cell (sub)population as referred to herein may constitute of a (sub)population of cells of a particular cell type characterized by a specific cell state.
When referring to induction, or alternatively suppression of a particular signature, preferable is meant induction or alternatively suppression (or upregulation or downregulation) of at least one gene/protein of the signature, such as for instance at least to, at least three, at least four, at least five, at least six, or all genes/proteins of the signature.
Signatures may be functionally validated as being uniquely associated with a particular phenotype of an intestinal epithelial cell, intestinal epithelial stem cell, or intestinal immune cell. Induction or suppression of a particular signature may consequentially be associated with or causally drive a particular phenotype.
Various aspects and embodiments of the invention may involve analyzing gene signature(s), protein signature(s), and/or other genetic signature(s) based on single cell analyses (e.g. single cell RNA sequencing) or alternatively based on cell population analyses, as is defined herein elsewhere.
As used herein the term “signature gene” means any gene or genes whose expression profile is associated with a specific cell type, subtype, or cell state of a specific cell type or subtype within a population of cells. The signature gene can be used to indicate the presence of a cell type, a subtype of the cell type, the state of the microenvironment of a population of cells, and/or the overall status of the entire cell population. Furthermore, the signature genes may be indicative of cells within a population of cells in vivo. Not being bound by a theory, the signature genes can be used to deconvolute the cells present in a tumor based on comparing them to data from bulk analysis of a tumor sample. The signature gene may indicate the presence of one particular cell type.
Markers as taught herein or genes or gene products comprised by or constituting gene or gene product signatures as taught herein, or the gene or gene product signatures as taught herein, may display AUC (area under the receiver-operating curve (ROC) as well-established in the art) value of 0.70 or more, e.g., 0.75 or more, preferably 0.80 or more, more preferably 0.85 or more, even more preferably 0.90 or more, and still more preferably 0.95 or more, e.g., 0.96, 0.97, 0.98, 0.99, or 1.00. An AUC value of 1 implies that the marker, gene, gene product or signature is a perfect classifier for a given outcome (e.g., a cell type or cluster). An AUC value of 0.50 implies no predictive value for the outcome.
A marker, for example a gene or gene product, for example a peptide, polypeptide, protein, or nucleic acid, or a group of two or more markers, is “measured” in a tested object (e.g., in or on a cell, cell population, tissue, organ, or organism, e.g., in a biological sample of a subject) when the presence or absence and/or quantity of the marker or the group of markers is detected or determined in the tested object, preferably substantially to the exclusion of other molecules and analytes, e.g., other genes or gene products.
Depending on factors that can be evaluated and decided on by a skilled person, such as inter alia the type of a marker (e.g., peptide, polypeptide, protein, or nucleic acid), the type of the tested object (e.g., a cell, cell population, tissue, organ, or organism, e.g., the type of biological sample of a subject, e.g., whole blood, plasma, serum, tissue biopsy), the expected abundance of the marker in the tested object, the type, robustness, sensitivity and/or specificity of the detection method used to detect the marker, etc., the marker may be measured directly in the tested object, or the tested object may be subjected to one or more processing steps aimed at achieving an adequate measurement of the marker.
The terms “quantity”, “amount” and “level” are synonymous and generally well-understood in the art. The terms as used throughout this specification may particularly refer to an absolute quantification of a marker in a tested object (e.g., in or on a cell, cell population, tissue, organ, or organism, e.g., in a biological sample of a subject), or to a relative quantification of a marker in a tested object, i.e., relative to another value such as relative to a reference value, or to a range of values indicating a base-line of the marker. Such values or ranges may be obtained as conventionally known.
An absolute quantity of a marker may be advantageously expressed as weight or as molar amount, or more commonly as a concentration, e.g., weight per volume or mol per volume. A relative quantity of a marker may be advantageously expressed as an increase or decrease or as a fold-increase or fold-decrease relative to another value, such as relative to a reference value. Performing a relative comparison between first and second variables (e.g., first and second quantities) may but need not require determining first the absolute values of the first and second variables. For example, a measurement method may produce quantifiable readouts (such as, e.g., signal intensities) for the first and second variables, wherein the readouts are a function of the value of the variables, and wherein the readouts may be directly compared to produce a relative value for the first variable vs. the second variable, without the actual need to first convert the readouts to absolute values of the respective variables.
Where a marker is detected in or on a cell, the cell may be conventionally denoted as positive (+) or negative (−) for the marker. Semi-quantitative denotations of marker expression in cells are also commonplace in the art, such as particularly in flow cytometry quantifications, for example, “dim” vs. “bright”, or “low” vs. “medium”/“intermediate” vs. “high”, or “−” vs. “+” vs. “++”, commonly controlled in flow cytometry quantifications by setting of the gates. Where a marker is quantified in or on a cell, absolute quantity of the marker may also be expressed for example as the number of molecules of the marker comprised by the cell.
Where a marker is detected and/or quantified on a single cell level in a cell population, the quantity of the marker may also be expressed for example as a percentage or fraction (by number) of cells comprised in the population that are positive for the marker, or as percentages or fractions (by number) of cells comprised in the population that are “dim” or “bright”, or that are “low” or “medium”/“intermediate” or “high”, or that are “−” or “+” or “++”. By means of an example, a sizeable proportion of the tested cells of the cell population may be positive for the marker, e.g., at least about 20%, at least about 40%, at least about 50%, at least about 60%, at least about 70%, at least about 80%, at least about 90%, at least about 95%, or up to 100%.
Any existing, available or conventional separation, detection and/or quantification methods may be used to measure the presence or absence (e.g., readout being present vs. absent; or detectable amount vs. undetectable amount) and/or quantity (e.g., readout being an absolute or relative quantity) of markers in a tested object (e.g., in or on a cell, cell population, tissue, organ, or organism, e.g., in a biological sample of a subject).
In certain examples, such methods may include biochemical assay methods, including inter alia assays of enzymatic activity, membrane channel activity, substance-binding activity, gene regulatory activity, or cell signalling activity of a marker, e.g., peptide, polypeptide, protein, or nucleic acid.
In other examples, such methods may include immunological assay methods, wherein the ability of an assay to separate, detect and/or quantify a marker (such as, preferably, peptide, polypeptide, or protein) is conferred by specific binding between a separable, detectable and/or quantifiable immunological binding agent (antibody) and the marker. Immunological assay methods include without limitation immunohistochemistry, immunocytochemistry, flow cytometry, mass cytometry, fluorescence activated cell sorting (FACS), fluorescence microscopy, fluorescence based cell sorting using microfluidic systems, immunoaffinity adsorption based techniques such as affinity chromatography, magnetic particle separation, magnetic activated cell sorting or bead based cell sorting using microfluidic systems, enzyme-linked immunosorbent assay (ELISA) and ELISPOT based techniques, radioimmunoassay (RIA), Western blot, etc.
In further examples, such methods may include mass spectrometry analysis methods. Generally, any mass spectrometric (MS) techniques that are capable of obtaining precise information on the mass of peptides, and preferably also on fragmentation and/or (partial) amino acid sequence of selected peptides (e.g., in tandem mass spectrometry, MS/MS; or in post source decay, TOF MS), may be useful herein for separation, detection and/or quantification of markers (such as, preferably, peptides, polypeptides, or proteins). Suitable peptide MS and MS/MS techniques and systems are well-known per se (see, e.g., Methods in Molecular Biology, vol. 146: “Mass Spectrometry of Proteins and Peptides”, by Chapman, ed., Humana Press 2000, ISBN 089603609x; Biemann 1990. Methods Enzymol 193: 455-79; or Methods in Enzymology, vol. 402: “Biological Mass Spectrometry”, by Burlingame, ed., Academic Press 2005, ISBN 9780121828073) and may be used herein. MS arrangements, instruments and systems suitable for biomarker peptide analysis may include, without limitation, matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) MS; MALDI-TOF post-source-decay (PSD); MALDI-TOF/TOF; surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF) MS; electrospray ionization mass spectrometry (ESI-MS); ESI-MS/MS; ESI-MS/(MS)n (n is an integer greater than zero); ESI 3D or linear (2D) ion trap MS; ESI triple quadrupole MS; ESI quadrupole orthogonal TOF (Q-TOF); ESI Fourier transform MS systems; desorption/ionization on silicon (DIOS); secondary ion mass spectrometry (SIMS); atmospheric pressure chemical ionization mass spectrometry (APCI-MS); APCI-MS/MS; APCI- (MS)n; atmospheric pressure photoionization mass spectrometry (APPI-MS); APPI-MS/MS; and APPI- (MS)n. Peptide ion fragmentation in tandem MS (MS/MS) arrangements may be achieved using manners established in the art, such as, e.g., collision induced dissociation (CID). Detection and quantification of markers by mass spectrometry may involve multiple reaction monitoring (MRM), such as described among others by Kuhn et al. 2004 (Proteomics 4: 1175-86). MS peptide analysis methods may be advantageously combined with upstream peptide or protein separation or fractionation methods, such as for example with the chromatographic and other methods.
In other examples, such methods may include chromatography methods. The term “chromatography” encompasses methods for separating substances, such as chemical or biological substances, e.g., markers, such as preferably peptides, polypeptides, or proteins, referred to as such and vastly available in the art. In a preferred approach, chromatography refers to a process in which a mixture of substances (analytes) carried by a moving stream of liquid or gas (“mobile phase”) is separated into components as a result of differential distribution of the analytes, as they flow around or over a stationary liquid or solid phase (“stationary phase”), between the mobile phase and the stationary phase. The stationary phase may be usually a finely divided solid, a sheet of filter material, or a thin film of a liquid on the surface of a solid, or the like. Chromatography is also widely applicable for the separation of chemical compounds of biological origin, such as, e.g., amino acids, proteins, fragments of proteins or peptides, etc.
Chromatography may be preferably columnar (i.e., wherein the stationary phase is deposited or packed in a column), preferably liquid chromatography, and yet more preferably HPLC. While particulars of chromatography are well known in the art, for further guidance see, e.g., Meyer M., 1998, ISBN: 047198373X, and “Practical HPLC Methodology and Applications”, Bidlingmeyer, B. A., John Wiley & Sons Inc., 1993. Exemplary types of chromatography include, without limitation, high-performance liquid chromatography (HPLC), normal phase HPLC (NP-HPLC), reversed phase HPLC (RP-HPLC), ion exchange chromatography (IEC), such as cation or anion exchange chromatography, hydrophilic interaction chromatography (HILIC), hydrophobic interaction chromatography (HIC), size exclusion chromatography (SEC) including gel filtration chromatography or gel permeation chromatography, chromatofocusing, affinity chromatography such as immunoaffinity, immobilised metal affinity chromatography, and the like.
Further techniques for separating, detecting and/or quantifying markers, such as preferably peptides, polypeptides, or proteins, may be used, optionally in conjunction with any of the above described analysis methods. Such methods include, without limitation, chemical extraction partitioning, isoelectric focusing (IEF) including capillary isoelectric focusing (CIEF), capillary isotachophoresis (CITP), capillary electrochromatography (CEC), and the like, one-dimensional polyacrylamide gel electrophoresis (PAGE), two-dimensional polyacrylamide gel electrophoresis (2D-PAGE), capillary gel electrophoresis (CGE), capillary zone electrophoresis (CZE), micellar electrokinetic chromatography (MEKC), free flow electrophoresis (FFE), etc.
In certain examples, such methods may include separating, detecting and/or quantifying markers at the nucleic acid level, more particularly RNA level, e.g., at the level of hnRNA, pre-mRNA, mRNA, or cDNA. Standard quantitative RNA or cDNA measurement tools known in the art may be used. Non-limiting examples include hybridisation-based analysis, microarray expression analysis, digital gene expression profiling (DGE), RNA-in-situ hybridisation (RISH), Northern-blot analysis and the like; PCR, RT-PCR, RT-qPCR, end-point PCR, digital PCR or the like; supported oligonucleotide detection, pyrosequencing, polony cyclic sequencing by synthesis, simultaneous bi-directional sequencing, single-molecule sequencing, single molecule real time sequencing, true single molecule sequencing, hybridization-assisted nanopore sequencing, sequencing by synthesis, single-cell RNA sequencing (sc-RNA seq), or the like. By means of an example, methods to profile the RNA content of large numbers of individual cells have been recently developed. To do so, special microfluidic devices have been developed to encapsulate each cell in an individual drop, associate the RNA of each cell with a ‘cell barcode’ unique to that cell/drop, measure the expression level of each RNA with sequencing, and then use the cell barcodes to determine which cell each RNA molecule came from.
In certain embodiments, the invention involves plate based single cell RNA sequencing (see, e.g., Picelli, S. et al., 2014, “Full-length RNA-seq from single cells using Smart-seq2” Nature protocols 9, 171-181, doi:10.1038/nprot.2014.006).
In certain embodiments, the invention involves high-throughput single-cell RNA-seq and/or targeted nucleic acid profiling (for example, sequencing, quantitative reverse transcription polymerase chain reaction, and the like) where the RNAs from different cells are tagged individually, allowing a single library to be created while retaining the cell identity of each read. In this regard reference is made to Macosko et al., 2015, “Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets” Cell 161, 1202-1214; International patent application number PCT/US2015/049178, published as WO2016/040476 on Mar. 17, 2016; Klein et al., 2015, “Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells” Cell 161, 1187-1201; International patent application number PCT/US2016/027734, published as WO2016168584A1 on Oct. 20, 2016; Zheng, et al., 2016, “Haplotyping germline and cancer genomes with high-throughput linked-read sequencing” Nature Biotechnology 34, 303-311; Zheng, et al., 2017, “Massively parallel digital transcriptional profiling of single cells” Nat. Commun. 8, 14049 doi: 10.1038/ncomms14049; International patent publication number WO2014210353A2; Zilionis, et al., 2017, “Single-cell barcoding and sequencing using droplet microfluidics” Nat Protoc. January; 12(1):44-73; Cao et al., 2017, “Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing” bioRxiv preprint first posted online Feb. 2, 2017, doi: dx.doi.org/10.1101/104844; Rosenberg et al., 2017, “Scaling single cell transcriptomics through split pool barcoding” bioRxiv preprint first posted online Feb. 2, 2017, doi: dx.doi.org/10.1101/105163; Vitak, et al., “Sequencing thousands of single-cell genomes with combinatorial indexing” Nature Methods, 14(3):302-308, 2017; Cao, et al., Comprehensive single-cell transcriptional profiling of a multicellular organism. Science, 357(6352):661-667, 2017; and Gierahn et al., “Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput” Nature Methods 14, 395-398 (2017), all the contents and disclosure of each of which are herein incorporated by reference in their entirety.
In certain embodiments, the invention involves single nucleus RNA sequencing. In this regard reference is made to Swiech et al., 2014, “In vivo interrogation of gene function in the mammalian brain using CRISPR-Cas9” Nature Biotechnology Vol. 33, pp. 102-106; Habib et al., 2016, “Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons” Science, Vol. 353, Issue 6302, pp. 925-928; Habib et al., 2017, “Massively parallel single-nucleus RNA-seq with DroNc-seq” Nat Methods. 2017 October; 14(10):955-958; and International patent application number PCT/US2016/059239, published as WO2017164936 on Sep. 28, 2017, which are herein incorporated by reference in their entirety.
In certain example embodiments, the tuft cell may be characterized by the expression one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, or ten or more genes or polypeptides listed in any one of Table 3-6 or 15A below. In certain example embodiments, the tuft cell is characterized by expression of 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more, 21 or more, 22 or more, 23 or more, 24 or more, or 25 or more of the genes or polypeptides listed in any one of Tables 3-6 or 15A below.
In another example embodiment, the tuft cell may be characterized by the expression 1 or more, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more, 21 or more, 22 or more, 23 or more, 24 or more, or 25 or more of the genes or polypeptides listed in Table 3.
In another example embodiment, the tuft cell may be characterized by the expression 1 or more, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more, 21 or more, 22 or more, 23 or more, 24 or more, or 25 or more of the genes or polypeptides listed in Table 4.
In another example embodiment, the tuft cell may be characterized by the expression 1 or more, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more, 21 or more, 22 or more, 23 or more, 24 or more, or 25 or more of the genes or polypeptides listed in Table 5.
In another example embodiment, the tuft cell may be characterized by the expression 1 or more, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more, 21 or more, 22 or more, 23 or more, 24 or more, or 25 or more of the genes or polypeptides listed in Table 6.
In another example embodiment, the tuft cell may be characterized by the expression 1 or more, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more, 21 or more, 22 or more, 23 or more, 24 or more, or 25 or more of the genes or polypeptides listed in Table 15A.
In another example embodiment, the tuft cell may be characterized by the expression of the genes or polypeptides listed in Table 3, Table 4, Table 5, Table 6, Table 8 or Table 15A.
In certain example embodiments, the tuft cell may be characterized by expression of Lrmp, Dclk1, Cd24a, Tas1r3, Ffar3, Sucnr1, Gabbr1, Drd3, Etv1, Gfi1b, Hmx2, Hmx3, Runx1, Jarid2, Nfatc1, Zfp710, Zbtb41, Spib, Foxe1, Sox9, Pou2f3, Ascl2, Ehf, Tcf4, Gprc5c, Sucnr1, Ccrl1, Gprc5a, Opn3, Vmn2r26 and Tas1r3. In another example embodiment, the tuft cell may be characterized by expression of Cd24a, Tas1r3, Ffar3, Sucnr1, Gabbr1 and Drd3. In another example embodiment, the tuft cell may be characterized by expression of Etv1, Gfi1b, Hmx2, Hmx3, Runx1, Jarid2, Nfatc1, Zfp710, Zbtb41, Spib, Foxe1, Sox9, Pou2f3, Ascl2, Ehf and Tcf4. In another example embodiment, the tuft cell may be characterized by expression of Etv1, Hmx2, Spib, Foxe1, Sox9, Pou2f3, Ascl2, Ehf and Tcf4. In another example embodiment, the tuft cell may be characterized by expression of Ffar3, Gprc5c, Sucnr1, Ccrl1, Gprc5a, Opn3, Vmn2r26 and Tas1r3. In another example embodiment, the tuft cell may be characterized by the expression of Etv1, Hmx2, Spib, Foxe1, Pou2f3, Sox9, Ascl2, Hoxa5, Hivep3, Ehf Tcf4, Mxd4, Hmx3, Hoxa3 and Nfatc1. In another example embodiment, the tuft cell may be characterized by expression of Lrmp, Gnat3, Gnb3, Plac8, Trpm5, Gng13, Ltc4s, Rgs13, Hck, Alox5ap, Avil, Alox5, Ptpn6, Atp2a3 and Pik2. In another example embodiment, the tuft cell may be characterized by expression of Rgs13, Rp141, Rps26, Zmiz1, Gpx3, Suox, Tslp and Socs1.
In certain example embodiments, the tuft cell may be characterized by primarily a chemosensory cell state. In certain example embodiments, the chemosensory cell state may be characterized by the expression of Trpm5, Pou2fs, Gnb3, Gng13, Atpb1b1, Fxyd6, Tas2R38, Tas2R105, Tas2R108, Tas1r3, or combinations thereof. In additional to a chemosensory role such tuft cells may play a role in sensing of bacterial infection, in particular gram-negative infection, as characterized by expression of Tas2R38, and/or regulation of breathing as characterized by expression of Tas2R105, Tas2R108, Tas1r3, or a combination thereof.
In certain other example embodiments, the tuft cell may be characterized by having primarily immune and/or inflammatory state characterized by the expression of Gfi1B, Spib, Sox9, Mgst3, Alox5ap. Ptprc (CD45), or a combination thereof.
Methods of Detecting and Isolating Cells
A further aspect of the invention thus relates to a method for detecting or quantifying intestinal epithelial cells, intestinal epithelial stem cells, intestinal immune cells or respiratory epithelial cells in a biological sample of a subject, or for isolating such cells from a biological sample of a subject, the method comprising: a) providing a biological sample of a subject; and b) detecting or quantifying in the biological sample intestinal epithelial cells, intestinal epithelial stem cells, or preferably intestinal epithelial cells as disclosed herein, or isolating from the biological sample such cells as disclosed herein.
The method may allow for detecting or concluding the presence or absence of the specified intestinal epithelial cells, intestinal epithelial stem cells, intestinal immune cells, or respiratory (e.g., airway) epithelial cells (preferably epithelial cells, e.g., tuft cells) in a tested object (e.g., in a cell population, tissue, organ, organism, or in a biological sample of a subject). The method may also allow to quantify the specified intestinal epithelial cells, intestinal epithelial stem cells, intestinal immune cells, or respiratory epithelial cells (preferably epithelial cells, e.g., tuft cells) in a tested object (e.g., in a cell population, tissue, organ, organism, or in a biological sample of a subject). The quantity of the specified cells in the tested object such as the biological sample may be suitably expressed for example as the number (count) of the specified cells per standard unit of volume (e.g., m1, μl or nl) or weight (e.g., g or mg or ng) of the tested object such as the biological sample ormay also be suitably expressed as a percentage or fraction (by number) of all cells comprised in the tested object such as the biological sample, or as a percentage or fraction (by number) of a select subset of the cells comprised in the tested object such as the biological sample, e.g., as a percentage or fraction (by number) intestinal epithelial cells, intestinal epithelial stem cells, intestinal immune cells, or respiratory epithelial cells (preferably epithelial cells, e.g., tuft cells) and of different (sub) types comprised in the tested object such as the biological sample (e.g., neuronal or immune tuft cells). The quantity of the specified cells in the tested object such as the biological sample may also be suitably represented by an absolute or relative quantity of a suitable surrogate analyte, such as a peptide, polypeptide, protein, or nucleic acid expressed or comprised by the specified cells.
In certain embodiments, methods to detect or conclude the presence or absence of a specified cell may be used to diagnose a disease or disorder. Specifically, the methods disclosed herein may be used to identify a particular tuft cell type, sub-type, cell state associated with presence or absence of a particular disease or disorder. For example, detection of increased tuft cells associated with an immune-like cell state may indicate the presence of inflammation, infection, or any other other diseases or conditions described herein.
The method may allow to isolate or purify the specified intestinal epithelial cells, intestinal epithelial stem cells, intestinal immune cells, or respiratory epithelial cells (preferably epithelial cells, e.g., tuft cells) from the tested object such as the biological sample. The terms “isolating” or “purifying” as used throughout this specification with reference to a particular component of a composition or mixture (e.g., the tested object such as the biological sample) encompass processes or techniques whereby such component is separated from one or more or (substantially) all other components of the composition or mixture (e.g., the tested object such as the biological sample). The terms do not require absolute purity. Instead, isolating or purifying the component will produce a discrete environment in which the abundance of the component relative to one or more or all other components is greater than in the starting composition or mixture (e.g., the tested object such as the biological sample). A discrete environment may denote a single medium, such as for example a single solution, dispersion, gel, precipitate, etc.
In some aspects the present disclosure refers to a method of identifying a cell or cell marker identified above, comprising: a) isolating target cells based on a marker specifically expressed in or on the cell or by label-free imaging flow cytometry; b) quantifying gene expression in the target cells by single cell sequencing, and c) clustering the target cells based on the gene expression by application of one or more algorithms, d) optionally determining a transcription signature for each cluster based at least in part on identifying differentially expressed genes between two or more clusters and between each cluster and the remaining cells as background, and e) optionally validating gene expression against cellular morphology.
In some examples of the present disclosure identifying differentially expressed transcripts comprises application of a supervised or unsupervised machine-learning model. A supervised machine learning model is for example selected from the group consisting of an analytical learning model, an artificial neural network model, a back propagation model, a boosting model, a Bayesian statistics model, a case-based model, a decision tree learning model, an inductive logic programming model, a Gaussian process regression model, a group method of data handling model, a kernel estimator model, a learning automata model, a minimum message length model, a multilinear subspace learning, a naïve bayes classifer model, a nearest neighbor model, a probably approximately correct (PAC) learning model, a ripple down rules model, a symbolic machine learning model, a subsymbolic machine learning model, a support vector machine learning model, a minimum complexity machine model, a random forest model, an ensemble of classifiers model, an ordinal classification model, a data pre-processing model, a handling imbalanced datasets model, a statistical relational learning model, a Proaftn model. An unsupervised machine learning model is for example selected from the group consisting of a k-means model, a mixture model, a hierarchical clustering model, an anomaly detection model, a neural network model, an expectation-maximization (EM) model, a method of moments model, or a blind signal separation technique.
These models are used separately or in combination with each other or in combination with any other machine-learning model, wherein a supervised model is combined with a supervised model, or an unsupervised model is combined with an unsupervised model or a supervised model is combined with an unsupervised model.
In other examples of the previous aspects (optional) validating gene expression against cellular morphology comprises sparse labeling the cell to enhance the expression of a fluorescent protein in the cell and combining the sparse labeling with fluorescent in situ hybridization (FISH) to validate the marker against cellular morphology in step e). In examples of the previous aspects FISH is for example combined with a specific antibody, double FISH or a transgenic reporter mouse line directed to a previously identified marker in the cell. For example, an enhancer element is inserted into a lentivirus or an adeno-associated virus (AAV) vector upstream of the fluorescent protein to enhance its expression.
A further aspect of the invention thus relates to a method for detecting or quantifying intestinal epithelial cells, intestinal epithelial stem cells, intestinal immune cells, or respiratory epithelial cells in a biological sample of a subject, or for isolating such cells from a biological sample of a subject, the method comprising: a) providing a biological sample of a subject; and b) detecting or quantifying in the biological sample intestinal epithelial cells, intestinal epithelial stem cells, or preferably intestinal epithelial cells as disclosed herein, or isolating from the biological sample such cells as disclosed herein.
The method may allow for detecting or concluding the presence or absence of the specified intestinal epithelial cells, intestinal epithelial stem cells, intestinal immune cells, or respiratory (e.g., airway) epithelial cells (preferably epithelial cells, e.g., tuft cells) in a tested object (e.g., in a cell population, tissue, organ, organism, or in a biological sample of a subject). The method may also allow to quantify the specified intestinal epithelial cells, intestinal epithelial stem cells, intestinal immune cells, or respiratory epithelial cells (preferably epithelial cells, e.g., tuft cells) in a tested object (e.g., in a cell population, tissue, organ, organism, or in a biological sample of a subject). The quantity of the specified cells in the tested object such as the biological sample may be suitably expressed for example as the number (count) of the specified cells per standard unit of volume (e.g., m1, μl or nl) or weight (e.g., g or mg or ng) of the tested object such as the biological sample ormay also be suitably expressed as a percentage or fraction (by number) of all cells comprised in the tested object such as the biological sample, or as a percentage or fraction (by number) of a select subset of the cells comprised in the tested object such as the biological sample, e.g., as a percentage or fraction (by number) intestinal epithelial cells, intestinal epithelial stem cells, intestinal immune cells, or respiratory epithelial cells (preferably epithelial cells, e.g., tuft cells) and of different (sub) types comprised in the tested object such as the biological sample (e.g., neuronal or immune tuft cells). The quantity of the specified cells in the tested object such as the biological sample may also be suitably represented by an absolute or relative quantity of a suitable surrogate analyte, such as a peptide, polypeptide, protein, or nucleic acid expressed or comprised by the specified cells.
The method may allow to isolate or purify the specified intestinal epithelial cells, intestinal epithelial stem cells, intestinal immune cells, or respiratory epithelial cells (preferably epithelial cells, e.g., tuft cells) from the tested object such as the biological sample. The terms “isolating” or “purifying” as used throughout this specification with reference to a particular component of a composition or mixture (e.g., the tested object such as the biological sample) encompass processes or techniques whereby such component is separated from one or more or (substantially) all other components of the composition or mixture (e.g., the tested object such as the biological sample). The terms do not require absolute purity. Instead, isolating or purifying the component will produce a discrete environment in which the abundance of the component relative to one or more or all other components is greater than in the starting composition or mixture (e.g., the tested object such as the biological sample). A discrete environment may denote a single medium, such as for example a single solution, dispersion, gel, precipitate, etc.
Isolating or purifying the specified intestinal epithelial cells, intestinal epithelial stem cells, intestinal immune cells, or respiratory epithelial cells (preferably epithelial cells, e.g., tuft cells) from the tested object such as the biological sample may increase the abundance of the specified cells relative to all other cells comprised in the tested object such as the biological sample, or relative to other cells of a select subset of the cells comprised in the tested object such as the biological sample.
By means of example, isolating or purifying the specified cells from the tested object such as the biological sample may yield a cell population, in which the specified cells constitute at least 40% (by number) of all cells of the cell population, for example, at least 45%, preferably at least 50%, at least 55%, more preferably at least 60%, at least 65%, still more preferably at least 70%, at least 75%, even more preferably at least 80%, at least 85%, and yet more preferably at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or even 100% of all cells of the cell population.
The intestinal epithelial cells, intestinal epithelial stem cells, intestinal immune cells, or respiratory epithelial cells (preferably epithelial cells, e.g., tuft cells) disclosed herein are generally described or characterized with reference to certain marker(s) or combination(s) of markers (such as genes or gene products, e.g., peptides, polypeptides, proteins, or nucleic acids) expressed or not expressed by the cells, or with reference to certain gene or gene product signature(s) comprised by the cells. Accordingly, the present methods for detecting, quantifying or isolating the specified cells may be marker-based or gene or gene product signature-based, i.e., may involve detection, quantification or isolation of cells expressing or not expressing marker(s) or combination(s) of markers the expression or lack of expression of which is taught herein as typifying or characterising the specified cells, or may involve detection, quantification or isolation of cells comprising gene or gene product signature(s) taught herein as typifying or characterising the specified cells.
Any existing, available or conventional separation, detection and/or quantification methods may be used to measure the presence or absence (e.g., readout being present vs. absent; or detectable amount vs. undetectable amount) and/or quantity (e.g., readout being an absolute or relative quantity) of the specified intestinal epithelial cells, intestinal epithelial stem cells, intestinal immune cells, or respiratory epithelial cells (preferably epithelial cells, e.g., tuft cells) in, or to isolate the specified cells from, a tested object (e.g., a cell population, tissue, organ, organism, or a biological sample of a subject). Such methods allow to detect, quantify or isolate the specified cells in or from the tested object (e.g., a cell population, tissue, organ, organism, or a biological sample of a subject) substantially to the exclusion of other cells comprised in the tested object.
Such methods may allow to detect, quantify or isolate the specified cells with sensitivity of at least 50%, at least 55%, at least 60%, at least 65%, preferably at least 70%, at least 75%, more preferably at least 80%, at least 85%, even more preferably at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or even 100%, and/or with specificity of at least 50%, at least 55%, at least 60%, at least 65%, preferably at least 70%, at least 75%, more preferably at least 80%, at least 85%, even more preferably at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or even 100%. By means of example, at least 40% (by number), for example at least 45%, preferably at least 50%, at least 55%, more preferably at least 60%, at least 65%, still more preferably at least 70%, at least 75%, even more preferably at least 80%, at least 85%, and yet more preferably at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or even 100% of all cells detected, quantified or isolated by such methods may correspond to the specified cells.
In certain embodiments, methods for detecting, quantifying or isolating the specified cells may comprise treatment(s) or step(s) which diminish or eliminate the viability of the cells. For example, methods which comprise measuring intracellular marker(s) typically necessitate permeabilization of the cell membrane and possibly fixation of the cells; and methods which comprise measuring nucleic acid marker(s) may typically necessitate obtaining nucleic acids (such as particularly RNA, more particularly mRNA) from the cells. In certain other embodiments, methods for detecting, quantifying or isolating the specified cells may substantially preserve the viability of the cells. For example, methods which comprise measuring extracellular or cell surface marker(s) need not disturb the integrity of the cell membrane and may not require fixation/permeabilization of the cells. By means of an example, methods for detecting, quantifying or isolating the specified cells may be configured such that at least 40% (by number), for example, at least 45%, preferably at least 50%, at least 55%, more preferably at least 60%, at least 65%, still more preferably at least 70%, at least 75%, even more preferably at least 80%, at least 85%, and yet more preferably at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or even 100% of the detected, quantified or isolated cells remain viable. The term “viable cells” as used throughout this specification refers to cells that can be qualified as viable by tests and assays known per se. For instance, the viability of cells may be measured using conventional dye exclusion assays, such as Trypan Blue exclusion assay or propidium iodide exclusion assay. In such assays, viable cells exclude the dye and hence remain unstained, while non-viable cells take up the dye and are stained. The cells and their uptake of the dye can be visualised and revealed by suitable techniques (e.g., conventional light microscopy, fluorescence microscopy, or flow cytometry), and viable (unstained) and non-viable (stained) cells in the tested sample can be counted.
In certain embodiments, methods for detecting, quantifying or isolating the specified intestinal epithelial cells, intestinal epithelial stem cells, intestinal immune cells, or respiratory epithelial cells (preferably epithelial cells, e.g., tuft cells) may be single-cell-based, i.e., may allow to discretely detect, quantify or isolate the specified cells as individual cells. In other embodiments, methods for detecting, quantifying or isolating the specified cells may be cell population-based, i.e., may only allow to detect, quantify or isolate the specified cells as a group or collection of cells, without providing information on or allowing to isolate individual cells.
Methods for detecting, quantifying or isolating the specified intestinal epithelial cells, intestinal epithelial stem cells, intestinal immune cells, or respiratory epithelial cells (preferably epithelial cells, e.g., tuft cells) may employ any of the above-described techniques for measuring markers, insofar the separation or the qualitative and/or quantitative measurement of the marker(s) can be correlated with or translated into detection, quantification or isolation of the specified cells. For example, any of the above-described biochemical assay methods, immunological assay methods, mass spectrometry analysis methods, chromatography methods, or nucleic acid analysis method, or combinations thereof for measuring markers, may be employed for detecting, quantifying or isolating the specified cells.
In certain embodiments, the intestinal epithelial cells, intestinal epithelial stem cells, intestinal immune cells, or respiratory epithelial cells (preferably epithelial cells, e.g., tuft cells) are detected, quantified or isolated using a technique selected from the group consisting of flow cytometry, fluorescence activated cell sorting, mass cytometry, fluorescence microscopy, affinity separation, magnetic cell separation, microfluidic separation, and combinations thereof.
Flow cytometry encompasses methods by which individual cells of a cell population are analysed by their optical properties (e.g., light absorbance, light scattering and fluorescence properties, etc.) as they pass in a narrow stream in single file through a laser beam. Flow cytometry methods include fluorescence activated cell sorting (FACS) methods by which a population of cells having particular optical properties are separated from other cells.
Elemental mass spectrometry-based flow cytometry, or mass cytometry, offers an approach to analyse cells by replacing fluorochrome-labelled binding reagents with mass tagged binding reagents, i.e., tagged with an element or isotope having a defined mass. In these methods, labelled particles are introduced into a mass cytometer, where they are individually atomised and ionised. The individual particles are then subjected to elemental analysis, which identifies and measures the abundance of the mass tags used. The identities and the amounts of the isotopic elements associated with each particle are then stored and analysed. Due to the resolution of elemental analysis and the number of elemental isotopes that can be used, it is possible to simultaneously measure up to 100 or more parameters on a single particle.
Fluorescence microscopy broadly encompasses methods by which individual cells of a cell population are microscopically analysed by their fluorescence properties. Fluorescence microscopy approaches may be manual or preferably automated.
Affinity separation also referred to as affinity chromatography broadly encompasses techniques involving specific interactions of cells present in a mobile phase, such as a suitable liquid phase (e.g., cell population in an aqueous suspension) with, and thereby adsorption of the cells to, a stationary phase, such as a suitable solid phase; followed by separation of the stationary phase from the remainder of the mobile phase; and recovery (e.g., elution) of the adsorbed cells from the stationary phase. Affinity separation may be columnar, or alternatively, may entail batch treatment, wherein the stationary phase is collected/separated from the liquid phases by suitable techniques, such as centrifugation or application of magnetic field (e.g., where the stationary phase comprises magnetic substrate, such as magnetic particles or beads). Accordingly, magnetic cell separation is also envisaged herein.
Microfluidic systems allow for accurate and high throughput cell detection, quantification and/or sorting, exploiting a variety of physical principles. Cell sorting on microchips provides numerous advantages by reducing the size of necessary equipment, eliminating potentially biohazardous aerosols, and simplifying the complex protocols commonly associated with cell sorting. The term “microfluidic system” as used throughout this specification broadly refers to systems having one or more fluid microchannels. Microchannels denote fluid channels having cross-sectional dimensions the largest of which are typically less than 1 mm, preferably less than 500 μm, more preferably less than 400 μm, more preferably less than 300 μm, more preferably less than 200 μm, e.g., 100 μm or smaller. Such microfluidic systems can be used for manipulating fluid and/or objects such as droplets, bubbles, capsules, particles, cells and the like. Microfluidic systems may allow for example for fluorescent label-based (e.g., employing fluorophore-conjugated binding agent(s), such as fluorophore-conjugated antibody(ies)), bead-based (e.g., bead-conjugated binding agent(s), such as bead-conjugated antibody(ies)), or label-free cell sorting (reviewed in Shields et al., Lab Chip. 2015, vol. 15: 1230-1249).
In certain embodiments, the aforementioned methods and techniques may employ agent(s) capable of specifically binding to one or more gene products, e.g., peptides, polypeptides, proteins, or nucleic acids, expressed or not expressed by the intestinal epithelial cells, intestinal epithelial stem cells, intestinal immune cells, or respiratory epithelial cells (preferably epithelial cells, e.g., tuft cells) as taught herein. In certain preferred embodiments, such one or more gene products, e.g., peptides, polypeptides, or proteins, may be expressed on the cell surface (i.e., cell surface markers, e.g., transmembrane peptides, polypeptides or proteins, or secreted peptides, polypeptides or proteins which remain associated with the cell surface). Hence, further disclosed are binding agents capable of specifically binding to markers, such as genes or gene products, e.g., peptides, polypeptides, proteins, or nucleic acids as taught herein. Binding agents as intended throughout this specification may include inter alia antibodies, aptamers, spiegelmers (L-aptamers), photoaptamers, protein, peptides, peptidomimetics, nucleic acids such as oligonucleotides (e.g., hybridisation probes or amplification or sequencing primers and primer pairs), small molecules, or combinations thereof.
Binding agents may be in various forms, e.g., lyophilised, free in solution, or immobilised on a solid phase. They may be, e.g., provided in a multi-well plate or as an array or microarray, or they may be packaged separately, individually, or in combination.
The term “specifically bind” as used throughout this specification means that an agent (denoted herein also as “specific-binding agent”) binds to one or more desired molecules or analytes (e.g., peptides, polypeptides, proteins, or nucleic acids) substantially to the exclusion of other molecules which are random or unrelated, and optionally substantially to the exclusion of other molecules that are structurally related. The term “specifically bind” does not necessarily require that an agent binds exclusively to its intended target(s). For example, an agent may be said to specifically bind to target(s) of interest if its affinity for such intended target(s) under the conditions of binding is at least about 2-fold greater, preferably at least about 5-fold greater, more preferably at least about 10-fold greater, yet more preferably at least about 25-fold greater, still more preferably at least about 50-fold greater, and even more preferably at least about 100-fold, or at least about 1000-fold, or at least about 104-fold, or at least about 105-fold, or at least about 106-fold or more greater, than its affinity for a non-target molecule, such as for a suitable control molecule (e.g., bovine serum albumin, casein).
Preferably, the specific binding agent may bind to its intended target(s) with affinity constant (KA) of such binding KA≥1×106 M−1, more preferably KA≥1×107 M−1, yet more preferably KA≥1×108 M−1, even more preferably KA≥1×109 M−1, and still more preferably KA≥1×1010 M−1 or KA≥1×1011 M−1 or KA≥1×1012 M−1, wherein KA=[SBA_T]/[SBA][T], SBA denotes the specific-binding agent, T denotes the intended target. Determination of KA can be carried out by methods known in the art, such as for example, using equilibrium dialysis and Scatchard plot analysis.
As used herein, the term “antibody” is used in its broadest sense and generally refers to any immunologic binding agent. The term specifically encompasses intact monoclonal antibodies, polyclonal antibodies, multivalent (e.g., 2-, 3- or more-valent) and/or multi-specific antibodies (e.g., bi- or more-specific antibodies) formed from at least two intact antibodies, and antibody fragments insofar they exhibit the desired biological activity (particularly, ability to specifically bind an antigen of interest, i.e., antigen-binding fragments), as well as multivalent and/or multi-specific composites of such fragments. The term “antibody” is not only inclusive of antibodies generated by methods comprising immunisation, but also includes any polypeptide, e.g., a recombinantly expressed polypeptide, which is made to encompass at least one complementarity-determining region (CDR) capable of specifically binding to an epitope on an antigen of interest. Hence, the term applies to such molecules regardless whether they are produced in vitro or in vivo.
An antibody may be any of IgA, IgD, IgE, IgG and IgM classes, and preferably IgG class antibody. An antibody may be a polyclonal antibody, e.g., an antiserum or immunoglobulins purified there from (e.g., affinity-purified). An antibody may be a monoclonal antibody or a mixture of monoclonal antibodies. Monoclonal antibodies can target a particular antigen or a particular epitope within an antigen with greater selectivity and reproducibility. By means of example and not limitation, monoclonal antibodies may be made by the hybridoma method first described by Kohler et al. 1975 (Nature 256: 495), or may be made by recombinant DNA methods (e.g., as in U.S. Pat. No. 4,816,567). Monoclonal antibodies may also be isolated from phage antibody libraries using techniques as described by Clackson et al. 1991 (Nature 352: 624-628) and Marks et al. 1991 (J Mol Biol 222: 581-597), for example.
Antibody binding agents may be antibody fragments. “Antibody fragments” comprise a portion of an intact antibody, comprising the antigen-binding or variable region thereof. Examples of antibody fragments include Fab, Fab′, F(ab′)2, Fv and scFv fragments, single domain (sd) Fv, such as VH domains, VL domains and VHH domains; diabodies; linear antibodies; single-chain antibody molecules, in particular heavy-chain antibodies; and multivalent and/or multispecific antibodies formed from antibody fragment(s), e.g., dibodies, tribodies, and multibodies. The above designations Fab, Fab′, F(ab′)2, Fv, scFv etc. are intended to have their art-established meaning.
The term antibody includes antibodies originating from or comprising one or more portions derived from any animal species, preferably vertebrate species, including, e.g., birds and mammals. Without limitation, the antibodies may be chicken, turkey, goose, duck, guinea fowl, quail or pheasant. Also without limitation, the antibodies may be human, murine (e.g., mouse, rat, etc.), donkey, rabbit, goat, sheep, guinea pig, camel (e.g., Camelus bactrianus and Camelus dromaderius), llama (e.g., Lama paccos, Lama glama or Lama vicugna) or horse. An antibody can include one or more amino acid deletions, additions and/or substitutions (e.g., conservative substitutions), insofar such alterations preserve its binding of the respective antigen. An antibody may also include one or more native or artificial modifications of its constituent amino acid residues (e.g., glycosylation, etc.).
Methods of producing polyclonal and monoclonal antibodies as well as fragments thereof are well known in the art, as are methods to produce recombinant antibodies or fragments thereof (see for example, Harlow and Lane, “Antibodies: A Laboratory Manual”, Cold Spring Harbour Laboratory, New York, 1988; Harlow and Lane, “Using Antibodies: A Laboratory Manual”, Cold Spring Harbour Laboratory, New York, 1999, ISBN 0879695447; “Monoclonal Antibodies: A Manual of Techniques”, by Zola, ed., CRC Press 1987, ISBN 0849364760; “Monoclonal Antibodies: A Practical Approach”, by Dean & Shepherd, eds., Oxford University Press 2000, ISBN 0199637229; Methods in Molecular Biology, vol. 248: “Antibody Engineering: Methods and Protocols”, Lo, ed., Humana Press 2004, ISBN 1588290921).
The term “aptamer” refers to single-stranded or double-stranded oligo-DNA, oligo-RNA or oligo-DNA/RNA or any analogue thereof that specifically binds to a target molecule such as a peptide. Advantageously, aptamers display fairly high specificity and affinity (e.g., KA in the order 1×109 M−1) for their targets. Aptamer production is described inter alia in U.S. Pat. No. 5,270,163; Ellington & Szostak 1990 (Nature 346: 818-822); Tuerk & Gold 1990 (Science 249: 505-510); or “The Aptamer Handbook: Functional Oligonucleotides and Their Applications”, by Klussmann, ed., Wiley-VCH 2006, ISBN 3527310592, incorporated by reference herein. The term “photoaptamer” refers to an aptamer that contains one or more photoreactive functional groups that can covalently bind to or crosslink with a target molecule. The term “spiegelmer” refers to an aptamer which includes L-DNA, L-RNA, or other left-handed nucleotide derivatives or nucleotide-like molecules. Aptamers containing left-handed nucleotides are resistant to degradation by naturally occurring enzymes, which normally act on substrates containing right-handed nucleotides. The term “peptidomimetic” refers to a non-peptide agent that is a topological analogue of a corresponding peptide. Methods of rationally designing peptidomimetics of peptides are known in the art. For example, the rational design of three peptidomimetics based on the sulphated 8-mer peptide CCK26-33, and of two peptidomimetics based on the 11-mer peptide Substance P, and related peptidomimetic design principles, are described in Horwell 1995 (Trends Biotechnol 13: 132-134).
The term “antibody-like protein scaffolds” or “engineered protein scaffolds” broadly encompasses proteinaceous non-immunoglobulin specific-binding agents, typically obtained by combinatorial engineering (such as site-directed random mutagenesis in combination with phage display or other molecular selection techniques). Usually, such scaffolds are derived from robust and small soluble monomeric proteins (such as Kunitz inhibitors or lipocalins) or from a stably folded extra-membrane domain of a cell surface receptor (such as protein A, fibronectin or the ankyrin repeat).
Such scaffolds have been extensively reviewed in Binz et al. (Engineering novel binding proteins from nonimmunoglobulin domains. Nat Biotechnol 2005, 23:1257-1268), Gebauer and Skerra (Engineered protein scaffolds as next-generation antibody therapeutics. Curr Opin Chem Biol. 2009, 13:245-55), Gill and Damle (Biopharmaceutical drug discovery using novel protein scaffolds. Curr Opin Biotechnol 2006, 17:653-658), Skerra (Engineered protein scaffolds for molecular recognition. J Mol Recognit 2000, 13:167-187), and Skerra (Alternative non-antibody scaffolds for molecular recognition. Curr Opin Biotechnol 2007, 18:295-304), and include without limitation affibodies, based on the Z-domain of staphylococcal protein A, a three-helix bundle of 58 residues providing an interface on two of its alpha-helices (Nygren, Alternative binding proteins: Affibody binding proteins developed from a small three-helix bundle scaffold. FEBS J 2008, 275:2668-2676); engineered Kunitz domains based on a small (ca. 58 residues) and robust, disulphide-crosslinked serine protease inhibitor, typically of human origin (e.g. LACI-D1), which can be engineered for different protease specificities (Nixon and Wood, Engineered protein inhibitors of proteases. Curr Opin Drug Discov Dev 2006, 9:261-268); monobodies or adnectins based on the 10th extracellular domain of human fibronectin III (1° fn3), which adopts an Ig-like beta-sandwich fold (94 residues) with 2-3 exposed loops, but lacks the central disulphide bridge (Koide and Koide, Monobodies: antibody mimics based on the scaffold of the fibronectin type III domain. Methods Mol Biol 2007, 352:95-109); anticalins derived from the lipocalins, a diverse family of eight-stranded beta-barrel proteins (ca. 180 residues) that naturally form binding sites for small ligands by means of four structurally variable loops at the open end, which are abundant in humans, insects, and many other organisms (Skerra, Alternative binding proteins: Anticalins-harnessing the structural plasticity of the lipocalin ligand pocket to engineer novel binding activities. FEBS J 2008, 275:2677-2683); DARPins, designed ankyrin repeat domains (166 residues), which provide a rigid interface arising from typically three repeated beta-turns (Stumpp et al., DARPins: a new generation of protein therapeutics. Drug Discov Today 2008, 13:695-701); avimers (multimerized LDLR-A module) (Silverman et al., Multivalent avimer proteins evolved by exon shuffling of a family of human receptor domains. Nat Biotechnol 2005, 23:1556-1561); and cysteine-rich knottin peptides (Kolmar, Alternative binding proteins: biological activity and therapeutic potential of cystine-knot miniproteins. FEBS J 2008, 275:2684-2690).
The term “oligonucleotide” as used throughout this specification refers to a nucleic acid (including nucleic acid analogues and mimetics) oligomer or polymer as defined herein. Preferably, an oligonucleotide, such as more particularly an antisense oligonucleotide, is (substantially) single-stranded. Oligonucleotides as intended herein may be preferably between about 10 and about 100 nucleoside units (i.e., nucleotides or nucleotide analogues) in length, preferably between about 15 and about 50, more preferably between about 20 and about 40, also preferably between about 20 and about 30. Oligonucleotides as intended herein may comprise one or more or all non-naturally occurring heterocyclic bases and/or one or more or all non-naturally occurring sugar groups and/or one or more or all non-naturally occurring inter-nucleoside linkages, the inclusion of which may improve properties such as, for example, increased stability in the presence of nucleases and increased hybridization affinity, increased tolerance for mismatches, etc. The reference to oligonucleotides may in particular but without limitation include hybridisation probes and/or amplification primers and/or sequencing primers, etc., as commonly used in nucleic acid detection technologies.
Nucleic acid binding agents, such as oligonucleotide binding agents, are typically at least partly antisense to a target nucleic acid of interest. The term “antisense” generally refers to an agent (e.g., an oligonucleotide) configured to specifically anneal with (hybridise to) a given sequence in a target nucleic acid, such as for example in a target DNA, hnRNA, pre-mRNA or mRNA, and typically comprises, consist essentially of or consist of a nucleic acid sequence that is complementary or substantially complementary to the target nucleic acid sequence. Antisense agents suitable for use herein, such as hybridisation probes or amplification or sequencing primers and primer pairs) may typically be capable of annealing with (hybridising to) the respective target nucleic acid sequences at high stringency conditions, and capable of hybridising specifically to the target under physiological conditions. The terms “complementary” or “complementarity” as used throughout this specification with reference to nucleic acids, refer to the normal binding of single-stranded nucleic acids under permissive salt (ionic strength) and temperature conditions by base pairing, preferably Watson-Crick base pairing. By means of example, complementary Watson-Crick base pairing occurs between the bases A and T, A and U or G and C. For example, the sequence 5′-A-G-U-3′ is complementary to sequence 5′-A-C-U-3′.
Binding agents as discussed herein may suitably comprise a detectable label. The term “label” refers to any atom, molecule, moiety or biomolecule that may be used to provide a detectable and preferably quantifiable read-out or property, and that may be attached to or made part of an entity of interest, such as a binding agent. Labels may be suitably detectable by for example mass spectrometric, spectroscopic, optical, colourimetric, magnetic, photochemical, biochemical, immunochemical or chemical means. Labels include without limitation dyes; radiolabels such as 32P, 33P, 35S, 125I, 131I; electron-dense reagents; enzymes (e.g., horse-radish peroxidase or alkaline phosphatase as commonly used in immunoassays); binding moieties such as biotin-streptavidin; haptens such as digoxigenin; luminogenic, phosphorescent or fluorogenic moieties; mass tags; and fluorescent dyes alone or in combination with moieties that may suppress or shift emission spectra by fluorescence resonance energy transfer (FRET).
In certain embodiments, the one or more binding agents may be one or more antibodies. In other embodiments, binding agents may be provided with a tag that permits detection with another agent (e.g., with a probe binding partner). Such tags may be, for example, biotin, streptavidin, his-tag, myc tag, maltose, maltose binding protein or any other kind of tag known in the art that has a binding partner. Example of associations which may be utilised in the probe:binding partner arrangement may be any, and includes, for example biotin:streptavidin, his-tag:metal ion (e.g., Ni2+), maltose:maltose binding protein, etc. In certain embodiments, the one or more binding agents are configured for use in a technique selected from the group consisting of flow cytometry, fluorescence activated cell sorting, mass cytometry, fluorescence microscopy, affinity separation, magnetic cell separation, microfluidic separation, and combinations thereof. In certain embodiments, the one or more binding agents are one or more antibodies.
A marker-binding agent conjugate may be associated with or attached to a detection agent to facilitate detection. Examples of detection agents include, but are not limited to, luminescent labels; colourimetric labels, such as dyes; fluorescent labels; or chemical labels, such as electroactive agents (e.g., ferrocyanide); enzymes; radioactive labels; or radiofrequency labels. The detection agent may be a particle. Examples of such particles include, but are not limited to, colloidal gold particles; colloidal sulphur particles; colloidal selenium particles; colloidal barium sulfate particles; colloidal iron sulfate particles; metal iodate particles; silver halide particles; silica particles; colloidal metal (hydrous) oxide particles; colloidal metal sulfide particles; colloidal lead selenide particles; colloidal cadmium selenide particles; colloidal metal phosphate particles; colloidal metal ferrite particles; any of the above-mentioned colloidal particles coated with organic or inorganic layers; protein or peptide molecules; liposomes; or organic polymer latex particles, such as polystyrene latex beads. Preferable particles may be colloidal gold particles.
Inflammatory Diseases of the Gut and Respiratory System
In certain embodiments, tuft cells are modulated to treat inflammatory diseases. In certain embodiments, tuft cells are modulated to shift immune-like tuft-2 cells to be more tuft-1 neuronal like. In certain embodiments, tuft cells are modulated to shift tuft-1 neuronal cells to be more immune-like tuft-2 like. In certain embodiments, a signature gene specific for a tuft cell is targeted to modulate tuft cells in vivo. In certain embodiments, specific tuft cells are targeted to reduce an inflammatory response. Targeted cells may be activated or inhibited. In certain embodiments, basal cells are targeted to differentiate into a specific subset of tuft cells. In certain embodiments, a signature gene specific for a tuft cell is targeted to modulate basal cells in vivo.
Inflammatory bowel disease (IBD) is a group of inflammatory conditions of the colon and small intestine, principally including Crohn's disease and ulcerative colitis, with other forms of IBD representing far fewer cases (e.g., collagenous colitis, lymphocytic colitis, diversion colitis, Behçet's disease and indeterminate colitis). Pathologically, Crohn's disease affects the full thickness of the bowel wall (e.g., transmural lesions) and can affect any part of the gastrointestinal tract, while ulcerative colitis is restricted to the mucosa (epithelial lining) of the colon and rectum.
Graft-versus-host disease (GVHD) is an immune-related disease that can occur following an allogeneic tissue transplant. It is commonly associated with stem cell or bone marrow transplants, but GVHD also applies to other forms of tissue graft. In GVHD immune cells of the tissue graft recognize the recipient host as foreign and attack the host's cells.
It has long been recognized that IBD and GVHD are diseases associated with increased immune activity. The causes of IBD, while not well understood, may be related to an aberrant immune response to the microbiota in genetically susceptible individuals. IBD affects over 1.4 million people in the United States and over 2.2 million in Europe and is on the increase. With both environmental and genetic factors playing a role in the development and progression of IBD, response to current treatments (e.g., anti-inflammatory drugs, immune system suppressors, antibiotics, surgery, and other symptom specific medications) are unpredictable.
Similarly, a fundamental feature of GVHD is increased immune activity. As yet, the pathophysiology underlying GVHD is not well understood. It is a significant cause of morbidity and mortality following allogenic haematopoietic stem-cell transplantation and thus the focus of much ongoing research. Despite the advances in understanding the pathophysiology (e.g., predisposing factors), a standardized therapeutic strategy is still lacking. Currently both acute and chronic forms of GVHD are treated using corticosteroids (e.g., anti-inflammatory treatments). There is a need for new approaches to treating IBD and GVHD.
Some of the genetic factors predisposing one to IBD are known, as explored in Daniel B. Graham and Ramnik J. Xavier “From Genetics of Inflammatory Bowel Disease Towards Mechanistic Insights” Trends Immunol. 2013 August; 34(8): 371-378 (incorporated herein). This disclosure provides a rationale for modulating intestinal epithelial cell balance, function, differentiation and/or activity for the treatment of both IBD and GVHD, and other disorders.
In certain embodiments, the IBD is Crohn's disease or ulcerative colitis. In certain embodiments, the IBD is collagenous colitis, lymphocytic colitis, diversion colitis, Behçet's disease, or indeterminate colitis.
In other embodiments, the GVHD is acute graft- versus-host disease (aGVHD) or chronic graft-versus-host disease (cGVHD).
Asthma is characterized by recurrent episodes of wheezing, shortness of breath, chest tightness, and coughing. Sputum may be produced from the lung by coughing but is often hard to bring up. During recovery from an attack, it may appear pus-like due to high levels of eosinophils. Symptoms are usually worse at night and in the early morning or in response to exercise or cold air. Some people with asthma rarely experience symptoms, usually in response to triggers, whereas others may have marked and persistent symptoms. Chronic rhino-sinusitis (CRS) is characterized by inflammation of the mucosal surfaces of the nose and para-nasal sinuses, and it often coexists with allergic asthma. Atopic dermatitis is a chronic inflammatory skin disease that is characterized by eosinophilic infiltration and high serum IgE levels. Similar to allergic asthma and CRS, atopic dermatitis has been associated with increased expression of TSLP, IL-25, and IL-33 in the skin. Primary eosinophilic gastrointestinal disorders (EGIDs), including eosinophilic esophagitis (EoE), eosinophilic gastritis, eosinophilic gastroenteritis, and eosinophilic colitis, are disorders that exhibit eosinophil-rich inflammation in the gastrointestinal tract in the absence of known causes for eosinophilia such as parasite infection and drug reaction.
In certain embodiments, tuft cells induce an TLC2 inflammatory response. A skilled person can readily determine diseases that can be treated by reducing an ILC2 inflammatory response. ILC2 cells and ILC2 inflammatory responses have been associated with allergic asthma, therapy resistant-asthma, steroid-resistant severe allergic airway inflammation, systemic steroid-dependent severe eosinophilic asthma, chronic rhino-sinusitis (CRS), atopic dermatitis, food allergies, persistence of chronic airway inflammation, and primary eosinophilic gastrointestinal disorders (EGIDs), including but not limited to eosinophilic esophagitis (EoE), eosinophilic gastritis, eosinophilic gastroenteritis, and eosinophilic colitis (see, e.g., Van Rijt et al., Type 2 innate lymphoid cells: at the cross-roads in allergic asthma, Seminars in Immunopathology July 2016, Volume 38, Issue 4, pp 483-496; Rivas et al., IL-4 production by group 2 innate lymphoid cells promotes food allergy by blocking regulatory T-cell function, J Allergy Clin Immunol. 2016 September; 138(3):801-811.e9; and Morita, Hideaki et al. Innate lymphoid cells in allergic and nonallergic inflammation, Journal of Allergy and Clinical Immunology, Volume 138, Issue 5, 1253-1264). In certain embodiments, modulation of tuft cells can be used to modulate ILC2 inflammatory responses.
In certain embodiments, tuft cells may be modulated to treat other diseases. In certain embodiments, the diseases are localized to a mucosal surface. The terms “disease” or “disorder” are used interchangeably throughout this specification, and refer to any alternation in state of the body or of some of the organs, interrupting or disturbing the performance of the functions and/or causing symptoms such as discomfort, dysfunction, distress, or even death to the person afflicted or those in contact with a person. A disease or disorder can also be related to a distemper, ailing, ailment, malady, disorder, sickness, illness, complaint, indisposition, or affliction.
In certain embodiments, the pathological condition may be an infection, inflammation, proliferative disease, autoimmune disease, or allergy.
The term “infection” as used herein refers to presence of an infective agent, such as a pathogen, e.g., a microorganism, in or on a subject, which, if its presence or growth were inhibited, would result in a benefit to the subject. Hence, the term refers to the state produced by the establishment, more particularly invasion and multiplication, of an infective agent, such as a pathogen, e.g., a microorganism, in or on a suitable host. An infection may produce tissue injury and progress to overt disease through a variety of cellular and toxic mechanisms.
The term “inflammation” generally refers to a response in vasculated tissues to cellular or tissue injury usually caused by physical, chemical and/or biological agents, that is marked in the acute form by the classical sequences of pain, heat, redness, swelling, and loss of function, and serves as a mechanism initiating the elimination, dilution or walling-off of noxious agents and/or of damaged tissue. Inflammation histologically involves a complex series of events, including dilation of the arterioles, capillaries, and venules with increased permeability and blood flow, exudation of fluids including plasma proteins, and leukocyte migration into the inflammatory focus.
Further, the term encompasses inflammation caused by extraneous physical or chemical injury or by biological agents, e.g., viruses, bacteria, fungi, protozoan or metazoan parasite infections, as well as inflammation which is seemingly unprovoked, e.g., which occurs in the absence of demonstrable injury or infection, inflammation responses to self-antigens (auto-immune inflammation), inflammation responses to engrafted xenogeneic or allogeneic cells, tissues or organs, inflammation responses to allergens, etc. The term covers both acute inflammation and chronic inflammation. Also, the term includes both local or localised inflammation, as well as systemic inflammation, i.e., where one or more inflammatory processes are not confined to a particular tissue but occur generally in the endothelium and/or other organ systems.
Systemic inflammatory conditions may particularly encompass systemic inflammatory response syndrome (SIRS) or sepsis. “SIRS” is a systemic inflammatory response syndrome with no signs of infection. It can be characterised by the presence of at least two of the four following clinical criteria: fever or hypothermia (temperature of 38.0° C.) or more, or temperature of 36.0° C. or less); tachycardia (at least 90 beats per minute); tachypnea (at least 20 breaths per minute or PaCO2 less than 4.3 kPa (32.0 mm Hg) or the need for mechanical ventilation); and an altered white blood cell (WBC) count of 12×106 cells/mL or more, or an altered WBC count of 4×106 cells/mL or less, or the presence of more than 10% band forms. “Sepsis” can generally be defined as SIRS with a documented infection, such as for example a bacterial infection. Infection can be diagnosed by standard textbook criteria or, in case of uncertainty, by an infectious disease specialist. Bacteraemia is defined as sepsis where bacteria can be cultured from blood. Sepsis may be characterised or staged as mild sepsis, severe sepsis (sepsis with acute organ dysfunction), septic shock (sepsis with refractory arterial hypotension), organ failure, multiple organ dysfunction syndrome and death.
The term “proliferative disease” generally refers to any disease or disorder characterised by neoplastic cell growth and proliferation, whether benign, pre-malignant, or malignant. The term proliferative disease generally includes all transformed cells and tissues and all cancerous cells and tissues. Proliferative diseases or disorders include, but are not limited to abnormal cell growth, benign tumours, premalignant or precancerous lesions, malignant tumors, and cancer.
The terms “tumor” or “tumor tissue” refer to an abnormal mass of tissue resulting from excessive cell division. A tumor or tumor tissue comprises “tumor cells” which are neoplastic cells with abnormal growth properties and no useful bodily function. Tumors, tumor tissue and tumor cells may be benign, pre-malignant or malignant, or may represent a lesion without any cancerous potential. A tumor or tumor tissue may also comprise “tumor-associated non-tumor cells”, e.g., vascular cells which form blood vessels to supply the tumor or tumor tissue. Non-tumor cells may be induced to replicate and develop by tumor cells, for example, the induction of angiogenesis in a tumor or tumor tissue.
The term “cancer” refers to a malignant neoplasm characterised by deregulated or unregulated cell growth. The term “cancer” includes primary malignant cells or tumors (e.g., those whose cells have not migrated to sites in the subject's body other than the site of the original malignancy or tumor) and secondary malignant cells or tumors (e.g., those arising from metastasis, the migration of malignant cells or tumor cells to secondary sites that are different from the site of the original tumor. The term “metastatic” or “metastasis” generally refers to the spread of a cancer from one organ or tissue to another non-adjacent organ or tissue. The occurrence of the proliferative disease in the other non-adjacent organ or tissue is referred to as metastasis.
As used throughout the present specification, the terms “autoimmune disease” or “autoimmune disorder” used interchangeably refer to a diseases or disorders caused by an immune response against a self-tissue or tissue component (self-antigen) and include a self-antibody response and/or cell-mediated response. The terms encompass organ-specific autoimmune diseases, in which an autoimmune response is directed against a single tissue, as well as non-organ specific autoimmune diseases, in which an autoimmune response is directed against a component present in two or more, several or many organs throughout the body.
Non-limiting examples of autoimmune diseases include but are not limited to acute disseminated encephalomyelitis (ADEM); Addison's disease; ankylosing spondylitis; antiphospholipid antibody syndrome (APS); aplastic anemia; autoimmune gastritis; autoimmune hepatitis; autoimmune thrombocytopenia; Behçet's disease; coeliac disease; dermatomyositis; diabetes mellitus type I; Goodpasture's syndrome; Graves' disease; Guillain-Barre syndrome (GBS); Hashimoto's disease; idiopathic thrombocytopenic purpura; inflammatory bowel disease (IBD) including Crohn's disease and ulcerative colitis; mixed connective tissue disease; multiple sclerosis (MS); myasthenia gravis; opsoclonus myoclonus syndrome (OMS); optic neuritis; Ord's thyroiditis; pemphigus; pernicious anaemia; polyarteritis nodosa; polymyositis; primary biliary cirrhosis; primary myoxedema; psoriasis; rheumatic fever; rheumatoid arthritis; Reiter's syndrome; scleroderma; Sjögren's syndrome; systemic lupus erythematosus; Takayasu's arteritis; temporal arteritis; vitiligo; warm autoimmune hemolytic anemia; or Wegener's granulomatosis.
Diagnosis, Prognosis, Monitoring
In certain embodiments, the markers described herein are used to make a diagnosis, prognosis or used to monitor a disease according to the methods described herein. In certain embodiments, markers are detected and/or quantified. In certain embodiments, cells are detected and/or quantified.
The terms “diagnosis” and “monitoring” are commonplace and well-understood in medical practice. By means of further explanation and without limitation the term “diagnosis” generally refers to the process or act of recognising, deciding on or concluding on a disease or condition in a subject on the basis of symptoms and signs and/or from results of various diagnostic procedures (such as, for example, from knowing the presence, absence and/or quantity of one or more biomarkers characteristic of the diagnosed disease or condition).
The term “monitoring” generally refers to the follow-up of a disease or a condition in a subject for any changes which may occur over time.
The terms “prognosing” or “prognosis” generally refer to an anticipation on the progression of a disease or condition and the prospect (e.g., the probability, duration, and/or extent) of recovery. A good prognosis of the diseases or conditions taught herein may generally encompass anticipation of a satisfactory partial or complete recovery from the diseases or conditions, preferably within an acceptable time period. A good prognosis of such may more commonly encompass anticipation of not further worsening or aggravating of such, preferably within a given time period. A poor prognosis of the diseases or conditions as taught herein may generally encompass anticipation of a substandard recovery and/or unsatisfactorily slow recovery, or to substantially no recovery or even further worsening of such.
The terms also encompass prediction of a disease. The terms “predicting” or “prediction” generally refer to an advance declaration, indication or foretelling of a disease or condition in a subject not (yet) having the disease or condition. For example, a prediction of a disease or condition in a subject may indicate a probability, chance or risk that the subject will develop the disease or condition, for example within a certain time period or by a certain age. The probability, chance or risk may be indicated inter alia as an absolute value, range or statistics, or may be indicated relative to a suitable control subject or subject population (such as, e.g., relative to a general, normal or healthy subject or subject population). Hence, the probability, chance or risk that a subject will develop a disease or condition may be advantageously indicated as increased or decreased, or as fold-increased or fold-decreased relative to a suitable control subject or subject population. As used herein, the term “prediction” of the conditions or diseases as taught herein in a subject may also particularly mean that the subject has a ‘positive’ prediction of such, i.e., that the subject is at risk of having such (e.g., the risk is significantly increased vis-à-vis a control subject or subject population). The term “prediction of no” diseases or conditions as taught herein as described herein in a subject may particularly mean that the subject has a ‘negative’ prediction of such, i.e., that the subject's risk of having such is not significantly increased vis-à-vis a control subject or subject population.
Cell-Based Therapeutics
The embodiments disclosed herein also provide cell-based therapeutic compositions comprising the isolated and/or modified tuft cells. The cell-based therapeutics may be used to restore homeostatic balance in one of the diseased tissues disclosed herein. For example, cell-based therapeutics may be used to deliver tuft cells modified to be primarily chemosensory or immune-like depending on the condition that needs to be treated. The isolated cells used in the cell based therapeutics may be allogenic or autologous. Tuft cells may be modified ex vivo and transferred to a subject in need thereof. In certain embodiments, a signature gene specific for a tuft cell is targeted to modulate tuft cells ex vivo. In certain embodiments, a signature gene specific for a tuft cell is targeted to modulate basal cells ex vivo. In certain embodiments, basal cells are differentiated into a specific subset of tuft cells.
A “pharmaceutical composition” refers to a composition that usually contains an excipient, such as a pharmaceutically acceptable carrier that is conventional in the art and that is suitable for administration to cells or to a subject.
The term “pharmaceutically acceptable” as used throughout this specification is consistent with the art and means compatible with the other ingredients of a pharmaceutical composition and not deleterious to the recipient thereof.
As used herein, “carrier” or “excipient” includes any and all solvents, diluents, buffers (such as, e.g., neutral buffered saline or phosphate buffered saline), solubilisers, colloids, dispersion media, vehicles, fillers, chelating agents (such as, e.g., EDTA or glutathione), amino acids (such as, e.g., glycine), proteins, disintegrants, binders, lubricants, wetting agents, emulsifiers, sweeteners, colorants, flavourings, aromatizers, thickeners, agents for achieving a depot effect, coatings, antifungal agents, preservatives, stabilisers, antioxidants, tonicity controlling agents, absorption delaying agents, and the like. The use of such media and agents for pharmaceutical active components is well known in the art. Such materials should be non-toxic and should not interfere with the activity of the cells or active components.
The precise nature of the carrier or excipient or other material will depend on the route of administration. For example, the composition may be in the form of a parenterally acceptable aqueous solution, which is pyrogen-free and has suitable pH, isotonicity and stability. For general principles in medicinal formulation, the reader is referred to Cell Therapy: Stem Cell Transplantation, Gene Therapy, and Cellular Immunotherapy, by G. Morstyn & W. Sheridan eds., Cambridge University Press, 1996; and Hematopoietic Stem Cell Therapy, E. D. Ball, J. Lister & P. Law, Churchill Livingstone, 2000.
The pharmaceutical composition can be applied parenterally, rectally, orally or topically. Preferably, the pharmaceutical composition may be used for intravenous, intramuscular, subcutaneous, peritoneal, peridural, rectal, nasal, pulmonary, mucosal, or oral application. In a preferred embodiment, the pharmaceutical composition according to the invention is intended to be used as an infuse. The skilled person will understand that compositions which are to be administered orally or topically will usually not comprise cells, although it may be envisioned for oral compositions to also comprise cells, for example when gastro-intestinal tract indications are treated. Each of the cells or active components (e.g., modulants, immunomodulants, antigens) as discussed herein may be administered by the same route or may be administered by a different route. By means of example, and without limitation, cells may be administered parenterally and other active components may be administered orally.
Liquid pharmaceutical compositions may generally include a liquid carrier such as water or a pharmaceutically acceptable aqueous solution. For example, physiological saline solution, tissue or cell culture media, dextrose or other saccharide solution or glycols such as ethylene glycol, propylene glycol or polyethylene glycol may be included.
The composition may include one or more cell protective molecules, cell regenerative molecules, growth factors, anti-apoptotic factors or factors that regulate gene expression in the cells. Such substances may render the cells independent of their environment.
Such pharmaceutical compositions may contain further components ensuring the viability of the cells therein. For example, the compositions may comprise a suitable buffer system (e.g., phosphate or carbonate buffer system) to achieve desirable pH, more usually near neutral pH, and may comprise sufficient salt to ensure isoosmotic conditions for the cells to prevent osmotic stress. For example, suitable solution for these purposes may be phosphate-buffered saline (PBS), sodium chloride solution, Ringer's Injection or Lactated Ringer's Injection, as known in the art. Further, the composition may comprise a carrier protein, e.g., albumin (e.g., bovine or human albumin), which may increase the viability of the cells.
Further suitably pharmaceutically acceptable carriers or additives are well known to those skilled in the art and for instance may be selected from proteins such as collagen or gelatine, carbohydrates such as starch, polysaccharides, sugars (dextrose, glucose and sucrose), cellulose derivatives like sodium or calcium carboxymethylcellulose, hydroxypropyl cellulose or hydroxypropylmethyl cellulose, pregelatinized starches, pectin agar, carrageenan, clays, hydrophilic gums (acacia gum, guar gum, arabic gum and xanthan gum), alginic acid, alginates, hyaluronic acid, polyglycolic and polylactic acid, dextran, pectins, synthetic polymers such as water-soluble acrylic polymer or polyvinylpyrrolidone, proteoglycans, calcium phosphate and the like.
If desired, cell preparation can be administered on a support, scaffold, matrix or material to provide improved tissue regeneration. For example, the material can be a granular ceramic, or a biopolymer such as gelatine, collagen, or fibrinogen. Porous matrices can be synthesized according to standard techniques (e.g., Mikos et al., Biomaterials 14: 323, 1993; Mikos et al., Polymer 35:1068, 1994; Cook et al., J. Biomed. Mater. Res. 35:513, 1997). Such support, scaffold, matrix or material may be biodegradable or non-biodegradable. Hence, the cells may be transferred to and/or cultured on suitable substrate, such as porous or non-porous substrate, to provide for implants.
For example, cells that have proliferated, or that are being differentiated in culture dishes, can be transferred onto three-dimensional solid supports in order to cause them to multiply and/or continue the differentiation process by incubating the solid support in a liquid nutrient medium of the invention, if necessary. Cells can be transferred onto a three-dimensional solid support, e.g. by impregnating the support with a liquid suspension containing the cells. The impregnated supports obtained in this way can be implanted in a human subject. Such impregnated supports can also be re-cultured by immersing them in a liquid culture medium, prior to being finally implanted. The three-dimensional solid support needs to be biocompatible so as to enable it to be implanted in a human. It may be biodegradable or non-biodegradable.
In some embodiments, the tuft cells or their progenitor cells are implanted in the subject as part of a composition further comprising a scaffold. In some embodiments, the scaffold is biodegradable.
In some embodiments, the scaffold comprises a natural fiber, a synthetic fiber, decellularized lung tissue, or a combination thereof.
In some embodiments, the natural fiber is selected from the group consisting of collagen, fibrin, silk, thrombin, chitosan, chitin, alginic acid, hyaluronic acid, and gelatin.
In some embodiments, the synthetic fiber is selected from the group consisting of: representative bio-degradable aliphatic polyesters such as polylactic acid (PLA), polyglycolic acid (PGA), poly(D,L-lactide-co-glycolide) (PLGA), poly(caprolactone), diol/diacid aliphatic polyester, polyester-amide/polyester-urethane, poly(valerolactone), poly(hydroxyl butyrate), polybutylene terephthalate (PBT), polyhydroxyhexanoate (PHH), polybutylene succinate (PBS), and poly(hydroxyl valerate).
The cells or cell populations can be administered in a manner that permits them to survive, grow, propagate and/or differentiate towards desired cell types (e.g. differentiation) or cell states. The cells or cell populations may be grafted to or may migrate to and engraft within the intended organ.
In certain embodiments, a pharmaceutical cell preparation as taught herein may be administered in a form of liquid composition. In embodiments, the cells or pharmaceutical composition comprising such can be administered systemically, topically, within an organ or at a site of organ dysfunction or lesion.
Preferably, the pharmaceutical compositions may comprise a therapeutically effective amount of the specified intestinal or respiratory epithelial cells, epithelial stem cells, or immune cells (preferably epithelial cells, e.g., tuft cells) and/or other active components. The term “therapeutically effective amount” refers to an amount which can elicit a biological or medicinal response in a tissue, system, animal or human that is being sought by a researcher, veterinarian, medical doctor or other clinician, and in particular can prevent or alleviate one or more of the local or systemic symptoms or features of a disease or condition being treated.
A further aspect of the invention provides a population of the intestinal or respiratory epithelial cells, epithelial stem cells, or immune cells (preferably epithelial cells, e.g., tuft cells) as taught herein. The terms “cell population” or “population” denote a set of cells having characteristics in common. The characteristics may include in particular the one or more marker(s) or gene or gene product signature(s) as taught herein. The intestinal or respiratory epithelial cells, epithelial stem cells, or immune cells (preferably epithelial cells, e.g., tuft cells) cells as taught herein may be comprised in a cell population. By means of example, the specified cells may constitute at least 40% (by number) of all cells of the cell population, for example, at least 45%, preferably at least 50%, at least 55%, more preferably at least 60%, at least 65%, still more preferably at least 70%, at least 75%, even more preferably at least 80%, at least 85%, and yet more preferably at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or even 100% of all cells of the cell population.
The isolated intestinal or respiratory epithelial cells, epithelial stem cells, or immune cells (preferably epithelial cells, e.g., tuft cells) of populations thereof as disclosed throughout this specification may be suitably cultured or cultivated in vitro. The term “in vitro” generally denotes outside, or external to, a body, e.g., an animal or human body. The term encompasses “ex vivo”.
The terms “culturing” or “cell culture” are common in the art and broadly refer to maintenance of cells and potentially expansion (proliferation, propagation) of cells in vitro. Typically, animal cells, such as mammalian cells, such as human cells, are cultured by exposing them to (i.e., contacting them with) a suitable cell culture medium in a vessel or container adequate for the purpose (e.g., a 96-, 24-, or 6-well plate, a T-25, T-75, T-150 or T-225 flask, or a cell factory), at art-known conditions conducive to in vitro cell culture, such as temperature of 37° C., 5% v/v CO2 and >95% humidity.
The term “medium” as used herein broadly encompasses any cell culture medium conducive to maintenance of cells, preferably conducive to proliferation of cells. Typically, the medium will be a liquid culture medium, which facilitates easy manipulation (e.g., decantation, pipetting, centrifugation, filtration, and such) thereof.
Methods of Modulating, Differentiation, and Treating
Embodiments disclosed herein provide methods of modulating epithelial cell proliferation, differentiation, maintenance, and/or function comprising administering to a subject in need thereof a tuft cell modulating agent. The methods may induce differentiation of progenitor cells into tuft cells or particular tuft cell sub-types disclosed herein. The methods may be used to induce shift in the relative amount of tuft cells in a given tissue as a whole or to push the balance of particular population of tuft cells towards one cell type or another. For example, in inflammatory disease, modulating agents may be used to reduce the number of inflammatory tuft cells and/or increase the number of non-inflammatory tuft cells or in order to reduce or mitigate tuft cell contributions to or induction of said inflammatory response.
Within the present specification, the terms “differentiation”, “differentiating” or derivatives thereof, denote the process by which an unspecialised or relatively less specialised cell becomes relatively more specialised. In the context of cell ontogeny, the adjective “differentiated” is a relative term. Hence, a “differentiated cell” is a cell that has progressed further down a certain developmental pathway than the cell it is being compared with. The differentiated cell may, for example, be a terminally differentiated cell, i.e., a fully specialised cell capable of taking up specialised functions in various tissues or organs of an organism, which may but need not be post-mitotic; or the differentiated cell may itself be a progenitor cell within a particular differentiation lineage which can further proliferate and/or differentiate.
A relatively more specialized cell may differ from an unspecialized or relatively less specialized cell in one or more demonstrable phenotypic characteristics, such as, for example, the presence, absence or level of expression of particular cellular components or products, e.g., RNA, proteins or other substances, activity of certain biochemical pathways, morphological appearance, proliferation capacity and/or kinetics, differentiation potential and/or response to differentiation signals, electrophysiological behaviour, etc., wherein such characteristics signify the progression of the relatively more specialised cell further along the developmental pathway. Non-limiting examples of differentiation may include, e.g., the change of a pluripotent stem cell into a given type of multipotent progenitor or stem cell, the change of a multipotent progenitor or stem cell into a given type of unipotent progenitor or stem cell, or the change of a unipotent progenitor or stem cell to more specialized cell types or to terminally specialised cells within a given cell lineage.
Any one or more of the several successive molecular mechanisms involved in the expression of a given gene or polypeptide may be targeted in the intestinal epithelial cells, intestinal epithelial stem cells, intestinal immune cells, or respiratory cells (preferably epithelial cells, e.g., tuft cells) cell modification as intended herein. Without limitation, these may include targeting the gene sequence (e.g., targeting the polypeptide-encoding, non-coding and/or regulatory portions of the gene sequence), the transcription of the gene into RNA, the polyadenylation and where applicable splicing and/or other post-transcriptional modifications of the RNA into mRNA, the localization of the mRNA into cell cytoplasm, where applicable other post-transcriptional modifications of the mRNA, the translation of the mRNA into a polypeptide chain, where applicable post-translational modifications of the polypeptide, and/or folding of the polypeptide chain into the mature conformation of the polypeptide. For compartmentalized polypeptides, such as secreted polypeptides and transmembrane polypeptides, this may further include targeting trafficking of the polypeptides, i.e., the cellular mechanism by which polypeptides are transported to the appropriate sub-cellular compartment or organelle, membrane, e.g. the plasma membrane, or outside the cell. Functional genomics can be used to modify cells for therapeutic purposes, and identify networks and pathways. For example, Graham et al. (“Functional genomics identifies negative regulatory nodes controlling phagocyte oxidative burst,” Nature Communications 6, Article number: 7838 (2015)) describes functional genetic screens to identify the phagocytic oxidative burst.
With the rapid advancement of genomic technology, it is now possible to associate genetic variation with phenotypes of intestinal epithelial cells, intestinal epithelial stem cells, intestinal immune cells, or respiratory epithelial cells (preferably epithelial cells, e.g., tuft cells) at the population level. In particular, genome-wide association studies (GWAS) have implicated genetic loci associated with risk for IBD and allowed for inference of new biological processes that contribute to disease. These studies highlight innate defense mechanisms such as antibacterial autophagy, superoxide generation during oxidative burst and reactive nitrogen species produced by iNOS. However, GWAS requires functional analysis to unlock new insights. For example, many risk loci are densely populated with coding genes, which complicates identification of causal genes. Even when fine mapping clearly identifies key genes, a majority have poorly defined functions in host immunity. Moreover, any given gene may have multiple functions depending on the cell type in which it is expressed as well as environmental cues. Such context-specific functions of regulatory genes are largely unexplored. Thus, human genetics offers an opportunity to leverage insight from large amounts of genetic variation within healthy and patient populations to interrogate mechanisms of immunity. Irrespective of their putative roles in IBD pathology, genes within risk loci are likely to be highly enriched for genes controlling signaling pathways. In certain embodiments, any gene as described herein is targeted. In certain embodiments, a GWAS gene is targeted. In certain embodiments, the gene is modulated by increasing or decreasing expression or activity of the gene.
The terms “increased” or “increase” or “upregulated” or “upregulate” as used herein generally mean an increase by a statically significant amount. For avoidance of doubt, “increased” means a statistically significant increase of at least 10% as compared to a reference level, including an increase of at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 100% or more, including, for example at least 2-fold, at least 3-fold, at least 4-fold, at least 5-fold, at least 10-fold increase or greater as compared to a reference level, as that term is defined herein.
The term “reduced” or “reduce” or “decrease” or “decreased” or “downregulate” or “downregulated” as used herein generally means a decrease by a statistically significant amount relative to a reference. For avoidance of doubt, “reduced” means statistically significant decrease of at least 10% as compared to a reference level, for example a decrease by at least 20%, at least 30%, at least 40%, at least t 50%, or least 60%, or least 70%, or least 80%, at least 90% or more, up to and including a 100% decrease (i.e., absent level as compared to a reference sample), or any decrease between 10-100% as compared to a reference level, as that term is defined herein. The term “abolish” or “abolished” may in particular refer to a decrease by 100%, i.e., absent level as compared to a reference sample.
It will be understood by the skilled person that treating as referred to herein encompasses enhancing treatment, or improving treatment efficacy. Treatment may include inhibition of an inflammatory response, tumor regression as well as inhibition of tumor growth, metastasis or tumor cell proliferation, or inhibition or reduction of otherwise deleterious effects associated with the tumor.
As used throughout this specification, the terms “treat”, “treating” and “treatment” refer to the alleviation or measurable lessening of one or more symptoms or measurable markers of a pathological condition such as a disease or disorder. Measurable lessening includes any statistically significant decline in a measurable marker or symptom. Generally, the terms encompass both curative treatments and treatments directed to reduce symptoms and/or slow progression of the disease. The terms encompass both the therapeutic treatment of an already developed pathological condition, as well as prophylactic or preventative measures, wherein the aim is to prevent or lessen the chances of incidence of a pathological condition. In certain embodiments, the terms may relate to therapeutic treatments. In certain other embodiments, the terms may relate to preventative treatments. Treatment of a chronic pathological condition during the period of remission may also be deemed to constitute a therapeutic treatment. The term may encompass ex vivo or in vivo treatments as appropriate in the context of the present invention.
As used throughout this specification, the terms “prevent”, “preventing” and “prevention” refer to the avoidance or delay in manifestation of one or more symptoms or measurable markers of a pathological condition, such as a disease or disorder. A delay in the manifestation of a symptom or marker is a delay relative to the time at which such symptom or marker manifests in a control or untreated subject with a similar likelihood or susceptibility of developing the pathological condition. The terms “prevent”, “preventing” and “prevention” include not only the avoidance or prevention of a symptom or marker of the pathological condition, but also a reduced severity or degree of any one of the symptoms or markers of the pathological condition, relative to those symptoms or markers in a control or non-treated individual with a similar likelihood or susceptibility of developing the pathological condition, or relative to symptoms or markers likely to arise based on historical or statistical measures of populations affected by the disease or disorder. By “reduced severity” is meant at least a 10% reduction in the severity or degree of a symptom or measurable marker relative to a control or reference, e.g., at least 15%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99% or even 100% (i.e., no symptoms or measurable markers).
Efficaciousness of treatment is determined in association with any known method for diagnosing or treating the particular disease. The invention comprehends a treatment method comprising any one of the methods or uses herein discussed.
The phrase “therapeutically effective amount” as used herein refers to a sufficient amount of a drug, agent, or compound to provide a desired therapeutic effect.
As used herein “patient” refers to any human being receiving or who may receive medical treatment and is used interchangeably herein with the term “subject”.
Modulating Agents
In certain embodiments, the tuft cell modulating agent may comprise a therapeutic antibody, antibody fragment, antibody-like protein scaffold, aptamer, protein, genetic modifying agent or small molecule.
The term “small molecule” refers to compounds, preferably organic compounds, with a size comparable to those organic molecules generally used in pharmaceuticals. The term excludes biological macromolecules (e.g., proteins, peptides, nucleic acids, etc.). Preferred small organic molecules range in size up to about 5000 Da, e.g., up to about 4000, preferably up to 3000 Da, more preferably up to 2000 Da, even more preferably up to about 1000 Da, e.g., up to about 900, 800, 700, 600 or up to about 500 Da.
In certain embodiments, the tuft cell modulating agent can refer to a protein-binding agent that permits modulation or activity of proteins or disrupts interactions of proteins and other biomolecules, such as but not limited to disrupting protein-protein interaction, ligand-receptor interaction, or protein-nucleic acid interaction. Agents can also refer to DNA targeting or RNA targeting agents. Agents may include a fragment, derivative and analog of an active agent. The terms “fragment,” “derivative” and “analog” when referring to polypeptides as used herein refers to polypeptides which either retain substantially the same biological function or activity as such polypeptides. An analog includes a proprotein which can be activated by cleavage of the proprotein portion to produce an active mature polypeptide. Such agents include, but are not limited to, antibodies (“antibodies” includes antigen-binding portions of antibodies such as epitope- or antigen-binding peptides, paratopes, functional CDRs; recombinant antibodies; chimeric antibodies; humanized antibodies; nanobodies; tribodies; midibodies; or antigen-binding derivatives, analogs, variants, portions, or fragments thereof), protein-binding agents, nucleic acid molecules, small molecules, recombinant protein, peptides, aptamers, avimers and protein-binding derivatives, portions or fragments thereof.
As used herein, a “blocking” antibody or an antibody “antagonist” is one which inhibits or reduces biological activity of the antigen(s) it binds. For example, an antagonist antibody may bind a surface receptor or ligand and inhibit the ability of the receptor and ligand to induce an ILC class 2 inflammatory response. In certain embodiments, the blocking antibodies or antagonist antibodies or portions thereof described herein completely inhibit the biological activity of the antigen(s).
Antibodies may act as agonists or antagonists of the recognized polypeptides. For example, the present invention includes antibodies which disrupt receptor/ligand interactions either partially or fully. The invention features both receptor-specific antibodies and ligand-specific antibodies. The invention also features receptor-specific antibodies which do not prevent ligand binding but prevent receptor activation. Receptor activation (i.e., signaling) may be determined by techniques described herein or otherwise known in the art. For example, receptor activation can be determined by detecting the phosphorylation (e.g., tyrosine or serine/threonine) of the receptor or of one of its down-stream substrates by immunoprecipitation followed by western blot analysis. In specific embodiments, antibodies are provided that inhibit ligand activity or receptor activity by at least 95%, at least 90%, at least 85%, at least 80%, at least 75%, at least 70%, at least 60%, or at least 50% of the activity in absence of the antibody.
The invention also features receptor-specific antibodies which both prevent ligand binding and receptor activation as well as antibodies that recognize the receptor-ligand complex. Likewise, encompassed by the invention are neutralizing antibodies which bind the ligand and prevent binding of the ligand to the receptor, as well as antibodies which bind the ligand, thereby preventing receptor activation, but do not prevent the ligand from binding the receptor. Further included in the invention are antibodies which activate the receptor. These antibodies may act as receptor agonists, i.e., potentiate or activate either all or a subset of the biological activities of the ligand-mediated receptor activation, for example, by inducing dimerization of the receptor. The antibodies may be specified as agonists, antagonists or inverse agonists for biological activities comprising the specific biological activities of the peptides disclosed herein. The antibody agonists and antagonists can be made using methods known in the art. See, e.g., PCT publication WO 96/40281; U.S. Pat. No. 5,811,097; Deng et al., Blood 92(6):1981-1988 (1998); Chen et al., Cancer Res. 58(16):3668-3678 (1998); Harrop et al., J. Immunol. 161(4):1786-1794 (1998); Zhu et al., Cancer Res. 58(15):3209-3214 (1998); Yoon et al., J. Immunol. 160(7):3170-3179 (1998); Prat et al., J. Cell. Sci. III (Pt2):237-247 (1998); Pitard et al., J. Immunol. Methods 205(2):177-190 (1997); Liautard et al., Cytokine 9(4):233-241 (1997); Carlson et al., J. Biol. Chem. 272(17):11295-11301 (1997); Taryman et al., Neuron 14(4):755-762 (1995); Muller et al., Structure 6(9):1153-1167 (1998); Bartunek et al., Cytokine 8(1):14-20 (1996).
The antibodies as defined for the present invention include derivatives that are modified, i.e., by the covalent attachment of any type of molecule to the antibody such that covalent attachment does not prevent the antibody from generating an anti-idiotypic response. For example, but not by way of limitation, the antibody derivatives include antibodies that have been modified, e.g., by glycosylation, acetylation, pegylation, phosphorylation, amidation, derivatization by known protecting/blocking groups, proteolytic cleavage, linkage to a cellular ligand or other protein, etc. Any of numerous chemical modifications may be carried out by known techniques, including, but not limited to specific chemical cleavage, acetylation, formylation, metabolic synthesis of tunicamycin, etc. Additionally, the derivative may contain one or more non-classical amino acids.
Methods for administering antibodies for therapeutic use is well known to one skilled in the art. In certain embodiments, small particle aerosols of antibodies or fragments thereof may be administered, preferably for treating a respiratory inflammatory disease (see e.g., Piazza et al., J. Infect. Dis., Vol. 166, pp. 1422-1424, 1992; and Brown, Aerosol Science and Technology, Vol. 24, pp. 45-56, 1996). In certain embodiments, antibodies are administered in metered-dose propellant driven aerosols. In preferred embodiments, antibodies are used as inhibitors or antagonists to depress inflammatory diseases or allergen-induced asthmatic responses. In certain embodiments, antibodies may be administered in liposomes, i.e., immunoliposomes (see, e.g., Maruyama et al., Biochim. Biophys. Acta, Vol. 1234, pp. 74-80, 1995). In certain embodiments, immunoconjugates, immunoliposomes or immunomicrospheres containing an agent of the present invention is administered by inhalation.
In certain embodiments, antibodies may be topically administered to mucosa, such as the oropharynx, nasal cavity, respiratory tract, gastrointestinal tract, eye such as the conjunctival mucosa, vagina, urogenital mucosa, or for dermal application. In certain embodiments, antibodies are administered to the nasal, bronchial or pulmonary mucosa. In order to obtain optimal delivery of the antibodies to the pulmonary cavity in particular, it may be advantageous to add a surfactant such as a phosphoglyceride, e.g. phosphatidylcholine, and/or a hydrophilic or hydrophobic complex of a positively or negatively charged excipient and a charged antibody of the opposite charge.
Other excipients suitable for pharmaceutical compositions intended for delivery of antibodies to the respiratory tract mucosa may be a) carbohydrates, e.g., monosaccharides such as fructose, galactose, glucose. D-mannose, sorbose, and the like; disaccharides, such as lactose, trehalose, cellobiose, and the like; cyclodextrins, such as 2-hydroxypropyl-β-cyclodextrin; and polysaccharides, such as raffinose, maltodextrins, dextrans, and the like; b) amino acids, such as glycine, arginine, aspartic acid, glutamic acid, cysteine, lysine and the like; c) organic salts prepared from organic acids and bases, such as sodium citrate, sodium ascorbate, magnesium gluconate, sodium gluconate, tromethamine hydrochloride, and the like: d) peptides and proteins, such as aspartame, human serum albumin, gelatin, and the like; e) alditols, such mannitol, xylitol, and the like, and f) polycationic polymers, such as chitosan or a chitosan salt or derivative.
For dermal application, the antibodies of the present invention (e.g. NMU antibodies) may suitably be formulated with one or more of the following excipients: solvents, buffering agents, preservatives, humectants, chelating agents, antioxidants, stabilizers, emulsifying agents, suspending agents, gel-forming agents, ointment bases, penetration enhancers, and skin protective agents.
Examples of solvents are e.g. water, alcohols, vegetable or marine oils (e.g. edible oils like almond oil, castor oil, cacao butter, coconut oil, corn oil, cottonseed oil, linseed oil, olive oil, palm oil, peanut oil, poppy seed oil, rapeseed oil, sesame oil, soybean oil, sunflower oil, and tea seed oil), mineral oils, fatty oils, liquid paraffin, polyethylene glycols, propylene glycols, glycerol, liquid polyalkylsiloxanes, and mixtures thereof.
Examples of buffering agents are e.g. citric acid, acetic acid, tartaric acid, lactic acid, hydrogenphosphoric acid, diethyl amine etc. Suitable examples of preservatives for use in compositions are parabens, such as methyl, ethyl, propyl p-hydroxybenzoate, butylparaben, isobutylparaben, isopropylparaben, potassium sorbate, sorbic acid, benzoic acid, methyl benzoate, phenoxyethanol, bronopol, bronidox, MDM hydantoin, iodopropynyl butylcarbamate, EDTA, benzalkonium chloride, and benzylalcohol, or mixtures of preservatives.
Examples of humectants are glycerin, propylene glycol, sorbitol, lactic acid, urea, and mixtures thereof.
Examples of antioxidants are butylated hydroxy anisole (BHA), ascorbic acid and derivatives thereof, tocopherol and derivatives thereof, cysteine, and mixtures thereof.
Examples of emulsifying agents are naturally occurring gums, e.g. gum acacia or gum tragacanth; naturally occurring phosphatides, e.g. soybean lecithin, sorbitan monooleate derivatives: wool fats; wool alcohols; sorbitan esters; monoglycerides; fatty alcohols; fatty acid esters (e.g. triglycerides of fatty acids); and mixtures thereof.
Examples of suspending agents are e.g. celluloses and cellulose derivatives such as, e.g., carboxymethyl cellulose, hydroxyethylcellulose, hydroxypropylcellulose, hydroxypropylmethylcellulose, carrageenan, acacia gum, arabic gum, tragacanth, and mixtures thereof.
Examples of gel bases, viscosity-increasing agents or components which are able to take up exudate from a wound are: liquid paraffin, polyethylene, fatty oils, colloidal silica or aluminum, zinc soaps, glycerol, propylene glycol, tragacanth, carboxyvinyl polymers, magnesium-aluminum silicates, Carbopol®, hydrophilic polymers such as, e.g. starch or cellulose derivatives such as, e.g., carboxymethylcellulose, hydroxyethylcellulose and other cellulose derivatives, water-swellable hydrocolloids, carragenans, hyaluronates (e.g. hyaluronate gel optionally containing sodium chloride), and alginates including propylene glycol alginate.
Examples of ointment bases are e.g. beeswax, paraffin, cetanol, cetyl palmitate, vegetable oils, sorbitan esters of fatty acids (Span), polyethylene glycols, and condensation products between sorbitan esters of fatty acids and ethylene oxide, e.g. polyoxyethylene sorbitan monooleate (Tween).
Examples of hydrophobic or water-emulsifying ointment bases are paraffins, vegetable oils, animal fats, synthetic glycerides, waxes, lanolin, and liquid polyalkylsiloxanes. Examples of hydrophilic ointment bases are solid macrogols (polyethylene glycols). Other examples of ointment bases are triethanolamine soaps, sulphated fatty alcohol and polysorbates.
Examples of other excipients are polymers such as carmelose, sodium carmelose, hydroxypropylmethylcellulose, hydroxyethylcellulose, hydroxypropylcellulose, pectin, xanthan gum, locust bean gum, acacia gum, gelatin, carbomer, emulsifiers like vitamin E, glyceryl stearates, cetearyl glucoside, collagen, carrageenan, hyaluronates and alginates and chitosans.
The dose of antibody required in humans to be effective in the treatment or prevention of allergic inflammation differs with the type and severity of the allergic condition to be treated, the type of allergen, the age and condition of the patient, etc. Typical doses of antibody to be administered are in the range of 1 μg to 1 g, preferably 1-1000 μg, more preferably 2-500, even more preferably 5-50, most preferably 10-20 μg per unit dosage form. In certain embodiments, infusion of antibodies of the present invention may range from 10-500 mg/m2.
Simple binding assays can be used to screen for or detect agents that bind to a target protein, or disrupt the interaction between proteins (e.g., a receptor and a ligand). Because certain targets of the present invention are transmembrane proteins, assays that use the soluble forms of these proteins rather than full-length protein can be used, in some embodiments. Soluble forms include, for example, those lacking the transmembrane domain and/or those comprising the IgV domain or fragments thereof which retain their ability to bind their cognate binding partners. Further, agents that inhibit or enhance protein interactions for use in the compositions and methods described herein, can include recombinant peptido-mimetics.
Detection methods useful in screening assays include antibody-based methods, detection of a reporter moiety, detection of cytokines as described herein, and detection of a gene signature as described herein.
Another variation of assays to determine binding of a receptor protein to a ligand protein is through the use of affinity biosensor methods. Such methods may be based on the piezoelectric effect, electrochemistry, or optical methods, such as ellipsometry, optical wave guidance, and surface plasmon resonance (SPR).
The disclosure also encompasses nucleic acid molecules, in particular those that inhibit a target gene. Exemplary nucleic acid molecules include aptamers, siRNA, artificial microRNA, interfering RNA or RNAi, dsRNA, ribozymes, antisense oligonucleotides, and DNA expression cassettes encoding said nucleic acid molecules. Preferably, the nucleic acid molecule is an antisense oligonucleotide. Antisense oligonucleotides (ASO) generally inhibit their target by binding target mRNA and sterically blocking expression by obstructing the ribosome. ASOs can also inhibit their target by binding target mRNA thus forming a DNA-RNA hybrid that can be a substance for RNase H. Preferred ASOs include Locked Nucleic Acid (LNA), Peptide Nucleic Acid (PNA), and morpholinos Preferably, the nucleic acid molecule is an RNAi molecule, i.e., RNA interference molecule. Preferred RNAi molecules include siRNA, shRNA, and artificial miRNA. The design and production of siRNA molecules is well known to one of skill in the art (e.g., Hajeri P B, Singh S K. Drug Discov Today. 2009 14(17-18):851-8). The nucleic acid molecule inhibitors may be chemically synthesized and provided directly to cells of interest. The nucleic acid compound may be provided to a cell as part of a gene delivery vehicle. Such a vehicle is preferably a liposome or a viral gene delivery vehicle.
There are a variety of techniques available for introducing nucleic acids into viable cells. The techniques vary depending upon whether the nucleic acid is transferred into cultured cells in vitro, or in vivo in the cells of the intended host. Techniques suitable for the transfer of nucleic acid into mammalian cells in vitro include the use of liposomes, electroporation, microinjection, cell fusion, DEAE-dextran, the calcium phosphate precipitation method, etc. The currently preferred in vivo gene transfer techniques include transfection with viral (typically retroviral) vectors and viral coat protein-liposome mediated transfection.
Genetic Modifying Agents
In certain embodiments, the one or more modulating agents may be a genetic modifying agent. The genetic modifying agent may comprise a CRISPR system, a zinc finger nuclease system, a TALEN, or a meganuclease.
In general, a CRISPR-Cas or CRISPR system as used in herein and in documents, such as WO 2014/093622 (PCT/US2013/074667), refers collectively to transcripts and other elements involved in the expression of or directing the activity of CRISPR-associated (“Cas”) genes, including sequences encoding a Cas gene, a tracr (trans-activating CRISPR) sequence (e.g. tracrRNA or an active partial tracrRNA), a tracr-mate sequence (encompassing a “direct repeat” and a tracrRNA-processed partial direct repeat in the context of an endogenous CRISPR system), a guide sequence (also referred to as a “spacer” in the context of an endogenous CRISPR system), or “RNA(s)” as that term is herein used (e.g., RNA(s) to guide Cas, such as Cas9, e.g. CRISPR RNA and transactivating (tracr) RNA or a single guide RNA (sgRNA) (chimeric RNA)) or other sequences and transcripts from a CRISPR locus. In general, a CRISPR system is characterized by elements that promote the formation of a CRISPR complex at the site of a target sequence (also referred to as a protospacer in the context of an endogenous CRISPR system). See, e.g, Shmakov et al. (2015) “Discovery and Functional Characterization of Diverse Class 2 CRISPR-Cas Systems”, Molecular Cell, DOI: dx.doi.org/10.1016/j.molcel.2015.10.008.
In certain embodiments, a protospacer adjacent motif (PAM) or PAM-like motif directs binding of the effector protein complex as disclosed herein to the target locus of interest. In some embodiments, the PAM may be a 5′ PAM (i.e., located upstream of the 5′ end of the protospacer). In other embodiments, the PAM may be a 3′ PAM (i.e., located downstream of the 5′ end of the protospacer). The term “PAM” may be used interchangeably with the term “PFS” or “protospacer flanking site” or “protospacer flanking sequence”.
In a preferred embodiment, the CRISPR effector protein may recognize a 3′ PAM. In certain embodiments, the CRISPR effector protein may recognize a 3′ PAM which is 5′H, wherein H is A, C or U.
In the context of formation of a CRISPR complex, “target sequence” refers to a sequence to which a guide sequence is designed to have complementarity, where hybridization between a target sequence and a guide sequence promotes the formation of a CRISPR complex. A target sequence may comprise RNA polynucleotides. The term “target RNA” refers to a RNA polynucleotide being or comprising the target sequence. In other words, the target RNA may be a RNA polynucleotide or a part of a RNA polynucleotide to which a part of the gRNA, i.e. the guide sequence, is designed to have complementarity and to which the effector function mediated by the complex comprising CRISPR effector protein and a gRNA is to be directed. In some embodiments, a target sequence is located in the nucleus or cytoplasm of a cell.
In certain example embodiments, the CRISPR effector protein may be delivered using a nucleic acid molecule encoding the CRISPR effector protein. The nucleic acid molecule encoding a CRISPR effector protein, may advantageously be a codon optimized CRISPR effector protein. An example of a codon optimized sequence, is in this instance a sequence optimized for expression in eukaryote, e.g., humans (i.e. being optimized for expression in humans), or for another eukaryote, animal or mammal as herein discussed; see, e.g., SaCas9 human codon optimized sequence in WO 2014/093622 (PCT/US2013/074667). Whilst this is preferred, it will be appreciated that other examples are possible and codon optimization for a host species other than human, or for codon optimization for specific organs is known. In some embodiments, an enzyme coding sequence encoding a CRISPR effector protein is a codon optimized for expression in particular cells, such as eukaryotic cells. The eukaryotic cells may be those of or derived from a particular organism, such as a plant or a mammal, including but not limited to human, or non-human eukaryote or animal or mammal as herein discussed, e.g., mouse, rat, rabbit, dog, livestock, or non-human mammal or primate. In some embodiments, processes for modifying the germ line genetic identity of human beings and/or processes for modifying the genetic identity of animals which are likely to cause them suffering without any substantial medical benefit to man or animal, and also animals resulting from such processes, may be excluded. In general, codon optimization refers to a process of modifying a nucleic acid sequence for enhanced expression in the host cells of interest by replacing at least one codon (e.g. about or more than about 1, 2, 3, 4, 5, 10, 15, 20, 25, 50, or more codons) of the native sequence with codons that are more frequently or most frequently used in the genes of that host cell while maintaining the native amino acid sequence. Various species exhibit particular bias for certain codons of a particular amino acid. Codon bias (differences in codon usage between organisms) often correlates with the efficiency of translation of messenger RNA (mRNA), which is in turn believed to be dependent on, among other things, the properties of the codons being translated and the availability of particular transfer RNA (tRNA) molecules. The predominance of selected tRNAs in a cell is generally a reflection of the codons used most frequently in peptide synthesis. Accordingly, genes can be tailored for optimal gene expression in a given organism based on codon optimization. Codon usage tables are readily available, for example, at the “Codon Usage Database” available at kazusa.or.jp/codon/and these tables can be adapted in a number of ways. See Nakamura, Y., et al. “Codon usage tabulated from the international DNA sequence databases: status for the year 2000” Nucl. Acids Res. 28:292 (2000). Computer algorithms for codon optimizing a particular sequence for expression in a particular host cell are also available, such as Gene Forge (Aptagen; Jacobus, PA), are also available. In some embodiments, one or more codons (e.g. 1, 2, 3, 4, 5, 10, 15, 20, 25, 50, or more, or all codons) in a sequence encoding a Cas correspond to the most frequently used codon for a particular amino acid.
In certain embodiments, the methods as described herein may comprise providing a Cas transgenic cell in which one or more nucleic acids encoding one or more guide RNAs are provided or introduced operably connected in the cell with a regulatory element comprising a promoter of one or more gene of interest. As used herein, the term “Cas transgenic cell” refers to a cell, such as a eukaryotic cell, in which a Cas gene has been genomically integrated. The nature, type, or origin of the cell are not particularly limiting according to the present invention. Also the way the Cas transgene is introduced in the cell may vary and can be any method as is known in the art. In certain embodiments, the Cas transgenic cell is obtained by introducing the Cas transgene in an isolated cell. In certain other embodiments, the Cas transgenic cell is obtained by isolating cells from a Cas transgenic organism. By means of example, and without limitation, the Cas transgenic cell as referred to herein may be derived from a Cas transgenic eukaryote, such as a Cas knock-in eukaryote. Reference is made to WO 2014/093622 (PCT/US13/74667), incorporated herein by reference. Methods of US Patent Publication Nos. 20120017290 and 20110265198 assigned to Sangamo BioSciences, Inc. directed to targeting the Rosa locus may be modified to utilize the CRISPR Cas system of the present invention. Methods of US Patent Publication No. 20130236946 assigned to Cellectis directed to targeting the Rosa locus may also be modified to utilize the CRISPR Cas system of the present invention. By means of further example reference is made to Platt et. al. (Cell; 159(2):440-455 (2014)), describing a Cas9 knock-in mouse, which is incorporated herein by reference. The Cas transgene can further comprise a Lox-Stop-polyA-Lox(LSL) cassette thereby rendering Cas expression inducible by Cre recombinase. Alternatively, the Cas transgenic cell may be obtained by introducing the Cas transgene in an isolated cell. Delivery systems for transgenes are well known in the art. By means of example, the Cas transgene may be delivered in for instance eukaryotic cell by means of vector (e.g., AAV, adenovirus, lentivirus) and/or particle and/or nanoparticle delivery, as also described herein elsewhere.
It will be understood by the skilled person that the cell, such as the Cas transgenic cell, as referred to herein may comprise further genomic alterations besides having an integrated Cas gene or the mutations arising from the sequence specific action of Cas when complexed with RNA capable of guiding Cas to a target locus.
In certain aspects the invention involves vectors, e.g. for delivering or introducing in a cell Cas and/or RNA capable of guiding Cas to a target locus (i.e. guide RNA), but also for propagating these components (e.g. in prokaryotic cells). A used herein, a “vector” is a tool that allows or facilitates the transfer of an entity from one environment to another. It is a replicon, such as a plasmid, phage, or cosmid, into which another DNA segment may be inserted so as to bring about the replication of the inserted segment. Generally, a vector is capable of replication when associated with the proper control elements. In general, the term “vector” refers to a nucleic acid molecule capable of transporting another nucleic acid to which it has been linked. Vectors include, but are not limited to, nucleic acid molecules that are single-stranded, double-stranded, or partially double-stranded; nucleic acid molecules that comprise one or more free ends, no free ends (e.g. circular); nucleic acid molecules that comprise DNA, RNA, or both; and other varieties of polynucleotides known in the art. One type of vector is a “plasmid,” which refers to a circular double stranded DNA loop into which additional DNA segments can be inserted, such as by standard molecular cloning techniques. Another type of vector is a viral vector, wherein virally-derived DNA or RNA sequences are present in the vector for packaging into a virus (e.g. retroviruses, replication defective retroviruses, adenoviruses, replication defective adenoviruses, and adeno-associated viruses (AAVs)). Viral vectors also include polynucleotides carried by a virus for transfection into a host cell. Certain vectors are capable of autonomous replication in a host cell into which they are introduced (e.g. bacterial vectors having a bacterial origin of replication and episomal mammalian vectors). Other vectors (e.g., non-episomal mammalian vectors) are integrated into the genome of a host cell upon introduction into the host cell, and thereby are replicated along with the host genome. Moreover, certain vectors are capable of directing the expression of genes to which they are operatively-linked. Such vectors are referred to herein as “expression vectors.” Common expression vectors of utility in recombinant DNA techniques are often in the form of plasmids.
Recombinant expression vectors can comprise a nucleic acid of the invention in a form suitable for expression of the nucleic acid in a host cell, which means that the recombinant expression vectors include one or more regulatory elements, which may be selected on the basis of the host cells to be used for expression, that is operatively-linked to the nucleic acid sequence to be expressed. Within a recombinant expression vector, “operably linked” is intended to mean that the nucleotide sequence of interest is linked to the regulatory element(s) in a manner that allows for expression of the nucleotide sequence (e.g. in an in vitro transcription/translation system or in a host cell when the vector is introduced into the host cell). With regards to recombination and cloning methods, mention is made of U.S. patent application Ser. No. 10/815,730, published Sep. 2, 2004 as US 2004-0171156 A1, the contents of which are herein incorporated by reference in their entirety. Thus, the embodiments disclosed herein may also comprise transgenic cells comprising the CRISPR effector system. In certain example embodiments, the transgenic cell may function as an individual discrete volume. In other words samples comprising a masking construct may be delivered to a cell, for example in a suitable delivery vesicle and if the target is present in the delivery vesicle the CRISPR effector is activated and a detectable signal generated.
The vector(s) can include the regulatory element(s), e.g., promoter(s). The vector(s) can comprise Cas encoding sequences, and/or a single, but possibly also can comprise at least 3 or 8 or 16 or 32 or 48 or 50 guide RNA(s) (e.g., sgRNAs) encoding sequences, such as 1-2, 1-3, 1-4 1-5, 3-6, 3-7, 3-8, 3-9, 3-10, 3-8, 3-16, 3-30, 3-32, 3-48, 3-50 RNA(s) (e.g., sgRNAs). In a single vector there can be a promoter for each RNA (e.g., sgRNA), advantageously when there are up to about 16 RNA(s); and, when a single vector provides for more than 16 RNA(s), one or more promoter(s) can drive expression of more than one of the RNA(s), e.g., when there are 32 RNA(s), each promoter can drive expression of two RNA(s), and when there are 48 RNA(s), each promoter can drive expression of three RNA(s). By simple arithmetic and well established cloning protocols and the teachings in this disclosure one skilled in the art can readily practice the invention as to the RNA(s) for a suitable exemplary vector such as AAV, and a suitable promoter such as the U6 promoter. For example, the packaging limit of AAV is ˜4.7 kb. The length of a single U6-gRNA (plus restriction sites for cloning) is 361 bp. Therefore, the skilled person can readily fit about 12-16, e.g., 13 U6-gRNA cassettes in a single vector. This can be assembled by any suitable means, such as a golden gate strategy used for TALE assembly (genome-engineering.org/taleffectors/). The skilled person can also use a tandem guide strategy to increase the number of U6-gRNAs by approximately 1.5 times, e.g., to increase from 12-16, e.g., 13 to approximately 18-24, e.g., about 19 U6-gRNAs. Therefore, one skilled in the art can readily reach approximately 18-24, e.g., about 19 promoter-RNAs, e.g., U6-gRNAs in a single vector, e.g., an AAV vector. A further means for increasing the number of promoters and RNAs in a vector is to use a single promoter (e.g., U6) to express an array of RNAs separated by cleavable sequences. And an even further means for increasing the number of promoter-RNAs in a vector, is to express an array of promoter-RNAs separated by cleavable sequences in the intron of a coding sequence or gene; and, in this instance it is advantageous to use a polymerase II promoter, which can have increased expression and enable the transcription of long RNA in a tissue specific manner. (see, e.g., nar.oxfordjournals. org/content/34/7/e53.short and nature.com/mt/journal/v16/n9/abs/mt2008144a.html). In an advantageous embodiment, AAV may package U6 tandem gRNA targeting up to about 50 genes. Accordingly, from the knowledge in the art and the teachings in this disclosure the skilled person can readily make and use vector(s), e.g., a single vector, expressing multiple RNAs or guides under the control or operatively or functionally linked to one or more promoters-especially as to the numbers of RNAs or guides discussed herein, without any undue experimentation.
The guide RNA(s) encoding sequences and/or Cas encoding sequences, can be functionally or operatively linked to regulatory element(s) and hence the regulatory element(s) drive expression. The promoter(s) can be constitutive promoter(s) and/or conditional promoter(s) and/or inducible promoter(s) and/or tissue specific promoter(s). The promoter can be selected from the group consisting of RNA polymerases, pol I, pol II, pol III, T7, U6, H1, retroviral Rous sarcoma virus (RSV) LTR promoter, the cytomegalovirus (CMV) promoter, the SV40 promoter, the dihydrofolate reductase promoter, the β-actin promoter, the phosphoglycerol kinase (PGK) promoter, and the EF1α promoter. An advantageous promoter is the promoter is U6.
Additional effectors for use according to the invention can be identified by their proximity to cas1 genes, for example, though not limited to, within the region 20 kb from the start of the cas1 gene and 20 kb from the end of the cas1 gene. In certain embodiments, the effector protein comprises at least one HEPN domain and at least 500 amino acids, and wherein the C2c2 effector protein is naturally present in a prokaryotic genome within 20 kb upstream or downstream of a Cas gene or a CRISPR array. Non-limiting examples of Cas proteins include Cas1, Cas1B, Cas2, Cas3, Cas4, Cas5, Cas6, Cas7, Cas8, Cas9 (also known as Csn1 and Csx12), Cas10, Csy1, Csy2, Csy3, Cse1, Cse2, Csc1, Csc2, Csa5, Csn2, Csm2, Csm3, Csm4, Csm5, Csm6, Cmr1, Cmr3, Cmr4, Cmr5, Cmr6, Csb1, Csb2, Csb3, Csx17, Csx14, Csx10, Csx16, CsaX, Csx3, Csx1, Csx15, Csf1, Csf2, Csf3, Csf4, homologues thereof, or modified versions thereof. In certain example embodiments, the C2c2 effector protein is naturally present in a prokaryotic genome within 20 kb upstream or downstream of a Cas 1 gene. The terms “orthologue” (also referred to as “ortholog” herein) and “homologue” (also referred to as “homolog” herein) are well known in the art. By means of further guidance, a “homologue” of a protein as used herein is a protein of the same species which performs the same or a similar function as the protein it is a homologue of. Homologous proteins may but need not be structurally related, or are only partially structurally related. An “orthologue” of a protein as used herein is a protein of a different species which performs the same or a similar function as the protein it is an orthologue of. Orthologous proteins may but need not be structurally related, or are only partially structurally related.
Guide Molecules
The methods described herein may be used to screen inhibition of CRISPR systems employing different types of guide molecules. As used herein, the term “guide sequence” and “guide molecule” in the context of a CRISPR-Cas system, comprises any polynucleotide sequence having sufficient complementarity with a target nucleic acid sequence to hybridize with the target nucleic acid sequence and direct sequence-specific binding of a nucleic acid-targeting complex to the target nucleic acid sequence. The guide sequences made using the methods disclosed herein may be a full-length guide sequence, a truncated guide sequence, a full-length sgRNA sequence, a truncated sgRNA sequence, or an E+F sgRNA sequence. In some embodiments, the degree of complementarity of the guide sequence to a given target sequence, when optimally aligned using a suitable alignment algorithm, is about or more than about 50%, 60%, 75%, 80%, 85%, 90%, 95%, 97.5%, 99%, or more. In certain example embodiments, the guide molecule comprises a guide sequence that may be designed to have at least one mismatch with the target sequence, such that a RNA duplex formed between the guide sequence and the target sequence. Accordingly, the degree of complementarity is preferably less than 99%. For instance, where the guide sequence consists of 24 nucleotides, the degree of complementarity is more particularly about 96% or less. In particular embodiments, the guide sequence is designed to have a stretch of two or more adjacent mismatching nucleotides, such that the degree of complementarity over the entire guide sequence is further reduced. For instance, where the guide sequence consists of 24 nucleotides, the degree of complementarity is more particularly about 96% or less, more particularly, about 92% or less, more particularly about 88% or less, more particularly about 84% or less, more particularly about 80% or less, more particularly about 76% or less, more particularly about 72% or less, depending on whether the stretch of two or more mismatching nucleotides encompasses 2, 3, 4, 5, 6 or 7 nucleotides, etc. In some embodiments, aside from the stretch of one or more mismatching nucleotides, the degree of complementarity, when optimally aligned using a suitable alignment algorithm, is about or more than about 50%, 60%, 75%, 80%, 85%, 90%, 95%, 97.5%, 99%, or more. Optimal alignment may be determined with the use of any suitable algorithm for aligning sequences, non-limiting example of which include the Smith-Waterman algorithm, the Needleman-Wunsch algorithm, algorithms based on the Burrows-Wheeler Transform (e.g., the Burrows Wheeler Aligner), ClustalW, Clustal X, BLAT, Novoalign (Novocraft Technologies; available at novocraft.com), ELAND (Illumina, San Diego, CA), SOAP (available at soap.genomics.org.cn), and Maq (available at maq.sourceforge.net). The ability of a guide sequence (within a nucleic acid-targeting guide RNA) to direct sequence-specific binding of a nucleic acid-targeting complex to a target nucleic acid sequence may be assessed by any suitable assay. For example, the components of a nucleic acid-targeting CRISPR system sufficient to form a nucleic acid-targeting complex, including the guide sequence to be tested, may be provided to a host cell having the corresponding target nucleic acid sequence, such as by transfection with vectors encoding the components of the nucleic acid-targeting complex, followed by an assessment of preferential targeting (e.g., cleavage) within the target nucleic acid sequence, such as by Surveyor assay as described herein. Similarly, cleavage of a target nucleic acid sequence (or a sequence in the vicinity thereof) may be evaluated in a test tube by providing the target nucleic acid sequence, components of a nucleic acid-targeting complex, including the guide sequence to be tested and a control guide sequence different from the test guide sequence, and comparing binding or rate of cleavage at or in the vicinity of the target sequence between the test and control guide sequence reactions. Other assays are possible, and will occur to those skilled in the art. A guide sequence, and hence a nucleic acid-targeting guide RNA may be selected to target any target nucleic acid sequence.
In certain embodiments, the guide sequence or spacer length of the guide molecules is from 15 to 50 nt. In certain embodiments, the spacer length of the guide RNA is at least 15 nucleotides. In certain embodiments, the spacer length is from 15 to 17 nt, e.g., 15, 16, or 17 nt, from 17 to 20 nt, e.g., 17, 18, 19, or 20 nt, from 20 to 24 nt, e.g., 20, 21, 22, 23, or 24 nt, from 23 to 25 nt, e.g., 23, 24, or 25 nt, from 24 to 27 nt, e.g., 24, 25, 26, or 27 nt, from 27-30 nt, e.g., 27, 28, 29, or 30 nt, from 30-35 nt, e.g., 30, 31, 32, 33, 34, or 35 nt, or 35 nt or longer. In certain example embodiment, the guide sequence is 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39 40, 41, 42, 43, 44, 45, 46, 47 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100 nt.
In some embodiments, the guide sequence is an RNA sequence of between 10 to 50 nt in length, but more particularly of about 20-30 nt advantageously about 20 nt, 23-25 nt or 24 nt. The guide sequence is selected so as to ensure that it hybridizes to the target sequence. This is described more in detail below. Selection can encompass further steps which increase efficacy and specificity.
In some embodiments, the guide sequence has a canonical length (e.g., about 15-30 nt) is used to hybridize with the target RNA or DNA. In some embodiments, a guide molecule is longer than the canonical length (e.g., >30 nt) is used to hybridize with the target RNA or DNA, such that a region of the guide sequence hybridizes with a region of the RNA or DNA strand outside of the Cas-guide target complex. This can be of interest where additional modifications, such deamination of nucleotides is of interest. In alternative embodiments, it is of interest to maintain the limitation of the canonical guide sequence length.
In some embodiments, the sequence of the guide molecule (direct repeat and/or spacer) is selected to reduce the degree secondary structure within the guide molecule. In some embodiments, about or less than about 75%, 50%, 40%, 30%, 25%, 20%, 15%, 10%, 5%, 1%, or fewer of the nucleotides of the nucleic acid-targeting guide RNA participate in self-complementary base pairing when optimally folded. Optimal folding may be determined by any suitable polynucleotide folding algorithm. Some programs are based on calculating the minimal Gibbs free energy. An example of one such algorithm is mFold, as described by Zuker and Stiegler (Nucleic Acids Res. 9 (1981), 133-148). Another example folding algorithm is the online webserver RNAfold, developed at Institute for Theoretical Chemistry at the University of Vienna, using the centroid structure prediction algorithm (see e.g., A. R. Gruber et al., 2008, Cell 106(1): 23-24; and PA Carr and GM Church, 2009, Nature Biotechnology 27(12): 1151-62).
In some embodiments, it is of interest to reduce the susceptibility of the guide molecule to RNA cleavage, such as to cleavage by Cas13. Accordingly, in particular embodiments, the guide molecule is adjusted to avoide cleavage by Cas13 or other RNA-cleaving enzymes.
In certain embodiments, the guide molecule comprises non-naturally occurring nucleic acids and/or non-naturally occurring nucleotides and/or nucleotide analogs, and/or chemically modifications. Preferably, these non-naturally occurring nucleic acids and non-naturally occurring nucleotides are located outside the guide sequence. Non-naturally occurring nucleic acids can include, for example, mixtures of naturally and non-naturally occurring nucleotides. Non-naturally occurring nucleotides and/or nucleotide analogs may be modified at the ribose, phosphate, and/or base moiety. In an embodiment of the invention, a guide nucleic acid comprises ribonucleotides and non-ribonucleotides. In one such embodiment, a guide comprises one or more ribonucleotides and one or more deoxyribonucleotides. In an embodiment of the invention, the guide comprises one or more non-naturally occurring nucleotide or nucleotide analog such as a nucleotide with phosphorothioate linkage, a locked nucleic acid (LNA) nucleotides comprising a methylene bridge between the 2′ and 4′ carbons of the ribose ring, or bridged nucleic acids (BNA). Other examples of modified nucleotides include 2′-O-methyl analogs, 2′-deoxy analogs, or 2′-fluoro analogs. Further examples of modified bases include, but are not limited to, 2-aminopurine, 5-bromo-uridine, pseudouridine, inosine, 7-methylguanosine. Examples of guide RNA chemical modifications include, without limitation, incorporation of 2′-O-methyl (M), 2′-O-methyl 3′ phosphorothioate (MS), S-constrained ethyl(cEt), or 2′-O-methyl 3′ thioPACE (MSP) at one or more terminal nucleotides. Such chemically modified guides can comprise increased stability and increased activity as compared to unmodified guides, though on-target vs. off-target specificity is not predictable. (See, Hendel, 2015, Nat Biotechnol. 33(9):985-9, doi: 10.1038/nbt.3290, published online 29 Jun. 2015 Ragdarm et al., 0215, PNAS, E7110-E7111; Allerson et al., J. Med. Chem. 2005, 48:901-904; Bramsen et al., Front. Genet., 2012, 3:154; Deng et al., PNAS, 2015, 112:11870-11875; Sharma et al., MedChemComm., 2014, 5:1454-1471; Hendel et al., Nat. Biotechnol. (2015) 33(9): 985-989; Li et al., Nature Biomedical Engineering, 2017, 1, 0066 DOI:10.1038/s41551-017-0066). In some embodiments, the 5′ and/or 3′ end of a guide RNA is modified by a variety of functional moieties including fluorescent dyes, polyethylene glycol, cholesterol, proteins, or detection tags. (See Kelly et al., 2016, J. Biotech. 233:74-83). In certain embodiments, a guide comprises ribonucleotides in a region that binds to a target RNA and one or more deoxyribonucletides and/or nucleotide analogs in a region that binds to Cas13. In an embodiment of the invention, deoxyribonucleotides and/or nucleotide analogs are incorporated in engineered guide structures, such as, without limitation, stem-loop regions, and the seed region. For Cas13 guide, in certain embodiments, the modification is not in the 5′-handle of the stem-loop regions. Chemical modification in the 5′-handle of the stem-loop region of a guide may abolish its function (see Li, et al., Nature Biomedical Engineering, 2017, 1:0066). In certain embodiments, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, or 75 nucleotides of a guide is chemically modified. In some embodiments, 3-5 nucleotides at either the 3′ or the 5′ end of a guide is chemically modified. In some embodiments, only minor modifications are introduced in the seed region, such as 2′-F modifications. In some embodiments, 2′-F modification is introduced at the 3′ end of a guide. In certain embodiments, three to five nucleotides at the 5′ and/or the 3′ end of the guide are chemicially modified with 2′-O-methyl (M), 2′-O-methyl 3′ phosphorothioate (MS), S-constrained ethyl(cEt), or 2′-O-methyl 3′ thioPACE (MSP). Such modification can enhance genome editing efficiency (see Hendel et al., Nat. Biotechnol. (2015) 33(9): 985-989). In certain embodiments, all of the phosphodiester bonds of a guide are substituted with phosphorothioates (PS) for enhancing levels of gene disruption. In certain embodiments, more than five nucleotides at the 5′ and/or the 3′ end of the guide are chemicially modified with 2′-O-Me, 2′-F or S-constrained ethyl(cEt). Such chemically modified guide can mediate enhanced levels of gene disruption (see Ragdarm et al., 0215, PNAS, E7110-E7111). In an embodiment of the invention, a guide is modified to comprise a chemical moiety at its 3′ and/or 5′ end. Such moieties include, but are not limited to amine, azide, alkyne, thio, dibenzocyclooctyne (DBCO), or Rhodamine. In certain embodiment, the chemical moiety is conjugated to the guide by a linker, such as an alkyl chain. In certain embodiments, the chemical moiety of the modified guide can be used to attach the guide to another molecule, such as DNA, RNA, protein, or nanoparticles. Such chemically modified guide can be used to identify or enrich cells generically edited by a CRISPR system (see Lee et al., eLife, 2017, 6:e25312, DOI:10.7554).
In some embodiments, the modification to the guide is a chemical modification, an insertion, a deletion or a split. In some embodiments, the chemical modification includes, but is not limited to, incorporation of 2′-O-methyl (M) analogs, 2′-deoxy analogs, 2-thiouridine analogs, N6-methyladenosine analogs, 2′-fluoro analogs, 2-aminopurine, 5-bromo-uridine, pseudouridine (Ψ), N1-methylpseudouridine (me1Ψ), 5-methoxyuridine(5moU), inosine, 7-methylguanosine, 2′-O-methyl 3′phosphorothioate (MS), S-constrained ethyl(cEt), phosphorothioate (PS), or 2′-O-methyl 3′thioPACE (MSP). In some embodiments, the guide comprises one or more of phosphorothioate modifications. In certain embodiments, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or 25 nucleotides of the guide are chemically modified. In certain embodiments, one or more nucleotides in the seed region are chemically modified. In certain embodiments, one or more nucleotides in the 3′-terminus are chemically modified. In certain embodiments, none of the nucleotides in the 5′-handle is chemically modified. In some embodiments, the chemical modification in the seed region is a minor modification, such as incorporation of a 2′-fluoro analog. In a specific embodiment, one nucleotide of the seed region is replaced with a 2′-fluoro analog. In some embodiments, 5 to 10 nucleotides in the 3′-terminus are chemically modified. Such chemical modifications at the 3′-terminus of the Cas13 CrRNA may improve Cas13 activity. In a specific embodiment, 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 nucleotides in the 3′-terminus are replaced with 2′-fluoro analogues. In a specific embodiment, 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 nucleotides in the 3′-terminus are replaced with 2′-O-methyl (M) analogs.
In some embodiments, the loop of the 5′-handle of the guide is modified. In some embodiments, the loop of the 5′-handle of the guide is modified to have a deletion, an insertion, a split, or chemical modifications. In certain embodiments, the modified loop comprises 3, 4, or 5 nucleotides. In certain embodiments, the loop comprises the sequence of UCUU, UUUU, UAUU, or UGUU.
In some embodiments, the guide molecule forms a stemloop with a separate non-covalently linked sequence, which can be DNA or RNA. In particular embodiments, the sequences forming the guide are first synthesized using the standard phosphoramidite synthetic protocol (Herdewijn, P., ed., Methods in Molecular Biology Col 288, Oligonucleotide Synthesis: Methods and Applications, Humana Press, New Jersey (2012)). In some embodiments, these sequences can be functionalized to contain an appropriate functional group for ligation using the standard protocol known in the art (Hermanson, G. T., Bioconjugate Techniques, Academic Press (2013)). Examples of functional groups include, but are not limited to, hydroxyl, amine, carboxylic acid, carboxylic acid halide, carboxylic acid active ester, aldehyde, carbonyl, chlorocarbonyl, imidazolylcarbonyl, hydrozide, semicarbazide, thio semicarbazide, thiol, maleimide, haloalkyl, sufonyl, ally, propargyl, diene, alkyne, and azide. Once this sequence is functionalized, a covalent chemical bond or linkage can be formed between this sequence and the direct repeat sequence. Examples of chemical bonds include, but are not limited to, those based on carbamates, ethers, esters, amides, imines, amidines, aminotrizines, hydrozone, disulfides, thioethers, thioesters, phosphorothioates, phosphorodithioates, sulfonamides, sulfonates, fulfones, sulfoxides, ureas, thioureas, hydrazide, oxime, triazole, photolabile linkages, C—C bond forming groups such as Diels-Alder cyclo-addition pairs or ring-closing metathesis pairs, and Michael reaction pairs.
In some embodiments, these stem-loop forming sequences can be chemically synthesized. In some embodiments, the chemical synthesis uses automated, solid-phase oligonucleotide synthesis machines with 2′-acetoxyethyl orthoester (2′-ACE) (Scaringe et al., J. Am. Chem. Soc. (1998) 120: 11820-11821; Scaringe, Methods Enzymol. (2000) 317: 3-18) or 2′-thionocarbamate (2′-TC) chemistry (Dellinger et al., J. Am. Chem. Soc. (2011) 133: 11540-11546; Hendel et al., Nat. Biotechnol. (2015) 33:985-989).
In certain embodiments, the guide molecule comprises (1) a guide sequence capable of hybridizing to a target locus and (2) a tracr mate or direct repeat sequence whereby the direct repeat sequence is located upstream (i.e., 5′) from the guide sequence. In a particular embodiment the seed sequence (i.e. the sequence essential critical for recognition and/or hybridization to the sequence at the target locus) of th guide sequence is approximately within the first 10 nucleotides of the guide sequence.
In a particular embodiment the guide molecule comprises a guide sequence linked to a direct repeat sequence, wherein the direct repeat sequence comprises one or more stem loops or optimized secondary structures. In particular embodiments, the direct repeat has a minimum length of 16 nts and a single stem loop. In further embodiments the direct repeat has a length longer than 16 nts, preferably more than 17 nts, and has more than one stem loops or optimized secondary structures. In particular embodiments the guide molecule comprises or consists of the guide sequence linked to all or part of the natural direct repeat sequence. A typical Type V or Type VI CRISPR-cas guide molecule comprises (in 3′ to 5′ direction or in 5′ to 3′ direction): a guide sequence a first complimentary stretch (the “repeat”), a loop (which is typically 4 or 5 nucleotides long), a second complimentary stretch (the “anti-repeat” being complimentary to the repeat), and a poly A (often poly U in RNA) tail (terminator). In certain embodiments, the direct repeat sequence retains its natural architecture and forms a single stem loop. In particular embodiments, certain aspects of the guide architecture can be modified, for example by addition, subtraction, or substitution of features, whereas certain other aspects of guide architecture are maintained. Preferred locations for engineered guide molecule modifications, including but not limited to insertions, deletions, and substitutions include guide termini and regions of the guide molecule that are exposed when complexed with the CRISPR-Cas protein and/or target, for example the stemloop of the direct repeat sequence.
In particular embodiments, the stem comprises at least about 4 bp comprising complementary X and Y sequences, although stems of more, e.g., 5, 6, 7, 8, 9, 10, 11 or 12 or fewer, e.g., 3, 2, base pairs are also contemplated. Thus, for example X2-10 and Y2-10 (wherein X and Y represent any complementary set of nucleotides) may be contemplated. In one aspect, the stem made of the X and Y nucleotides, together with the loop will form a complete hairpin in the overall secondary structure; and, this may be advantageous and the amount of base pairs can be any amount that forms a complete hairpin. In one aspect, any complementary X:Y basepairing sequence (e.g., as to length) is tolerated, so long as the secondary structure of the entire guide molecule is preserved. In one aspect, the loop that connects the stem made of X:Y basepairs can be any sequence of the same length (e.g., 4 or 5 nucleotides) or longer that does not interrupt the overall secondary structure of the guide molecule. In one aspect, the stemloop can further comprise, e.g. an MS2 aptamer. In one aspect, the stem comprises about 5-7 bp comprising complementary X and Y sequences, although stems of more or fewer basepairs are also contemplated. In one aspect, non-Watson Crick basepairing is contemplated, where such pairing otherwise generally preserves the architecture of the stemloop at that position.
In particular embodiments the natural hairpin or stemloop structure of the guide molecule is extended or replaced by an extended stemloop. It has been demonstrated that extension of the stem can enhance the assembly of the guide molecule with the CRISPR-Cas proten (Chen et al. Cell. (2013); 155(7): 1479-1491). In particular embodiments the stem of the stemloop is extended by at least 1, 2, 3, 4, 5 or more complementary basepairs (i.e. corresponding to the addition of 2, 4, 6, 8, 10 or more nucleotides in the guide molecule). In particular embodiments these are located at the end of the stem, adjacent to the loop of the stemloop.
In particular embodiments, the susceptibility of the guide molecule to RNAses or to decreased expression can be reduced by slight modifications of the sequence of the guide molecule which do not affect its function. For instance, in particular embodiments, premature termination of transcription, such as premature transcription of U6 Pol-III, can be removed by modifying a putative Pol-III terminator (4 consecutive U's) in the guide molecules sequence. Where such sequence modification is required in the stemloop of the guide molecule, it is preferably ensured by a basepair flip.
In a particular embodiment, the direct repeat may be modified to comprise one or more protein-binding RNA aptamers. In a particular embodiment, one or more aptamers may be included such as part of optimized secondary structure. Such aptamers may be capable of binding a bacteriophage coat protein as detailed further herein.
In some embodiments, the guide molecule forms a duplex with a target RNA comprising at least one target cytosine residue to be edited. Upon hybridization of the guide RNA molecule to the target RNA, the cytidine deaminase binds to the single strand RNA in the duplex made accessible by the mismatch in the guide sequence and catalyzes deamination of one or more target cytosine residues comprised within the stretch of mismatching nucleotides.
A guide sequence, and hence a nucleic acid-targeting guide RNA may be selected to target any target nucleic acid sequence. The target sequence may be mRNA.
In certain embodiments, the target sequence should be associated with a PAM (protospacer adjacent motif) or PFS (protospacer flanking sequence or site); that is, a short sequence recognized by the CRISPR complex. Depending on the nature of the CRISPR-Cas protein, the target sequence should be selected such that its complementary sequence in the DNA duplex (also referred to herein as the non-target sequence) is upstream or downstream of the PAM. In the embodiments of the present invention where the CRISPR-Cas protein is a Cas13 protein, the compelementary sequence of the target sequence is downstream or 3′ of the PAM or upstream or 5′ of the PAM. The precise sequence and length requirements for the PAM differ depending on the Cas13 protein used, but PAMs are typically 2-5 base pair sequences adjacent the protospacer (that is, the target sequence). Examples of the natural PAM sequences for different Cas13 orthologues are provided herein below and the skilled person will be able to identify further PAM sequences for use with a given Cas13 protein.
Further, engineering of the PAM Interacting (PI) domain may allow programing of PAM specificity, improve target site recognition fidelity, and increase the versatility of the CRISPR-Cas protein, for example as described for Cas9 in Kleinstiver B P et al. Engineered CRISPR-Cas9 nucleases with altered PAM specificities. Nature. 2015 Jul. 23; 523(7561):481-5. doi: 10.1038/nature14592. As further detailed herein, the skilled person will understand that Cas13 proteins may be modified analogously.
In particular embodiment, the guide is an escorted guide. By “escorted” is meant that the CRISPR-Cas system or complex or guide is delivered to a selected time or place within a cell, so that activity of the CRISPR-Cas system or complex or guide is spatially or temporally controlled. For example, the activity and destination of the 3 CRISPR-Cas system or complex or guide may be controlled by an escort RNA aptamer sequence that has binding affinity for an aptamer ligand, such as a cell surface protein or other localized cellular component. Alternatively, the escort aptamer may for example be responsive to an aptamer effector on or in the cell, such as a transient effector, such as an external energy source that is applied to the cell at a particular time.
The escorted CRISPR-Cas systems or complexes have a guide molecule with a functional structure designed to improve guide molecule structure, architecture, stability, genetic expression, or any combination thereof. Such a structure can include an aptamer.
Aptamers are biomolecules that can be designed or selected to bind tightly to other ligands, for example using a technique called systematic evolution of ligands by exponential enrichment (SELEX; Tuerk C, Gold L: “Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase.” Science 1990, 249:505-510). Nucleic acid aptamers can for example be selected from pools of random-sequence oligonucleotides, with high binding affinities and specificities for a wide range of biomedically relevant targets, suggesting a wide range of therapeutic utilities for aptamers (Keefe, Anthony D., Supriya Pai, and Andrew Ellington. “Aptamers as therapeutics.” Nature Reviews Drug Discovery 9.7 (2010): 537-550). These characteristics also suggest a wide range of uses for aptamers as drug delivery vehicles (Levy-Nissenbaum, Etgar, et al. “Nanotechnology and aptamers: applications in drug delivery.” Trends in biotechnology 26.8 (2008): 442-449; and, Hicke B J, Stephens A W. “Escort aptamers: a delivery service for diagnosis and therapy.” J Clin Invest 2000, 106:923-928.). Aptamers may also be constructed that function as molecular switches, responding to a que by changing properties, such as RNA aptamers that bind fluorophores to mimic the activity of green fluorescent protein (Paige, Jeremy S., Karen Y. Wu, and Samie R. Jaffrey. “RNA mimics of green fluorescent protein.” Science 333.6042 (2011): 642-646). It has also been suggested that aptamers may be used as components of targeted siRNA therapeutic delivery systems, for example targeting cell surface proteins (Zhou, Jiehua, and John J. Rossi. “Aptamer-targeted cell-specific RNA interference.” Silence 1.1 (2010): 4).
Accordingly, in particular embodiments, the guide molecule is modified, e.g., by one or more aptamer(s) designed to improve guide molecule delivery, including delivery across the cellular membrane, to intracellular compartments, or into the nucleus. Such a structure can include, either in addition to the one or more aptamer(s) or without such one or more aptamer(s), moiety(ies) so as to render the guide molecule deliverable, inducible or responsive to a selected effector. The invention accordingly comprehends an guide molecule that responds to normal or pathological physiological conditions, including without limitation pH, hypoxia, 02 concentration, temperature, protein concentration, enzymatic concentration, lipid structure, light exposure, mechanical disruption (e.g. ultrasound waves), magnetic fields, electric fields, or electromagnetic radiation.
Light responsiveness of an inducible system may be achieved via the activation and binding of cryptochrome-2 and CIB1. Blue light stimulation induces an activating conformational change in cryptochrome-2, resulting in recruitment of its binding partner CIB1. This binding is fast and reversible, achieving saturation in <15 sec following pulsed stimulation and returning to baseline <15 min after the end of stimulation. These rapid binding kinetics result in a system temporally bound only by the speed of transcription/translation and transcript/protein degradation, rather than uptake and clearance of inducing agents. Crytochrome-2 activation is also highly sensitive, allowing for the use of low light intensity stimulation and mitigating the risks of phototoxicity. Further, in a context such as the intact mammalian brain, variable light intensity may be used to control the size of a stimulated region, allowing for greater precision than vector delivery alone may offer.
The invention contemplates energy sources such as electromagnetic radiation, sound energy or thermal energy to induce the guide. Advantageously, the electromagnetic radiation is a component of visible light. In a preferred embodiment, the light is a blue light with a wavelength of about 450 to about 495 nm. In an especially preferred embodiment, the wavelength is about 488 nm. In another preferred embodiment, the light stimulation is via pulses. The light power may range from about 0-9 mW/cm2. In a preferred embodiment, a stimulation paradigm of as low as 0.25 sec every 15 sec should result in maximal activation.
The chemical or energy sensitive guide may undergo a conformational change upon induction by the binding of a chemical source or by the energy allowing it act as a guide and have the Cas13 CRISPR-Cas system or complex function. The invention can involve applying the chemical source or energy so as to have the guide function and the Cas13 CRISPR-Cas system or complex function; and optionally further determining that the expression of the genomic locus is altered.
There are several different designs of this chemical inducible system: 1. ABI-PYL based system inducible by Abscisic Acid (ABA) (see, e.g., stke.sciencemag.org/cgi/content/abstract/sigtrans; 4/164/rs2), 2. FKBP-FRB based system inducible by rapamycin (or related chemicals based on rapamycin) (see, e.g., nature.com/nmeth/joumal/v2/n6/full/nmeth763.html), 3. GID1-GAIbased system inducibleby Gibberellin (GA) (see, e.g., nature.com/nchembio/joumal/v8/n5/full/nchembio.922.html).
A chemical inducible system can be an estrogen receptor (ER) based system inducible by 4-hydroxytamoxifen (40HT) (see, e.g., pnas.org/content/104/3/1027.abstract). A mutated ligand-binding domain of the estrogen receptor called ERT2 translocates into the nucleus of cells upon binding of 4-hydroxytamoxifen. In further embodiments of the invention any naturally occurring or engineered derivative of any nuclear receptor, thyroid hormone receptor, retinoic acid receptor, estrogren receptor, estrogen-related receptor, glucocorticoid receptor, progesterone receptor, androgen receptor may be used in inducible systems analogous to the ER based inducible system.
Another inducible system is based on the design using Transient receptor potential (TRP) ion channel based system inducible by energy, heat or radio-wave (see, e.g., sciencemag.org/content/336/6081/604). These TRP family proteins respond to different stimuli, including light and heat. When this protein is activated by light or heat, the ion channel will open and allow the entering of ions such as calcium into the plasma membrane. This influx of ions will bind to intracellular ion interacting partners linked to a polypeptide including the guide and the other components of the Cas13 CRISPR-Cas complex or system, and the binding will induce the change of sub-cellular localization of the polypeptide, leading to the entire polypeptide entering the nucleus of cells. Once inside the nucleus, the guide protein and the other components of the Cas13 CRISPR-Cas complex will be active and modulating target gene expression in cells.
While light activation may be an advantageous embodiment, sometimes it may be disadvantageous especially for in vivo applications in which the light may not penetrate the skin or other organs. In this instance, other methods of energy activation are contemplated, in particular, electric field energy and/or ultrasound which have a similar effect.
Electric field energy is preferably administered substantially as described in the art, using one or more electric pulses of from about 1 Volt/cm to about 10 kVolts/cm under in vivo conditions. Instead of or in addition to the pulses, the electric field may be delivered in a continuous manner. The electric pulse may be applied for between 1 μs and 500 milliseconds, preferably between 1 μs and 100 milliseconds. The electric field may be applied continuously or in a pulsed manner for 5 about minutes.
As used herein, ‘electric field energy’ is the electrical energy to which a cell is exposed. Preferably the electric field has a strength of from about 1 Volt/cm to about 10 kVolts/cm or more under in vivo conditions (see WO97/49450).
As used herein, the term “electric field” includes one or more pulses at variable capacitance and voltage and including exponential and/or square wave and/or modulated wave and/or modulated square wave forms. References to electric fields and electricity should be taken to include reference the presence of an electric potential difference in the environment of a cell. Such an environment may be set up by way of static electricity, alternating current (AC), direct current (DC), etc, as known in the art. The electric field may be uniform, non-uniform or otherwise, and may vary in strength and/or direction in a time dependent manner.
Single or multiple applications of electric field, as well as single or multiple applications of ultrasound are also possible, in any order and in any combination. The ultrasound and/or the electric field may be delivered as single or multiple continuous applications, or as pulses (pulsatile delivery).
Electroporation has been used in both in vitro and in vivo procedures to introduce foreign material into living cells. With in vitro applications, a sample of live cells is first mixed with the agent of interest and placed between electrodes such as parallel plates. Then, the electrodes apply an electrical field to the cell/implant mixture. Examples of systems that perform in vitro electroporation include the Electro Cell Manipulator ECM600 product, and the Electro Square Porator T820, both made by the BTX Division of Genetronics, Inc (see U.S. Pat. No. 5,869,326).
The known electroporation techniques (both in vitro and in vivo) function by applying a brief high voltage pulse to electrodes positioned around the treatment region. The electric field generated between the electrodes causes the cell membranes to temporarily become porous, whereupon molecules of the agent of interest enter the cells. In known electroporation applications, this electric field comprises a single square wave pulse on the order of 1000 V/cm, of about 100 .mu.s duration. Such a pulse may be generated, for example, in known applications of the Electro Square Porator T820.
Preferably, the electric field has a strength of from about 1 V/cm to about 10 kV/cm under in vitro conditions. Thus, the electric field may have a strength of 1 V/cm, 2 V/cm, 3 V/cm, 4 V/cm, 5 V/cm, 6 V/cm, 7 V/cm, 8 V/cm, 9 V/cm, 10 V/cm, 20 V/cm, 50 V/cm, 100 V/cm, 200 V/cm, 300 V/cm, 400 V/cm, 500 V/cm, 600 V/cm, 700 V/cm, 800 V/cm, 900 V/cm, 1 kV/cm, 2 kV/cm, 5 kV/cm, 10 kV/cm, 20 kV/cm, 50 kV/cm or more. More preferably from about 0.5 kV/cm to about 4.0 kV/cm under in vitro conditions. Preferably the electric field has a strength of from about 1 V/cm to about 10 kV/cm under in vivo conditions. However, the electric field strengths may be lowered where the number of pulses delivered to the target site are increased. Thus, pulsatile delivery of electric fields at lower field strengths is envisaged.
Preferably the application of the electric field is in the form of multiple pulses such as double pulses of the same strength and capacitance or sequential pulses of varying strength and/or capacitance. As used herein, the term “pulse” includes one or more electric pulses at variable capacitance and voltage and including exponential and/or square wave and/or modulated wave/square wave forms.
Preferably the electric pulse is delivered as a waveform selected from an exponential wave form, a square wave form, a modulated wave form and a modulated square wave form.
A preferred embodiment employs direct current at low voltage. Thus, Applicants disclose the use of an electric field which is applied to the cell, tissue or tissue mass at a field strength of between 1V/cm and 20V/cm, for a period of 100 milliseconds or more, preferably 15 minutes or more.
Ultrasound is advantageously administered at a power level of from about 0.05 W/cm2 to about 100 W/cm2. Diagnostic or therapeutic ultrasound may be used, or combinations thereof.
As used herein, the term “ultrasound” refers to a form of energy which consists of mechanical vibrations the frequencies of which are so high they are above the range of human hearing. Lower frequency limit of the ultrasonic spectrum may generally be taken as about 20 kHz. Most diagnostic applications of ultrasound employ frequencies in the range 1 and 15 MHz′ (From Ultrasonics in Clinical Diagnosis, P. N. T. Wells, ed., 2nd. Edition, Publ. Churchill Livingstone [Edinburgh, London & NY, 1977]).
Ultrasound has been used in both diagnostic and therapeutic applications. When used as a diagnostic tool (“diagnostic ultrasound”), ultrasound is typically used in an energy density range of up to about 100 mW/cm2 (FDA recommendation), although energy densities of up to 750 mW/cm2 have been used. In physiotherapy, ultrasound is typically used as an energy source in a range up to about 3 to 4 W/cm2 (WHO recommendation). In other therapeutic applications, higher intensities of ultrasound may be employed, for example, HIFU at 100 W/cm up to 1 kW/cm2 (or even higher) for short periods of time. The term “ultrasound” as used in this specification is intended to encompass diagnostic, therapeutic and focused ultrasound.
Focused ultrasound (FUS) allows thermal energy to be delivered without an invasive probe (see Morocz et al 1998 Journal of Magnetic Resonance Imaging Vol. 8, No. 1, pp. 136-142. Another form of focused ultrasound is high intensity focused ultrasound (HIFU) which is reviewed by Moussatov et al in Ultrasonics (1998) Vol. 36, No. 8, pp. 893-900 and TranHuuHue et al in Acustica (1997) Vol. 83, No. 6, pp. 1103-1106.
Preferably, a combination of diagnostic ultrasound and a therapeutic ultrasound is employed. This combination is not intended to be limiting, however, and the skilled reader will appreciate that any variety of combinations of ultrasound may be used. Additionally, the energy density, frequency of ultrasound, and period of exposure may be varied.
Preferably the exposure to an ultrasound energy source is at a power density of from about 0.05 to about 100 Wcm-2. Even more preferably, the exposure to an ultrasound energy source is at a power density of from about 1 to about 15 Wcm-2.
Preferably the exposure to an ultrasound energy source is at a frequency of from about 0.015 to about 10.0 MHz. More preferably the exposure to an ultrasound energy source is at a frequency of from about 0.02 to about 5.0 MHz or about 6.0 MHz. Most preferably, the ultrasound is applied at a frequency of 3 MHz.
Preferably the exposure is for periods of from about 10 milliseconds to about 60 minutes. Preferably the exposure is for periods of from about 1 second to about 5 minutes. More preferably, the ultrasound is applied for about 2 minutes. Depending on the particular target cell to be disrupted, however, the exposure may be for a longer duration, for example, for 15 minutes.
Advantageously, the target tissue is exposed to an ultrasound energy source at an acoustic power density of from about 0.05 Wcm-2 to about 10 Wcm-2 with a frequency ranging from about 0.015 to about 10 MHz (see WO 98/52609). However, alternatives are also possible, for example, exposure to an ultrasound energy source at an acoustic power density of above 100 Wcm-2, but for reduced periods of time, for example, 1000 Wcm-2 for periods in the millisecond range or less.
Preferably the application of the ultrasound is in the form of multiple pulses; thus, both continuous wave and pulsed wave (pulsatile delivery of ultrasound) may be employed in any combination. For example, continuous wave ultrasound may be applied, followed by pulsed wave ultrasound, or vice versa. This may be repeated any number of times, in any order and combination. The pulsed wave ultrasound may be applied against a background of continuous wave ultrasound, and any number of pulses may be used in any number of groups.
Preferably, the ultrasound may comprise pulsed wave ultrasound. In a highly preferred embodiment, the ultrasound is applied at a power density of 0.7 Wcm-2 or 1.25 Wcm-2 as a continuous wave. Higher power densities may be employed if pulsed wave ultrasound is used.
Use of ultrasound is advantageous as, like light, it may be focused accurately on a target. Moreover, ultrasound is advantageous as it may be focused more deeply into tissues unlike light. It is therefore better suited to whole-tissue penetration (such as but not limited to a lobe of the liver) or whole organ (such as but not limited to the entire liver or an entire muscle, such as the heart) therapy. Another important advantage is that ultrasound is a non-invasive stimulus which is used in a wide variety of diagnostic and therapeutic applications. By way of example, ultrasound is well known in medical imaging techniques and, additionally, in orthopedic therapy. Furthermore, instruments suitable for the application of ultrasound to a subject vertebrate are widely available and their use is well known in the art.
In particular embodiments, the guide molecule is modified by a secondary structure to increase the specificity of the CRISPR-Cas system and the secondary structure can protect against exonuclease activity and allow for 5′ additions to the guide sequence also referred to herein as a protected guide molecule.
In one aspect, the invention provides for hybridizing a “protector RNA” to a sequence of the guide molecule, wherein the “protector RNA” is an RNA strand complementary to the 3′ end of the guide molecule to thereby generate a partially double-stranded guide RNA. In an embodiment of the invention, protecting mismatched bases (i.e. the bases of the guide molecule which do not form part of the guide sequence) with a perfectly complementary protector sequence decreases the likelihood of target RNA binding to the mismatched basepairs at the 3′ end. In particular embodiments of the invention, additional sequences comprising an extented length may also be present within the guide molecule such that the guide comprises a protector sequence within the guide molecule. This “protector sequence” ensures that the guide molecule comprises a “protected sequence” in addition to an “exposed sequence” (comprising the part of the guide sequence hybridizing to the target sequence). In particular embodiments, the guide molecule is modified by the presence of the protector guide to comprise a secondary structure such as a hairpin. Advantageously there are three or four to thirty or more, e.g., about 10 or more, contiguous base pairs having complementarity to the protected sequence, the guide sequence or both. It is advantageous that the protected portion does not impede thermodynamics of the CRISPR-Cas system interacting with its target. By providing such an extension including a partially double stranded guide molecule, the guide molecule is considered protected and results in improved specific binding of the CRISPR-Cas complex, while maintaining specific activity.
In particular embodiments, use is made of a truncated guide (tru-guide), i.e. a guide molecule which comprises a guide sequence which is truncated in length with respect to the canonical guide sequence length. As described by Nowak et al. (Nucleic Acids Res (2016) 44 (20): 9555-9564), such guides may allow catalytically active CRISPR-Cas enzyme to bind its target without cleaving the target RNA. In particular embodiments, a truncated guide is used which allows the binding of the target but retains only nickase activity of the CRISPR-Cas enzyme.
CRISPR RNA-Targeting Effector Proteins
In one example embodiment, the CRISPR system effector protein is an RNA-targeting effector protein. In certain embodiments, the CRISPR system effector protein is a Type VI CRISPR system targeting RNA (e.g., Cas13a, Cas13b, Cas13c or Cas13d). Example RNA-targeting effector proteins include Cas13b and C2c2 (now known as Cas13a). It will be understood that the term “C2c2” herein is used interchangeably with “Cas13a”. “C2c2” is now referred to as “Cas13a”, and the terms are used interchangeably herein unless indicated otherwise. As used herein, the term “Cas13” refers to any Type VI CRISPR system targeting RNA (e.g., Cas13a, Cas13b, Cas13c or Cas13d). When the CRISPR protein is a C2c2 protein, a tracrRNA is not required. C2c2 has been described in Abudayyeh et al. (2016) “C2c2 is a single-component programmable RNA-guided RNA-targeting CRISPR effector”; Science; DOI: 10.1126/science.aaf5573; and Shmakov et al. (2015) “Discovery and Functional Characterization of Diverse Class 2 CRISPR-Cas Systems”, Molecular Cell, DOI: dx.doi.org/10.1016/j.molcel.2015.10.008; which are incorporated herein in their entirety by reference. Cas13b has been described in Smargon et al. (2017) “Cas13b Is a Type VI-B CRISPR-Associated RNA-Guided RNases Differentially Regulated by Accessory Proteins Csx27 and Csx28,” Molecular Cell. 65, 1-13; dx.doi.org/10.1016/j.molcel.2016.12.023., which is incorporated herein in its entirety by reference.
In some embodiments, one or more elements of a nucleic acid-targeting system is derived from a particular organism comprising an endogenous CRISPR RNA-targeting system. In certain example embodiments, the effector protein CRISPR RNA-targeting system comprises at least one HEPN domain, including but not limited to the HEPN domains described herein, HEPN domains known in the art, and domains recognized to be HEPN domains by comparison to consensus sequence motifs. Several such domains are provided herein. In one non-limiting example, a consensus sequence can be derived from the sequences of C2c2 or Cas13b orthologs provided herein. In certain example embodiments, the effector protein comprises a single HEPN domain. In certain other example embodiments, the effector protein comprises two HEPN domains.
In one example embodiment, the effector protein comprise one or more HEPN domains comprising a RxxxxH motif sequence. The RxxxxH motif sequence can be, without limitation, from a HEPN domain described herein or a HEPN domain known in the art. RxxxxH motif sequences further include motif sequences created by combining portions of two or more HEPN domains. As noted, consensus sequences can be derived from the sequences of the orthologs disclosed in U.S. Provisional Patent Application 62/432,240 entitled “Novel CRISPR Enzymes and Systems,” U.S. Provisional Patent Application 62/471,710 entitled “Novel Type VI CRISPR Orthologs and Systems” filed on Mar. 15, 2017, and U.S. Provisional Patent Application 62/484,786 entitled “Novel Type VI CRISPR Orthologs and Systems,” filed on Apr. 12, 2017.
In certain other example embodiments, the CRISPR system effector protein is a C2c2 nuclease. The activity of C2c2 may depend on the presence of two HEPN domains. These have been shown to be RNase domains, i.e. nuclease (in particular an endonuclease) cutting RNA. C2c2 HEPN may also target DNA, or potentially DNA and/or RNA. On the basis that the HEPN domains of C2c2 are at least capable of binding to and, in their wild-type form, cutting RNA, then it is preferred that the C2c2 effector protein has RNase function. Regarding C2c2 CRISPR systems, reference is made to U.S. Provisional 62/351,662 filed on Jun. 17, 2016 and U.S. Provisional 62/376,377 filed on Aug. 17, 2016. Reference is also made to U.S. Provisional 62/351,803 filed on Jun. 17, 2016. Reference is also made to U.S. Provisional 62/432,240 entitled “Novel Crispr Enzymes and Systems” filed Dec. 9, 2016. Reference is further made to East-Seletsky et al. “Two distinct RNase activities of CRISPR-C2c2 enable guide-RNA processing and RNA detection” Nature doi:10/1038/nature19802 and Abudayyeh et al. “C2c2 is a single-component programmable RNA-guided RNA targeting CRISPR effector” bioRxiv doi:10.1101/054742.
In certain embodiments, the C2c2 effector protein is from an organism of a genus selected from the group consisting of: Leptotrichia, Listeria, Corynebacter, Sutterella, Legionella, Treponema, Filifactor, Eubacterium, Streptococcus, Lactobacillus, Mycoplasma, Bacteroides, Flavivirus, Flavobacterium, Sphaerochaeta, Azospirillum, Gluconacetobacter, Neisseria, Roseburia, Parvibaculum, Staphylococcus, Nitratifractor, Mycoplasma, Campylobacter, and Lachnospira, or the C2c2 effector protein is an organism selected from the group consisting of: Leptotrichia shahii, Leptotrichia, wadei, Listeria seeligeri, Clostridium aminophilum, Carnobacterium gallinarum, Paludibacter propionicigenes, Listeria weihenstephanensis, or the C2c2 effector protein is a L. wadei F0279 or L. wadei F0279 (Lw2) C2C2 effector protein. In another embodiment, the one or more guide RNAs are designed to detect a single nucleotide polymorphism, splice variant of a transcript, or a frameshift mutation in a target RNA or DNA.
In certain example embodiments, the RNA-targeting effector protein is a Type VI-B effector protein, such as Cas13b and Group 29 or Group 30 proteins. In certain example embodiments, the RNA-targeting effector protein comprises one or more HEPN domains. In certain example embodiments, the RNA-targeting effector protein comprises a C-terminal HEPN domain, a N-terminal HEPN domain, or both. Regarding example Type VI-B effector proteins that may be used in the context of this invention, reference is made to U.S. application Ser. No. 15/331,792 entitled “Novel CRISPR Enzymes and Systems” and filed Oct. 21, 2016, International Patent Application No. PCT/US2016/058302 entitled “Novel CRISPR Enzymes and Systems”, and filed Oct. 21, 2016, and Smargon et al. “Cas13b is a Type VI-B CRISPR-associated RNA-Guided RNase differentially regulated by accessory proteins Csx27 and Csx28” Molecular Cell, 65, 1-13 (2017); dx.doi.org/10.1016/j.molcel.2016.12.023, and U.S. Provisional Application No. to be assigned, entitled “Novel Cas13b Orthologues CRISPR Enzymes and System” filed Mar. 15, 2017. In particular embodiments, the Cas13b enzyme is derived from Bergeyella zoohelcum. 
In certain example embodiments, the RNA-targeting effector protein is a Cas13c effector protein as disclosed in U.S. Provisional Patent Application No. 62/525,165 filed Jun. 26, 2017, and PCT Application No. US 2017/047193 filed Aug. 16, 2017.
In some embodiments, one or more elements of a nucleic acid-targeting system is derived from a particular organism comprising an endogenous CRISPR RNA-targeting system. In certain embodiments, the CRISPR RNA-targeting system is found in Eubacterium and Ruminococcus. In certain embodiments, the effector protein comprises targeted and collateral ssRNA cleavage activity. In certain embodiments, the effector protein comprises dual HEPN domains. In certain embodiments, the effector protein lacks a counterpart to the Helical-1 domain of Cas13a. In certain embodiments, the effector protein is smaller than previously characterized class 2 CRISPR effectors, with a median size of 928 aa. This median size is 190 aa (17%) less than that of Cas13c, more than 200 aa (18%) less than that of Cas13b, and more than 300 aa (26%) less than that of Cas13a. In certain embodiments, the effector protein has no requirement for a flanking sequence (e.g., PFS, PAM).
In certain embodiments, the effector protein locus structures include a WYL domain containing accessory protein (so denoted after three amino acids that were conserved in the originally identified group of these domains; see, e.g., WYL domain IPR026881). In certain embodiments, the WYL domain accessory protein comprises at least one helix-turn-helix (HTH) or ribbon-helix-helix (RHH) DNA-binding domain. In certain embodiments, the WYL domain containing accessory protein increases both the targeted and the collateral ssRNA cleavage activity of the RNA-targeting effector protein. In certain embodiments, the WYL domain containing accessory protein comprises an N-terminal RHH domain, as well as a pattern of primarily hydrophobic conserved residues, including an invariant tyrosine-leucine doublet corresponding to the original WYL motif. In certain embodiments, the WYL domain containing accessory protein is WYLL. WYL1 is a single WYL-domain protein associated primarily with Ruminococcus. 
In other example embodiments, the Type VI RNA-targeting Cas enzyme is Cas13d. In certain embodiments, Cas13d is Eubacterium siraeum DSM 15702 (EsCas13d) or Ruminococcus sp. N15.MGS-57 (RspCas13d) (see, e.g., Yan et al., Cas13d Is a Compact RNA-Targeting Type VI CRISPR Effector Positively Modulated by a WYL-Domain-Containing Accessory Protein, Molecular Cell (2018), doi.org/10.1016/j.molcel.2018.02.028). RspCas13d and EsCas13d have no flanking sequence requirements (e.g., PFS, PAM).
Cas13 RNA Editing
In one aspect, the invention provides a method of modifying or editing a target transcript in a eukaryotic cell. In some embodiments, the method comprises allowing a CRISPR-Cas effector module complex to bind to the target polynucleotide to effect RNA base editing, wherein the CRISPR-Cas effector module complex comprises a Cas effector module complexed with a guide sequence hybridized to a target sequence within said target polynucleotide, wherein said guide sequence is linked to a direct repeat sequence. In some embodiments, the Cas effector module comprises a catalytically inactive CRISPR-Cas protein. In some embodiments, the guide sequence is designed to introduce one or more mismatches to the RNA/RNA duplex formed between the target sequence and the guide sequence. In particular embodiments, the mismatch is an A-C mismatch. In some embodiments, the Cas effector may associate with one or more functional domains (e.g. via fusion protein or suitable linkers). In some embodiments, the effector domain comprises one or more cytindine or adenosine deaminases that mediate endogenous editing of via hydrolytic deamination. In particular embodiments, the effector domain comprises the adenosine deaminase acting on RNA (ADAR) family of enzymes. In particular embodiments, the adenosine deaminase protein or catalytic domain thereof capable of deaminating adenosine or cytidine in RNA or is an RNA specific adenosine deaminase and/or is a bacterial, human, cephalopod, or Drosophila adenosine deaminase protein or catalytic domain thereof, preferably TadA, more preferably ADAR, optionally huADAR, optionally (hu)ADAR1 or (hu)ADAR2, preferably huADAR2 or catalytic domain thereof.
The present application relates to modifying a target RNA sequence of interest (see, e.g, Cox et al., Science. 2017 Nov. 24; 358(6366):1019-1027). Using RNA-targeting rather than DNA targeting offers several advantages relevant for therapeutic development. First, there are substantial safety benefits to targeting RNA: there will be fewer off-target events because the available sequence space in the transcriptome is significantly smaller than the genome, and if an off-target event does occur, it will be transient and less likely to induce negative side effects. Second, RNA-targeting therapeutics will be more efficient because they are cell-type independent and not have to enter the nucleus, making them easier to deliver.
A further aspect of the invention relates to the method and composition as envisaged herein for use in prophylactic or therapeutic treatment, preferably wherein said target locus of interest is within a human or animal and to methods of modifying an Adenine or Cytidine in a target RNA sequence of interest, comprising delivering to said target RNA, the composition as described herein. In particular embodiments, the CRISPR system and the adenosine deaminase, or catalytic domain thereof, are delivered as one or more polynucleotide molecules, as a ribonucleoprotein complex, optionally via particles, vesicles, or one or more viral vectors. In particular embodiments, the invention thus comprises compositions for use in therapy. This implies that the methods can be performed in vivo, ex vivo or in vitro. In particular embodiments, when the target is a human or animal target, the method is carried out ex vivo or in vitro.
A further aspect of the invention relates to the method as envisaged herein for use in prophylactic or therapeutic treatment, preferably wherein said target of interest is within a human or animal and to methods of modifying an Adenine or Cytidine in a target RNA sequence of interest, comprising delivering to said target RNA, the composition as described herein. In particular embodiments, the CRISPR system and the adenonsine deaminase, or catalytic domain thereof, are delivered as one or more polynucleotide molecules, as a ribonucleoprotein complex, optionally via particles, vesicles, or one or more viral vectors.
In one aspect, the invention provides a method of generating a eukaryotic cell comprising a modified or edited gene. In some embodiments, the method comprises (a) introducing one or more vectors into a eukaryotic cell, wherein the one or more vectors drive expression of one or more of. Cas effector module, and a guide sequence linked to a direct repeat sequence, wherein the Cas effector module associate one or more effector domains that mediate base editing, and (b) allowing a CRISPR-Cas effector module complex to bind to a target polynucleotide to effect base editing of the target polynucleotide within said disease gene, wherein the CRISPR-Cas effector module complex comprises a Cas effector module complexed with the guide sequence that is hybridized to the target sequence within the target polynucleotide, wherein the guide sequence may be designed to introduce one or more mismatches between the RNA/RNA duplex formed between the guide sequence and the target sequence. In particular embodiments, the mismatch is an A-C mismatch. In some embodiments, the Cas effector may associate with one or more functional domains (e.g. via fusion protein or suitable linkers). In some embodiments, the effector domain comprises one or more cytidine or adenosine deaminases that mediate endogenous editing of via hydrolytic deamination. In particular embodiments, the effector domain comprises the adenosine deaminase acting on RNA (ADAR) family of enzymes. In particular embodiments, the adenosine deaminase protein or catalytic domain thereof capable of deaminating adenosine or cytidine in RNA or is an RNA specific adenosine deaminase and/or is a bacterial, human, cephalopod, or Drosophila adenosine deaminase protein or catalytic domain thereof, preferably TadA, more preferably ADAR, optionally huADAR, optionally (hu)ADAR1 or (hu)ADAR2, preferably huADAR2 or catalytic domain thereof.
A further aspect relates to an isolated cell obtained or obtainable from the methods described herein comprising the composition described herein or progeny of said modified cell, preferably wherein said cell comprises a hypoxanthine or a guanine in replace of said Adenine in said target RNA of interest compared to a corresponding cell not subjected to the method. In particular embodiments, the cell is a eukaryotic cell, preferably a human or non-human animal cell, optionally a therapeutic T cell or an antibody-producing B-cell.
In some embodiments, the modified cell is a therapeutic T cell, such as a T cell suitable for adoptive cell transfer therapies (e.g., CAR-T therapies). The modification may result in one or more desirable traits in the therapeutic T cell, as described further herein.
The invention further relates to a method for cell therapy, comprising administering to a patient in need thereof the modified cell described herein, wherein the presence of the modified cell remedies a disease in the patient. In one embodiment, the modified cell for cell therapy is a epithelial cell (e.g., tuft cell).
The present invention may be further illustrated and extended based on aspects of CRISPR-Cas development and use as set forth in the following articles and particularly as relates to delivery of a CRISPR protein complex and uses of an RNA guided endonuclease in cells and organisms:

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        each of which is incorporated herein by reference, may be considered in the practice of the instant invention, and discussed briefly below:
    
    
    Cong et al. engineered type II CRISPR-Cas systems for use in eukaryotic cells based on both Streptococcus thermophilus Cas9 and also Streptococcus pyogenes Cas9 and demonstrated that Cas9 nucleases can be directed by short RNAs to induce precise cleavage of DNA in human and mouse cells. Their study further showed that Cas9 as converted into a nicking enzyme can be used to facilitate homology-directed repair in eukaryotic cells with minimal mutagenic activity. Additionally, their study demonstrated that multiple guide sequences can be encoded into a single CRISPR array to enable simultaneous editing of several at endogenous genomic loci sites within the mammalian genome, demonstrating easy programmability and wide applicability of the RNA-guided nuclease technology. This ability to use RNA to program sequence specific DNA cleavage in cells defined a new class of genome engineering tools. These studies further showed that other CRISPR loci are likely to be transplantable into mammalian cells and can also mediate mammalian genome cleavage. Importantly, it can be envisaged that several aspects of the CRISPR-Cas system can be further improved to increase its efficiency and versatility.
    Jiang et al. used the clustered, regularly interspaced, short palindromic repeats (CRISPR)-associated Cas9 endonuclease complexed with dual-RNAs to introduce precise mutations in the genomes of Streptococcus pneumoniae and Escherichia coli. The approach relied on dual-RNA:Cas9-directed cleavage at the targeted genomic site to kill unmutated cells and circumvents the need for selectable markers or counter-selection systems. The study reported reprogramming dual-RNA:Cas9 specificity by changing the sequence of short CRISPR RNA (crRNA) to make single- and multinucleotide changes carried on editing templates. The study showed that simultaneous use of two crRNAs enabled multiplex mutagenesis. Furthermore, when the approach was used in combination with recombineering, in S. pneumoniae, nearly 100% of cells that were recovered using the described approach contained the desired mutation, and in E. coli, 65% that were recovered contained the mutation.
    Wang et al. (2013) used the CRISPR-Cas system for the one-step generation of mice carrying mutations in multiple genes which were traditionally generated in multiple steps by sequential recombination in embryonic stem cells and/or time-consuming intercrossing of mice with a single mutation. The CRISPR-Cas system will greatly accelerate the in vivo study of functionally redundant genes and of epistatic gene interactions.
    Konermann et al. (2013) addressed the need in the art for versatile and robust technologies that enable optical and chemical modulation of DNA-binding domains based CRISPR Cas9 enzyme and also Transcriptional Activator Like Effectors.
    Ran et al. (2013-A) described an approach that combined a Cas9 nickase mutant with paired guide RNAs to introduce targeted double-strand breaks. This addresses the issue of the Cas9 nuclease from the microbial CRISPR-Cas system being targeted to specific genomic loci by a guide sequence, which can tolerate certain mismatches to the DNA target and thereby promote undesired off-target mutagenesis. Because individual nicks in the genome are repaired with high fidelity, simultaneous nicking via appropriately offset guide RNAs is required for double-stranded breaks and extends the number of specifically recognized bases for target cleavage. The authors demonstrated that using paired nicking can reduce off-target activity by 50- to 1,500-fold in cell lines and to facilitate gene knockout in mouse zygotes without sacrificing on-target cleavage efficiency. This versatile strategy enables a wide variety of genome editing applications that require high specificity.
    Hsu et al. (2013) characterized SpCas9 targeting specificity in human cells to inform the selection of target sites and avoid off-target effects. The study evaluated >700 guide RNA variants and SpCas9-induced indel mutation levels at >100 predicted genomic off-target loci in 293T and 293FT cells. The authors that SpCas9 tolerates mismatches between guide RNA and target DNA at different positions in a sequence-dependent manner, sensitive to the number, position and distribution of mismatches. The authors further showed that SpCas9-mediated cleavage is unaffected by DNA methylation and that the dosage of SpCas9 and guide RNA can be titrated to minimize off-target modification. Additionally, to facilitate mammalian genome engineering applications, the authors reported providing a web-based software tool to guide the selection and validation of target sequences as well as off-target analyses.
    Ran et al. (2013-B) described a set of tools for Cas9-mediated genome editing via non-homologous end joining (NHEJ) or homology-directed repair (HDR) in mammalian cells, as well as generation of modified cell lines for downstream functional studies. To minimize off-target cleavage, the authors further described a double-nicking strategy using the Cas9 nickase mutant with paired guide RNAs. The protocol provided by the authors experimentally derived guidelines for the selection of target sites, evaluation of cleavage efficiency and analysis of off-target activity. The studies showed that beginning with target design, gene modifications can be achieved within as little as 1-2 weeks, and modified clonal cell lines can be derived within 2-3 weeks.
    Shalem et al. described a new way to interrogate gene function on a genome-wide scale. Their studies showed that delivery of a genome-scale CRISPR-Cas9 knockout (GeCKO) library targeted 18,080 genes with 64,751 unique guide sequences enabled both negative and positive selection screening in human cells. First, the authors showed use of the GeCKO library to identify genes essential for cell viability in cancer and pluripotent stem cells. Next, in a melanoma model, the authors screened for genes whose loss is involved in resistance to vemurafenib, a therapeutic that inhibits mutant protein kinase BRAF. Their studies showed that the highest-ranking candidates included previously validated genes NF1 and MED12 as well as novel hits NF2, CUL3, TADA2B, and TADA1. The authors observed a high level of consistency between independent guide RNAs targeting the same gene and a high rate of hit confirmation, and thus demonstrated the promise of genome-scale screening with Cas9.
    Nishimasu et al. reported the crystal structure of Streptococcus pyogenes Cas9 in complex with sgRNA and its target DNA at 2.5 A° resolution. The structure revealed a bilobed architecture composed of target recognition and nuclease lobes, accommodating the sgRNA:DNA heteroduplex in a positively charged groove at their interface. Whereas the recognition lobe is essential for binding sgRNA and DNA, the nuclease lobe contains the HNH and RuvC nuclease domains, which are properly positioned for cleavage of the complementary and non-complementary strands of the target DNA, respectively. The nuclease lobe also contains a carboxyl-terminal domain responsible for the interaction with the protospacer adjacent motif (PAM). This high-resolution structure and accompanying functional analyses have revealed the molecular mechanism of RNA-guided DNA targeting by Cas9, thus paving the way for the rational design of new, versatile genome-editing technologies.
    Wu et al. mapped genome-wide binding sites of a catalytically inactive Cas9 (dCas9) from Streptococcus pyogenes loaded with single guide RNAs (sgRNAs) in mouse embryonic stem cells (mESCs). The authors showed that each of the four sgRNAs tested targets dCas9 to between tens and thousands of genomic sites, frequently characterized by a 5-nucleotide seed region in the sgRNA and an NGG protospacer adjacent motif (PAM). Chromatin inaccessibility decreases dCas9 binding to other sites with matching seed sequences; thus 70% of off-target sites are associated with genes. The authors showed that targeted sequencing of 295 dCas9 binding sites in mESCs transfected with catalytically active Cas9 identified only one site mutated above background levels. The authors proposed a two-state model for Cas9 binding and cleavage, in which a seed match triggers binding but extensive pairing with target DNA is required for cleavage.
    Platt et al. established a Cre-dependent Cas9 knockin mouse. The authors demonstrated in vivo as well as ex vivo genome editing using adeno-associated virus (AAV)-, lentivirus-, or particle-mediated delivery of guide RNA in neurons, immune cells, and endothelial cells.
    Hsu et al. (2014) is a review article that discusses generally CRISPR-Cas9 history from yogurt to genome editing, including genetic screening of cells.
    Wang et al. (2014) relates to a pooled, loss-of-function genetic screening approach suitable for both positive and negative selection that uses a genome-scale lentiviral single guide RNA (sgRNA) library.
    Doench et al. created a pool of sgRNAs, tiling across all possible target sites of a panel of six endogenous mouse and three endogenous human genes and quantitatively assessed their ability to produce null alleles of their target gene by antibody staining and flow cytometry. The authors showed that optimization of the PAM improved activity and also provided an on-line tool for designing sgRNAs.
    Swiech et al. demonstrate that AAV-mediated SpCas9 genome editing can enable reverse genetic studies of gene function in the brain.
    Konermann et al. (2015) discusses the ability to attach multiple effector domains, e.g., transcriptional activator, functional and epigenomic regulators at appropriate positions on the guide such as stem or tetraloop with and without linkers.
    Zetsche et al. demonstrates that the Cas9 enzyme can be split into two and hence the assembly of Cas9 for activation can be controlled.
    Chen et al. relates to multiplex screening by demonstrating that a genome-wide in vivo CRISPR-Cas9 screen in mice reveals genes regulating lung metastasis.
    Ran et al. (2015) relates to SaCas9 and its ability to edit genomes and demonstrates that one cannot extrapolate from biochemical assays.
    Shalem et al. (2015) described ways in which catalytically inactive Cas9 (dCas9) fusions are used to synthetically repress (CRISPRi) or activate (CRISPRa) expression, showing. advances using Cas9 for genome-scale screens, including arrayed and pooled screens, knockout approaches that inactivate genomic loci and strategies that modulate transcriptional activity.
    Xu et al. (2015) assessed the DNA sequence features that contribute to single guide RNA (sgRNA) efficiency in CRISPR-based screens. The authors explored efficiency of CRISPR-Cas9 knockout and nucleotide preference at the cleavage site. The authors also found that the sequence preference for CRISPRi/a is substantially different from that for CRISPR-Cas9 knockout.
    Parnas et al. (2015) introduced genome-wide pooled CRISPR-Cas9 libraries into dendritic cells (DCs) to identify genes that control the induction of tumor necrosis factor (Tnf) by bacterial lipopolysaccharide (LPS). Known regulators of Tlr4 signaling and previously unknown candidates were identified and classified into three functional modules with distinct effects on the canonical responses to LPS.
    Ramanan et al (2015) demonstrated cleavage of viral episomal DNA (cccDNA) in infected cells. The HBV genome exists in the nuclei of infected hepatocytes as a 3.2 kb double-stranded episomal DNA species called covalently closed circular DNA (cccDNA), which is a key component in the HBV life cycle whose replication is not inhibited by current therapies. The authors showed that sgRNAs specifically targeting highly conserved regions of HBV robustly suppresses viral replication and depleted cccDNA.
    Nishimasu et al. (2015) reported the crystal structures of SaCas9 in complex with a single guide RNA (sgRNA) and its double-stranded DNA targets, containing the 5′-TTGAAT-3′ PAM and the 5′-TTGGGT-3′ PAM. A structural comparison of SaCas9 with SpCas9 highlighted both structural conservation and divergence, explaining their distinct PAM specificities and orthologous sgRNA recognition.
    Canver et al. (2015) demonstrated a CRISPR-Cas9-based functional investigation of non-coding genomic elements. The authors we developed pooled CRISPR-Cas9 guide RNA libraries to perform in situ saturating mutagenesis of the human and mouse BCL11A enhancers which revealed critical features of the enhancers.
    Zetsche et al. (2015) reported characterization of Cpf1, a class 2 CRISPR nuclease from Francisella novicida U112 having features distinct from Cas9. Cpf1 is a single RNA-guided endonuclease lacking tracrRNA, utilizes a T-rich protospacer-adjacent motif, and cleaves DNA via a staggered DNA double-stranded break.
    Shmakov et al. (2015) reported three distinct Class 2 CRISPR-Cas systems. Two system CRISPR enzymes (C2c1 and C2c3) contain RuvC-like endonuclease domains distantly related to Cpf1. Unlike Cpf1, C2c1 depends on both crRNA and tracrRNA for DNA cleavage. The third enzyme (C2c2) contains two predicted HEPN RNase domains and is tracrRNA independent.
    Slaymaker et al (2016) reported the use of structure-guided protein engineering to improve the specificity of Streptococcus pyogenes Cas9 (SpCas9). The authors developed “enhanced specificity” SpCas9 (eSpCas9) variants which maintained robust on-target cleavage with reduced off-target effects.
    Cox et al., (2017) reported the use of catalytically inactive Cas13 (dCas13) to direct adenosine-to-inosine deaminase activity by ADAR2 (adenosine deaminase acting on RNA type 2) to transcripts in mammalian cells. The system, referred to as RNA Editing for Programmable A to I Replacement (REPAIR), has no strict sequence constraints and can be used to edit full-length transcripts. The authors further engineered the system to create a high-specificity variant and minimized the system to facilitate viral delivery.


The methods and tools provided herein are may be designed for use with or Cas13, a type II nuclease that does not make use of tracrRNA. Orthologs of Cas13 have been identified in different bacterial species as described herein. Further type II nucleases with similar properties can be identified using methods described in the art (Shmakov et al. 2015, 60:385-397; Abudayeh et al. 2016, Science, 5; 353(6299)). In particular embodiments, such methods for identifying novel CRISPR effector proteins may comprise the steps of selecting sequences from the database encoding a seed which identifies the presence of a CRISPR Cas locus, identifying loci located within 10 kb of the seed comprising Open Reading Frames (ORFs) in the selected sequences, selecting therefrom loci comprising ORFs of which only a single ORF encodes a novel CRISPR effector having greater than 700 amino acids and no more than 90% homology to a known CRISPR effector. In particular embodiments, the seed is a protein that is common to the CRISPR-Cas system, such as Cas1. In further embodiments, the CRISPR array is used as a seed to identify new effector proteins.
Also, “Dimeric CRISPR RNA-guided FokI nucleases for highly specific genome editing”, Shengdar Q. Tsai, Nicolas Wyvekens, Cyd Khayter, Jennifer A. Foden, Vishal Thapar, Deepak Reyon, Mathew J. Goodwin, Martin J. Aryee, J. Keith Joung Nature Biotechnology 32(6): 569-77 (2014), relates to dimeric RNA-guided FokI Nucleases that recognize extended sequences and can edit endogenous genes with high efficiencies in human cells.
With respect to general information on CRISPR/Cas Systems, components thereof, and delivery of such components, including methods, materials, delivery vehicles, vectors, particles, and making and using thereof, including as to amounts and formulations, as well as CRISPR-Cas-expressing eukaryotic cells, CRISPR-Cas expressing eukaryotes, such as a mouse, reference is made to: U.S. Pat. Nos. 8,999,641, 8,993,233, 8,697,359, 8,771,945, 8,795,965, 8,865,406, 8,871,445, 8,889,356, 8,889,418, 8,895,308, 8,906,616, 8,932,814, and 8,945,839; US Patent Publications US 2014-0310830 (U.S. application Ser. No. 14/105,031), US 2014-0287938 A1 (U.S. application Ser. No. 14/213,991), US 2014-0273234 A1 (U.S. application Ser. No. 14/293,674), US2014-0273232 A1 (U.S. application Ser. No. 14/290,575), US 2014-0273231 (U.S. application Ser. No. 14/259,420), US 2014-0256046 A1 (U.S. application Ser. No. 14/226,274), US 2014-0248702 A1 (U.S. application Ser. No. 14/258,458), US 2014-0242700 A1 (U.S. application Ser. No. 14/222,930), US 2014-0242699 A1 (U.S. application Ser. No. 14/183,512), US 2014-0242664 A1 (U.S. application Ser. No. 14/104,990), US 2014-0234972 A1 (U.S. application Ser. No. 14/183,471), US 2014-0227787 A1 (U.S. application Ser. No. 14/256,912), US 2014-0189896 A1 (U.S. application Ser. No. 14/105,035), US 2014-0186958 (U.S. application Ser. No. 14/105,017), US 2014-0186919 A1 (U.S. application Ser. No. 14/104,977), US 2014-0186843 A1 (U.S. application Ser. No. 14/104,900), US 2014-0179770 A1 (U.S. application Ser. No. 14/104,837) and US 2014-0179006 A1 (U.S. application Ser. No. 14/183,486), US 2014-0170753 (U.S. application Ser. No. 14/183,429); US 2015-0184139 (U.S. application Ser. No. 14/324,960); 14/054,414 European Patent Applications EP 2 771 468 (EP13818570.7), EP 2 764 103 (EP13824232.6), and EP 2 784 162 (EP14170383.5); and PCT Patent Publications WO2014/093661 (PCT/US2013/074743), WO2014/093694 (PCT/US2013/074790), WO2014/093595 (PCT/US2013/074611), WO2014/093718 (PCT/US2013/074825), WO2014/093709 (PCT/US2013/074812), WO2014/093622 (PCT/US2013/074667), WO2014/093635 (PCT/US2013/074691), WO2014/093655 (PCT/US2013/074736), WO2014/093712 (PCT/US2013/074819), WO2014/093701 (PCT/US2013/074800), WO2014/018423 (PCT/US2013/051418), WO2014/204723 (PCT/US2014/041790), WO2014/204724 (PCT/US2014/041800), WO2014/204725 (PCT/US2014/041803), WO2014/204726 (PCT/US2014/041804), WO2014/204727 (PCT/US2014/041806), WO2014/204728 (PCT/US2014/041808), WO2014/204729 (PCT/US2014/041809), WO2015/089351 (PCT/US2014/069897), WO2015/089354 (PCT/US2014/069902), WO2015/089364 (PCT/US2014/069925), WO2015/089427 (PCT/US2014/070068), WO2015/089462 (PCT/US2014/070127), WO2015/089419 (PCT/US2014/070057), WO2015/089465 (PCT/US2014/070135), WO2015/089486 (PCT/US2014/070175), WO2015/058052 (PCT/US2014/061077), WO2015/070083 (PCT/US2014/064663), WO2015/089354 (PCT/US2014/069902), WO2015/089351 (PCT/US2014/069897), WO2015/089364 (PCT/US2014/069925), WO2015/089427 (PCT/US2014/070068), WO2015/089473 (PCT/US2014/070152), WO2015/089486 (PCT/US2014/070175), WO2016/049258 (PCT/US2015/051830), WO2016/094867 (PCT/US2015/065385), WO2016/094872 (PCT/US2015/065393), WO2016/094874 (PCT/US2015/065396), WO2016/106244 (PCT/US2015/067177).
Mention is also made of U.S. application 62/180,709, 17 Jun. 2015, PROTECTED GUIDE RNAS (PGRNAS); U.S. application 62/091,455, filed, 12 Dec. 2014, PROTECTED GUIDE RNAS (PGRNAS); U.S. application 62/096,708, 24 Dec. 2014, PROTECTED GUIDE RNAS (PGRNAS); U.S. applications 62/091,462, 12 Dec. 2014, 62/096,324, 23 Dec. 2014, 62/180,681, 17 Jun. 2015, and 62/237,496, 5 Oct. 2015, DEAD GUIDES FOR CRISPR TRANSCRIPTION FACTORS; U.S. application 62/091,456, 12 Dec. 2014 and 62/180,692, 17 Jun. 2015, ESCORTED AND FUNCTIONALIZED GUIDES FOR CRISPR-CAS SYSTEMS; U.S. application 62/091,461, 12 Dec. 2014, DELIVERY, USE AND THERAPEUTIC APPLICATIONS OF THE CRISPR-CAS SYSTEMS AND COMPOSITIONS FOR GENOME EDITING AS TO HEMATOPOIETIC STEM CELLS (HSCs); U.S. application 62/094,903, 19 Dec. 2014, UNBIASED IDENTIFICATION OF DOUBLE-STRAND BREAKS AND GENOMIC REARRANGEMENT BY GENOME-WISE INSERT CAPTURE SEQUENCING; U.S. application 62/096,761, 24 Dec. 2014, ENGINEERING OF SYSTEMS, METHODS AND OPTIMIZED ENZYME AND GUIDE SCAFFOLDS FOR SEQUENCE MANIPULATION; U.S. application 62/098,059, 30 Dec. 2014, 62/181,641, 18 Jun. 2015, and 62/181,667, 18 Jun. 2015, RNA-TARGETING SYSTEM; U.S. application 62/096,656, 24 Dec. 2014 and 62/181,151, 17 Jun. 2015, CRISPR HAVING OR ASSOCIATED WITH DESTABILIZATION DOMAINS; U.S. application 62/096,697, 24 Dec. 2014, CRISPR HAVING OR ASSOCIATED WITH AAV; U.S. application 62/098,158, 30 Dec. 2014, ENGINEERED CRISPR COMPLEX INSERTIONAL TARGETING SYSTEMS; U.S. application 62/151,052, 22 Apr. 2015, CELLULAR TARGETING FOR EXTRACELLULAR EXOSOMAL REPORTING; U.S. application 62/054,490, 24 Sep. 2014, DELIVERY, USE AND THERAPEUTIC APPLICATIONS OF THE CRISPR-CAS SYSTEMS AND COMPOSITIONS FOR TARGETING DISORDERS AND DISEASES USING PARTICLE DELIVERY COMPONENTS; U.S. application 61/939,154, 12 Feb. 2014, SYSTEMS, METHODS AND COMPOSITIONS FOR SEQUENCE MANIPULATION WITH OPTIMIZED FUNCTIONAL CRISPR-CAS SYSTEMS; U.S. application 62/055,484, 25 Sep. 2014, SYSTEMS, METHODS AND COMPOSITIONS FOR SEQUENCE MANIPULATION WITH OPTIMIZED FUNCTIONAL CRISPR-CAS SYSTEMS; U.S. application 62/087,537, 4 Dec. 2014, SYSTEMS, METHODS AND COMPOSITIONS FOR SEQUENCE MANIPULATION WITH OPTIMIZED FUNCTIONAL CRISPR-CAS SYSTEMS; U.S. application 62/054,651, 24 Sep. 2014, DELIVERY, USE AND THERAPEUTIC APPLICATIONS OF THE CRISPR-CAS SYSTEMS AND COMPOSITIONS FOR MODELING COMPETITION OF MULTIPLE CANCER MUTATIONS IN VIVO; U.S. application 62/067,886, 23 Oct. 2014, DELIVERY, USE AND THERAPEUTIC APPLICATIONS OF THE CRISPR-CAS SYSTEMS AND COMPOSITIONS FOR MODELING COMPETITION OF MULTIPLE CANCER MUTATIONS IN VIVO; U.S. applications 62/054,675, 24 Sep. 2014 and 62/181,002, 17 Jun. 2015, DELIVERY, USE AND THERAPEUTIC APPLICATIONS OF THE CRISPR-CAS SYSTEMS AND COMPOSITIONS IN NEURONAL CELLS/TISSUES; U.S. application 62/054,528, 24 Sep. 2014, DELIVERY, USE AND THERAPEUTIC APPLICATIONS OF THE CRISPR-CAS SYSTEMS AND COMPOSITIONS IN IMMUNE DISEASES OR DISORDERS; U.S. application 62/055,454, 25 Sep. 2014, DELIVERY, USE AND THERAPEUTIC APPLICATIONS OF THE CRISPR-CAS SYSTEMS AND COMPOSITIONS FOR TARGETING DISORDERS AND DISEASES USING CELL PENETRATION PEPTIDES (CPP); U.S. application 62/055,460, 25 Sep. 2014, MULTIFUNCTIONAL-CRISPR COMPLEXES AND/OR OPTIMIZED ENZYME LINKED FUNCTIONAL-CRISPR COMPLEXES; U.S. application 62/087,475, 4 Dec. 2014 and 62/181,690, 18 Jun. 2015, FUNCTIONAL SCREENING WITH OPTIMIZED FUNCTIONAL CRISPR-CAS SYSTEMS; U.S. application 62/055,487, 25 Sep. 2014, FUNCTIONAL SCREENING WITH OPTIMIZED FUNCTIONAL CRISPR-CAS SYSTEMS; U.S. application 62/087,546, 4 Dec. 2014 and 62/181,687, 18 Jun. 2015, MULTIFUNCTIONAL CRISPR COMPLEXES AND/OR OPTIMIZED ENZYME LINKED FUNCTIONAL-CRISPR COMPLEXES; and U.S. application 62/098,285, 30 Dec. 2014, CRISPR MEDIATED IN VIVO MODELING AND GENETIC SCREENING OF TUMOR GROWTH AND METASTASIS.
Mention is made of U.S. applications 62/181,659, 18 Jun. 2015 and 62/207,318, 19 Aug. 2015, ENGINEERING AND OPTIMIZATION OF SYSTEMS, METHODS, ENZYME AND GUIDE SCAFFOLDS OF CAS9 ORTHOLOGS AND VARIANTS FOR SEQUENCE MANIPULATION. Mention is made of U.S. applications 62/181,663, 18 Jun. 2015 and 62/245,264, 22 Oct. 2015, NOVEL CRISPR ENZYMES AND SYSTEMS, U.S. applications 62/181,675, 18 Jun. 2015, 62/285,349, 22 Oct. 2015, 62/296,522, 17 Feb. 2016, and 62/320,231, 8 Apr. 2016, NOVEL CRISPR ENZYMES AND SYSTEMS, U.S. application 62/232,067, 24 Sep. 2015, U.S. application Ser. No. 14/975,085, 18 Dec. 2015, European application No. 16150428.7, U.S. application 62/205,733, 16 Aug. 2015, U.S. application 62/201,542, 5 Aug. 2015, U.S. application 62/193,507, 16 Jul. 2015, and U.S. application 62/181,739, 18 Jun. 2015, each entitled NOVEL CRISPR ENZYMES AND SYSTEMS and of U.S. application 62/245,270, 22 Oct. 2015, NOVEL CRISPR ENZYMES AND SYSTEMS. Mention is also made of U.S. application 61/939,256, 12 Feb. 2014, and WO 2015/089473 (PCT/US2014/070152), 12 Dec. 2014, each entitled ENGINEERING OF SYSTEMS, METHODS AND OPTIMIZED GUIDE COMPOSITIONS WITH NEW ARCHITECTURES FOR SEQUENCE MANIPULATION. Mention is also made of PCT/US2015/045504, 15 Aug. 2015, U.S. application 62/180,699, 17 Jun. 2015, and U.S. application 62/038,358, 17 Aug. 2014, each entitled GENOME EDITING USING CAS9 NICKASES.
Each of these patents, patent publications, and applications, and all documents cited therein or during their prosecution (“appln cited documents”) and all documents cited or referenced in the appln cited documents, together with any instructions, descriptions, product specifications, and product sheets for any products mentioned therein or in any document therein and incorporated by reference herein, are hereby incorporated herein by reference, and may be employed in the practice of the invention. All documents (e.g., these patents, patent publications and applications and the appln cited documents) are incorporated herein by reference to the same extent as if each individual document was specifically and individually indicated to be incorporated by reference.
In particular embodiments, pre-complexed guide RNA and CRISPR effector protein, (optionally, adenosine deaminase fused to a CRISPR protein or an adaptor) are delivered as a ribonucleoprotein (RNP). RNPs have the advantage that they lead to rapid editing effects even more so than the RNA method because this process avoids the need for transcription. An important advantage is that both RNP delivery is transient, reducing off-target effects and toxicity issues. Efficient genome editing in different cell types has been observed by Kim et al. (2014, Genome Res. 24(6):1012-9), Paix et al. (2015, Genetics 204(1):47-54), Chu et al. (2016, BMC Biotechnol. 16:4), and Wang et al. (2013, Cell. 9; 153(4):910-8).
In particular embodiments, the ribonucleoprotein is delivered by way of a polypeptide-based shuttle agent as described in WO2016161516. WO2016161516 describes efficient transduction of polypeptide cargos using synthetic peptides comprising an endosome leakage domain (ELD) operably linked to a cell penetrating domain (CPD), to a histidine-rich domain and a CPD. Similarly these polypeptides can be used for the delivery of CRISPR-effector based RNPs in eukaryotic cells.
Tale Systems
As disclosed herein editing can be made by way of the transcription activator-like effector nucleases (TALENs) system. Transcription activator-like effectors (TALEs) can be engineered to bind practically any desired DNA sequence. Exemplary methods of genome editing using the TALEN system can be found for example in Cermak T. Doyle E L. Christian M. Wang L. Zhang Y. Schmidt C, et al. Efficient design and assembly of custom TALEN and other TAL effector-based constructs for DNA targeting. Nucleic Acids Res. 2011; 39:e82; Zhang F. Cong L. Lodato S. Kosuri S. Church G M. Arlotta P Efficient construction of sequence-specific TAL effectors for modulating mammalian transcription. Nat Biotechnol. 2011; 29:149-153 and U.S. Pat. Nos. 8,450,471, 8,440,431 and 8,440,432, all of which are specifically incorporated by reference.
In advantageous embodiments of the invention, the methods provided herein use isolated, non-naturally occurring, recombinant or engineered DNA binding proteins that comprise TALE monomers as a part of their organizational structure that enable the targeting of nucleic acid sequences with improved efficiency and expanded specificity.
Naturally occurring TALEs or “wild type TALEs” are nucleic acid binding proteins secreted by numerous species of proteobacteria. TALE polypeptides contain a nucleic acid binding domain composed of tandem repeats of highly conserved monomer polypeptides that are predominantly 33, 34 or 35 amino acids in length and that differ from each other mainly in amino acid positions 12 and 13. In advantageous embodiments the nucleic acid is DNA. As used herein, the term “polypeptide monomers”, or “TALE monomers” will be used to refer to the highly conserved repetitive polypeptide sequences within the TALE nucleic acid binding domain and the term “repeat variable di-residues” or “RVD” will be used to refer to the highly variable amino acids at positions 12 and 13 of the polypeptide monomers. As provided throughout the disclosure, the amino acid residues of the RVD are depicted using the IUPAC single letter code for amino acids. A general representation of a TALE monomer which is comprised within the DNA binding domain is X1-11-(X12×13)-X14-33 or 34 or 35, where the subscript indicates the amino acid position and X represents any amino acid. X12×13 indicate the RVDs. In some polypeptide monomers, the variable amino acid at position 13 is missing or absent and in such polypeptide monomers, the RVD consists of a single amino acid. In such cases the RVD may be alternatively represented as X*, where X represents X12 and (*) indicates that X13 is absent. The DNA binding domain comprises several repeats of TALE monomers and this may be represented as (X1-11-(X12×13)-X14-33 or 34 or 35)z, where in an advantageous embodiment, z is at least 5 to 40. In a further advantageous embodiment, z is at least 10 to 26.
The TALE monomers have a nucleotide binding affinity that is determined by the identity of the amino acids in its RVD. For example, polypeptide monomers with an RVD of NI preferentially bind to adenine (A), polypeptide monomers with an RVD of NG preferentially bind to thymine (T), polypeptide monomers with an RVD of HD preferentially bind to cytosine (C) and polypeptide monomers with an RVD of NN preferentially bind to both adenine (A) and guanine (G). In yet another embodiment of the invention, polypeptide monomers with an RVD of IG preferentially bind to T. Thus, the number and order of the polypeptide monomer repeats in the nucleic acid binding domain of a TALE determines its nucleic acid target specificity. In still further embodiments of the invention, polypeptide monomers with an RVD of NS recognize all four base pairs and may bind to A, T, G or C. The structure and function of TALEs is further described in, for example, Moscou et al., Science 326:1501 (2009); Boch et al., Science 326:1509-1512 (2009); and Zhang et al., Nature Biotechnology 29:149-153 (2011), each of which is incorporated by reference in its entirety.
The TALE polypeptides used in methods of the invention are isolated, non-naturally occurring, recombinant or engineered nucleic acid-binding proteins that have nucleic acid or DNA binding regions containing polypeptide monomer repeats that are designed to target specific nucleic acid sequences.
As described herein, polypeptide monomers having an RVD of HN or NH preferentially bind to guanine and thereby allow the generation of TALE polypeptides with high binding specificity for guanine containing target nucleic acid sequences. In a preferred embodiment of the invention, polypeptide monomers having RVDs RN, NN, NK, SN, NH, KN, HN, NQ, HH, RG, KH, RH and SS preferentially bind to guanine. In a much more advantageous embodiment of the invention, polypeptide monomers having RVDs RN, NK, NQ, HH, KH, RH, SS and SN preferentially bind to guanine and thereby allow the generation of TALE polypeptides with high binding specificity for guanine containing target nucleic acid sequences. In an even more advantageous embodiment of the invention, polypeptide monomers having RVDs HH, KH, NH, NK, NQ, RH, RN and SS preferentially bind to guanine and thereby allow the generation of TALE polypeptides with high binding specificity for guanine containing target nucleic acid sequences. In a further advantageous embodiment, the RVDs that have high binding specificity for guanine are RN, NH RH and KH. Furthermore, polypeptide monomers having an RVD of NV preferentially bind to adenine and guanine. In more preferred embodiments of the invention, polypeptide monomers having RVDs of H*, HA, KA, N*, NA, NC, NS, RA, and S* bind to adenine, guanine, cytosine and thymine with comparable affinity.
The predetermined N-terminal to C-terminal order of the one or more polypeptide monomers of the nucleic acid or DNA binding domain determines the corresponding predetermined target nucleic acid sequence to which the TALE polypeptides will bind. As used herein the polypeptide monomers and at least one or more half polypeptide monomers are “specifically ordered to target” the genomic locus or gene of interest. In plant genomes, the natural TALE-binding sites always begin with a thymine (T), which may be specified by a cryptic signal within the non-repetitive N-terminus of the TALE polypeptide; in some cases this region may be referred to as repeat 0. In animal genomes, TALE binding sites do not necessarily have to begin with a thymine (T) and TALE polypeptides may target DNA sequences that begin with T, A, G or C. The tandem repeat of TALE monomers always ends with a half-length repeat or a stretch of sequence that may share identity with only the first 20 amino acids of a repetitive full length TALE monomer and this half repeat may be referred to as a half-monomer (FIG. 8), which is included in the term “TALE monomer”. Therefore, it follows that the length of the nucleic acid or DNA being targeted is equal to the number of full polypeptide monomers plus two.
As described in Zhang et al., Nature Biotechnology 29:149-153 (2011), TALE polypeptide binding efficiency may be increased by including amino acid sequences from the “capping regions” that are directly N-terminal or C-terminal of the DNA binding region of naturally occurring TALEs into the engineered TALEs at positions N-terminal or C-terminal of the engineered TALE DNA binding region. Thus, in certain embodiments, the TALE polypeptides described herein further comprise an N-terminal capping region and/or a C-terminal capping region.
An exemplary amino acid sequence of a N-terminal capping region is:







(SEQ. I.D. No. 1)


M D P I R S R T P S P A R E L L S G P Q P D G V Q


 


P T A D R G V S P P A G G P L D G L P A R R T M S


 


R T R L P S P P A P S P A F S A D S F S D L L R Q


 


F D P S L F N T S L F D S L P P F G A H H T E A A


 


T G E W D E V Q S G L R A A D A P P P T M R V A V


 


T A A R P P R A K P A P R R R A A Q P S D A S P A


 


A Q V D L R T L G Y S Q Q Q Q E K I K P K V R S T


 


V A Q H H E A L V G H G F T H A H I V A L S Q H P


 


A A L G T V A V K Y Q D M I A A L P E A T H E A I


 


V G V G K Q W S G A R A L E A L L T V A G E L R G


 


P P L Q L D T G Q L L K I A K R G G V T A V E A V


 


H A W R N A L T G A P L N






An exemplary amino acid sequence of a C-terminal capping region is:







(SEQ. I.D. No. 2)


R P A L E S I V A Q L S R P D P A L A A L T N D H


 


L V A L A C L G G R P A L D A V K K G L P H A P A


 


L I K R T N R R I P E R T S H R V A D H A Q V V R


 


V L G F F Q C H S H P A Q A F D D A M T Q F G M S


 


R H G L L Q L F R R V G V T E L E A R S G T L P P


 


A S Q R W D R I L Q A S G M K R A K P S P T S T Q


 


T P D Q A S L H A F A D S L E R D L D A P S P M H


 


E G D Q T R A S






As used herein the predetermined “N-terminus” to “C terminus” orientation of the N-terminal capping region, the DNA binding domain comprising the repeat TALE monomers and the C-terminal capping region provide structural basis for the organization of different domains in the d-TALEs or polypeptides of the invention.
The entire N-terminal and/or C-terminal capping regions are not necessary to enhance the binding activity of the DNA binding region. Therefore, in certain embodiments, fragments of the N-terminal and/or C-terminal capping regions are included in the TALE polypeptides described herein.
In certain embodiments, the TALE polypeptides described herein contain a N-terminal capping region fragment that included at least 10, 20, 30, 40, 50, 54, 60, 70, 80, 87, 90, 94, 100, 102, 110, 117, 120, 130, 140, 147, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260 or 270 amino acids of an N-terminal capping region. In certain embodiments, the N-terminal capping region fragment amino acids are of the C-terminus (the DNA-binding region proximal end) of an N-terminal capping region. As described in Zhang et al., Nature Biotechnology 29:149-153 (2011), N-terminal capping region fragments that include the C-terminal 240 amino acids enhance binding activity equal to the full length capping region, while fragments that include the C-terminal 147 amino acids retain greater than 80% of the efficacy of the full length capping region, and fragments that include the C-terminal 117 amino acids retain greater than 50% of the activity of the full-length capping region.
In some embodiments, the TALE polypeptides described herein contain a C-terminal capping region fragment that included at least 6, 10, 20, 30, 37, 40, 50, 60, 68, 70, 80, 90, 100, 110, 120, 127, 130, 140, 150, 155, 160, 170, 180 amino acids of a C-terminal capping region. In certain embodiments, the C-terminal capping region fragment amino acids are of the N-terminus (the DNA-binding region proximal end) of a C-terminal capping region. As described in Zhang et al., Nature Biotechnology 29:149-153 (2011), C-terminal capping region fragments that include the C-terminal 68 amino acids enhance binding activity equal to the full length capping region, while fragments that include the C-terminal 20 amino acids retain greater than 50% of the efficacy of the full length capping region.
In certain embodiments, the capping regions of the TALE polypeptides described herein do not need to have identical sequences to the capping region sequences provided herein. Thus, in some embodiments, the capping region of the TALE polypeptides described herein have sequences that are at least 50%, 60%, 70%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical or share identity to the capping region amino acid sequences provided herein. Sequence identity is related to sequence homology. Homology comparisons may be conducted by eye, or more usually, with the aid of readily available sequence comparison programs. These commercially available computer programs may calculate percent (%) homology between two or more sequences and may also calculate the sequence identity shared by two or more amino acid or nucleic acid sequences. In some preferred embodiments, the capping region of the TALE polypeptides described herein have sequences that are at least 95% identical or share identity to the capping region amino acid sequences provided herein.
Sequence homologies may be generated by any of a number of computer programs known in the art, which include but are not limited to BLAST or FASTA. Suitable computer program for carrying out alignments like the GCG Wisconsin Bestfit package may also be used. Once the software has produced an optimal alignment, it is possible to calculate % homology, preferably % sequence identity. The software typically does this as part of the sequence comparison and generates a numerical result.
In advantageous embodiments described herein, the TALE polypeptides of the invention include a nucleic acid binding domain linked to the one or more effector domains. The terms “effector domain” or “regulatory and functional domain” refer to a polypeptide sequence that has an activity other than binding to the nucleic acid sequence recognized by the nucleic acid binding domain. By combining a nucleic acid binding domain with one or more effector domains, the polypeptides of the invention may be used to target the one or more functions or activities mediated by the effector domain to a particular target DNA sequence to which the nucleic acid binding domain specifically binds.
In some embodiments of the TALE polypeptides described herein, the activity mediated by the effector domain is a biological activity. For example, in some embodiments the effector domain is a transcriptional inhibitor (i.e., a repressor domain), such as an mSin interaction domain (SID). SID4X domain or a Kruppel-associated box (KRAB) or fragments of the KRAB domain. In some embodiments the effector domain is an enhancer of transcription (i.e. an activation domain), such as the VP16, VP64 or p65 activation domain. In some embodiments, the nucleic acid binding is linked, for example, with an effector domain that includes but is not limited to a transposase, integrase, recombinase, resolvase, invertase, protease, DNA methyltransferase, DNA demethylase, histone acetylase, histone deacetylase, nuclease, transcriptional repressor, transcriptional activator, transcription factor recruiting, protein nuclear-localization signal or cellular uptake signal.
In some embodiments, the effector domain is a protein domain which exhibits activities which include but are not limited to transposase activity, integrase activity, recombinase activity, resolvase activity, invertase activity, protease activity, DNA methyltransferase activity, DNA demethylase activity, histone acetylase activity, histone deacetylase activity, nuclease activity, nuclear-localization signaling activity, transcriptional repressor activity, transcriptional activator activity, transcription factor recruiting activity, or cellular uptake signaling activity. Other preferred embodiments of the invention may include any combination the activities described herein.
ZN-Finger Nucleases
Other preferred tools for genome editing for use in the context of this invention include zinc finger systems and TALE systems. One type of programmable DNA-binding domain is provided by artificial zinc-finger (ZF) technology, which involves arrays of ZF modules to target new DNA-binding sites in the genome. Each finger module in a ZF array targets three DNA bases. A customized array of individual zinc finger domains is assembled into a ZF protein (ZFP).
ZFPs can comprise a functional domain. The first synthetic zinc finger nucleases (ZFNs) were developed by fusing a ZF protein to the catalytic domain of the Type IIS restriction enzyme FokI. (Kim, Y. G. et al., 1994, Chimeric restriction endonuclease, Proc. Natl. Acad. Sci. U.S.A. 91, 883-887; Kim, Y. G. et al., 1996, Hybrid restriction enzymes: zinc finger fusions to Fok I cleavage domain. Proc. Natl. Acad. Sci. U.S.A. 93, 1156-1160). Increased cleavage specificity can be attained with decreased off target activity by use of paired ZFN heterodimers, each targeting different nucleotide sequences separated by a short spacer. (Doyon, Y. et al., 2011, Enhancing zinc-finger-nuclease activity with improved obligate heterodimeric architectures. Nat. Methods 8, 74-79). ZFPs can also be designed as transcription activators and repressors and have been used to target many genes in a wide variety of organisms. Exemplary methods of genome editing using ZFNs can be found for example in U.S. Pat. Nos. 6,534,261, 6,607,882, 6,746,838, 6,794,136, 6,824,978, 6,866,997, 6,933,113, 6,979,539, 7,013,219, 7,030,215, 7,220,719, 7,241,573, 7,241,574, 7,585,849, 7,595,376, 6,903,185, and 6,479,626, all of which are specifically incorporated by reference.
Meganucleases
As disclosed herein editing can be made by way of meganucleases, which are endodeoxyribonucleases characterized by a large recognition site (double-stranded DNA sequences of 12 to 40 base pairs). Exemplary method for using meganucleases can be found in U.S. Pat. Nos. 8,163,514; 8,133,697; 8,021,867; 8,119,361; 8,119,381; 8,124,369; and 8,129,134, which are specifically incorporated by reference.
In certain embodiments, any of the nucleases, including the modified nucleases as described herein, may be used in the methods, compositions, and kits according to the invention. In particular embodiments, nuclease activity of an unmodified nuclease may be compared with nuclease activity of any of the modified nucleases as described herein, e.g. to compare for instance off-target or on-target effects. Alternatively, nuclease activity (or a modified activity as described herein) of different modified nucleases may be compared, e.g. to compare for instance off-target or on-target effects.
Also provided herein are compositions for use in carrying out the methods of the invention. More particularly, non-naturally occurring or engineered compositions are provided which comprise one or more of the elements required to ensure genomic perturbation. In particular embodiments, the compositions comprise one or more of the (modified) DNA binding protein, and/or a guide RNA. In particular embodiments, the composition comprises a vector. In further particular embodiments, the vector comprises a polynucleotide encoding a gRNA. In particular embodiments, the vector comprises two or more guide RNAs. The two or more guide RNAs may target a different target (so as to ensure multiplex targeting) or the same target, in which case the different guide RNAs will target different sequences within the same target sequence. Where provided in a vector the different guide RNAs may be under common control of the same promotor, or may be each be under control of the same or different promoters.
In certain embodiments, a modulant may comprise silencing one or more endogenous genes.
As used herein, “gene silencing” or “gene silenced” in reference to an activity of an RNAi molecule, for example a siRNA or miRNA refers to a decrease in the mRNA level in a cell for a target gene by at least about 5%, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, about 99%, about 100% of the mRNA level found in the cell without the presence of the miRNA or RNA interference molecule. In one preferred embodiment, the mRNA levels are decreased by at least about 70%, about 80%, about 90%, about 95%, about 99%, about 100%.
As used herein, the term “RNAi” refers to any type of interfering RNA, including but not limited to, siRNAi, shRNAi, endogenous microRNA and artificial microRNA. For instance, it includes sequences previously identified as siRNA, regardless of the mechanism of down-stream processing of the RNA (i.e. although siRNAs are believed to have a specific method of in vivo processing resulting in the cleavage of mRNA, such sequences can be incorporated into the vectors in the context of the flanking sequences described herein). The term “RNAi” can include both gene silencing RNAi molecules, and also RNAi effector molecules which activate the expression of a gene.
As used herein, a “siRNA” refers to a nucleic acid that forms a double stranded RNA, which double stranded RNA has the ability to reduce or inhibit expression of a gene or target gene when the siRNA is present or expressed in the same cell as the target gene. The double stranded RNA siRNA can be formed by the complementary strands. In one embodiment, a siRNA refers to a nucleic acid that can form a double stranded siRNA. The sequence of the siRNA can correspond to the full-length target gene, or a subsequence thereof. Typically, the siRNA is at least about 15-50 nucleotides in length (e.g., each complementary sequence of the double stranded siRNA is about 15-50 nucleotides in length, and the double stranded siRNA is about 15-50 base pairs in length, preferably about 19-30 base nucleotides, preferably about 20-25 nucleotides in length, e.g., 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 nucleotides in length).
As used herein “shRNA” or “small hairpin RNA” (also called stem loop) is a type of siRNA. In one embodiment, these shRNAs are composed of a short, e.g. about 19 to about 25 nucleotide, antisense strand, followed by a nucleotide loop of about 5 to about 9 nucleotides, and the analogous sense strand. Alternatively, the sense strand can precede the nucleotide loop structure and the antisense strand can follow.
The terms “microRNA” or “miRNA” are used interchangeably herein are endogenous RNAs, some of which are known to regulate the expression of protein-coding genes at the posttranscriptional level. Endogenous microRNAs are small RNAs naturally present in the genome that are capable of modulating the productive utilization of mRNA. The term artificial microRNA includes any type of RNA sequence, other than endogenous microRNA, which is capable of modulating the productive utilization of mRNA. MicroRNA sequences have been described in publications such as Lim, et al., Genes & Development, 17, p. 991-1008 (2003), Lim et al Science 299, 1540 (2003), Lee and Ambros Science, 294, 862 (2001), Lau et al., Science 294, 858-861 (2001), Lagos-Quintana et al, Current Biology, 12, 735-739 (2002), Lagos Quintana et al, Science 294, 853-857 (2001), and Lagos-Quintana et al, RNA, 9, 175-179 (2003), which are incorporated by reference. Multiple microRNAs can also be incorporated into a precursor molecule. Furthermore, miRNA-like stem-loops can be expressed in cells as a vehicle to deliver artificial miRNAs and short interfering RNAs (siRNAs) for the purpose of modulating the expression of endogenous genes through the miRNA and or RNAi pathways.
As used herein, “double stranded RNA” or “dsRNA” refers to RNA molecules that are comprised of two strands. Double-stranded molecules include those comprised of a single RNA molecule that doubles back on itself to form a two-stranded structure. For example, the stem loop structure of the progenitor molecules from which the single-stranded miRNA is derived, called the pre-miRNA (Bartel et al. 2004. Cell 1 16:281-297), comprises a dsRNA molecule.
In certain embodiments, a modulant may comprise (i) a DNA-binding portion configured to specifically bind to the endogenous gene and (ii) an effector domain mediating a biological activity.
In certain embodiments, the DNA-binding portion may comprises a zinc finger protein or DNA-binding domain thereof, a transcription activator-like effector (TALE) protein or DNA-binding domain thereof, or an RNA-guided protein or DNA-binding domain thereof.
In certain embodiments, the DNA-binding portion may comprise (i) Cas9 or Cpf1 or any Cas protein described herein modified to eliminate its nuclease activity, or (ii) DNA-binding domain of Cas9 or Cpf1 or any Cas protein described herein.
In some embodiments, the effector domain may be a transcriptional inhibitor (i.e., a repressor domain), such as an mSin interaction domain (SID). SID4X domain or a Kruppel-associated box (KRAB) or fragments of the KRAB domain. In some embodiments the effector domain may be an enhancer of transcription (i.e. an activation domain), such as the VP16, VP64 or p65 activation domain. In some embodiments, the nucleic acid binding portion may be linked, for example, with an effector domain that includes but is not limited to a transposase, integrase, recombinase, resolvase, invertase, protease, DNA methyltransferase, DNA demethylase, histone acetylase, histone deacetylase, nuclease, transcriptional repressor, transcriptional activator, transcription factor recruiting, protein nuclear-localization signal or cellular uptake signal. In some embodiments, the effector domain may be a protein domain which exhibits activities which include but are not limited to transposase activity, integrase activity, recombinase activity, resolvase activity, invertase activity, protease activity, DNA methyltransferase activity, DNA demethylase activity, histone acetylase activity, histone deacetylase activity, nuclease activity, nuclear-localization signaling activity, transcriptional repressor activity, transcriptional activator activity, transcription factor recruiting activity, or cellular uptake signaling activity.
Other preferred embodiments of the invention may include any combination the activities described herein. In certain embodiments, a modulant may comprise introducing one or more endogenous genes and/or one or more exogenous genes in expressible format into an immune cell, in accordance with the practice of transgenesis as taught elsewhere in this specification.
The term “immune cell” as used throughout this specification generally encompasses any cell derived from a hematopoietic stem cell that plays a role in the immune response. The term is intended to encompass immune cells both of the innate or adaptive immune system. The immune cell as referred to herein may be a leukocyte, at any stage of differentiation (e.g., a stem cell, a progenitor cell, a mature cell) or any activation stage. Immune cells include lymphocytes (such as natural killer cells, T cells (including, e.g., thymocytes, Th or Tc; Th1, Th2, Th17, Thαβ, CD4+, CD8+, effector Th, memory Th, regulatory Th, CD4+/CD8+ thymocytes, CD4−/CD8− thymocytes, γδ T cells, etc.) or B-cells (including, e.g., pro-B cells, early pro-B cells, late pro-B cells, pre-B cells, large pre-B cells, small pre-B cells, immature or mature B-cells, producing antibodies of any isotype, T1 B-cells, T2, B-cells, naïve B-cells, GC B-cells, plasmablasts, memory B-cells, plasma cells, follicular B-cells, marginal zone B-cells, B-1 cells, B-2 cells, regulatory B cells, etc.), such as for instance, monocytes (including, e.g., classical, non-classical, or intermediate monocytes), (segmented or banded) neutrophils, eosinophils, basophils, mast cells, histiocytes, microglia, including various subtypes, maturation, differentiation, or activation stages, such as for instance hematopoietic stem cells, myeloid progenitors, lymphoid progenitors, myeloblasts, promyelocytes, myelocytes, metamyelocytes, monoblasts, promonocytes, lymphoblasts, prolymphocytes, small lymphocytes, macrophages (including, e.g., Kupffer cells, stellate macrophages, M1 or M2 macrophages), (myeloid or lymphoid) dendritic cells (including, e.g., Langerhans cells, conventional or myeloid dendritic cells, plasmacytoid dendritic cells, mDC-1, mDC-2, Mo-DC, HP-DC, veiled cells), granulocytes, polymorphonuclear cells, antigen-presenting cells (APC), etc.
The invention provides compositions and methods for modulating T cell and intestinal or respiratory epithelial cell balance. As used herein, the term “modulating” includes up-regulation of, or otherwise increasing, the expression of one or more genes, down-regulation of, or otherwise decreasing, the expression of one or more genes, inhibiting or otherwise decreasing the expression, activity and/or function of one or more gene products, and/or enhancing or otherwise increasing the expression, activity and/or function of one or more gene products. The term “modulate” broadly denotes a qualitative and/or quantitative alteration, change or variation in that which is being modulated. Where modulation can be assessed quantitatively—for example, where modulation comprises or consists of a change in a quantifiable variable such as a quantifiable property of a cell or where a quantifiable variable provides a suitable surrogate for the modulation—modulation specifically encompasses both increase (e.g., activation) or decrease (e.g., inhibition) in the measured variable. The term encompasses any extent of such modulation, e.g., any extent of such increase or decrease, and may more particularly refer to statistically significant increase or decrease in the measured variable. By means of example, modulation may encompass an increase in the value of the measured variable by at least about 10%, e.g., by at least about 20%, preferably by at least about 30%, e.g., by at least about 40%, more preferably by at least about 50%, e.g., by at least about 75%, even more preferably by at least about 100%, e.g., by at least about 150%, 200%, 250%, 300%, 400% or by at least about 500%, compared to a reference situation without the modulation; or modulation may encompass a decrease or reduction in the value of the measured variable by at least about 10%, e.g., by at least about 20%, by at least about 30%, e.g., by at least about 40%, by at least about 50%, e.g., by at least about 60%, by at least about 70%, e.g., by at least about 80%, by at least about 90%, e.g., by at least about 95%, such as by at least about 96%, 97%, 98%, 99% or even by 100%, compared to a reference situation without the modulation. Preferably, modulation may be specific or selective, hence, one or more desired phenotypic aspects of a cell or cell population may be modulated without substantially altering other (unintended, undesired) phenotypic aspect(s).
In certain embodiments, an modulant may comprise altering expression and/or activity of one or more endogenous genes of the cell. The term “altered expression” denotes that the modification of the cell alters, i.e., changes or modulates, the expression of the recited gene(s) or polypeptides(s). The term “altered expression” encompasses any direction and any extent of the alteration. Hence, “altered expression” may reflect qualitative and/or quantitative change(s) of expression, and specifically encompasses both increase (e.g., activation or stimulation) or decrease (e.g., inhibition) of expression.
“Modulating” can also involve effecting a change (which can either be an increase or a decrease) in affinity, avidity, specificity and/or selectivity of a target gene or cell, such as a cell surface gene (e.g., receptor or ligand). “Modulating” can also mean effecting a change with respect to one or more biological or physiological mechanisms, effects, responses, functions, pathways or activities in which the target gene or cell (or in which its substrate(s), ligand(s) or pathway(s) are involved, such as its signaling pathway or metabolic pathway and their associated biological or physiological effects) is involved. Again, as will be clear to the skilled person, such an action as an agonist or an antagonist can be determined in any suitable manner and/or using any suitable assay known or described herein (e.g., in vitro or cellular assay), depending on the target gene or cell involved.
Modulating can, for example, also involve allosteric modulation of the target and/or reducing or inhibiting the binding of the target to one of its substrates or ligands and/or competing with a natural ligand, substrate for binding to the target. Modulating can also involve activating the target or the mechanism or pathway in which it is involved. Modulating can for example also involve effecting a change in respect of the folding or confirmation of the target, or in respect of the ability of the target to fold, to change its conformation (for example, upon binding of a ligand), to associate with other (sub)units, or to disassociate. Modulating can for example also involve effecting a change in the ability of the target to signal, phosphorylate, dephosphorylate, and the like.
As used herein, the term “modulating T cell balance” includes the modulation of any of a variety of T cell-related functions and/or activities, including by way of non-limiting example, controlling or otherwise influencing the networks that regulate T cell differentiation; controlling or otherwise influencing the networks that regulate T cell maintenance, for example, over the lifespan of a T cell; controlling or otherwise influencing the networks that regulate T cell function; controlling or otherwise influencing the networks that regulate helper T cell (Th cell) differentiation; controlling or otherwise influencing the networks that regulate Th cell maintenance, for example, over the lifespan of a Th cell; controlling or otherwise influencing the networks that regulate Th cell function; controlling or otherwise influencing the networks that regulate Th17 cell differentiation; controlling or otherwise influencing the networks that regulate Th17 cell maintenance, for example, over the lifespan of a Th17 cell; controlling or otherwise influencing the networks that regulate Th17 cell function; controlling or otherwise influencing the networks that regulate regulatory T cell (Treg) differentiation; controlling or otherwise influencing the networks that regulate Treg cell maintenance, for example, over the lifespan of a Treg cell; controlling or otherwise influencing the networks that regulate Treg cell function; controlling or otherwise influencing the networks that regulate other CD4+ T cell differentiation; controlling or otherwise influencing the networks that regulate other CD4+ T cell maintenance; controlling or otherwise influencing the networks that regulate other CD4+ T cell function; manipulating or otherwise influencing the ratio of T cells such as, for example, manipulating or otherwise influencing the ratio of Th17 cells to other T cell types such as Tregs or other CD4+ T cells; manipulating or otherwise influencing the ratio of different types of Th17 cells such as, for example, pathogenic Th17 cells and non-pathogenic Th17 cells; manipulating or otherwise influencing at least one function or biological activity of a T cell; manipulating or otherwise influencing at least one function or biological activity of Th cell; manipulating or otherwise influencing at least one function or biological activity of a Treg cell; manipulating or otherwise influencing at least one function or biological activity of a Th17 cell; and/or manipulating or otherwise influencing at least one function or biological activity of another CD4+ T cell.
As used herein, the term “modulating enteric cell balance” comprises cell differentiation types, rates, activity levels, death rate, and more.
The invention provides T cell modulating agents that modulate T cell balance. For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to regulate, influence or otherwise impact the level(s) of and/or balance between T cell types, e.g., between Th17 and other T cell types, for example, regulatory T cells (Tregs), and/or Th17 activity and inflammatory potential.
As used herein, terms such as “Th17 cell” and/or “Th17 phenotype” and all grammatical variations thereof refer to a differentiated T helper cell that expresses one or more cytokines selected from the group the consisting of interleukin 17A (IL-17A), interleukin 17F (IL-17F), and interleukin 17A/F heterodimer (IL17-AF). As used herein, terms such as “Th1 cell” and/or “Th1 phenotype” and all grammatical variations thereof refer to a differentiated T helper cell that expresses interferon gamma (IFNγ). As used herein, terms such as “Th2 cell” and/or “Th2 phenotype” and all grammatical variations thereof refer to a differentiated T helper cell that expresses one or more cytokines selected from the group the consisting of interleukin 4 (IL-4), interleukin 5 (IL-5) and interleukin 13 (IL-13). As used herein, terms such as “Treg cell” and/or “Treg phenotype” and all grammatical variations thereof refer to a differentiated T cell that expresses Foxp3.
For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to regulate, influence or otherwise impact the level of and/or balance between Th17 phenotypes, and/or Th17 activity and inflammatory potential. Suitable T cell modulating agents include an antibody, a soluble polypeptide, a polypeptide agent, a peptide agent, a nucleic acid agent, a nucleic acid ligand, or a small molecule agent.
For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to regulate, influence or otherwise impact the level of and/or balance between Th17 cell types, e.g., between pathogenic and non-pathogenic Th17 cells. For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to regulate, influence or otherwise impact the level of and/or balance between pathogenic and non-pathogenic Th17 activity.
For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to influence or otherwise impact the differentiation of a population of T cells, for example toward Th17 cells, with or without a specific pathogenic distinction, or away from Th17 cells, with or without a specific pathogenic distinction.
For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to influence or otherwise impact the differentiation of a population of T cells, for example toward a non-Th17 T cell subset or away from a non-Th17 cell subset. For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to induce T cell plasticity, i.e., converting Th17 cells into a different subtype, or into a new state.
For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to induce T cell plasticity, e.g., converting Th17 cells into a different subtype, or into a new state.
For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to achieve any combination of the above.
The terms “pathogenic” or “non-pathogenic” as used herein are not to be construed as implying that one cell phenotype is more desirable than the other.
In some embodiments, the invention provides a method of activating therapeutic immunity by exploiting the blockade of immune checkpoints. The progression of a productive immune response requires that a number of immunological checkpoints be passed. Immunity response is regulated by the counterbalancing of stimulatory and inhibitory signal.
One skilled in the art will appreciate that the T cell modulating agents have a variety of uses. For example, the T cell modulating agents are used as therapeutic agents as described herein. The T cell modulating agents can be used as reagents in screening assays, diagnostic kits or as diagnostic tools, or these T cell modulating agents can be used in competition assays to generate therapeutic reagents.
Adoptive Cell Transfer (ACT)
In certain embodiments, a cell-based therapeutic includes engraftment of the cells of the present invention (e.g., tuft cells). As used herein, the term “engraft” or “engraftment” refers to the process of cell incorporation into a tissue of interest in vivo through contact with existing cells of the tissue.
Given the linkage between T cells and epithelial cell differentiation, function and activity, the invention also contemplates the adoptive cell transfer for the modulation of epithelial cells. Adoptive cell therapy or adoptive cell transfer (ACT) can refer to the transfer of cells, most commonly immune-derived cells, back into the same patient or into a new recipient host with the goal of transferring the immunologic functionality and characteristics into the new host. If possible, use of autologous cells helps the recipient by minimizing GVHD issues. The adoptive transfer of autologous tumor infiltrating lymphocytes (TIL) (Besser et al., (2010) Clin. Cancer Res 16 (9) 2646-55; Dudley et al., (2002) Science 298 (5594): 850-4; and Dudley et al., (2005) Journal of Clinical Oncology 23 (10): 2346-57.) or genetically re-directed peripheral blood mononuclear cells (Johnson et al., (2009) Blood 114 (3): 535-46; and Morgan et al., (2006) Science 314(5796) 126-9) has been used to successfully treat patients with advanced solid tumors, including melanoma and colorectal carcinoma, as well as patients with CD19-expressing hematologic malignancies (Kalos et al., (2011) Science Translational Medicine 3 (95): 95ra73).
Aspects of the invention involve the adoptive transfer of immune system cells, such as T cells, specific for selected antigens, such as tumor associated antigens (see Maus et al., 2014, Adoptive Immunotherapy for Cancer or Viruses, Annual Review of Immunology, Vol. 32: 189-225; Rosenberg and Restifo, 2015, Adoptive cell transfer as personalized immunotherapy for human cancer, Science Vol. 348 no. 6230 pp. 62-68; Restifo et al., 2015, Adoptive immunotherapy for cancer: harnessing the T cell response. Nat. Rev. Immunol. 12(4): 269-281; and Jenson and Riddell, 2014, Design and implementation of adoptive therapy with chimeric antigen receptor-modified T cells. Immunol Rev. 257(1): 127-144). Various strategies may, for example, be employed to genetically modify T cells by altering the specificity of the T cell receptor (TCR), for example, by introducing new TCR a and R chains with selected peptide specificity (see U.S. Pat. No. 8,697,854; PCT Patent Publications: WO2003020763, WO2004033685, WO2004044004, WO2005114215, WO2006000830, WO2008038002, WO2008039818, WO2004074322, WO2005113595, WO2006125962, WO2013166321, WO2013039889, WO2014018863, WO2014083173; U.S. Pat. No. 8,088,379).
As an alternative to, or addition to, TCR modifications, chimeric antigen receptors (CARs) may be used in order to generate immunoresponsive cells, such as T cells, specific for selected targets, such as malignant cells, with a wide variety of receptor chimera constructs having been described (see U.S. Pat. Nos. 5,843,728; 5,851,828; 5,912,170; 6,004,811; 6,284,240; 6,392,013; 6,410,014; 6,753,162; 8,211,422; and, PCT Publication WO9215322).
In general, CARs are comprised of an extracellular domain, a transmembrane domain, and an intracellular domain, wherein the extracellular domain comprises an antigen-binding domain that is specific for a predetermined target. While the antigen-binding domain of a CAR is often an antibody or antibody fragment (e.g., a single chain variable fragment, scFv), the binding domain is not particularly limited so long as it results in specific recognition of a target. For example, in some embodiments, the antigen-binding domain may comprise a receptor, such that the CAR is capable of binding to the ligand of the receptor. Alternatively, the antigen-binding domain may comprise a ligand, such that the CAR is capable of binding the endogenous receptor of that ligand.
The antigen-binding domain of a CAR is generally separated from the transmembrane domain by a hinge or spacer. The spacer is also not particularly limited, and it is designed to provide the CAR with flexibility. For example, a spacer domain may comprise a portion of a human Fc domain, including a portion of the CH3 domain, or the hinge region of any immunoglobulin, such as IgA, IgD, IgE, IgG, or IgM, or variants thereof. Furthermore, the hinge region may be modified so as to prevent off-target binding by FcRs or other potential interfering objects. For example, the hinge may comprise an IgG4 Fc domain with or without a S228P, L235E, and/or N297Q mutation (according to Kabat numbering) in order to decrease binding to FcRs. Additional spacers/hinges include, but are not limited to, CD4, CD8, and CD28 hinge regions.
The transmembrane domain of a CAR may be derived either from a natural or from a synthetic source. Where the source is natural, the domain may be derived from any membrane-bound or transmembrane protein. Transmembrane regions of particular use in this disclosure may be derived from CD8, CD28, CD3, CD45, CD4, CD5, CDS, CD9, CD 16, CD22, CD33, CD37, CD64, CD80, CD86, CD 134, CD137, CD 154, TCR. Alternatively the transmembrane domain may be synthetic, in which case it will comprise predominantly hydrophobic residues such as leucine and valine. Preferably a triplet of phenylalanine, tryptophan and valine will be found at each end of a synthetic transmembrane domain. Optionally, a short oligo- or polypeptide linker, preferably between 2 and 10 amino acids in length may form the linkage between the transmembrane domain and the cytoplasmic signaling domain of the CAR. A glycine-serine doublet provides a particularly suitable linker.
Alternative CAR constructs may be characterized as belonging to successive generations. First-generation CARs typically consist of a single-chain variable fragment of an antibody specific for an antigen, for example comprising a VL linked to a VH of a specific antibody, linked by a flexible linker, for example by a CD8α hinge domain and a CD8α transmembrane domain, to the transmembrane and intracellular signaling domains of either CD3ζ or FcRγ (scFv-CD3ζ or scFv-FcRγ; see U.S. Pat. Nos. 7,741,465; 5,912,172; 5,906,936). Second-generation CARs incorporate the intracellular domains of one or more costimulatory molecules, such as CD28, OX40 (CD134), or 4-1BB (CD137) within the endodomain (for example scFv-CD28/OX40/4-1BB-CD3ζ; see U.S. Pat. Nos. 8,911,993; 8,916,381; 8,975,071; 9,101,584; 9,102,760; 9,102,761).
Third-generation CARs include a combination of costimulatory endodomains, such a CD3ζ-chain, CD97, GDI1a-CD18, CD2, ICOS, CD27, CD2, CD7, LIGHT, LFA-1, NKG2C, B7-H3, CD30, CD40, PD-1, CD154, CDS, OX40, 4-1BB, or CD28 signaling domains (for example scFv-CD28-4-1BB-CD3ζ or scFv-CD28-OX40-CD3ζ; see U.S. Pat. Nos. 8,906,682; 8,399,645; 5,686,281; PCT Publication No. WO2014134165; PCT Publication No. WO2012079000). Alternatively, costimulation may be orchestrated by expressing CARs in antigen-specific T cells, chosen so as to be activated and expanded following engagement of their native αβTCR, for example by antigen on professional antigen-presenting cells, with attendant costimulation. In addition, additional engineered receptors may be provided on the immunoresponsive cells, for example to improve targeting of a T cell attack and/or minimize side effects.
Alternatively, T cells expressing CARs may be further modified to reduce or eliminate expression of endogenous TCRs in order to reduce off-target effects. Reduction or elimination of endogenous TCRs can reduce off-target effects and increase the effectiveness of the T cells (U.S. Pat. No. 9,181,527). T cells stably lacking expression of a functional TCR may be produced using a variety of approaches. T cells internalize, sort, and degrade the entire T cell receptor as a complex, with a half-life of about 10 hours in resting T cells and 3 hours in stimulated T cells (von Essen, M. et al. 2004. J. Immunol. 173:384-393). Proper functioning of the TCR complex requires the proper stoichiometric ratio of the proteins that compose the TCR complex. TCR function also requires two functioning TCR zeta proteins with ITAM motifs. The activation of the TCR upon engagement of its MHC-peptide ligand requires the engagement of several TCRs on the same T cell, which all must signal properly. Thus, if a TCR complex is destabilized with proteins that do not associate properly or cannot signal optimally, the T cell will not become activated sufficiently to begin a cellular response.
Accordingly, in some embodiments, TCR expression may be eliminated using RNA interference (e.g., shRNA, siRNA, miRNA, etc.), CRISPR, or other methods that target the nucleic acids encoding specific TCRs (e.g., TCR-α and TCR-β) and/or CD3 chains in primary T cells. By blocking expression of one or more of these proteins, the T cell will no longer produce one or more of the key components of the TCR complex, thereby destabilizing the TCR complex and preventing cell surface expression of a functional TCR.
In some instances, CAR may also comprise a switch mechanism for controlling expression and/or activation of the CAR. For example, a CAR may comprise an extracellular, transmembrane, and intracellular domain, in which the extracellular domain comprises a target-specific binding element that comprises a label, binding domain, or tag that is specific for a molecule other than the target antigen that is expressed on or by a target cell. In such embodiments, the specificity of the CAR is provided by a second construct that comprises a target antigen binding domain (e.g., an scFv or a bispecific antibody that is specific for both the target antigen and the label or tag on the CAR) and a domain that is recognized by or binds to the label, binding domain, or tag on the CAR. See, e.g., WO 2013/044225, WO 2016/000304, WO 2015/057834, WO 2015/057852, WO 2016/070061, U.S. Pat. No. 9,233,125, US 2016/0129109. In this way, a T cell that expresses the CAR can be administered to a subject, but the CAR cannot bind its target antigen until the second composition comprising an antigen-specific binding domain is administered.
Alternative switch mechanisms include CARs that require multimerization in order to activate their signaling function (see, e.g., US 2015/0368342, US 2016/0175359, US 2015/0368360) and/or an exogenous signal, such as a small molecule drug (US 2016/0166613, Yung et al., Science, 2015), in order to elicit a T cell response. Some CARs may also comprise a “suicide switch” to induce cell death of the CAR T cells following treatment (Buddee et al., PLoS One, 2013) or to downregulate expression of the CAR following binding to the target antigen (WO 2016/011210).
Various techniques may be used to transform target immunoresponsive cells, such as protoplast fusion, lipofection, transfection or electroporation. A wide variety of vectors may be used, such as retroviral vectors, lentiviral vectors, adenoviral vectors, adeno-associated viral vectors, plasmids or transposons, such as a Sleeping Beauty transposon (see U.S. Pat. Nos. 6,489,458; 7,148,203; 7,160,682; 7,985,739; 8,227,432), may be used to introduce CARs, for example using 2nd generation antigen-specific CARs signaling through CD3 (and either CD28 or CD137. Viral vectors may for example include vectors based on HIV, SV40, EBV, HSV or BPV.
Cells that are targeted for transformation may for example include T cells, Natural Killer (NK) cells, cytotoxic T lymphocytes (CTL), regulatory T cells, human embryonic stem cells, tumor-infiltrating lymphocytes (TIL) or a pluripotent stem cell from which lymphoid cells may be differentiated. T cells expressing a desired CAR may for example be selected through co-culture with γ-irradiated activating and propagating cells (AaPC), which co-express the cancer antigen and co-stimulatory molecules. The engineered CAR T cells may be expanded, for example by co-culture on AaPC in presence of soluble factors, such as IL-2 and IL-21. This expansion may for example be carried out so as to provide memory CAR+ T cells (which may for example be assayed by non-enzymatic digital array and/or multi-panel flow cytometry). In this way, CAR T cells may be provided that have specific cytotoxic activity against antigen-bearing tumors (optionally in conjunction with production of desired chemokines such as interferon-γ). CAR T cells of this kind may for example be used in animal models, for example to treat tumor xenografts.
Approaches such as the foregoing may be adapted to provide methods of treating and/or increasing survival of a subject having a disease, such as a neoplasia, for example by administering an effective amount of an immunoresponsive cell comprising an antigen recognizing receptor that binds a selected antigen, wherein the binding activates the immunoresponsive cell, thereby treating or preventing the disease (such as a neoplasia, a pathogen infection, an autoimmune disorder, or an allogeneic transplant reaction).
In one embodiment, the treatment can be administrated into patients undergoing an immunosuppressive treatment. The cells or population of cells, may be made resistant to at least one immunosuppressive agent due to the inactivation of a gene encoding a receptor for such immunosuppressive agent. Not being bound by a theory, the immunosuppressive treatment should help the selection and expansion of the immunoresponsive or T cells according to the invention within the patient.
The administration of the cells or population of cells according to the present invention may be carried out in any convenient manner, including by aerosol inhalation, injection, ingestion, transfusion, implantation or transplantation. The cells or population of cells may be administered to a patient subcutaneously, intradermally, intratumorally, intranodally, intramedullary, intramuscularly, intrathecally, by intravenous or intralymphatic injection, or intraperitoneally. In some embodiments, the disclosed CARs may be delivered or administered into a cavity formed by the resection of tumor tissue (i.e. intracavity delivery) or directly into a tumor prior to resection (i.e. intratumoral delivery). In one embodiment, the cell compositions of the present invention are preferably administered by intravenous injection.
The administration of the cells or population of cells can consist of the administration of 104-109 cells per kg body weight, preferably 105 to 106 cells/kg body weight including all integer values of cell numbers within those ranges. Dosing in CAR T cell therapies may for example involve administration of from 106 to 109 cells/kg, with or without a course of lymphodepletion, for example with cyclophosphamide. The cells or population of cells can be administrated in one or more doses. In another embodiment, the effective amount of cells are administrated as a single dose. In another embodiment, the effective amount of cells are administrated as more than one dose over a period time. Timing of administration is within the judgment of managing physician and depends on the clinical condition of the patient. The cells or population of cells may be obtained from any source, such as a blood bank or a donor. While individual needs vary, determination of optimal ranges of effective amounts of a given cell type for a particular disease or conditions are within the skill of one in the art. An effective amount means an amount which provides a therapeutic or prophylactic benefit. The dosage administrated will be dependent upon the age, health and weight of the recipient, kind of concurrent treatment, if any, frequency of treatment and the nature of the effect desired.
In another embodiment, the effective amount of cells or composition comprising those cells are administrated parenterally. The administration can be an intravenous administration. The administration can be directly done by injection within a tumor.
To guard against possible adverse reactions, engineered immunoresponsive cells may be equipped with a transgenic safety switch, in the form of a transgene that renders the cells vulnerable to exposure to a specific signal. For example, the herpes simplex viral thymidine kinase (TK) gene may be used in this way, for example by introduction into allogeneic T lymphocytes used as donor lymphocyte infusions following stem cell transplantation (Greco, et al., Improving the safety of cell therapy with the TK-suicide gene. Front. Pharmacol. 2015; 6: 95). In such cells, administration of a nucleoside prodrug such as ganciclovir or acyclovir causes cell death. Alternative safety switch constructs include inducible caspase 9, for example triggered by administration of a small-molecule dimerizer that brings together two nonfunctional icasp9 molecules to form the active enzyme. A wide variety of alternative approaches to implementing cellular proliferation controls have been described (see U.S. Patent Publication No. 20130071414; PCT Patent Publication WO2011146862; PCT Patent Publication WO2014011987; PCT Patent Publication WO2013040371; Zhou et al. BLOOD, 2014, 123/25:3895-3905; Di Stasi et al., The New England Journal of Medicine 2011; 365:1673-1683; Sadelain M, The New England Journal of Medicine 2011; 365:1735-173; Ramos et al., Stem Cells 28(6):1107-15 (2010)).
In a further refinement of adoptive therapies, genome editing may be used to tailor immunoresponsive cells to alternative implementations, for example providing edited CAR T cells (see Poirot et al., 2015, Multiplex genome edited T cell manufacturing platform for “off-the-shelf” adoptive T cell immunotherapies, Cancer Res 75 (18): 3853). Cells may be edited using any CRISPR system and method of use thereof as described herein. CRISPR systems may be delivered to an immune cell by any method described herein. In preferred embodiments, cells are edited ex vivo and transferred to a subject in need thereof. Immunoresponsive cells, CAR T cells or any cells used for adoptive cell transfer may be edited. Editing may be performed to eliminate potential alloreactive T cell receptors (TCR), disrupt the target of a chemotherapeutic agent, block an immune checkpoint, activate a T cell, and/or increase the differentiation and/or proliferation of functionally exhausted or dysfunctional CD8+ T cells (see PCT Patent Publications: WO2013176915, WO2014059173, WO2014172606, WO2014184744, and WO2014191128). Editing may result in inactivation of a gene.
By inactivating a gene it is intended that the gene of interest is not expressed in a functional protein form. In a particular embodiment, the CRISPR system can specifically catalyze cleavage in one targeted gene thereby inactivating the targeted gene. The nucleic acid strand breaks caused are commonly repaired through the distinct mechanisms of homologous recombination or non-homologous end joining (NHEJ). However, NHEJ is an imperfect repair process that often results in changes to the DNA sequence at the site of the cleavage. Repair via NHEJ often results in small insertions or deletions (Indel) and can be used for the creation of specific gene knockouts. Cells in which a cleavage induced mutagenesis event has occurred can be identified and/or selected by well-known methods in the art.
T cell receptors (TCR) are cell surface receptors that participate in the activation of T cells in response to the presentation of antigen. The TCR is generally made from two chains, α and β, which assemble to form a heterodimer and associates with the CD3-transducing subunits to form the T cell receptor complex present on the cell surface. Each α and β chain of the TCR consists of an immunoglobulin-like N-terminal variable (V) and constant (C) region, a hydrophobic transmembrane domain, and a short cytoplasmic region. As for immunoglobulin molecules, the variable region of the α and β chains are generated by V(D)J recombination, creating a large diversity of antigen specificities within the population of T cells. However, in contrast to immunoglobulins that recognize intact antigen, T cells are activated by processed peptide fragments in association with an MHC molecule, introducing an extra dimension to antigen recognition by T cells, known as MHC restriction. Recognition of MHC disparities between the donor and recipient through the T cell receptor leads to T cell proliferation and the potential development of graft versus host disease (GVHD). The inactivation of TCRa or TCRO can result in the elimination of the TCR from the surface of T cells preventing recognition of alloantigen and thus GVHD. However, TCR disruption generally results in the elimination of the CD3 signaling component and alters the means of further T cell expansion.
Allogeneic cells are rapidly rejected by the host immune system. It has been demonstrated that, allogeneic leukocytes present in non-irradiated blood products will persist for no more than 5 to 6 days (Boni, Muranski et al. 2008 Blood 1; 112(12):4746-54). Thus, to prevent rejection of allogeneic cells, the host's immune system usually has to be suppressed to some extent. However, in the case of adoptive cell transfer the use of immunosuppressive drugs also have a detrimental effect on the introduced therapeutic T cells. Therefore, to effectively use an adoptive immunotherapy approach in these conditions, the introduced cells would need to be resistant to the immunosuppressive treatment. Thus, in a particular embodiment, the present invention further comprises a step of modifying T cells to make them resistant to an immunosuppressive agent, preferably by inactivating at least one gene encoding a target for an immunosuppressive agent. An immunosuppressive agent is an agent that suppresses immune function by one of several mechanisms of action. An immunosuppressive agent can be, but is not limited to a calcineurin inhibitor, a target of rapamycin, an interleukin-2 receptor α-chain blocker, an inhibitor of inosine monophosphate dehydrogenase, an inhibitor of dihydrofolic acid reductase, a corticosteroid or an immunosuppressive antimetabolite. The present invention allows conferring immunosuppressive resistance to T cells for immunotherapy by inactivating the target of the immunosuppressive agent in T cells. As non-limiting examples, targets for an immunosuppressive agent can be a receptor for an immunosuppressive agent such as: CD52, glucocorticoid receptor (GR), a FKBP family gene member and a cyclophilin family gene member.
Immune checkpoints are inhibitory pathways that slow down or stop immune reactions and prevent excessive tissue damage from uncontrolled activity of immune cells. In certain embodiments, the immune checkpoint targeted is the programmed death-1 (PD-1 or CD279) gene (PDCD1). In other embodiments, the immune checkpoint targeted is cytotoxic T-lymphocyte-associated antigen (CTLA-4). In additional embodiments, the immune checkpoint targeted is another member of the CD28 and CTLA4 Ig superfamily such as BTLA, LAG3, ICOS, PDL1 or KIR. In further additional embodiments, the immune checkpoint targeted is a member of the TNFR superfamily such as CD40, OX40, CD137, GITR, CD27 or TIM-3.
Additional immune checkpoints include Src homology 2 domain-containing protein tyrosine phosphatase 1 (SHP-1) (Watson H A, et al., SHP-1: the next checkpoint target for cancer immunotherapy?Biochem Soc Trans. 2016 Apr. 15; 44(2):356-62). SHP-1 is a widely expressed inhibitory protein tyrosine phosphatase (PTP). In T-cells, it is a negative regulator of antigen-dependent activation and proliferation. It is a cytosolic protein, and therefore not amenable to antibody-mediated therapies, but its role in activation and proliferation makes it an attractive target for genetic manipulation in adoptive transfer strategies, such as chimeric antigen receptor (CAR) T cells. Immune checkpoints may also include T cell immunoreceptor with Ig and ITIM domains (TIGIT/Vstm3/WUCAM/VSIG9) and VISTA (Le Mercier I, et al., (2015) Beyond CTLA-4 and PD-1, the generation Z of negative checkpoint regulators. Front. Immunol. 6:418).
WO2014172606 relates to the use of MT1 and/or MT1 inhibitors to increase proliferation and/or activity of exhausted CD8+ T-cells and to decrease CD8+ T-cell exhaustion (e.g., decrease functionally exhausted or unresponsive CD8+ immune cells). In certain embodiments, metallothioneins are targeted by gene editing in adoptively transferred T cells.
In certain embodiments, targets of gene editing may be at least one targeted locus involved in the expression of an immune checkpoint protein. Such targets may include, but are not limited to CTLA4, PPP2CA, PPP2CB, PTPN6, PTPN22, PDCD1, ICOS (CD278), PDL1, KIR, LAG3, HAVCR2, BTLA, CD160, TIGIT, CD96, CRTAM, LAIR1, SIGLEC7, SIGLEC9, CD244 (2B4), TNFRSF10B, TNFRSF10A, CASP8, CASP10, CASP3, CASP6, CASP7, FADD, FAS, TGFBRII, TGFRBRI, SMAD2, SMAD3, SMAD4, SMAD10, SKI, SKIL, TGIF1, IL10RA, IL10RB, HMOX2, IL6R, IL6ST, EIF2AK4, CSK, PAG1, SIT1, FOXP3, PRDM1, BATF, VISTA, GUCY1A2, GUCY1A3, GUCY1B2, GUCY1B3, MT1, MT2, CD40, OX40, CD137, GITR, CD27, SHP-1 or TIM-3. In preferred embodiments, the gene locus involved in the expression of PD-1 or CTLA-4 genes is targeted. In other preferred embodiments, combinations of genes are targeted, such as but not limited to PD-1 and TIGIT. In preferred embodiments, the novel genes or gene combinations described herein are targeted or modulated.
In other embodiments, at least two genes are edited. Pairs of genes may include, but are not limited to PD1 and TCRα, PD1 and TCRβ, CTLA-4 and TCRα, CTLA-4 and TCRβ, LAG3 and TCRα, LAG3 and TCRβ, Tim3 and TCRα, Tim3 and TCRβ, BTLA and TCRα, BTLA and TCRβ, BY55 and TCRα, BY55 and TCRβ, TIGIT and TCRα, TIGIT and TCRβ, B7H5 and TCRα, B7H5 and TCRβ, LAIR1 and TCRα, LAIR1 and TCRβ, SIGLEC10 and TCRα, SIGLEC10 and TCRβ, 2B4 and TCRα, 2B4 and TCRβ.
Whether prior to or after genetic modification of the T cells, the T cells can be activated and expanded generally using methods as described, for example, in U.S. Pat. Nos. 6,352,694; 6,534,055; 6,905,680; 5,858,358; 6,887,466; 6,905,681; 7,144,575; 7,232,566; 7,175,843; 5,883,223; 6,905,874; 6,797,514; 6,867,041; and 7,572,631. T cells can be expanded in vitro or in vivo.
Immune cells may be obtained using any method known in the art. In one embodiment T cells that have infiltrated a tumor are isolated. T cells may be removed during surgery. T cells may be isolated after removal of tumor tissue by biopsy. T cells may be isolated by any means known in the art. In one embodiment the method may comprise obtaining a bulk population of T cells from a tumor sample by any suitable method known in the art. For example, a bulk population of T cells can be obtained from a tumor sample by dissociating the tumor sample into a cell suspension from which specific cell populations can be selected. Suitable methods of obtaining a bulk population of T cells may include, but are not limited to, any one or more of mechanically dissociating (e.g., mincing) the tumor, enzymatically dissociating (e.g., digesting) the tumor, and aspiration (e.g., as with a needle).
The bulk population of T cells obtained from a tumor sample may comprise any suitable type of T cell. Preferably, the bulk population of T cells obtained from a tumor sample comprises tumor infiltrating lymphocytes (TILs).
The tumor sample may be obtained from any mammal. Unless stated otherwise, as used herein, the term “mammal” refers to any mammal including, but not limited to, mammals of the order Lagomorpha, such as rabbits; the order Carnivora, including Felines (cats) and Canines (dogs); the order Artiodactyla, including Bovines (cows) and Swines (pigs); or of the order Perissodactyla, including Equines (horses). The mammals may be non-human primates, e.g., of the order Primates, Ceboids, or Simoids (monkeys) or of the order Anthropoids (humans and apes). In some embodiments, the mammal may be a mammal of the order Rodentia, such as mice and hamsters. Preferably, the mammal is a non-human primate or a human. An especially preferred mammal is the human.
T cells can be obtained from a number of sources, including peripheral blood mononuclear cells, bone marrow, lymph node tissue, spleen tissue, and tumors. In certain embodiments of the present invention, T cells can be obtained from a unit of blood collected from a subject using any number of techniques known to the skilled artisan, such as Ficoll separation. In one preferred embodiment, cells from the circulating blood of an individual are obtained by apheresis or leukapheresis. The apheresis product typically contains lymphocytes, including T cells, monocytes, granulocytes, B cells, other nucleated white blood cells, red blood cells, and platelets. In one embodiment, the cells collected by apheresis may be washed to remove the plasma fraction and to place the cells in an appropriate buffer or media for subsequent processing steps. In one embodiment of the invention, the cells are washed with phosphate buffered saline (PBS). In an alternative embodiment, the wash solution lacks calcium and may lack magnesium or may lack many if not all divalent cations. Initial activation steps in the absence of calcium lead to magnified activation. As those of ordinary skill in the art would readily appreciate a washing step may be accomplished by methods known to those in the art, such as by using a semi-automated “flow-through” centrifuge (for example, the Cobe 2991 cell processor) according to the manufacturer's instructions. After washing, the cells may be resuspended in a variety of biocompatible buffers, such as, for example, Ca-free, Mg-free PBS. Alternatively, the undesirable components of the apheresis sample may be removed and the cells directly resuspended in culture media.
In another embodiment, T cells are isolated from peripheral blood lymphocytes by lysing the red blood cells and depleting the monocytes, for example, by centrifugation through a PERCOLL™ gradient. A specific subpopulation of T cells, such as CD28+, CD4+, CDC, CD45RA+, and CD45RO+ T cells, can be further isolated by positive or negative selection techniques. For example, in one preferred embodiment, T cells are isolated by incubation with anti-CD3/anti-CD28 (i.e., 3×28)-conjugated beads, such as DYNABEADS® M-450 CD3/CD28 T, or XCYTE DYNABEADS™ for a time period sufficient for positive selection of the desired T cells. In one embodiment, the time period is about 30 minutes. In a further embodiment, the time period ranges from 30 minutes to 36 hours or longer and all integer values there between. In a further embodiment, the time period is at least 1, 2, 3, 4, 5, or 6 hours. In yet another preferred embodiment, the time period is 10 to 24 hours. In one preferred embodiment, the incubation time period is 24 hours. For isolation of T cells from patients with leukemia, use of longer incubation times, such as 24 hours, can increase cell yield. Longer incubation times may be used to isolate T cells in any situation where there are few T cells as compared to other cell types, such in isolating tumor infiltrating lymphocytes (TIL) from tumor tissue or from immunocompromised individuals. Further, use of longer incubation times can increase the efficiency of capture of CD8+ T cells.
Enrichment of a T cell population by negative selection can be accomplished with a combination of antibodies directed to surface markers unique to the negatively selected cells. A preferred method is cell sorting and/or selection via negative magnetic immunoadherence or flow cytometry that uses a cocktail of monoclonal antibodies directed to cell surface markers present on the cells negatively selected. For example, to enrich for CD4+ cells by negative selection, a monoclonal antibody cocktail typically includes antibodies to CD14, CD20, CD11b, CD16, HLA-DR, and CD8.
Further, monocyte populations (i.e., CD14+ cells) may be depleted from blood preparations by a variety of methodologies, including anti-CD14 coated beads or columns, or utilization of the phagocytotic activity of these cells to facilitate removal. Accordingly, in one embodiment, the invention uses paramagnetic particles of a size sufficient to be engulfed by phagocytotic monocytes. In certain embodiments, the paramagnetic particles are commercially available beads, for example, those produced by Life Technologies under the trade name Dynabeads™. In one embodiment, other non-specific cells are removed by coating the paramagnetic particles with “irrelevant” proteins (e.g., serum proteins or antibodies). Irrelevant proteins and antibodies include those proteins and antibodies or fragments thereof that do not specifically target the T cells to be isolated. In certain embodiments the irrelevant beads include beads coated with sheep anti-mouse antibodies, goat anti-mouse antibodies, and human serum albumin.
In brief, such depletion of monocytes is performed by preincubating T cells isolated from whole blood, apheresed peripheral blood, or tumors with one or more varieties of irrelevant or non-antibody coupled paramagnetic particles at any amount that allows for removal of monocytes (approximately a 20:1 bead:cell ratio) for about 30 minutes to 2 hours at 22 to 37 degrees C., followed by magnetic removal of cells which have attached to or engulfed the paramagnetic particles. Such separation can be performed using standard methods available in the art. For example, any magnetic separation methodology may be used including a variety of which are commercially available, (e.g., DYNAL® Magnetic Particle Concentrator (DYNAL MPC®)). Assurance of requisite depletion can be monitored by a variety of methodologies known to those of ordinary skill in the art, including flow cytometric analysis of CD14 positive cells, before and after depletion.
For isolation of a desired population of cells by positive or negative selection, the concentration of cells and surface (e.g., particles such as beads) can be varied. In certain embodiments, it may be desirable to significantly decrease the volume in which beads and cells are mixed together (i.e., increase the concentration of cells), to ensure maximum contact of cells and beads. For example, in one embodiment, a concentration of 2 billion cells/ml is used. In one embodiment, a concentration of 1 billion cells/ml is used. In a further embodiment, greater than 100 million cells/ml is used. In a further embodiment, a concentration of cells of 10, 15, 20, 25, 30, 35, 40, 45, or 50 million cells/ml is used. In yet another embodiment, a concentration of cells from 75, 80, 85, 90, 95, or 100 million cells/ml is used. In further embodiments, concentrations of 125 or 150 million cells/ml can be used. Using high concentrations can result in increased cell yield, cell activation, and cell expansion. Further, use of high cell concentrations allows more efficient capture of cells that may weakly express target antigens of interest, such as CD28-negative T cells, or from samples where there are many tumor cells present (i.e., leukemic blood, tumor tissue, etc). Such populations of cells may have therapeutic value and would be desirable to obtain. For example, using high concentration of cells allows more efficient selection of CD8+ T cells that normally have weaker CD28 expression.
In a related embodiment, it may be desirable to use lower concentrations of cells. By significantly diluting the mixture of T cells and surface (e.g., particles such as beads), interactions between the particles and cells is minimized. This selects for cells that express high amounts of desired antigens to be bound to the particles. For example, CD4+ T cells express higher levels of CD28 and are more efficiently captured than CD8+ T cells in dilute concentrations. In one embodiment, the concentration of cells used is 5×106/ml. In other embodiments, the concentration used can be from about 1×105/ml to 1×106/ml, and any integer value in between.
T cells can also be frozen. Wishing not to be bound by theory, the freeze and subsequent thaw step provides a more uniform product by removing granulocytes and to some extent monocytes in the cell population. After a washing step to remove plasma and platelets, the cells may be suspended in a freezing solution. While many freezing solutions and parameters are known in the art and will be useful in this context, one method involves using PBS containing 20% DMSO and 8% human serum albumin, or other suitable cell freezing media, the cells then are frozen to −80° C. at a rate of 1° per minute and stored in the vapor phase of a liquid nitrogen storage tank. Other methods of controlled freezing may be used as well as uncontrolled freezing immediately at −20° C. or in liquid nitrogen.
T cells for use in the present invention may also be antigen-specific T cells. For example, tumor-specific T cells can be used. In certain embodiments, antigen-specific T cells can be isolated from a patient of interest, such as a patient afflicted with a cancer or an infectious disease. In one embodiment neoepitopes are determined for a subject and T cells specific to these antigens are isolated. Antigen-specific cells for use in expansion may also be generated in vitro using any number of methods known in the art, for example, as described in U.S. Patent Publication No. US 20040224402 entitled, Generation And Isolation of Antigen-Specific T Cells, or in U.S. Pat. Nos. 6,040,177. Antigen-specific cells for use in the present invention may also be generated using any number of methods known in the art, for example, as described in Current Protocols in Immunology, or Current Protocols in Cell Biology, both published by John Wiley & Sons, Inc., Boston, Mass.
In a related embodiment, it may be desirable to sort or otherwise positively select (e.g. via magnetic selection) the antigen specific cells prior to or following one or two rounds of expansion. Sorting or positively selecting antigen-specific cells can be carried out using peptide-MHC tetramers (Altman, et al., Science. 1996 Oct. 4; 274(5284):94-6). In another embodiment, the adaptable tetramer technology approach is used (Andersen et al., 2012 Nat Protoc. 7:891-902). Tetramers are limited by the need to utilize predicted binding peptides based on prior hypotheses, and the restriction to specific HLAs. Peptide-MHC tetramers can be generated using techniques known in the art and can be made with any MHC molecule of interest and any antigen of interest as described herein. Specific epitopes to be used in this context can be identified using numerous assays known in the art. For example, the ability of a polypeptide to bind to MHC class I may be evaluated indirectly by monitoring the ability to promote incorporation of 125I labeled β2-microglobulin (β2m) into MHC class I/β2m/peptide heterotrimeric complexes (see Parker et al., J. Immunol. 152:163, 1994).
In one embodiment cells are directly labeled with an epitope-specific reagent for isolation by flow cytometry followed by characterization of phenotype and TCRs. In one T cells are isolated by contacting the T cell specific antibodies. Sorting of antigen-specific T cells, or generally any cells of the present invention, can be carried out using any of a variety of commercially available cell sorters, including, but not limited to, MoFlo sorter (DakoCytomation, Fort Collins, Colo.), FACSAria™, FACSArray™, FACSVantage™, BD™ LSR II, and FACSCalibur™ (BD Biosciences, San Jose, Calif.).
In a preferred embodiment, the method comprises selecting cells that also express CD3. The method may comprise specifically selecting the cells in any suitable manner. Preferably, the selecting is carried out using flow cytometry. The flow cytometry may be carried out using any suitable method known in the art. The flow cytometry may employ any suitable antibodies and stains. Preferably, the antibody is chosen such that it specifically recognizes and binds to the particular biomarker being selected. For example, the specific selection of CD3, CD8, TIM-3, LAG-3, 4-1BB, or PD-1 may be carried out using anti-CD3, anti-CD8, anti-TIM-3, anti-LAG-3, anti-4-1BB, or anti-PD-1 antibodies, respectively. The antibody or antibodies may be conjugated to a bead (e.g., a magnetic bead) or to a fluorochrome. Preferably, the flow cytometry is fluorescence-activated cell sorting (FACS). TCRs expressed on T cells can be selected based on reactivity to autologous tumors. Additionally, T cells that are reactive to tumors can be selected for based on markers using the methods described in patent publication Nos. WO2014133567 and WO2014133568, herein incorporated by reference in their entirety. Additionally, activated T cells can be selected for based on surface expression of CD107a.
In one embodiment of the invention, the method further comprises expanding the numbers of T cells in the enriched cell population. Such methods are described in U.S. Pat. No. 8,637,307 and is herein incorporated by reference in its entirety. The numbers of T cells may be increased at least about 3-fold (or 4-, 5-, 6-, 7-, 8-, or 9-fold), more preferably at least about 10-fold (or 20-, 30-, 40-, 50-, 60-, 70-, 80-, or 90-fold), more preferably at least about 100-fold, more preferably at least about 1,000 fold, or most preferably at least about 100,000-fold. The numbers of T cells may be expanded using any suitable method known in the art. Exemplary methods of expanding the numbers of cells are described in patent publication No. WO 2003057171, U.S. Pat. No. 8,034,334, and U.S. Patent Application Publication No. 2012/0244133, each of which is incorporated herein by reference.
In one embodiment, ex vivo T cell expansion can be performed by isolation of T cells and subsequent stimulation or activation followed by further expansion. In one embodiment of the invention, the T cells may be stimulated or activated by a single agent. In another embodiment, T cells are stimulated or activated with two agents, one that induces a primary signal and a second that is a co-stimulatory signal. Ligands useful for stimulating a single signal or stimulating a primary signal and an accessory molecule that stimulates a second signal may be used in soluble form. Ligands may be attached to the surface of a cell, to an Engineered Multivalent Signaling Platform (EMSP), or immobilized on a surface. In a preferred embodiment both primary and secondary agents are co-immobilized on a surface, for example a bead or a cell. In one embodiment, the molecule providing the primary activation signal may be a CD3 ligand, and the co-stimulatory molecule may be a CD28 ligand or 4-1BB ligand.
“Activation” generally refers to the state of a cell, such as preferably T cell, following sufficient cell surface moiety ligation (e.g., interaction between the T cell receptor on the surface of a T cell (such as naturally-occurring TCR or genetically engineered TCR, e.g., chimeric antigen receptor, CAR) and MHIC-bound antigen peptide presented on the surface of the immune cell as taught herein) to induce a noticeable biochemical or morphological change of the cell, such as preferably T cell. In particular, “activation” may refer to the state of a T cell that has been sufficiently stimulated to induce detectable cellular proliferation of the T cell. Activation can also encompass induced cytokine production, and detectable T cell effector functions, e.g., regulatory or cytolytic effector functions. The T cells and immune cells may be may be suitably contacted by admixing the T cells and immune cells in an aqueous composition, e.g., in a culture medium, in sufficient numbers and for a sufficient duration of time to produce the desired T cell activation.
Use of T Cell Modulating Agents
Suitable T cell modulating agent(s) for use in any of the compositions and methods provided herein include an antibody, a soluble polypeptide, a polypeptide agent, a peptide agent, a nucleic acid agent, a nucleic acid ligand, or a small molecule agent. By way of non-limiting example, suitable T cell modulating agents or agents for use in combination with one or more T cell modulating agents are shown below in Table 1.







TABLE 1







T cell Modulating Agents








Target
Agent





CCR6
prostaglandin E2, lipopolysaccharide, mip-3alpha, vegf, rantes, calcium,



bortezomib, ccl4, larc, tarc, lipid, E. coli B5 lipopolysaccharide


CCR5
cholesterol, cyclosporin a, glutamine, methionine, guanine, simvastatin,



threonine, indinavir, lipoxin A4, cysteine, prostaglandin E2, zinc, dapta, 17-



alpha-ethinylestradiol, polyacrylamide, progesterone, zidovudine, rapamycin,



rantes, glutamate, alanine, valine, ccl4, quinine, NSC 651016, methadone,



pyrrolidine dithiocarbamate, palmitate, nor-binaltorphimine, interferon beta-



1a, vitamin-e, tak779, lipopolysaccharide, cisplatin, albuterol, fluvoxamine,



vicriviroc, bevirimat, carbon tetrachloride, galactosylceramide, ATP-gamma-S,



cytochalasin d, hemozoin, CP 96345, tyrosine, etravirine, vitamin d, mip



1alpha, ammonium, tyrosine sulfate, isoleucine, isopentenyl diphosphate, il



10, serine, N-acetyl-L-cysteine, histamine, cocaine, ritonavir, tipranavir,



aspartate, atazanavir, tretinoin, ATP, ribavirin, butyrate, N-nitro-L-arginine



methyl ester, larc, buthionine sulfoximine, DAPTA, aminooxypentane-rantes,



triamcinolone acetonide, shikonin, actinomycin d, bucladesine, aplaviroc,



nevirapine, N-formyl-Met-Leu-Phe, cyclosporin A, lipoarabinomannan,



nucleoside, sirolimus, morphine, mannose, calcium, heparin, c-d4i, pge2, beta-



estradiol, mdms, dextran sulfate, dexamethasone, arginine, ivig, mcp 2, cyclic



amp, U 50488H, N-methyl-D-aspartate, hydrogen peroxide, 8-



carboxamidocyclazocine, latex, groalpha, xanthine, ccl3, retinoic acid,



Maraviroc, sdf 1, opiate, efavirenz, estrogen, bicyclam, enfuvirtide, filipin,



bleomycin, polysaccharide, tarc, pentoxifylline, E. coli B5 lipopolysaccharide,



methylcellulose, maraviroc


ITGA3
SP600125, paclitaxel, decitabine, e7820, retinoid, U0126, serine, retinoic acid,



tyrosine, forskolin, Ca2+


IRF4
prostaglandin E2, phorbol myristate acetate, lipopolysaccharide, A23187,



tacrolimus, trichostatin A, stallimycin, imatinib, cyclosporin A, tretinoin,



bromodeoxyuridine, ATP-gamma-S, ionomycin


BATF
Cyclic AMP, serine, tacrolimus, beta-estradiol, cyclosporin A, leucine


RBPJ
zinc, tretinoin


PROCR
lipopolysaccharide, cisplatin, fibrinogen, 1,10-phenanthroline, 5-N-



ethylcarboxamido adenosine, cystathionine, hirudin, phospholipid,



Drotrecogin alfa, vegf, Phosphatidylethanolamine, serine, gamma-



carboxyglutamic acid, calcium, warfarin, endotoxin, curcumin, lipid, nitric



oxide


ZEB1
resveratrol, zinc, sulforafan, sorafenib, progesterone, PD-0332991,



dihydrotestosterone, silibinin, LY294002, 4-hydroxytamoxifen, valproic acid,



beta-estradiol, forskolin, losartan potassium, fulvestrant, vitamin d


POU2AF1
terbutaline, phorbol myristate acetate, bucladesine, tyrosine, ionomycin,



KT5720, H89


EGR1
ghrelin, ly294002, silicone, sodium, propofol, 1,25 dihydroxy vitamin d3,



tetrodotoxin, threonine, cyclopiazonic acid, urea, quercetin, ionomycin, 12-o-



tetradecanoylphorbol 13-acetate, fulvestrant, phenylephrine, formaldehyde,



cysteine, leukotriene C4, prazosin, LY379196, vegf, rapamycin, leupeptin, pd



98, 059, ruboxistaurin, pCPT-cAMP, methamphetamine, nitroprusside, H-7,



Ro31-8220, phosphoinositide, lysophosphatidylcholine, bufalin, calcitriol,



leuprolide, isobutylmethylxanthine, potassium chloride, acetic acid,



cyclothiazide, quinolinic acid, tyrosine, adenylate, resveratrol, topotecan,



genistein, thymidine, D-glucose, mifepristone, lysophosphatidic acid,



leukotriene D4, carbon monoxide, poly rI:rC-RNA, sp 600125, agar, cocaine, 4-



nitroquinoline-1-oxide, tamoxifen, lead, fibrinogen, tretinoin, atropine,



mithramycin, K+, epigallocatechin-gallate, ethylenediaminetetraacetic acid,



h2o2, carbachol, sphingosine-1-phosphate, iron, 5-hydroxytryptamine,



amphetamine, SP600125, actinomycin d, SB203580, cyclosporin A,



norepinephrine, okadaic acid, ornithine, LY294002, pge2, beta-estradiol,



glucose, erlotinib, arginine, 1-alpha, 25-dihydroxy vitamin D3,



dexamethasone, pranlukast, phorbol myristate acetate, nimodipine,



desipramine, cyclic amp, N-methyl-D-aspartate, atipamezole, acadesine,



losartan, salvin, methylnitronitrosoguanidine, EGTA, gf 109203x,



nitroarginine, 5-N-ethylcarboxamido adenosine, 15-deoxy-delta-12,14 -PGJ 2,



dbc-amp, manganese superoxide, di(2-ethylhexyl) phthalate, egcg, mitomycin



C,6,7-dinitroquinoxaline-2,3-dione, GnRH-A, estrogen, ribonucleic acid,



imipramine, bapta, L-triiodothyronine, prostaglandin, forskolin, nogalamycin,



losartan potassium, lipid, vincristine, 2-amino-3-phosphonopropionic acid,



prostacyclin, methylnitrosourea, cyclosporin a, vitamin K3, thyroid hormone,



diethylstilbestrol, D-tubocurarine, tunicamycin, caffeine, phorbol, guanine,



bisindolylmaleimide, apomorphine, arachidonic acid, SU6656, prostaglandin



E2, zinc, ptx1, progesterone, cyclosporin H, phosphatidylinositol, U0126,



hydroxyapatite, epoprostenol, glutamate, 5fluorouracil, indomethacin, 5-



fluorouracil, RP 73401, Ca2+, superoxide, trifluoperazine, nitric oxide,



lipopolysaccharide, cisplatin, diazoxide, tgf beta1, calmidazolium, anisomycin,



paclitaxel, sulindac sulfide, ganciclovir, gemcitabine, testosterone, ag 1478,



glutamyl-Se-methylselenocysteine, doxorubicin, tolbutamide, cytochalasin d,



PD98059, leucine, SR 144528, cyclic AMP, matrigel, haloperidol, serine, sb



203580, triiodothyronine, reverse, N-acetyl-L-cysteine, ethanol, s-nitroso-n-



acetylpenicillamine, curcumin, l-nmma, H89, tpck, calyculin a,



chloramphenicol, A23187, dopamine, platelet activating factor, arsenite,



selenomethylselenocysteine, ropinirole, saralasin, methylphenidate,



gentamicin, reserpine, triamcinolone acetonide, methyl methanesulfonate,



wortmannin, thapsigargin, deferoxamine, calyculin A, peptidoglycan,



dihydrotestosterone, calcium, phorbol-12-myristate, ceramide, nmda, 6-



cyano-7-nitroquinoxaline-2,3-dione, hydrogen peroxide, carrageenan, sch



23390, linsidomine, oxygen, clonidine, fluoxetine, retinoid, troglitazone,



retinoic acid, epinephrine, n acetylcysteine, KN-62, carbamylcholine, 2-amino-



5-phosphonovaleric acid, oligonucleotide, gnrh, rasagiline, 8-bromo-cAMP,



muscarine, tacrolimus, kainic acid, chelerythrine, inositol 1,4,5



trisphosphate, yohimbine, acetylcholine, atp, 15-deoxy-delta-12,14-



prostaglandin j2, ryanodine, CpG oligonucleotide, cycloheximide, BAPTA-AM,



phenylalanine


ETV6
lipopolysaccharide, retinoic acid, prednisolone, valproic acid, tyrosine,



cerivastatin, vegf, agar, imatinib, tretinoin


IL17RA
rantes, lipopolysaccharide, 17-alpha-ethinylestradiol, camptothecin, E. coli B5



lipopolysaccharide


EGR2
phorbol myristate acetate, lipopolysaccharide, platelet activating factor,



carrageenan, edratide, 5-N-ethylcarboxamido adenosine, potassium chloride,



dbc-amp, tyrosine, PD98059, camptothecin, formaldehyde, prostaglandin E2,



leukotriene C4, zinc, cyclic AMP, GnRH-A, bucladesine, thapsigargin, kainic



acid, cyclosporin A, mifepristone, leukotriene D4, LY294002, L-



triiodothyronine, calcium, beta-estradiol, H89, dexamethasone, cocaine


SP4
betulinic acid, zinc, phorbol myristate acetate, LY294002, methyl 2-cyano-3,



12-dioxoolean-1,9-dien-28-oate, beta-estradiol, Ca2+


IRF8
oligonucleotide, chloramphenicol, lipopolysaccharide, estrogen, wortmannin,



pirinixic acid, carbon monoxide, retinoic acid, tyrosine


NFKB1
Bay 11-7085, Luteolin, Triflusal, Bay 11-7821, Thalidomide, Caffeic acid



phenethyl ester, Pranlukast


TSC22D3
phorbol myristate acetate, prednisolone, sodium, dsip, tretinoin, 3-



deazaneplanocin, gaba, PD98059, leucine, triamcinolone acetonide,



prostaglandin E2, steroid, norepinephrine, U0126, acth, calcium, ethanol,



beta-estradiol, lipid, chloropromazine, arginine, dexamethasone


PML
lipopolysaccharide, glutamine, thyroid hormone, cadmium, lysine, tretinoin,



bromodeoxyuridine, etoposide, retinoid, pic 1, arsenite, arsenic trioxide,



butyrate, retinoic acid, alpha-retinoic acid, h2o2, camptothecin, cysteine,



leucine, zinc, actinomycin d, proline, stallimycin, U0126


IL12RB1
prostaglandin E2, phorbol myristate acetate, lipopolysaccharide, bucladesine,



8-bromo-cAMP, gp 130, AGN194204, galactosylceramide-alpha, tyrosine,



ionomycin, dexamethasone, il-12


IL21R
azathioprine, lipopolysaccharide, okadaic acid, E. coli B5 lipopolysaccharide,



calyculin A


NOTCH1
interferon beta-1a, lipopolysaccharide, cisplatin, tretinoin, oxygen, vitamin



B12, epigallocatechin-gallate, isobutylmethylxanthine, threonine,



apomorphine, matrigel, trichostatin A, vegf, 2-acetylaminofluorene,



rapamycin, dihydrotestosterone, poly rI:rC-RNA, hesperetin, valproic acid,



asparagine, lipid, curcumin, dexamethasone, glycogen, CpG oligonucleotide,



nitric oxide


ETS2
oligonucleotide


MINA
phorbol myristate acetate, 4-hydroxytamoxifen


SMARCA4
cyclic amp, cadmium, lysine, tretinoin, latex, androstane, testosterone,



sucrose, tyrosine, cysteine, zinc, oligonucleotide, estrogen, steroid,



trichostatin A, tpmp, progesterone, histidine, atp, trypsinogen, glucose, agar,



lipid, arginine, vancomycin, dihydrofolate


FAS
hoechst 33342, ly294002, 2-chlorodeoxyadenosine, glutamine, cd 437,



tetrodotoxin, cyclopiazonic acid, arsenic trioxide, phosphatidylserine,



niflumic acid, gliadin, ionomycin, safrole oxide, methotrexate, rubitecan,



cysteine, propentofylline, vegf, boswellic acids, rapamycin, pd 98, 059,



captopril, methamphetamine, vesnarinone, tetrapeptide, oridonin, raltitrexed,



pirinixic acid, nitroprusside, H-7, beta-boswellic acid, adriamycin,



concanamycin a, etoposide, trastuzumab, cyclophosphamide, ifn-alpha,



tyrosine, rituximab, selenodiglutathione, chitosan, omega-N-methylarginine,



creatinine, resveratrol, topotecan, genistein, trichostatin A, decitabine,



thymidine, D-glucose, mifepristone, tetracycline, Sn50 peptide, poly rI:rC-



RNA, actinomycin D, sp 600125, doxifluridine, agar, ascorbic acid,



acetaminophen, aspirin, tamoxifen, okt3, edelfosine, sulforafan, aspartate,



antide, n, n-dimethylsphingosine, epigallocatechin-gallate, N-nitro-L-arginine



methyl ester, h2o2, cerulenin, sphingosine-1-phosphate, SP600125, sodium



nitroprusside, glycochenodeoxycholic acid, ceramides, actinomycin d,



SB203580, cyclosporin A, morphine, LY294002, n(g)-nitro-l-arginine methyl



ester, 4-hydroxynonenal, piceatannol, valproic acid, beta-estradiol, 1-alpha,



25-dihydroxy vitamin D3, arginine, dexamethasone, sulfadoxine, phorbol



myristate acetate, beta-lapachone, nitrofurantoin, chlorambucil,



methylnitronitrosoguanidine, CD 437, opiate, egcg, mitomycin C, estrogen,



ribonucleic acid, fontolizumab, tanshinone iia, recombinant human



endostatin, fluoride, L-triiodothyronine, bleomycin, forskolin, nonylphenol,



zymosan A, vincristine, daunorubicin, prednisolone, cyclosporin a, vitamin K3,



diethylstilbestrol, deoxyribonucleotide, suberoylanilide hydroxamic acid,



orlistat, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide,



rottierin, arachidonic acid, ibuprofen, prostaglandin E2, toremifene,



depsipeptide, ochratoxin A, (glc)4, phosphatidylinositol, mitomycin c, rantes,



sphingosine, indomethacin, 5fluorouracil, phosphatidylcholine, 5-fluorouracil,



mg 132, thymidylate, trans-cinnamaldehyde, sterol, polyadenosine



diphosphate ribose, nitric oxide, vitamin e succinate, lipopolysaccharide,



cisplatin, herbimycin a, 5-aza-2′deoxycytidine, proteasome inhibitor PSI, 2,5-



hexanedione, epothilone B, caffeic acid phenethyl ester, glycerol 3-phosphate,



tgf beta1, anisomycin, paclitaxel, gemcitabine, medroxyprogesterone acetate,



hymecromone, testosterone, ag 1478, doxorubicin, S-nitroso-N-



acetylpenicillamine, adpribose, sulforaphane, vitamin d, annexin-v, lactate,



reactive oxygen species, sb 203580, serine, N-acetyl-L-cysteine, dutp,



infliximab, ethanol, curcumin, cytarabine, tpck, calyculin a, dopamine, gp 130,



bromocriptine, apicidin, fatty acid, citrate, glucocorticoid, arsenite, butyrate,



peplomycin, oxaliplatin, camptothecin, benzyloxycarbonyl-Leu-Leu-Leu



aldehyde, clofibrate, carbon, wortmannin, fludarabine, N-(3-



(aminomethyl)benzyl)acetamidine, sirolimus, peptidoglycan, c2ceramide,



dihydrotestosterone, 7-aminoactinomycin d, carmustine, heparin, ceramide,



paraffin, mitoxantrone, docosahexaenoic acid, vitamin a, ivig, hydrogen



peroxide, 7-ethyl-10-hydroxy-camptothecin, oxygen, pydrin, bortezomib,



retinoic acid, 1,4-phenylenebis(methylene)selenocyanate, teriflunomide,



epinephrine, n acetylcysteine, noxa, irinotecan, oligonucleotide, d-api,



rasagiline, 8-bromo-cAMP, atpo, agarose, fansidar, clobetasol propionate,



teniposide, aurintricarboxylic acid, polysaccharide, CpG oligonucleotide,



cycloheximide


IRF1
tamoxifen, chloramphenicol, polyinosinic-polycytidylic acid, inosine



monophosphate, suberoylanilide hydroxamic acid, butyrate, iron, gliadin,



zinc, actinomycin d, deferoxamine, phosphatidylinositol, adenine, ornithine,



rantes, calcium, 2′,5′-oligoadenylate, pge2, poly(i-c), indoleamine, arginine,



estradiol, nitric oxide, etoposide, adriamycin, oxygen, retinoid, guanylate,



troglitazone, ifn-alpha, retinoic acid, tyrosine, adenylate, am 580, guanosine,



oligonucleotide, estrogen, thymidine, tetracycline, serine, sb 203580, pdtc,



lipid, cycloheximide


MYC
cd 437, 1, 25 dihydroxy vitamin d3, phenethyl isothiocyanate, threonine,



arsenic trioxide, salicylic acid, quercetin, prostaglandin E1, ionomycin, 12-o-



tetradecanoylphorbol 13-acetate, fulvestrant, phenylephrine, fisetin, 4-



coumaric acid, dihydroartemisinin, 3-deazaadenosine, nitroprusside, pregna-



4,17-diene-3,16-dione, adriamycin, bromodeoxyuridine, AGN194204, STA-



9090, isobutylmethylxanthine, potassium chloride, docetaxel, quinolinic acid,



5,6,7,8-tetrahydrobiopterin, propranolol, delta 7-pga1, topotecan, AVI-4126,



trichostatin A, decitabine, thymidine, D-glucose, mifepristone, poly rI:rC-RNA,



letrozole, L-threonine, 5-hydroxytryptamine, bucladesine, SB203580, 1′-



acetoxychavicol acetate, cyclosporin A, okadaic acid, dfmo, LY294002, hmba,



piceatannol, 2′,5′-oligoadenylate, 4-hydroxytamoxifen, butylbenzyl phthalate,



dexamethasone, ec 109, phosphatidic acid, grape seed extract, phorbol



myristate acetate, coumermycin, tosylphenylalanyl chloromethyl ketone, CD



437, di(2-ethylhexyl) phthalate, butyrine, cytidine, sodium arsenite,



tanshinone iia, L-triiodothyronine, niacinamide, glycogen, daunorubicin,



vincristine, carvedilol, bizelesin, 3-deazaneplanocin, phorbol, neplanocin a,



panobinostat, [alcl], phosphatidylinositol, U0126,



dichlororibofuranosylbenzimidazole, flavopiridol, 5-fluorouracil, verapamil,



cyclopamine, nitric oxide, cisplatin, hrgbetal, 5,6-dichloro-1-beta-d-



ribofuranosylbenzimidazole, amsacrine, gemcitabine, aristeromycin,



medroxyprogesterone acetate, gambogic acid, leucine, alpha-naphthyl acetate,



cyclic AMP, reactive oxygen species, PD 180970, curcumin, chloramphenicol,



A23187, crocidolite asbestos, 6-hydroxydopamine, cb 33, arsenite,



gentamicin, benzyloxycarbonyl-Leu-Leu-Leu aldehyde, clofibrate,



wortmannin, sirolimus, ceramide, melphalan, 3M-001, linsidomine, CP-55940,



hyaluronic acid, ethionine, clonidine, retinoid, bortezomib, oligonucleotide,



methyl 2-cyano-3,12-dioxoolean-1,9-dien-28-oate, tacrolimus, embelin,



methyl-beta-cyclodextrin, 3M-011, folate, ly294002, PP1, hydroxyurea,



aclarubicin, phenylbutyrate, PD 0325901, methotrexate, Cd2+, prazosin, vegf,



rapamycin, alanine, phenobarbital, pd 98, 059, trapoxin, 4-



hydroperoxycyclophosphamide, methamphetamine, s-(1,2-dichlorovinyl)-l-



cysteine, aphidicolin, vesnarinone, ADI PEG20, pirinixic acid, wp631, H-7,



carbon tetrachloride, bufalin, 2,2-dimethylbutyric acid, etoposide, calcitriol,



trastuzumab, cyclophosphamide, harringtonine, tyrosine, N(6)-(3-



iodobenzyl)-5′-N-methylcarboxamidoadenosine, resveratrol, thioguanine,



genistein, S-nitroso-N-acetyl-DL-penicillamine, zearalenone, lysophosphatidic



acid, Sn50 peptide, roscovitine, actinomycin D, propanil, agar, tamoxifen,



acetaminophen, imatinib, tretinoin, mithramycin, ATP, epigallocatechin-



gallate, ferric ammonium citrate, acyclic retinoid, L-cysteine, nitroblue



tetrazolium, actinomycin d, sodium nitroprusside, 1,2-dimethylhydrazine,



dibutyl phthalate, ornithine, 4-hydroxynonenal, beta-estradiol, 1-alpha, 25-



dihydroxy vitamin D3, cyproterone acetate, nimodipine, nitrofurantoin,



temsirolimus,



15-deoxy-delta-12,14-PGJ 2, estrogen, ribonucleic acid, ciprofibrate, alpha-



amanitin, SB 216763, bleomycin, forskolin, prednisolone, cyclosporin a,



thyroid hormone, tunicamycin, phosphorothioate, suberoylanilide



hydroxamic acid, pga2, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium



bromide, benzamide riboside, bisindolylmaleimide, SU6656, prostaglandin



E2, depsipeptide, zidovudine, cerivastatin, progesterone, sethoxydim,



indomethacin, mg 132, mezerein, pyrrolidine dithiocarbamate, vitamin e



succinate, herbimycin a, 5-aza-2′deoxycytidine, lipopolysaccharide, diazoxide,



anisomycin, paclitaxel, sodium dodecylsulfate, nilotinib, oxysterol,



doxorubicin, lipofectamine, PD98059, steroid, delta-12-pgj2, serine, H-8, N-



acetyl-L-cysteine, ethanol, n-(4-hydroxyphenyl)retinamide, tiazofurin,



cytarabine, H89, 10-hydroxycamptothecin, everolimus, lactacystin, n(1),



n(12)-bis(ethyl)spermine, silibinin, glucocorticoid, butyrate, camptothecin,



triamcinolone acetonide, tocotrienol, n-ethylmaleimide, phorbol 12,13-



didecanoate, thapsigargin, deferoxamine, R59949, bryostatin 1, paraffin,



romidepsin, vitamin a, docosahexaenoic acid, hydrogen peroxide, droloxifene,



saikosaponin, fluoxetine, retinoic acid, n acetylcysteine, dithiothreitol,



cordycepin, agarose, 8-bromo-cAMP, D-galactosamine, tachyplesin i,



theophylline, metoprolol, SU6657, 15-deoxy-delta-12,14-prostaglandin j2,



dmso, 2-amino-5-azotoluene, cycloheximide









It will be appreciated that administration of therapeutic entities in accordance with the invention will be administered with suitable carriers, excipients, and other agents that are incorporated into formulations to provide improved transfer, delivery, tolerance, and the like. A multitude of appropriate formulations can be found in the formulary known to all pharmaceutical chemists: Remington's Pharmaceutical Sciences (15th ed, Mack Publishing Company, Easton, PA (1975)), particularly Chapter 87 by Blaug, Seymour, therein. These formulations include, for example, powders, pastes, ointments, jellies, waxes, oils, lipids, lipid (cationic or anionic) containing vesicles (such as Lipofectin™), DNA conjugates, anhydrous absorption pastes, oil-in-water and water-in-oil emulsions, emulsions carbowax (polyethylene glycols of various molecular weights), semi-solid gels, and semi-solid mixtures containing carbowax.
Any of the foregoing mixtures may be appropriate in treatments and therapies in accordance with the present invention, provided that the active ingredient in the formulation is not inactivated by the formulation and the formulation is physiologically compatible and tolerable with the route of administration. See also Baldrick P. “Pharmaceutical excipient development: the need for preclinical guidance.” Regul. Toxicol Pharmacol. 32(2):210-8 (2000), Wang W. “Lyophilization and development of solid protein pharmaceuticals.” Int. J. Pharm. 203(1-2):1-60 (2000), Charman WN “Lipids, lipophilic drugs, and oral drug delivery-some emerging concepts.” J Pharm Sci. 89(8):967-78 (2000), Powell et al. “Compendium of excipients for parenteral formulations” PDA J Pharm Sci Technol. 52:238-311 (1998) and the citations therein for additional information related to formulations, excipients and carriers well known to pharmaceutical chemists.
In yet other embodiments, the methods of the disclosure include administering to a subject in need thereof an effective amount (e.g., therapeutically effective amount or prophylactically effective amount) of the treatments provided herein. Such treatment may be supplemented with other known treatments, such as surgery on the subject. In certain embodiments, the surgery is strictureplasty, resection (e.g., bowel resection, colon resection), colectomy, surgery for abscesses and fistulas, proctocolectomy, restorative proctocolectomy, vaginal surgery, cataract surgery, or a combination thereof.
Diseases that may be treated by the foregoing include, without limitation, infection, inflammation, immune-related disorders or aberrant immune responses.
Diseases with an abberant or pathologic immune response include include, for example, Acquired Immunodeficiency Syndrome (AIDS, which is a viral disease with an autoimmune component), Crohn's disease, systemic lupus erythematosus, ulcerative colitis, multiple sclerosis (MS), inflammatory bowel disease and chronic and acute inflammatory disorders. Examples of inflammatory disorders include asthma, atopic allergy, allergy, eczema, glomerulonephritis, graft vs. host disease.
Administration of a modulating agent to a patient suffering from a disorder or aberrant or condition considered successful if any of a variety of laboratory or clinical objectives is achieved, such as if symptoms associated with the disorder or condition is alleviated, reduced, inhibited or does not progress to a further, i.e., worse, state.
A therapeutically effective amount of an agent relates generally to the amount needed to achieve a therapeutic objective, and will depend on the specificity of agent for its specific target, the rate and route of administration, and the like. Where polypeptide-based agents are used, the smallest fragment that specifically binds to the target and retains therapeutic function is preferred. Such fragments can be synthesized chemically and/or produced by recombinant DNA technology. (See, e.g., Marasco et al., Proc. Natl. Acad. Sci. USA, 90: 7889-7893 (1993)). The formulation can also contain more than one active compound as necessary for the particular indication being treated, preferably those with complementary activities that do not adversely affect each other.
Therapy or treatment according to the invention may be performed alone or in conjunction with another therapy, and may be provided at home, the doctor's office, a clinic, a hospital's outpatient department, or a hospital. Treatment generally begins at a hospital so that the doctor can observe the therapy's effects closely and make any adjustments that are needed. The duration of the therapy depends on the age and condition of the patient, the stage of the cancer, and how the patient responds to the treatment. Additionally, a person having a greater risk of developing an inflammatory response (e.g., a person who is genetically predisposed or predisposed to allergies or a person having a disease characterized by episodes of inflammation) may receive prophylactic treatment to inhibit or delay symptoms of the disease.
It will be appreciated that administration of therapeutic entities in accordance with the invention will be administered with suitable carriers, excipients, and other agents that are incorporated into formulations to provide improved transfer, delivery, tolerance, and the like. A multitude of appropriate formulations can be found in the formulary known to all pharmaceutical chemists: Remington's Pharmaceutical Sciences (15th ed, Mack Publishing Company, Easton, PA (1975)), particularly Chapter 87 by Blaug, Seymour, therein. These formulations include, for example, powders, pastes, ointments, jellies, waxes, oils, lipids, lipid (cationic or anionic) containing vesicles (such as Lipofectin™), DNA conjugates, anhydrous absorption pastes, oil-in-water and water-in-oil emulsions, emulsions carbowax (polyethylene glycols of various molecular weights), semi-solid gels, and semi-solid mixtures containing carbowax. Any of the foregoing mixtures may be appropriate in treatments and therapies in accordance with the present invention, provided that the active ingredient in the formulation is not inactivated by the formulation and the formulation is physiologically compatible and tolerable with the route of administration. See also Baldrick P. “Pharmaceutical excipient development: the need for preclinical guidance.” Regul. Toxicol Pharmacol. 32(2):210-8 (2000), Wang W. “Lyophilization and development of solid protein pharmaceuticals.” Int. J. Pharm. 203(1-2):1-60 (2000), Charman W N “Lipids, lipophilic drugs, and oral drug delivery-some emerging concepts.” J Pharm Sci. 89(8):967-78 (2000), Powell et al. “Compendium of excipients for parenteral formulations” PDA J Pharm Sci Technol. 52:238-311 (1998) and the citations therein for additional information related to formulations, excipients and carriers well known to pharmaceutical chemists.
The medicaments of the invention are prepared in a manner known to those skilled in the art, for example, by means of conventional dissolving, lyophilizing, mixing, granulating or confectioning processes. Methods well known in the art for making formulations are found, for example, in Remington: The Science and Practice of Pharmacy, 20th ed., ed. A. R. Gennaro, 2000, Lippincott Williams & Wilkins, Philadelphia, and Encyclopedia of Pharmaceutical Technology, eds. J. Swarbrick and J. C. Boylan, 1988-1999, Marcel Dekker, New York.
Administration of medicaments of the invention may be by any suitable means that results in a compound concentration that is effective for treating or inhibiting (e.g., by delaying) the development of a disease. The compound is admixed with a suitable carrier substance, e.g., a pharmaceutically acceptable excipient that preserves the therapeutic properties of the compound with which it is administered. One exemplary pharmaceutically acceptable excipient is physiological saline. The suitable carrier substance is generally present in an amount of 1-95% by weight of the total weight of the medicament. The medicament may be provided in a dosage form that is suitable for administration. Thus, the medicament may be in form of, e.g., tablets, capsules, pills, powders, granulates, suspensions, emulsions, solutions, gels including hydrogels, pastes, ointments, creams, plasters, drenches, delivery devices, injectables, implants, sprays, or aerosols.
The agents disclosed herein (e.g., cells, agonists or antagonists) may be used in a pharmaceutical composition when combined with a pharmaceutically acceptable carrier. Such compositions comprise a therapeutically-effective amount of the agent and a pharmaceutically acceptable carrier. Such a composition may also further comprise (in addition to an agent and a carrier) diluents, fillers, salts, buffers, stabilizers, solubilizers, and other materials well known in the art. Compositions comprising the agent can be administered in the form of salts provided the salts are pharmaceutically acceptable. Salts may be prepared using standard procedures known to those skilled in the art of synthetic organic chemistry.
The term “pharmaceutically acceptable salts” refers to salts prepared from pharmaceutically acceptable non-toxic bases or acids including inorganic or organic bases and inorganic or organic acids. Salts derived from inorganic bases include aluminum, ammonium, calcium, copper, ferric, ferrous, lithium, magnesium, manganic salts, manganous, potassium, sodium, zinc, and the like. Particularly preferred are the ammonium, calcium, magnesium, potassium, and sodium salts. Salts derived from pharmaceutically acceptable organic non-toxic bases include salts of primary, secondary, and tertiary amines, substituted amines including naturally occurring substituted amines, cyclic amines, and basic ion exchange resins, such as arginine, betaine, caffeine, choline, N,N′-dibenzylethylenediamine, diethylamine, 2-diethylaminoethanol, 2-dimethylaminoethanol, ethanolamine, ethylenediamine, N-ethyl-morpholine, N-ethylpiperidine, glucamine, glucosamine, histidine, hydrabamine, isopropylamine, lysine, methylglucamine, morpholine, piperazine, piperidine, polyamine resins, procaine, purines, theobromine, triethylamine, trimethylamine, tripropylamine, tromethamine, and the like. The term “pharmaceutically acceptable salt” further includes all acceptable salts such as acetate, lactobionate, benzenesulfonate, laurate, benzoate, malate, bicarbonate, maleate, bisulfate, mandelate, bitartrate, mesylate, borate, methylbromide, bromide, methylnitrate, calcium edetate, methylsulfate, camsylate, mucate, carbonate, napsylate, chloride, nitrate, clavulanate, N-methylglucamine, citrate, ammonium salt, dihydrochloride, oleate, edetate, oxalate, edisylate, pamoate (embonate), estolate, palmitate, esylate, pantothenate, fumarate, phosphate/diphosphate, gluceptate, polygalacturonate, gluconate, salicylate, glutamate, stearate, glycollylarsanilate, sulfate, hexylresorcinate, subacetate, hydrabamine, succinate, hydrobromide, tannate, hydrochloride, tartrate, hydroxynaphthoate, teoclate, iodide, tosylate, isothionate, triethiodide, lactate, panoate, valerate, and the like which can be used as a dosage form for modifying the solubility or hydrolysis characteristics or can be used in sustained release or pro-drug formulations. It will be understood that, as used herein, references to specific agents (e.g., neuromedin U receptor agonists or antagonists), also include the pharmaceutically acceptable salts thereof.
Methods of administrating the pharmacological compositions, including agents, cells, agonists, antagonists, antibodies or fragments thereof, to an individual include, but are not limited to, intradermal, intrathecal, intramuscular, intraperitoneal, intravenous, subcutaneous, intranasal, epidural, by inhalation, and oral routes. The compositions can be administered by any convenient route, for example by infusion or bolus injection, by absorption through epithelial or mucocutaneous linings (for example, oral mucosa, rectal and intestinal mucosa, and the like), ocular, and the like and can be administered together with other biologically-active agents. Administration can be systemic or local. In addition, it may be advantageous to administer the composition into the central nervous system by any suitable route, including intraventricular and intrathecal injection. Pulmonary administration may also be employed by use of an inhaler or nebulizer, and formulation with an aerosolizing agent. It may also be desirable to administer the agent locally to the area in need of treatment; this may be achieved by, for example, and not by way of limitation, local infusion during surgery, topical application, by injection, by means of a catheter, by means of a suppository, or by means of an implant.
Various delivery systems are known and can be used to administer the pharmacological compositions including, but not limited to, encapsulation in liposomes, microparticles, microcapsules; minicells; polymers; capsules; tablets; and the like. In one embodiment, the agent may be delivered in a vesicle, in particular a liposome. In a liposome, the agent is combined, in addition to other pharmaceutically acceptable carriers, with amphipathic agents such as lipids which exist in aggregated form as micelles, insoluble monolayers, liquid crystals, or lamellar layers in aqueous solution. Suitable lipids for liposomal formulation include, without limitation, monoglycerides, diglycerides, sulfatides, lysolecithin, phospholipids, saponin, bile acids, and the like. Preparation of such liposomal formulations is within the level of skill in the art, as disclosed, for example, in U.S. Pat. Nos. 4,837,028 and 4,737,323. In yet another embodiment, the pharmacological compositions can be delivered in a controlled release system including, but not limited to: a delivery pump (See, for example, Saudek, et al., New Engl. J. Med. 321: 574 (1989) and a semi-permeable polymeric material (See, for example, Howard, et al., J. Neurosurg. 71: 105 (1989)). Additionally, the controlled release system can be placed in proximity of the therapeutic target (e.g., a tumor), thus requiring only a fraction of the systemic dose. See, for example, Goodson, In: Medical Applications of Controlled Release, 1984. (CRC Press, Boca Raton, Fla.).
The amount of the agents which will be effective in the treatment of a particular disorder or condition will depend on the nature of the disorder or condition, and may be determined by standard clinical techniques by those of skill within the art. In addition, in vitro assays may optionally be employed to help identify optimal dosage ranges. The precise dose to be employed in the formulation will also depend on the route of administration, and the overall seriousness of the disease or disorder, and should be decided according to the judgment of the practitioner and each patient's circumstances. Ultimately, the attending physician will decide the amount of the agent with which to treat each individual patient. In certain embodiments, the attending physician will administer low doses of the agent and observe the patient's response. Larger doses of the agent may be administered until the optimal therapeutic effect is obtained for the patient, and at that point the dosage is not increased further. In general, the daily dose range for a small molecule, protein or protein derivative thereof may be within the range of from about 0.001 mg to about 100 mg per kg body weight of a mammal, preferably 0.01 mg to about 50 mg per kg, and most preferably 0.1 to 10 mg per kg, in single or divided doses. On the other hand, it may be necessary to use dosages outside these limits in some cases. In certain embodiments, suitable dosage ranges for intravenous administration of an agent are generally about 5-500 micrograms (μg) of active compound per kilogram (Kg) body weight. Suitable dosage ranges for intranasal administration are generally about 0.01 pg/kg body weight to 1 mg/kg body weight. In certain embodiments, a composition containing an agent of the present invention is subcutaneously injected in adult patients with dose ranges of approximately 5 to 5000 μg/human and preferably approximately 5 to 500 μg/human as a single dose. Effective doses may be extrapolated from dose-response curves derived from in vitro or animal model test systems. Ultimately the attending physician will decide on the appropriate duration of therapy using compositions of the present invention. Dosage will also vary according to the age, weight and response of the individual patient.
Perturbation Screening
In certain embodiments, the gene signatures described herein are screened by perturbation of target genes within said signatures. Methods and tools for genome-scale screening of perturbations in single cells using CRISPR-Cas9 have been described, herein referred to as perturb-seq (see e.g., Dixit et al., “Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens” 2016, Cell 167, 1853-1866; Adamson et al., “A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response” 2016, Cell 167, 1867-1882; and International publication serial number WO/2017/075294). The present invention is compatible with perturb-seq, such that signature genes may be perturbed and the perturbation may be identified and assigned to the proteomic and gene expression readouts of single cells. In certain embodiments, signature genes may be perturbed in single cells and gene expression analyzed. Not being bound by a theory, networks of genes that are disrupted due to perturbation of a signature gene may be determined. Understanding the network of genes effected by a perturbation may allow for a gene to be linked to a specific pathway that may be targeted to modulate the signature and treat a disease (e.g., inflammatory disease, cancer, autoimmune disease). Thus, in certain embodiments, perturb-seq is used to discover novel drug targets.
Orthologs and Homologs
The terms “orthologue” (also referred to as “ortholog” herein) and “homologue” (also referred to as “homolog” herein) are well known in the art. By means of further guidance, a “homologue” of a protein as used herein is a protein of the same species which performs the same or a similar function as the protein it is a homologue of. Homologous proteins may but need not be structurally related, or are only partially structurally related. An “orthologue” of a protein as used herein is a protein of a different species which performs the same or a similar function as the protein it is an orthologue of. Orthologous proteins may but need not be structurally related, or are only partially structurally related. Thus, when reference is made to mouse genes and proteins, it is understood that the same is believed to apply to the corresponding ortholog in humans or other species.
Likewise, when referencing Cas9 and other proteins, it is understood to likewise apply to orthologs and homologs.
The CRISPR-CRISPR associated (Cas) systems of bacterial and archaeal adaptive immunity are some such systems that show extreme diversity of protein composition and genomic loci architecture. The CRISPR-Cas system loci has more than 50 gene families and there is no strictly universal genes indicating fast evolution and extreme diversity of loci architecture. So far, adopting a multi-pronged approach, there is comprehensive cas gene identification of about 395 profiles for 93 Cas proteins. Classification includes signature gene profiles plus signatures of locus architecture. A new classification of CRISPR-Cas systems is proposed in which these systems are broadly divided into two classes, Class 1 with multi-subunit effector complexes and Class 2 with single-subunit effector modules exemplified by the Cas9 protein. Novel effector proteins associated with Class 2 CRISPR-Cas systems may be developed as powerful genome engineering tools and the prediction of putative novel effector proteins and their engineering and optimization is important.
The effector protein may comprise a chimeric effector protein comprising a first fragment from a first effector protein ortholog and a second fragment from a second effector protein ortholog, and wherein the first and second effector protein orthologs are different. At least one of the first and second effector protein orthologs may comprise an effector protein from an organism comprising Bergeyella, Prevotella, Porphyromonas, Bacteroides, Alistipes, Riemerella, Myroides, Flavobacterium, Capnocytophaga, Chryseobacterium, Paludibacter, Phaeodactylibacter or Psychroflexus. 
In certain embodiments, the effector protein, particularly a Group 29 or Group 30 effector protein effector protein may be at least 700 amino acids long. In preferred embodiments, the effector protein may be about 1100 to about 1500 amino acids long, e.g., about 1100 to about 1200 amino acids long, or about 1200 to about 1300 amino acids long, or about 1300 to about 1400 amino acids long, or about 1400 to about 1500 amino acids long, e.g., about 900, about 1000, about 1100, about 1200, about 1300, about 1400, about 1500, about 1600, about 1700, or about 1800 amino acids long.
In certain embodiments, the Group 29 or Group 30 effector proteins as intended herein may be associated with a locus comprising short CRISPR repeats between 30 and 40 bp long, more typically between 34 and 38 bp long, even more typically between 36 and 37 bp long, e.g., 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, or 40 bp long. In certain embodiments the CRISPR repeats are long or dual repeats between 80 and 350 bp long such as between 80 and 200 bp long, even more typically between 86 and 88 bp long, e.g., 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, or 90 bp long.
Orthologous proteins may but need not be structurally related, or are only partially structurally related. In particular embodiments, the homologue or orthologue of a Group 29 or Group 30 protein as referred to herein has a sequence homology or identity of at least 80%, more preferably at least 85%, even more preferably at least 90%, such as for instance at least 95% with the Group 29 or Group 30 effector protein. In a preferred embodiment, the Group 29 or Group 30 effector protein may be an ortholog of an organism of a genus which includes but is not limited to Bergeyella, Prevotella, Porphyromonas, Bacteroides, Alistipes, Riemerella, Myroides, Flavobacterium, Capnocytophaga, Chryseobacterium, Phaeodactylibacter, Paludibacter or Psychroflexus. Some methods of identifying orthologs of CRISPR system enzymes may involve identifying tracr sequences in genomes of interest. Identification of tracr sequences may relate to the following steps: Search for the direct repeats or tracr mate sequences in a database to identify a CRISPR region comprising a CRISPR enzyme. Search for homologous sequences in the CRISPR region flanking the CRISPR enzyme in both the sense and antisense directions. Look for transcriptional terminators and secondary structures. Identify any sequence that is not a direct repeat or a tracr mate sequence but has more than 50% identity to the direct repeat or tracr mate sequence as a potential tracr sequence. Take the potential tracr sequence and analyze for transcriptional terminator sequences associated therewith.
It will be appreciated that any of the functionalities described herein may be engineered into CRISPR enzymes from other orthologs, including chimeric enzymes comprising fragments from multiple orthologs. Examples of such orthologs are described elsewhere herein. Thus, chimeric enzymes may comprise fragments of CRISPR enzyme orthologs of an organism which includes but is not limited to Bergeyella, Prevotella, Porphyromonas, Bacteroides, Alistipes, Riemerella, Myroides, Flavobacterium, Capnocytophaga, Chryseobacterium, Phaeodactylibacter, Paludibacter or Psychroflexus. A chimeric enzyme can comprise a first fragment and a second fragment, and the fragments can be of CRISPR enzyme orthologs of organisms of genuses herein mentioned or of species herein mentioned; advantageously the fragments are from CRISPR enzyme orthologs of different species.







TABLE 1







Representative Type VI-B Effectors and Accessory Proteins















Cas13b
Csx27/28
#
CRISPR-


Cas13b size


Species (Genome Accession)
Accession
Accession
Spacers
Cas?
Cas1?
Cas2?
(aa)

















Paludibacter propionicigenes
WP_013446107.1
NA
8
N
N
N
1155


WB4 (NC_014734.1)


Prevotella sp. P5-60
WP_044074780.1
NA
5
Y
?
?
1091


(NZ_JXQJ01000080.1)


Prevotella sp. P4-76
WP_044072147.1
NA
0
?
?
?
1091


(NZ_JXQI01000021.1)


Prevotella sp. P5-125
WP_044065294.1
NA
11
?
?
?
1091


(NZ_JXQL01000055.1)


Prevotella sp. P5-119
WP_042518169.1
NA
11
?
?
?
1091


(NZ_JXQK01000043.1)


Capnocytophaga canimorsus
WP_013997271.1
WP_013997274.1
51
Y
Y
Y
1200


Cc5 (NC_015846.1)


Phaeodactylibacter xiamenensis
WP_044218239.1
WP_044218241.1
19
?
?
?
1132


(NZ_JPOS01000018.1)


Porphyromonas gingivalis W83
WP_005873511.1
WP_005873518.1
7
Y
Y
Y
1136


(NC_002950.2)


Porphyromonas gingivalis
WP_021665475.1
WP_021665476.1
3
?
?
?
1136


F0570 (NZ_KI259168.1)


Porphyromonas gingivalis ATCC
WP_012458151.1
WP_012458152.1
12
Y
Y
Y
1136


33277 (NC_010729.1)


Porphyromonas gingivalis
ERJ81987.1
ERJ81988.1
0
?
?
?
1136


F0185 (AWVC01000122.1)


Porphyromonas gingivalis
WP_021677657.1
WP_021677658.1
6
?
?
?
1136


F0185 (NZ_KI259960.1)


Porphyromonas gingivalis SJD2
WP_023846767.1
WP_005873518.1
4
?
?
?
1136


(NZ_KI629875.1)


Porphyromonas gingivalis
ERJ65637.1
ERJ65638.1
3
?
?
?
1136


F0568 (AWUU01000145.1)


Porphyromonas gingivalis
ERJ87335.1
ERJ87336.1
2
?
?
?
1136


W4087 (AWVE01000130.1)


Porphyromonas gingivalis
WP_021680012.1
WP_005873518.1
4
?
?
?
1136


W4087 (NZ_KI260263.1)


Porphyromonas gingivalis
WP_021663197.1
WP_021663198.1
6
?
?
?
1136


F0568 (NZ_KI258981.1)


Porphyromonas gingivalis
WP_061156637.1
WP_005873518.1
11
?
?
?
1136


(NZ_LOEL01000010.1)


Porphyromonas gulae
WP_039445055.1
WP_039445052.1
10
?
?
?
1136


(NZ_JRAQ01000019.1)


Bacteroides pyogenes F0041
ERI81700.1
ERI81699.1
5
?
?
?
1116


(KE993153.1)


Bacteroides pyogenes JCM
WP_034542281.1
WP_034542279.1
18
?
?
?
1116


10003 (NZ_BAIU01000001.1)


Alistipes sp. ZOR0009
WP_047447901.1
NA
7
?
?
?
954


(NZ_JTLD01000029.1)


Flavobacterium branchiophilum
WP_014084666.1
WP_014084665.1
19
Y
N
Y
1151


FL-15 (NC_016001.1)


Prevotella sp. MA2016
WP_036929175.1
NA
7
?
?
?
1323


(NZ_JHUW01000010.1)


Myroides odoratimimus CCUG
EHO06562.1
EHO06560.1
2
?
?
?
1160


10230 (AGEC02000017.1)


Myroides odoratimimus CCUG
EKB06014.1
EKB06015.1
0
?
?
?
1158


3837 (AGZK01000016.1)


Myroides odoratimimus CCUG
WP_006265509.1
WP_006265510.1
0
?
?
?
1158


3837 (NZ_JH815535.1)


Myroides odoratimimus CCUG
WP_006261414.1
WP_006261415.1
0
?
?
?
1158


12901 (NZ_JH590834.1)


Myroides odoratimimus CCUG
EHO08761.1
EHO08762.1
0
?
?
?
1158


12901 (AGED01000033.1)


Myroides odoratimimus
WP_058700060.1
WP_006261415.1
10
Y
Y
Y
1160


(NZ_CP013690.1)


Bergeyella zoohelcum ATCC
EKB54193.1
EKB54194.1
9
?
?
?
1225


43767 (AGYA01000037.1)


Capnocytophaga cynodegmi
WP_041989581.1
WP_041989578.1
7
?
?
?
1219


(NZ_CDOD01000002.1)


Bergeyella zoohelcum ATCC
WP_002664492.1
WP_034985946.1
8
Y
Y
Y
1225


43767 (NZ_JH932293.1)


Flavobacterium sp. 316
WP_045968377.1
NA
0
?
?
?
1156


(NZ_JYGZ01000003.1)


Psychroflexus torquis ATCC
WP_015024765.1
NA
16
Y
Y
Y
1146


700755 (NC_018721.1)


Flavobacterium columnare ATCC
WP_014165541.1
NA
7
Y
Y
Y
1180


49512 (NC_016510.2)


Flavobacterium columnare
WP_060381855.1
NA
5
Y
Y
Y
1214


(NZ_CP013992.1)


Flavobacterium columnare
WP_063744070.1
NA
3
Y
Y
Y
1214


(NZ_CP015107.1)


Flavobacterium columnare
WP_065213424.1
NA
14
Y
Y
Y
1215


(NZ_CP016277.1)


Chryseobacterium sp. YR477
WP_047431796.1
NA
0
?
?
?
1146


(NZ_KN549099.1)


Riemerella anatipestifer ATCC
WP_004919755.1
WP_004919758.1
12
Y
Y
Y
1096


11845 = DSM 15868


(NC_014738.1)


Riemerella anatipestifer RA-CH-
WP_015345620.1
WP_004919758.1
12
Y
Y
Y
949


2 (NC_020125.1)


Riemerella anatipestifer
WP_049354263.1
WP_004919758.1
11
Y
Y
Y
949


(NZ_CP007504.1)


Riemerella anatipestifer
WP_061710138.1
WP_061710139.1
13
?
?
?
951


(NZ_LUDU01000012.1)


Riemerella anatipestifer
WP_064970887.1
WP_064970885.1
4
?
?
?
1096


(NZ_LUDI01000010.1)


Prevotella saccharolytica F0055
EKY00089.1
EKY00090.1
0
?
?
?
1151


(AMEP01000091.1)


Prevotella saccharolytica JCM
WP_051522484.1
NA
5
Y
Y
Y
1152


17484 (NZ_BAKN01000001.1)


Prevotella buccae ATCC 33574
EFU31981.1
EFU31982.1
16
?
?
?
1128


(AEPD01000005.1)


Prevotella buccae ATCC 33574
WP_004343973.1
WP_004343974.1
16
Y
Y
Y
1128


(NZ_GL586311.1)


Prevotella buccae D17
WP_004343581.1
WP_004343582.1
8
?
?
?
1128


(NZ_GG739967.1)


Prevotella sp. MSX73
WP_007412163.1
WP_036927782.1
13
?
?
?
1128


(NZ_ALJQ01000043.1)


Prevotella pallens ATCC 700821
EGQ18444.1
EGQ18443.1
4
?
?
?
1126


(AFPY01000052.1)


Prevotella pallens ATCC 700821
WP_006044833.1
WP_050795200.1
4
?
?
?
1126


(NZ_GL982513.1)


Prevotella intermedia ATCC
WP_036860899.1
WP_050795200.1
11
?
?
?
1127


25611 = DSM 20706


(NZ_JAEZ01000017.1)


Prevotella intermedia
WP_061868553.1
NA
27
?
?
?
1121


(NZ_LBGT01000010.1)


Prevotella intermedia 17
AFJ07523.1
AFJ07898.1
16
N
N
N
1135


(CP003502.1)


Prevotella intermedia
WP_050955369.1
WP_014708440.1
16
N
N
N
1133


(NZ_AP014926.1)


Prevotella intermedia
BAU18623.1
BAU18624.1
6
N
N
N
1134


(AP014598.1)


Prevotella intermedia ZT
KJJ86756.1
KJJ86755.1
2
?
?
?
1126


(ATMK01000017.1)


Prevotella aurantiaca JCM 15754
WP_025000926.1
WP_036889078.1
5
?
?
?
1125


(NZ_BAKF01000019.1)


Prevotella pleuritidis F0068
WP_021584635.1
WP_021584705.1
6
?
?
?
1140


(NZ_AWET01000045.1)


Prevotella pleuritidis JCM 14110
WP_036931485.1
WP_024991772.1
7
?
?
?
1117


(NZ_BAJN01000005.1)


Prevotella falsenii
WP_036884929.1
WP_051527348.1
10
?
?
?
1134


DSM 22864 = JCM 15124


(NZ_BAJY01000004.1)


Porphyromonas gulae
WP_039418912.1
WP_052073447.1
11
Y
Y
Y
1176


(NZ_JRAT01000012.1)


Porphyromonas sp. COT-052
WP_039428968.1
WP_050563578.1
12
?
?
?
1176


OH4946 (NZ_JQZY01000014.1)


Porphyromonas gulae
WP_039442171.1
WP_050563578.1
9
?
?
?
1175


(NZ_JRFD01000046.1)


Porphyromonas gulae
WP_039431778.1
WP_046201041.1
2
?
?
?
1176


(NZ_JRAJ01000010.1)


Porphyromonas gulae
WP_046201018.1
WP_046201041.1
4
?
?
?
1176


(NZ_KQ040500.1)


Porphyromonas gulae
WP_039434803.1
WP_039434800.1
20
?
?
?
1176


(NZ_JRAL01000022.1)


Porphyromonas gulae
WP_039419792.1
WP_052078041.1
9
?
?
?
1120


(NZ_JRAI01000002.1)


Porphyromonas gulae
WP_039426176.1
WP_039426172.1
6
?
?
?
1120


(NZ_JRAK01000129.1)


Porphyromonas gulae
WP_039437199.1
WP_052102013.1
0
?
?
?
1120


(NZ_KN294104.1)


Porphyromonas gingivalis
WP_013816155.1
WP_043890185.1
2
Y
Y
Y
1120


TDC60 (NC_015571.1)


Porphyromonas gingivalis ATCC
WP_012458414.1
WP_012458413.1
4
Y
Y
Y
1120


33277 (NC_010729.1)


Porphyromonas gingivalis
WP_058019250.1
WP_043898408.1
6
Y
Y
Y
1176


A7A1-28 (NZ_CP013131.1)


Porphyromonas gingivalis JCVI
EOA10535.1
EOA10563.1
5
?
?
?
1176


SC001 (APMB01000175.1)


Porphyromonas gingivalis W50
WP_005874195.1
WP_010955981.1
2
?
?
?
1176


(NZ_AJZS01000051.1)


Porphyromonas gingivalis
WP_052912312.1
WP_010955981.1
7
Y
Y
Y
1176


(NZ_CP011995.1)


Porphyromonas gingivalis AJW4
WP_053444417.1
WP_043898408.1
11
N
N
N
1120


(NZ_CP011996.1)


Porphyromonas gingivalis
WP_039417390.1
WP_021665928.1
5
Y
Y
Y
1120


(NZ_CP007756.1)


Porphyromonas gingivalis
WP_061156470.1
WP_021663076.1
5
?
?
?
1120


(NZ_LOEL01000001.1)









Kit

The terms “kit” and “kit of parts” as used throughout this specification refer to a product containing components necessary for carrying out the specified methods (e.g., methods for detecting, quantifying or isolating intestinal epithelial cells, intestinal epithelial stem cells, intestinal immune cells, or respiratory epithelial cells (preferably epithelial cells, e.g., tuft cells) as taught herein), packed so as to allow their transport and storage. Materials suitable for packing the components comprised in a kit include crystal, plastic (e.g., polyethylene, polypropylene, polycarbonate), bottles, flasks, vials, ampules, paper, envelopes, or other types of containers, carriers or supports. Where a kit comprises a plurality of components, at least a subset of the components (e.g., two or more of the plurality of components) or all of the components may be physically separated, e.g., comprised in or on separate containers, carriers or supports. The components comprised in a kit may be sufficient or may not be sufficient for carrying out the specified methods, such that external reagents or substances may not be necessary or may be necessary for performing the methods, respectively.
Typically, kits and kit of parts are employed in conjunction with standard laboratory equipment, such as liquid handling equipment, environment (e.g., temperature) controlling equipment, analytical instruments, etc. In addition to the recited binding agents(s) as taught herein, such as for example, antibodies, hybridisation probes, amplification and/or sequencing primers, optionally provided on arrays or microarrays, the present kits may also include some or all of solvents, buffers (such as for example but without limitation histidine-buffers, citrate-buffers, succinate-buffers, acetate-buffers, phosphate-buffers, formate buffers, benzoate buffers, TRIS (Tris(hydroxymethyl)-aminomethane) buffers or maleate buffers, or mixtures thereof), enzymes (such as for example but without limitation thermostable DNA polymerase), detectable labels, detection reagents, and control formulations (positive and/or negative), useful in the specified methods. Typically, the kits and kit of parts may also include instructions for use thereof, such as on a printed insert or on a computer readable medium. The terms may be used interchangeably with the term “article of manufacture”, which broadly encompasses any man-made tangible structural product, when used in the present context.
In certain embodiments, the kit of parts or article of manufacture may comprise a microfluidic system.
The invention having now been described by way of written description, those of skill in the art will recognize that the invention can be practiced in a variety of embodiments and that the foregoing description and examples below are for purposes of illustration and not limitation of the claims that follow.
Although the present invention and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined in the appended claims.
The invention is further described in the following examples, which do not limit the scope of the invention described in the claims.
EXAMPLES
Example 1—A Single-Cell Atlas Identifies all Known Populations of Epithelial Cells in the Small Intestine
Here, Applicants performed an scRNA-seq survey of 53,193 epithelial cells of the small intestine (SI) in homeostasis and during infection. Applicants identified gene signatures, key transcription factors (TFs) and specific G protein-coupled receptors (GPCRs) for each major small intestinal differentiated cell type. Applicants distinguished proximal and distal enterocytes and their stem cells, established a novel classification of different enteroendocrine subtypes, and identified previously unrecognized heterogeneity within both Paneth and tuft cells. Finally, Applicants demonstrated how these cell types and states adaptively change is response to different infections.
Applicants profiled a total of 53,193 individual cells across this study (Table 2). Applicants estimated the required cell numbers using a general statistical model based on the negative binomial distribution for random sampling (Methods). There are seven known cell-types in the intestinal epithelium, and in order to provide an unbiased estimate, Applicants arbitrarily allow for as many as twice this number. The statistical framework suggested that to achieve a 99% probability of sampling at least 50 cells from each of 14 expected cell types, where the rarest cell type is present at a fraction of 1%, Applicants needed to sequence 7,500 cells (Methods).









TABLE 2






Number
Single-cell


Dataset
of cells
platform

















Atlas (droplet)
7216
3′-droplet Full-length




plate


Atlas (plate)
1522


Infection models (10X)
9842
3′-droplet


Salmonella infection
2,029
3′-droplet Full-length




plate


Infection models (SS2)
389


RANKL-treated organoids
5434
3′-droplet


Follicle-associated epithelium (FAE)
4700
3′-droplet


Spatial regions
11665
3′-droplet


Paneth cell enrichment
10396
3′-droplet


Total
53193









Applicants used droplet-based massively-parallel single cell RNA-Seq24 (Methods) to transcriptionally profile EpCAM+ epithelial cells from the small intestine of C57BL/6 wild-type and Lgr5-GFP knock-in mice6 (FIG. 1a). Applicants measured 8,882 single-cell profiles, removed 1,402 low quality cells (<800 genes detected; Methods) and 264 contaminating immune cells (Methods), retaining 7,216 cells for all subsequent analyses (median 42,697 transcripts per cell, median 1,659 genes detected per cell; FIG. 7a), with excellent reproducibility between replicates (n=6 mice, mean r=0.95, FIG. 7c-f).
Unsupervised clustering of the data partitioned the cells into 15 distinct groups. First, Applicants built a k-nearest neighbor graph on a low-dimensional representation of the cellular expression data using principal component analysis (PCA), and partitioned this graph into 15 discrete clusters using the Infomap algorithm25,26, each comprising transcriptionally similar cells (Methods). The clusters, each of which contained cells from all mice and replicate experiments (FIG. 7c,g), were visualized using t-stochastic neighborhood embedding26-28 (tSNE) (FIG. 1b).
Applicants labeled the 15 clusters post hoc based on the expression of signatures of known marker genes (FIG. 7g), showing that each is associated with a distinct cell type or state, including the major post-mitotic cell-types: enterocyte, goblet, Paneth, enteroendocrine and tuft cells (FIG. 1b). Applicants scored proliferating cells with a cell-cycle signature that Applicants previously developed from single-cell profiles29 to distinguish between dividing stem or progenitor cells and fully differentiated, post-mitotic cells. To enrich for M cells, found only above Peyer's patches, Applicants isolated and analyzed the follicle associated epithelium (FAE) in a separate set of experiments (below). The enteroendocrine, Paneth, goblet, stem and tuft cells were each represented by a single (1:1 matching) cluster (FIG. 1b and FIG. 7g). While the term ‘enterocytes’ is occasionally used to refer to all intestinal epithelial cells, in this study Applicants use the term to refer exclusively to absorptive enterocytes, which are the most abundant cell type in the intestinal epithelium1. This subset of cells was partitioned across seven clusters representing distinct stages of maturation (FIG. 1b, FIG. 7g). Of note, a recent study30 identified the same major cell-type clusters of IECs without these distinctions between various stages of enterocyte differentiation. The proportions of common differentiated IEC types, such as goblet cells (7.1%) and enterocyte (44.6%), were consistent with their expected abundances given the crypt-enriched isolation protocol Applicants used (Methods, FIG. 7d), with the exception of Paneth cells, which were under-represented in the data (3.6% compared to the expected 5%31). Conversely, the proportions of enteroendocrine and tuft cells were 4.3% and 2.3%, respectively, significantly higher than current estimates11, 12, 14. To improve Paneth cell capture, Applicants devised a sorting strategy to better capture large cells. Profiling an additional 10,396 epithelial cells identified 1,449 Paneth cells (13.9%) in two distinct clusters (FIG. 10M), but no additional novel cell-types. Applicants thus expect that all cell-types with >0.75% prevalence were detected in the survey at 99% confidence.
Applicants validated the atlas by independently profiling single epithelial cells that were sorted by FACS followed by an established full-length scRNA-seq protocol32 (FIG. 1a and FIGS. 7b and 2a). Applicants profiled 1,853 single cells, filtered isolated immune cells and lower quality cells (<3,000 genes per cell; Methods), and retained a high-quality subset of 1,522 single cells for analysis, with high reproducibility across mice (n=10 mice, FIG. 8a). The measured cell profiles had much higher coverage (median 1.06 million reads per cell, median of 6,009 genes per cell; FIG. 7b). The same clustering procedure (using the 15 significant PCs in this data; Methods) identified 8 clusters, and overall recapitulated the same post-mitotic cluster groups (FIG. 8a), but without finer distinctions by maturity and location among the enterocytes (below), as expected given the much smaller cell number. This highlights the importance of collecting a large number of scRNA-seq profiles to make finer distinctions26.
Applicants also profiled trachea single cells and verified a tuft cell gene signature that was consistent across the intestine and trachea (FIG. 27-29). Applicants identified transcription factors specific to tuft cells in the trachea and these were consistent with transcription factor expression in the intestine (FIG. 30).
Example 2—Distinct Cell Types are Characterized by Specific Signatures, TFs and Receptors
Relying on the high congruence between the two approaches, Applicants defined high-confidence consensus expression signatures for each cell type (Methods), highlighting known markers (corroborating the labels) and novel ones suggesting specific functions (FIG. 1c, FIG. 8b and Tables 3-5). For example, the Paneth cell consensus signature identified Mptx2, a mucosal pentraxin, with unknown function33, (FIG. 1c, FIG. 8b,c, Table 5), which Applicants validated using single-molecule fluorescence in situ hybridization (smFISH, Methods) (FIG. 1d,e). From the deeper, full length RNA-seq dataset, Applicants also identified Mptx1, another mucosal pentraxin, as specific to Paneth cells (FDR<0.001, Mann-Whitney U-test, Table 4). Other Pentraxins include C reactive protein (CRP) and serum amyloid P component protein (SAP), secreted proteins that play a role in host defense against pathogenic bacteria34. In addition, the two Paneth cell subsets expressed distinct panels of anti-microbial alpha-defensins (FIG. 10n).







TABLE 3







Marker genes (3′ droplet-based data) for intestinal epithelial cell-types

















Enterocyte 
Enterocyte 
Enterocyte
Enterocyte







Entero-
Immature 
Immature 
Mature 
Mature 







endocrine
Distal
Proximal
Distal
Proximal
Goblet
Paneth
Stem
TA (G2)
Tuft





Chgb
Reg3g
Casp6
Tmigd1
Apoa4
Agr2
Gm15284
Gkn3
Stmn1
Lrmp


Chga
Gsdmc4

Fabp6
Fabp1
Spink4
Gm14851

Tubb5
Alox5ap


Gfra3
Prss32

Slc51b
Apoc2
Fcgbp
Defa21


Rgs13


Cpe
Krt8

Slc51a
Rbp2
Tff3
Defa22


Sh2d6


Tac1


Mep1a
Apoc3
Muc2
AY761184


Ltc4s


Fam183b


Fam151a
Leap2
Zg16
Defa24


Avil


Hmgn3


Naaladl1
Cyp2b10
Clca1
Defa17


Hck


Cck


Slc34a2
Cyp3a11
Ccl6
Lyz1


Dclk1


Fev


Plb1
Lct
Klk1
Defa-rs1


Snrnp25


Gch1


Nudt4
Gsta1
Tpsg1
Defa3


Cd24a


Pcsk1n


Dpep1
Gstm1
Ccl9
Mptx2


Trpm5


Bex2


Pmp22
Gstm3
Txndc5
Ang4


Kctd12


Neurog3


Xpnpep2
Ephx2
Smim14_
Defa26


Aldh2







ENSMUSG00000037822






Ngfrap1


Muc3
Ms4a10
Tspan13
Gm15292


Il13ra1


Vwa5b2


Neu1
Fam213a
Atoh1



Gng13


Resp18


Clec2h
Cbr1
Lrrc26



Tmem176a


Sct


Phgr1
Adh6a
Ramp1



Skap2


Aplp1


2200002D01Rik
Cyb5r3
Galnt12



Ptpn6


Scgn


Prss30
Dhrs1
Mmp7



Ly6g6f


Neurod1


Cubn
Ifi27l2b
Qsox1



Fyb


Nkx2-2


Plec
Cyb5a
Fkbp11



Adh1


Insm1


Fgf15
Cyp3a25
Rep15



Tmem176b


Vim


Crip1
Gda
Tmsb10



Hpgds


Rbp4


Krt20
Ckb
Pla2g10



Reep5


Isl1


Dhcr24
Prap1
Tsta3



Ptpn18


Ddc


Myo15b
Cgref1
Pdia6



Spib


Mtch1


Amn
Dnase1
Sdf2l1



Bpgm


Tph1


Enpep
Aldh1a1
S100a6



Galk1


Cldn4


Anpep
Khk
Manf



Matk


Scg5


Slc7a9
Lpgat1
Slc12a8



Tuba1a


Maged1


Ocm
Treh
Creb3l1



1810046K07Rik


Rprml


Anxa2
Reg3a
Sh3bgrl3



Hmx2


Cryba2


Aoc1
Acsl5
Spdef



Ccdc28b


Rph3al


Ceacam20
Ace
Tpd52



Ethe1


Celf3


Arf6
Aldob
Pdia5



Limd2


Cacna1a


Abcb1a
H2-Q2
Cmpk1



Sh2d7


Trp53i11


Xpnpep1
Rdh7
Serp1



Ccdc109b


Gpx3


Vnn1
Ckmt1
Tmed3



Tspan6


Pcsk1


Cndp2
Cyp3a13
Selm



Smpx


Fabp5


Nostrin
P4hb
Creb3l4



Vav1


Fxyd6


Slc13a1
Mdh1
Smim6



Ly6g6d


Cplx2


Aspa
Ppap2a
Krtcap2



Pik3r5


Cdkn1c


Maf
Slc2a2
Bace2



Nebl


Rundc3a


Myh14
Cox7a1
Stard3nl



Plcg2


Pycr2



Sec14l2
Bcas1



Rbm38


Myl7



Gsta4
Nans



Vdac3


Ffar2



Mme
C1galt1c1



Krt18


Prnp



Retsat
Xbp1



Asah1


Rimbp2



Mttp
Hpd



Cd47


Slc25a4



Creb3l3
Slc50a1



Krt23


Bambi



Slc5a1
Guk1



Bcl2l14


Itm2c



Sult1b1
Tmed9



Lima1


Cacna2d1



Hsd17b6
Ssr4



Pygl


Fgd2



Scp2
Hgfac



Itpr2


Gadd45a



Cyb5b
Ostc



Inpp5j


Cited2



Cyp2c65
Creld2



Pea15a


Olfm1



Gpx4
Sec61b



Rac2


Slc39a2



Xdh
Gale



Pou2f3


Ptov1



Cyp2d26
Kdelr2



Atp2a3


Rab3c



Ugdh
Ssr2



Bmx


Tox3



Gstm6
Ern2



Acot7


Cdkn1a



Ndufa1
Ergic1



Gnai2


Anxa6



Gpd1
AW112010



Alox5


Krt7



Cyp2c66
Gcnt3



Ppp3ca


Btg2




Guca2a



Ptgs1


Cnot6l




Klf4



Calm2


Riiad1




Sep15



Zfp428


Marcksl1




Galnt7



Tmem141


Pax6




Uap1



Myo1b


Wbp5




Dnajc10



Siglecf


Scg3




Ddost



Pla2g4a


Nisch




Oit1



Inpp5b


Gstz1




Foxa3



Fam221a


Bax




Tm9sf3



Bub3


Gm43861




Cracr2b



Arpc5


Slc18a1




Vimp



Pla2g16


Gng4




Capn9



1110007C09Rik







Scin



Gimap1







Pdia3



Coprs







Rnase1



Lect2







Calr



Nrgn







Wars



Agt







Snhg18



Ffar3







Dap



Tmem45b







Ttc39a



Ccdc23







Dad1



Rgs2







Tnfaip8



Mlip







Tram1



Csk







Kdelr3



2210016L21Rik







Arf4



St6galnac2







Cmtm7



Ildr1











Gprc5c











Mocs2











Nrep











Pik3cg











Malat1











Sec14l1











Ndufaf3











Inpp5d











Pim3











Tmem9











Gga2











Nt5c3





Significance cut-offs: FDR (max): 0.05, Log2 fold-change: 0.5






Significance cut-offs: FDR (max):0.05, Log 2 fold-change: 0.5







TABLE 4







Marker genes (full-length plate-based data) for intestinal epithelial cell-types

















Enterocyte
Enterocyte









progenitor
progenitor







Enteroendocrine
Enterocyte
(early)
(late)
Goblet
Paneth
Stem
TA
Tuft

















Gfra3
Mep1b
Slc16a1
Ccnb1
Clca3
Defa23
Lgr5
Alox5ap


Chgb
Anpep

Cdc20
Zg16
Gm15284
Gkn3
Hck


Trp53i11
Gsta1

Cenpa
Fcgbp
Defa17
Ascl2
Lrmp


Neurod1
Apoa1

Cdkn3
Tff3
Defa-rs7
Olfm4
Avil


Vwa5b2
Gm3776

Cdc25c
Agr2
AY761184
Rgmb
Trpm5


Cck
Igsf9

Ccnb2
Scin
Defa-rs1
Igfbp4
Spib


Rfx6
Il18

Kif22
Pdia5
Gm7849
2210407C18Rik
Rgs13


Prnp
Ace2

Ube2c
Tpsg1
Gm14851
Jun
Ltc4s


Pcsk1
Creb3l3

Sapcd2
Chst4
Defa3
Pdgfa
Pygl


Syt13
Krt20

Rbp7
Bcas1
Defa22
Soat1
Sh2d7


Rph3al
Slc9a3

Ccna2
Bace2
Gm21498
Tnfrsf19
Dclk1


Fabp5
Dpep1

Aurka
Galnt12
Defa26
Cyp2e1
Alox5


Pam
Slc25a45

Cdkn2d
Rep15
Defa4
Fstl1
Pik3r5


Scgn
Rbp2

Kif23
S100a6
Defa20
H2-Eb1
Fyb


Aplp1
Ms4a18

Nek2
Capn9
Defa25
Ifitm3
Vav1


Fev
Reg3b

Birc5
Spdef
Gm14850
Prelp
Matk


Scg5
Reg3a

Plk1
Atoh1
Defa5
Scn2b
Tspan6


Celf3
Clec2h

Tacc3
Guca2a
Defa24
A930009A15Rik
Strip2


Resp18
Slc51b

Melk
Pla2g10
Gm15292
H2-Ab1
Pou2f3


Neurog3
Cyp2d26

Cdca3
Muc2
Defa-ps1
Slc1a2
1810046K07Rik


Maged1
Adh6a

Hmmr
Mlph
Gm15315
Cd74
Ptpn6


Scg3
Bco2

Spc25
AW112010
Mptx2
Sp5
Bmx


Pax4
Slc3a1

Tpx2
Scnn1a
Gm15299
Noxa1
Tuba1a


Olfm1
Cyp3a13

Arhgef39
Ern2
Gm10104
Rgcc
Espn


Cplx2
Slc16a5

Bub1b
Ttc39a
Lyz1
Sorbs2
Plcb2


Isl1
Btnl1

1190002F15Rik
Liph
Clps
Sectm1b
Ffar3


Gpx3
2010106E10Rik

Kif4
C1galt1c1
Defa21
H2-Aa
Ccdc109b


Anxa6
Maob

Mad2l1
Kcnk6
Reg4
Cdo1
Plcg2


Gng4
Sis

Fbxl8
Creb3l4
Pnliprp2
Slc14a1
Ly6g6f


Mreg
Acad11

Gpsm2
Slc12a8
Defa6
Clca2
Hpgds


Map1b
Edn2

Ckap2l
Efcab4a
Pla2g2a
Tifa
Pea15a


Bex2
Spink3

Knstrn
Ptprr
Itln1
Pls3
Ly6g6d


Baiap3
H2-Q1

Id1
Klk1
Mmp7
Hmgcs2
Pik3cg


Disp2
Sult2b1

Cmc2
Tnfaip8
Gm21002
Arid5b
Inpp5d


1700086L19Rik
Slc7a7

1810065E05Rik
Lrrc26
Gm7861
Agr3
Ccdc28b


Lrp11
1700019G17Rik

Cenpe
C1galt1
Ang4
Slc12a2
Snrnp25


Rimbp2
Dgat2

Pif1
Galnt7
Gm15308
Rassf5
Kctd12


Snap25
Enpep

Ckap5
Fam174b
Habp2
Rnf43
Siglec5


Klhdc8b
Fmo5

Cnih4
Sgsm3
Pnliprp1
Nrn1
Skap2


Foxa2
2010001E11Rik

Spc24
Galnt3
Gm6696
Lamb3
Ccdc129


Gck
Fam3b


Spats2l
Mptx1
Cd44
Nebl


Pcsk1n
Slc26a6


Ccl9
Fam46c
Axin2
Gprc5c


Gdap1l1
Mpp1


Sytl2
Samd5
Slc27a2
Rgs22


Map3k15
Ces1f


Car8
Lyz2
Afap1l1
Gfi1b


Kcnh6
Apoa4


Uap1
C4bp
Ccdc3
Hmx3


Kcnb2
Slc5a11


Asph
1810010D01Rik
Lrig1
Cbr3


Prodh2
2010003K11Rik


Slc50a1
Apoc2
Noxo1
Pfkfb3


Bex1
Eci3


Smim14
AY761185
Cdk6
Prss53


Lhfpl2
Cyp4f14


Creb3l1
Defb1
Amica1
Itpr2


Fam183b
Btnl6


Hgfac
Pla2g2f
Tgif1
Limd2


Nkx2-2
Ace


Stard3nl
Copz2
Tns3
Cd300lf


Pax6
Hsd17b6


Tspan13
Scgb2b7
Nr2e3
Chn2


Adprm
Rdh7


Gsn
Scgb2b19
Efna4
Smpx


Dbpht2
Alpi


Capn8
Scgb2b20
Rnf32
Ptgs1


Myt1
Gpd1


Gcnt3
Klf15
Prss23
A4galt


Kcnk16
Ptprh


Txndc5
Sntb1
2010009K17Rik
Rac2


Tac1
Papss2


Atp2c2
Ggh
Smoc2
Csk


Scarb1
Ggt1


Hpd
Cd244
Mecom
Slco4a1


Acadsb
Aldh1a1


Bhlhe40
Gm15293
Esrrg
Ptpn18


Vim
Naaladl1


Tfcp2l1
Gm7325
Aqp1
Chat


Xpnpep2
Agpat9


Qsox1
Fzd9
Znrf3
Hebp1


Acsl6
H2-Q2


St3gal6
Fgfrl1
Grb7
Ppp1r14c


Bcmo1
Hsd17b2


Rap1gap
Tesc
Phgdh
Dgki


Parp6
Exoc3l4


Kctd14
Slc1a4
2410004N09Rik
Inpp5j


Plxnb1
Hpgd


Kdelr3
Lamb1
Clca4
Tppp3


Cnot6l
Gnpda1


Galnt10
Darc
Aqp4
Gng13


Ncald
Gm1332


Dnajc10
Ddx26b
Lcp1
Ildr1


Scg2
Ms4a10


Sytl4
Slc30a2
E030011005Rik
Cwh43


Phldb2
Gm7092


Hid1
Hspb8
Snhg1
Il17rb


Peg3
Ugt2a3


Samhd1
Sync
BC064078
Ncf2


Mapre3
Upp1


Fkbp11
Slc16a7
Car12
Fut2


Ids
Lrrc19


Galnt5
Hapln4
Zbtb38
Coprs


Amigo2
Fmo4


Tmed3
Insrr
Cdca7
Ddah1


Dner
Hkdc1


Ica1
Acvr1c
Fam13a
Tmem116


Syp
Nr1h3


Pqlc3
Syne4
Shisa2
Sucnr1


Tox3
Themis3


Tmem123
Acox2
Dtx4
Tmem176a


Insm1
Agmo


Sdf2l1
Dkk3
Slc19a2
Ccrl1


Adora3
Slc6a20a


S100a14
Ang2
Fam115c
1110007C09Rik


Tmem106c
Soat2


Ergic1
Ang6
Mir703
Adcy5


Sstr1
Ces2a


Efcab4b
Thbs1
Cd14
Fnbp1


Cbfa2t2
Bcl2l15


Foxa3
Dll3
Mettl20
Plk2


Slc39a2
Entpd5


Stx17
Ang5
Myo9a
Hmx2


Rasd1
Cndp2


AI597468

App
Tmem141


Cacna2d1
Tmem37


Fxyd3

Clic6
Krt23


Ngfrap1
Gda


Cd97

Wee1
Gprc5a


Rab36
Abcg5


Csrp1

2410006H16Rik
Rgs2


Akna
Ces2c


Pdia6

Lancl1
Camk2b


Ghrl
Mogat2


Tinagl1

1500012F01Rik
Fes


Gpr116
Abhd3


Rcan3

Casp12
Bpgm


2610301B20Rik
St3gal4


Fam114a1

Sh3rf1
Acacb


Rbfox2
Gm8909


Cmtm7

Lrp4
Il13ra1


Pde1c
Slc5a1


Ppapdc1b

Arhgef26
Zfp428


Mapk8ip2
Tubal3


Mon1a

Etv6
Ppp1r3b


Scn3a
Gstm3


Slc7a4

1700024F13Rik
Ccnj


Sstr5
Sphk1


Tnfrsf21

Cttnbp2
Bcl2l14


Lypd1
Slc26a3


Tor3a

Slc16a13
Tmem229a


Marcks
Tmem106a


Adrbk1

Htr4
Ethe1


Riiad1
Slc27a4


P2rx4

Pdxk
Runx1


Trit1
Sowaha


Myo5c

Immp2l
Gga2


Ptpru
Slc6a4


Nipal2

Rps15a-ps6
Apobec1


Apbb1
Mme


Tmem39a

Rps15a-ps4
Serpini1


Galr3
Adamtsl5


Sil1

Nap1l1
St6galnac6


Rapgef4
Aldh1l1


Slc17a9

Sdc4
Fbxl21


Sphkap
Gpt


Mcf2l

Epn3
9030624J02Rik


Golim4
Igsf5


Rasa4

Sipa1l1
Inpp5b


Nefm
Emp1


Cgref1

Wfdc15b
Samd14


Cdk2ap1
Cox7a1


Galk2

Zfp341
Pgm2l1


Tubb3
Ugt2b5


Wars

Ngef
Pla2g4a


Tmem182
Apoc3


Gm9994

Nrg4
Ptprc


Fam135a
Abcg8


Edem1

Csad
Aldh2


Fam43a
Ugt2b36


Mia3

Rpl34-ps1
Ifi27l1


Golga7b
Pex11a


Slc35a1

Rin2
Pnpla3


Slc26a4
Osgin1


Tm9sf3

Cd81
Jarid2


Chd7
Gsta4


Fhl1

Irf2bp2
Rgs19


Cerkl
Slc28a1


Sec24d

Sesn3
Reep5


Cplx1
Gm11437


Sel1l3

Phlpp1
Tiparp


Galr1
Nat8


Tmed9

Yap1
Gnai2


Gpr119
Nr1i3


Cd9

Mfge8
Fam49a


Fam160a2
Slc51a


Rasd2

Zfp825
Cacna2d2


Pcp4l1
Fabp1


Edem2

Itga1
Ypel2


Efcab1
Abcc2


Golph3l

Pcdh8
Cd24a


Maml3
Apob


Arfip2

Vdr
Acot7


Ap3b2
Mical2


Tsta3

Kcnq1
Svil


Trf
Mgat4c


Tvp23b

Slc28a2
Abhd16a


Rab31
H2-Bl


Rnf39

Zfp36l1
Fam101a


Hnrnph3
Hdhd3


E130003G02Rik

Urod
Trim40


Ffar1
Sec23a


Aacs

Rgs12
Trak1


Emb
Slc7a9


Chrm1

Nfib
Sec14l1


Th
Tmem86a


Fut4

Sdsl
4930539E08Rik


Ptprn
Npc1l1


Vps37c

Nfia
Smtn


Prkar1b
Btnl2


Creld2


Galk1


Dock4
Acot9


Ikbip


Tbc1d1


Kirrel2
Paqr7


Nans


Tmem176b


Sh2d5
Cblc


Tpd52


Fcna


Tmem130
Tmem253


Tmem214


Abhd2


Pde11a
Smlr1


Anxa3


Hsbp1l1


Nek5
Abhd6


Rassf6


Slc4a8


Azi1
Amn


Bcat2


Myo1b


5430425J12Rik
Pbld2


Tmem159


Tmem38b


Pnmal1
Mttp


Stxbp6


Hk1


Dnahc9
Ap2a2


Slc30a7


Neurl1a


Rnf122
Ptk6


Mansc1


Dmxl2


Chst11
Vwce


Gfpt1


Bub3


Tekt2
Cideb


Gmppb


Ptprj


Mum1l1
Sco2


Sybu


Trib2


Trpm2
Gramd3


Srd5a1


Stard5


Map9
Apol10a


Tram1


Ubtd1


Ctif
Dpyd


Slc39a7


Slc41a3


Btbd17
Abat


Tmem248


Plekhg5


Lrrc16b
Slc46a1


Bet1l


Rbm38


Rufy2
Adtrp


Sec23ip


Fam57a


Ambp
Xdh


Cog6


Eef2k


Pkia
Tgfbi


Rab3d


Cables2


Pitpnc1
Chp2


D630039A03Rik


Fbxo25


Mapkbp1
Gyk


Prrc1


Ap1s2


Unc13a
Khk


Appl2


1300002K09Rik


Gatm
Lct


1810055G02Rik


Ero1lb


Slc35d3
Atp6v0a2


Synj2


Clmn


Spred3
Rhbg


1700066B19Rik


Fam49b


Zc3h12c
Tmem82


Arfgap1


Cpvl


Mapk15
Galm


Oit1


Prr15


March4
AA986860


Ehd4


Lpcat4


Pax6os1
Shpk


Stx5a


Tmem74b


Neurod2
Slc15a1


Plcb1


Mn1


Cidea
Cyp4f40


Ptger4


Eppk1


Klhl32
Sult1b1


Slc39a11


Samd9l


Hrh3
Slc13a1


5033406O09Rik


Tmem245


Slc8a1
Cml1


Pllp


Glyctk


Klhl31
Pm20d1


Gpr20


Aldh3a2


Gfra1
Fahd1


Spink4


Ppp3ca


Adgb
Trim31


Nfkb2


Cpne3


Lhx1
H2-T3


Tmco3


Slc4a7


Plk5
0610005C13Rik


Mllt3


Nfatc1



Optn


Gmppa


Kit



Clec2e


D10Bwg1379e


Fam117b



Myo7a


Cdk5rap3


Nradd



Slc37a4


Smim6


Tmem121



Ppargc1a


Parm1


Cpm



Stom


Fam69a


Asah1



Reep6


1810007106Rik


Slc9a9



Cmbl


Kcnh3


Ubl7



Cdkn2b


Tspan1


Abca3



Pgm2


B3gnt7


Pde6d



Maf


Entpd4


Bmp2



Mia2


Kdelr2


Kdm4a



Slc11a2


Sppl2a


Camkk2



Spsb1


Impad1


Arhgap8



Tmem236


Mgat3


Agt



Cd36


Cpd


Ptpra



Treh


Asns


Adh1



Gstk1


Hyou1


Dusp14



Lipe


Uba7


Clic4



Tmem139


Dnajc3


Gimap1



Cyp2c66


Golt1b


Cpne5



Gsdmd


Pygb


Ceacam2



Ocm


Manf


Zfp710



Srxn1


Xbp1


Gcnt1



Lmbr1l


Galntl6


B4galt5



Lpgat1


Hspa13


Suco



Fez2


Rab27b


Pim3



Slc52a2


Rasef


Ogdhl



Mocos


Itga2


Oas1g



Nek3


Gorasp1


Dcp1b



Tm6sf2


Pck1


Myzap



Agpat2


Pgm3


Cdkn1a



Slc23a2


Galnt6


Cd37



Xkr9


Vimp


Brms1



Tob1


Golga5


Lrrc42



Clcn2


Sec16a


Pld2



Hectd3


Eif2ak3


Tmem9



Tbc1d22a


Osbpl2


Cpeb4



Naip1


Zfp467


Ssx2ip



Ctss


Hdlbp


Ddah2



Slc9a2


Cbfa2t3


Tmem65



Cdc42ep2


Zbp1


5430417L22Rik



9030617O03Rik


B3gnt5


2210016L21Rik



Mall


Far1


Msi2



Pla2g12b


0610007N19Rik


B4galt4



Rhod


Zfp330


Rabgap1l



Kbtbd11


Gcc2


Pik3r3



Acox1


Lman1


Nt5c3



Arhgap26


Lamc2


Palld



Trim30d


Herpud1


AA467197



Tcn2


Slc10a7


Pip5k1b



Mylk


Serp1


Krt18



Thnsl2


Scamp1


Map1a



Fam213b


Gal3st2


Lmf1



Dhrs1


Odf21


Arhgef28



Adh4


Hilpda


Nsfl1c



Dgkq


Cog3


Txndc16



Ces2e


Alyref2


Pstpip2



Aldh1a7


Galnt4


Ttll11



Myo5b


Prr24


Exph5



Dnm1


Litaf


2700086A05Rik



Frk


Fam98a


Gadd45a



Tsc22d3


Pcsk9


Plekhs1



Slc35f5


Zbtb8a


Fam188a



2200002D01Rik


Tmem63a


Jmy



Cyp2c65


Dap


Atat1



S100g


Trim47


Arhgef2



Ugdh


Ssr3


Lmbr1



Cyp2c68


Edem3


Rhoc



Hagh


Tst


Card10



Xpnpep1


Ang


Kcnj16



Cobl


Slc38a10


Arhgap4



Epb4.1l3


Guk1


Acsl4



Mep1a


Pcsk7


Rhog



Hnf4g


Trabd


Fam221a



Parp9


Gfi1


Dynlt1b



Cyp2j6


Gnpnat1


C2



Sgpl1


Pdxdc1


Zbtb41



Pccb


Hspa5


Socs1



Abcg2


Slc35a2


Atp6ap1



Slc2a2


Slc37a3


Fam171a1



Ephx2


Arl1


Wnk2



Kcnk5


Smim5


Kcnd3



Lrp1


Ccnd3


Slc27a1



Tmem135


Sar1a


Atxn1



Dak


F2rl1


Rabgap1



Dusp12


Stt3a


Myrfl



Gpr128


Tdrd7


Crot



Abcb1a


Spcs3


Tm4sf4



Tmem252


Sidt1


Ube2j1



Slc7a8


Pdia3


Sort1



4931406C07Rik


Lss


Lima1



Tm4sf5


Cmpk1


Mov10



Akr1b7


Naga


Lca5



Tmem230


Sh3bgrl3


Gimap9



Acbd4


Slc41a2


Mlip



Crat


Ostc


1110008P14Rik



Pcsk5


Fgfr3


Ckap4



Galt


Fut8


Tor4a



Gm10768


Ggcx


Rmdn1



Cyp3a25


Plac9a


Oas2



Gstp2


Sec61b


Dsp



Ilvbl


Bscl2


Sox9



Urgcp


Golm1


Osbpl3



Chchd7


Klf4


Kif21b



Car4


Ssr4


Tbcb



Slc13a2


Srprb


Arap2



Epha1


Yipf6


Casp3



Dab1


Clptm1l


Enc1



Gstm6


Id4


Il25



Sept9


Arf4


Lman2l



Adipor2


Gale


Zmiz1



Cast


Eif4ebp1


Nav2



Abp1


Srpr


Atp2a3



Casp6


Tbc1d30


Gimap8



Itga3


Akr1c14


Folr1



Rilp


Zc3h7a


Fn1



Tmem41a


D17Wsu104e


Hspa4l



Nkiras2


S100a16


Sufu



March6


Mknk2


Atp8a1



Gm9926


Tmprss2


Vps53



Plin3


Tc2n


Rgs14



Rab11fip3


Slc35c1


Gm17660



Retsat


Ufsp2


Pdcl



Arg2


Tmem165


Shkbp1



Slc39a5


Tmsb10


Oas1a



Pepd


Sec62


Pkp1



Idh1


Bet1


Ccdc23



Ccdc134


Cyp51


Il4ra



Mgam


Fam3c


1700112E06Rik



Ugt2b34


Mfsd7a


Dvl1



Ceacam20


Slc37a1


Zfhx3



Slc2a9


Cmtm8


Adam22



Frmd8


Adam9


Gramd1c



Smpdl3a


Art2a-ps


Tmem45b



Apol10b


Capns1


Unc5b



Slc5a9


Syt7


Mical3



Gna11


Pdia4


Kctd13



Pls1


Slc22a23


Ak7



Rab17


Yipf5


Tcta



Lgals3


H2-T9


Nek7



Slc25a37


Atf4


D730039F16Rik



Ppap2a


Ick


Plekho2



Gpr155


Srm


Myo6



Cml5


Plaur


Chdh



Spns2


Pyroxd1


Opn3



Acot11


Fry


Tle3



Vmp1


Cyp2j9


Ttll10



Mertk


Sep15


Strada



2510049J12Rik


Sc4mol


Ypel3



Zzef1


Stk38l


Cmip



Bche


Bmp8a


Cachd1



Abcd3


Spryd3


Pigc



Aqp11


Gne


Atp6v1d



Gcnt2


Aldh3b2


Rdx



Acsl5


Rell1


S100a11



Gng12


Krtcap2


Spa17



Cda


Sec23b


Gimap5



Fcgrt


St3gal1


Cystm1



Gm6034


Tmem56


Zdhhc17



Sema4g


Tulp4


Lect2



Zfyve21


Capn7


Vdac3



Pfkfb4


Gpr180


Hspb11



D130043K22Rik


Txndc11


Gm4952



Cyp4v3


Copb2


Slc16a2



C530008M17Rik


Calr


Abhd5



Ptdss1


Homer2


Rhbdf1



Gm766


Ssr2


Cblb



Tbc1d24


Tbrg1


Nfe2l3



Cyb5b


Jtb


Pla2g16



Maoa


Syvn1


Sept8



Vat1


Morf4l2


Gpcpd1



Ehhadh


Rpn2


Psd3



Naprt1


Ugp2


Anxa11



Slc3a2


H13


Slc25a12



Dhrs11


Slc16a6


Ehf



Sh3tc1


Slc39a1


Akr1b10



Irak2


Gm1123


Dapp1



Btnl4


Copg1


Vmn2r26



Stx12


Ssr1


Esyt1



Dgat1


Tmed2


Ppt1



Acaa1a


Ank3


Cd47



Cyp4f16


Tmbim4


Chi3l1



Btnl5


Rpn1


Mical1



Snx9


Uggt1


Gna14



Ahnak


Utp11l


Pacs2



Fam109a


Ppib


Lyn



Edn3


Camsap3


Rmnd5a



Ccl25


Ddost


Ankrd12



Zdhhc7


Mesdc1


BC022687



Ppp1r14d


4930404N11Rik


Rit1



Slc43a2


Sh3bgrl2


Camta2



Faah


Golgb1


Mocs2



Tymp


B3gnt3


Usp49



Acy1


Dcbld2


Nrbp2



Cyb5r3


Spcs2


Ifnar2



Rnf13


Sec61a1


Epha4



Rxra


Cant1


Arl5a



Dqx1


Tpcn1


Rgl2



Snx13


Gorasp2


St18



Acnat1


Pmm2


BC016579



Ticam1


Ano7


Tead1



Sidt2


Rrbp1


Enpp4



Fam78a


Pacsin1


Tmem158



Aldh18a1


Srp72


Tnfaip3



Rmdn3


Tnk2


Gys1



Sat1


Eif2ak4


Hivep2



Ckmt1


Sec22b


Cap1



Txlng


Tars


Slc4a2



Slc31a1


Slc1a5


Map4k4



Slc25a36


Copb1


Desi1



Slc25a34


Yif1b


H2-D1



AU040320


Etnk1


Man2a1



Marc2


Ramp1


Cyp17a1



Aldob


Cltb


Cyhr1



Gm7030


Slc22a15


Morf4l1



Decr1


Kif13a


Mllt4



Sh3d21


Yipf3


Phf17



Ugt1a1


Ift20


Stox2



Ccs


Ufl1


Hist3h2a



Kifc3


Tm9sf2


Hdac6



Slc18b1


Syngr2


Prox1



Aprt


Nucb1


Dtnb



Slc22a1


Gmds


Lrch4



Acp6


Sec61g


Spire2



Ogdh


Rfc1


Klf6



Tfg


C2cd2l


Rab5b



Tstd1


Smim3


Anxa4



Klc4


Hsp90b1


Rab4b



Itpk1


Srp9


Iqsec1



Bmp3


Ost4


Pdpk1



Pld1


Tmem183a


Stk40



Ezr


Dnajb11


Gde1



Coro2a


Tom1l1


Mtmr11



Ckb


Sh3pxd2a


Cib2



Farp2


Ier3ip1


March2



Pxdc1





Capg



Sar1b





Narf



Scp2





Mgst3



Ggact





Angel1



Cst6





Bicd1



Sft2d2





Ifitm1



Abr





Stx3



Glt28d2





S100a1



Slc34a2





Omd



Fam160a1





0610040J01Rik



Pcyt1a





Arpc5



Tep1





Homer3



Hadha





Cdc42se1



Ccdc88c





Abcc3



Lpcat3





Hsf2



Tbc1d14





Pnpla6



Gucd1





Ccdc68



Acadm





Fryl



2210404O07Rik





Lmtk2



Mvp





Tas1r3



Actn4





4931406H21Rik



Tspan15





Uspl1



Rufy3





Ajuba



Mcu





Kalrn



Spint1





Basp1



Sfxn1





Pip5kl1



Alas1





Slc26a2



Nipsnap3b





Atp2b2



Tor1aip2





Smug1



Casp1





Myadm



Bpnt1





D330041H03Rik



Baiap2l1





Wdfy2



Ifngr2





Trim38



Pex19





Arf3



Myl12b





Scand1



0610008F07Rik





Dpysl2



Atp1a1





Ndufaf3



Itfg3





Sik1



Dnpep





Wdr7



Akr7a5





Sfxn3



Dlst





Kcnq4



Ugt1a7c





Mll1



Myo1d





Hsbp1



Tmem120a





Calml4



Cdh17





Atf7ip



Acaa2





Gpr137b-ps



Apol11b





Hap1



Hadh





Kctd15



Casp7





Prcp



Acp5





9430023L20Rik



Rfk





Gmip



Aldh9a1





Cmtm3



Vipr1





Madd



Txndc17





Krt222



Phgr1





Nsf



Eno1





Klhl28



Hsd17b4





Pparg



Slc39a4





Eml3



Nlrp6





Phlda1



Pttg1ip





P2rx1



Il17rc





Pde9a



Sqrdl





Otud7b



Net1





Tfpi2



Lad1





Rilpl2



Gm5177





Klf3



Mdh2





Gyg



2210016F16Rik





4930455F23Rik



Erbb3





Armcx1



Proz





Lzts2



Tax1bp3





Plek



Pgd





Vamp8



Sult1d1





Stat2



Gpi1





Znf512b



Prap1





Ptplad1



Lypla1





1110058L19Rik









Tmem160









Tmem51









Cdhr5









Stk38









Atp13a2









Nptn









Sirt5









Gabarapl2









Nudt14









2010111I01Rik









Alkbh7









Slc18a3









4930427A07Rik









Ttll7









Acss2









Siae





Significance cut-offs: FDR (max): 0.05, Log2 fold-change: 0.5













TABLE 5







Consensus (full-length platebased and 3′ droplet-based)


signatures for post-mitotic intestinal epithelial cells
















Enterocyte
Enterocyte


Goblet
Paneth
Tuft
Enteroendocrine
(Proximal)
(Distal)





Agr2
Gm15284
Alox5ap
Chgb
Gsta1
Tmigd1


Fcgbp
AY761184
Lrmp
Gfra3
Rbp2
Fabp6


Tff3
Defa17
Hck
Cck
Adh6a
Slc51b


Clca1
Gm14851
Avil
Vwa5b2
Apoa4
Slc51a


Zg16
Defa22
Rgs13
Neurod1
Reg3a
Mep1a


Tpsg1
Defa-rs1
Ltc4s
Fev
Creb3l3
Fam151a


Muc2
Defa3
Trpm5
Aplp1
Cyp3a13
Naaladl1


Galnt12
Defa24
Dclk1
Scgn
Cyp2d26
Slc34a2


Atoh1
Defa26
Spib
Neurog3
Ms4a10
Plb1


Rep15
Defa21
Fyb
Resp18
Ace
Nudt4


S100a6
Lyz1
Ptpn6
Trp53i11
Aldh1a1
Dpep1


Pdia5
Gm15292
Matk
Bex2
Rdh7
Pmp22


Klk1
Mptx2
Snrnp25
Rph3al
H2-Q2
Xpnpep2


Pla2g10
Ang4
Sh2d7
Scg5
Hsd17b6
Muc3


Spdef

Ly6g6f
Pcsk1
Gstm3
Neu1


Lrrc26

Kctd12
Isl1
Gda
Clec2h


Ccl9

1810046K07Rik
Maged1
Apoc3
Phgr1


Bace2

Hpgds
Fabp5
Gpd1
2200002D01Rik


Bcas1

Tuba1a
Celf3
Fabp1
Prss30


Slc12a8

Pik3r5
Pcsk1n
Slc5a1
Cubn


Smim14

Vav1
Fam183b
Mme
Plec


Tspan13

Tspan6
Prnp
Cox7a1
Fgf15


Txndc5

Skap2
Tac1
Gsta4
Crip1


Creb3l4

Pygl
Gpx3
Lct
Krt20


C1galt1c1

Ccdc109b
Cplx2
Khk
Dhcr24


Creb3l1

Ccdc28b
Nkx2-2
Mttp
Myo15b


Qsox1

Plcg2
Olfm1
Xdh
Amn


Guca2a

Ly6g6d
Vim
Sult1b1
Enpep


Scin

Alox5
Rimbp2
Treh
Anpep


Ern2

Pou2f3
Anxa6
Lpgat1
Slc7a9


AW112010

Gng13
Scg3
Dhrs1
Ocm


Fkbp11

Bmx
Ngfrap1
Cyp2c66
Anxa2


Capn9

Ptpn18
Insm1
Ephx2
Aoc1


Stard3nl

Nebl
Gng4
Cyp2c65
Ceacam20


Slc50a1

Limd2
Pax6
Cyp3a25
Arf6


Sdf2l1

Pea15a
Cnot61
Slc2a2
Abcb1a


Hgfac

Tmem176a
Cacna2d1
Ugdh
Xpnpep1


Galnt7

Smpx
Tox3
Gstm6
Vnn1


Hpd

Itpr2
Slc39a2
Retsat
Cndp2


Ttc39a

Il13ra1
Riiad1
Ppap2a
Nostrin


Tmed3

Siglecf

Acsl5
Slc13a1


Pdia6

Ffar3

Cyb5r3
Aspa


Uap1

Rac2

Cyb5b
Maf


Gcnt3

Hmx2

Ckmt1
Myh14


Tnfaip8

Bpgm

Aldob


Dnajc10

Inpp5j

Ckb


Ergic1

Ptgs1

Scp2


Tsta3

Aldh2

Prap1


Kdelr3

Pik3cg


Foxa3

Cd24a


Tpd52

Ethe1


Tmed9

Inpp5d


Spink4

Krt23


Nans

Gprc5c


Cmtm7

Reep5


Creld2

Csk


Tm9sf3

BCl2l14


Wars

Tmem141


Smim6

Coprs


Manf

Tmem176b


Oit1

1110007C09Rik


Tram1

Ildr1


Kdelr2

Galk1


Xbp1

Zfp428


Serp1

Rgs2


Vimp

Inpp5b


Guk1

Gnai2


Sh3bgrl3

Pla2g4a


Cmpk1

Acot7


Tmsb10

Rbm38


Dap

Gga2


Ostc

Myo1b


Ssr4

Adh1


Sec61b

Bub3


Pdia3

Sec14l1


Gale

Asahi


Klf4

Ppp3ca


Krtcap2

Agt


Arf4

Gimap1


Sep15

Krt18


Ssr2

Pim3


Ramp1

2210016L21Rik


Calr

Tmem9


Ddost

Lima1




Fam221a




Nt5c3




Atp2a3




Mlip




Vdac3




Ccdc23




Tmem45b




Cd47




Lect2




Pla2g16




Mocs2




Arpc5




Ndufaf3





Significance cut-offs: FDR (max): 0.05, Log2 fold-change: 0.5 in both datasets






Next, leveraging the higher sensitivity of the plate-based, full-length scRNA-seq data, Applicants also identified enriched TFs, GPCRs and leucine-rich repeat (LRR) proteins (Methods) for each of the major cell types (FIG. 1f,g, FIG. 8d,e and Table 6). Among TFs, these included several Kruippel-like family (KLF) TFs specific to secretory subtypes, such as Klf4, a known regulator of goblet cell development35, and novel KLFs, including Klf15, expressed at significantly higher levels by Paneth cells, and Klf3 and Klf6 by tuft cells. Among cell-type enriched GPCRs (FIG. 1g, FIG. 8d and Table 6), the known sensory cell types (tuft and EECs) were most prominently represented, each with more than 10 enriched receptors. These included many nutrient-sensing receptors expressed on the EECs (e.g., Gpbar1-a, a bile acid receptor36, and Gpr119, a sensor for food intake and glucose homeostasis37) and Drd3, a dopamine receptor (FIG. 8d) enriched in tuft cells. The family of pattern recognition receptors (PRR) containing LRR domains are variably deployed on surfaces ofthe normal intestinal epithelium. Interestingly, Tlr2 and its co-receptor Cd14 had a significantly higher expression (FDR<0.5, Methods) in the stem cell population (FIG. 8e). In sum, Applicants identified and characterized all major cell-types of the villous epithelium at single-cell resolution.







TABLE 6A







Transcription factors (TFs) (full-length plate-based data)

















Enterocyte
Enterocyte









progenitor
progenitor



Entero-



Stem
TA
(early)
(late)
Enterocyte
Goblet
Paneth
endocrine
Tuft





Ascl2
Zfp808
Zbtb44
Id1
Creb3l3
Spdef
Klf15
Neurod1
Spib


Jun
Ctcf
Zfp72
Pias4
Nr1h3
Atoh1
Nr4a1
Rfx6
Pou2f3


Sp5
Zfp101
Zfp709
Foxm1
Nr1i3
Creb3l4
Zfp667
Fev
Gfi1b


Arid5b
Zfp652

Nfyc
Maf
Creb3l1

Neurog3
Hmx3


Tgif1


Mycn
Tsc22d3
Bhlhe40

Pax4
Hmx2


Nr2e3


Hmgb2
Hnf4g
Foxa3

Isl1
Runx1


Mecom



Rxra
Nfkb2

Foxa2
Jarid2


Esrrg



Batf2
Xbp1

Nkx2-2
Nfatc1


Zbtb38



Zbtb7b
Zfp467

Pax6
Zfp710


Etv6




Litaf

Myt1
Zbtb41


Tgif2




Zbtb8a

Peg3
Sox9


Nr1d2




Klf4

Tox3
Zmiz1


Zfp341




Id4

Insm1
Zfhx3


Hes1




Atf4

Etv5
Nfe2l3


Nfix




Dnajc1

Sox4
Ehf


Repin1




Tulp4

Zfp68
Camta2


Zfp825




Foxp1

Lmx1a
St18


Vdr




Nfxl1

Lcorl
Tead1


Gtf2i






Zfp7
Hivep2


Nfib






Vezf1
Prox1


Nfia






Gm5595
Klf6


Relb






Pbx1
Hsf2


Hmga1-rs1






Zfp787
Pparg


Pms1






Zfp62
Klf3


Gm6710






Hhex
Stat2


Atf7






Etv1
Znf512b


Zfp956






Zfp92



Esr1






Neurod2



Hmga2






Zfp329



0610010B08Rik






Zfp71-rs1



Hmga1






Zfp30



Myc






Zkscan1



Zfp317






Lhx1



Nfic






Rcor2



Zfp13






Zfp266



Bcl6






Sp4



Foxq1






Atoh8



Zfp119b






Bach1



Hnf4a






Zfp236



Trp53






Rfx2



Zfp369






Zfp189



Zfp1






Plag1










Zfp821










Zglp1










Nanog
















TABLE 6B







G-coupled protein receptors (GPCRs) (full-length plate-based data)

















Enterocyte
Enterocyte









progenitor
progenitor



Entero-



Stem
TA
(early)
(late)
Enterocyte
Goblet
Paneth
endocrine
Tuft





Lgr5



Gpr128
Cd97
Fzd9
Adora3
Ffar3


Htr4



Gpr160
Chrm1
Darc
Sstr1
Gprc5c


Fzd7



Lpar1
Ptger4
Ccrl2
Gpr116
Sucnr1


Gpr110




Gpr20

Sstr5
Ccrl1


Lphn2




F2rl1

Gpr22
Gprc5a







Ffar2

Galr3
Opn3







Mtnr1a

Galr1
Vmn2r26







P2ry4

Gpr119
Tas1r3









Ffar1










Adora2a










Cxcr7










Gpr6










Hrh3










Gpbar1










Chrm4










Glp1r










Htr1d
















TABLE 6C







Leucine-rich repeat (LRR) proteins (full-length plate-based data)

















Enterocyte
Enterocyte









progenitor
progenitor



Entero-



Stem
TA
(early)
(late)
Enterocyte
Goblet
Paneth
endocrine
Tuft
















Lgr5
Fbxl8
Lrrc19
Lrrc26
Insrr
Cnot6l
Lrrc42


Prelp
Lrrc47
Fam211a
Amigo3

Amigo2
1700112E06Rik


Lrig1




Fbxl16
Cmip


Cd14




Lrrc16b
Lrch4


Phlpp1




Nxf7
Omd


Tlr2




Tpbg



Ciita




1810043G02Rik



Rtn4rl1





Significance cut-offs: FDR (max): 0.5






Example 3—Distinct Regulators are Associated with the Proliferation-Differentiation and Proximal-Distal Axes
The largest components of variation (PC-1 and PC-2) between single cells in the atlas reflect the processes of proliferation and differentiation in the small intestine (FIG. 9a). Applicants thus used the cell-type signatures (Table 4) to embed each cell in a three-dimensional space (FIG. 2a), such that its location corresponds to its lineage fate, and to its stage of differentiation towards that fate (Methods). Applicants confirmed that Lgr5-expressing cells were positioned at the base of the embedding (FIG. 2a, left). Scoring of a cell-cycle state signature29 highlighted the presence of rapidly proliferating cells above the stem cells (FIG. 2a, center), with a somewhat lower expression of stemness related genes, but not yet expressing markers for differentiated cell types, corresponding to TA progenitor cells, as previously suggested38. The distinct “leaves” on top reflected Muc2-expressing goblet cells, Dclk1-expressing Tuft cells, and Chgb-expressing EECs (FIG. 9b), whereas the expression of the enterocyte marker Alpi gradually increased along a dense branch of cells moving towards the enterocyte lineage (FIG. 2a, right). Although the vast majority of these Alpi-expressing cells are well on their way to the enterocyte lineage, a small subset co-expresses Alpi and crypt-specific markers (Slc12a2, Ascl2, Axin2, and Lgr5) (data not shown), consistent with a recent report39.
Focusing on the abundant population of enterocytes, Applicants used diffusion maps40 to place them in a pseudo-temporal order (FIG. 2b-e). Several recent studies41,42 have shown that cellular differentiation and fate determination can be modeled as a dynamic process on a high-dimensional manifold, which can be inspected by ordering cells—sampled simultaneously from an ongoing asynchronous process—in pseudo-time. In this case, considering the first and third diffusion components (DC-1 and 3) highlighted a trajectory from stem-like to progenitor to immature enterocytes (FIG. 2b, FIG. 9c-e and FIG. 10a-c).
DC-2 captured a process of branching lineage commitment between enterocytes of the proximal (duodenum and jejunum) and distal (ileum) small intestine (FIG. 2c, FIG. 10d-f), emphasizing the adaptation of enterocytes to absorb different carbon sources, from easy to digest carbohydrates to more complex molecules such as fat. Applicants tested this prediction, by profiling another 11,665 single cells from the duodenum, jejunum and ileum separately (n=2 mice, FIG. 10h), and recovering genes differentially expressed in the 1,041 absorptive enterocytes from the different regions (Methods). Indeed, of the 64 and 44 genes identified as signature genes for mature proximal and distal enterocytes, respectively, (Methods, FIG. 1c and Table 3), 60 and 23, respectively, were also differentially expressed (FDR<0.05 Mann-Whitney U-test) between proximal (duodenum and jejunum) and distal (ileum) regions (FIG. 10i). Furthermore, smFISH confirmed the regional distribution of enterocytes expressing Lct and Fabp6 markers43 in the duodenum and ileum, respectively (FIG. 10j). Most marker genes of the two Paneth cell subsets (FIG. 10n) are enriched (FDR<0.05) in proximal or distal gut respectively, confirming that they reflect regional distinctions (FIG. 10o); the novel marker Mptx2 showed no regional specificity (Table 10). Finally, the stem cells in each region also express region-specific markers (FIG. 10p), which when examined in either the non-regional (Fig. q) or the regional (FIG. 10l) diffusion maps mark distinct ISC subsets, each likely foreshadowing the eventual distinct enterocytes from the corresponding region (FIG. 10l).







TABLE 10







DE results [droplet-data], ranked by Log2 fold-


change Paneth-1 (distal) vs. Paneth-2 (proximal)













Mean expression
Mean expression






(Log2 TPM + 1)
(Log2 TPM + 1)


Gene symbol
Paneth-1
Paneth-2
log2fc
p
p. adj















Defa20
8.43307629
4.191569275
4.241507015
 4.59E−198
 1.29E−193


Gm15308
6.747161753
2.622293721
4.124868032
 1.82E−194
 2.55E−190


Defa22
8.938663197
4.944112099
3.994551098
 2.63E−177
 2.45E−173


Defa21
8.979216936
5.396694643
3.582522293
 3.65E−165
 2.56E−161


Guca2a
3.927258003
1.966457829
1.960800174
 3.25E−158
 1.82E−154


Gm15315
2.93782559
1.484980923
1.452844667
1.03E−82
2.40E−79


Gm21002
1.426481352
0.165194501
1.261286851
 2.44E−108
 9.77E−105


Nupr1
2.47171844
1.419388432
1.052330007
2.80E−90
7.85E−87


Gm10104
3.266743446
2.254967422
1.011776024
1.48E−90
4.61E−87


Gm1123
1.646936159
0.685262667
0.961673491
4.34E−71
8.69E−68


Agr2
2.958898977
2.108410455
0.850488522
9.78E−50
1.61E−46


Muc2
2.572337443
1.749806632
0.822530811
6.74E−49
1.05E−45


Gm15293
1.67374113
0.895914849
0.777826282
1.16E−44
1.55E−41


Pnliprp2
2.801230998
2.134237786
0.666993213
1.69E−11
7.07E−09


Tspan1
1.333205915
0.716544122
0.616661793
2.21E−46
3.09E−43


Itln1
7.721067156
7.13664624
0.584420916
1.35E−15
8.40E−13


Pglyrp1
2.681719453
2.143612461
0.538106992
5.20E−43
6.62E−40


mt-Atp6
4.984661454
4.469107748
0.515553706
6.48E−12
2.79E−09


Guca2b
3.555007019
4.08419426
−0.529187242
2.46E−42
3.00E−39


Gm15292
4.123432202
4.663038688
−0.539606487
1.24E−25
1.20E−22


Gm15299
2.822490385
3.416108207
−0.593617822
5.07E−36
5.91E−33


Defa17
4.869214625
5.476804872
−0.607590247
3.46E−58
6.45E−55


Clps
5.793805073
6.504310944
−0.710505871
1.18E−50
2.07E−47


Defa23
2.958117378
3.6903216
−0.732204222
3.84E−21
3.36E−18


Gm14851
8.518496669
9.343126247
−0.824629578
3.56E−83
9.06E−80


Gm15284
9.174886103
10.05353355
−0.878647448
6.25E−73
1.35E−69


AY761184
8.318749405
9.553086427
−1.234337022
 4.50E−104
 1.57E−100


Rnase1
1.026127868
2.459104539
−1.432976671
 3.18E−111
 1.48E−107









Finally, Applicants identified TFs with specific expression patterns in different regions of the diffusion map (Methods), associating regulators with early enterocyte lineage commitment (known: Sox444, and novel: Batf2, Mxd3 and Foxm1) (FIG. 2d and FIG. 10g), or with proximal and distal intestinal identity (known: Gata4, Nr1 h445-46 and novel: Creb3l3, Jund, Osr2, Nr1i3) (FIG. 2e).
Example 4—Taxonomy of Enteroendocrine Cells is Defined by Hierarchical Hormone Expression
Enteroendocrine cells (EECs) are key sensors of nutrients and microbial metabolites11,12 that secrete diverse hormones and function as metabolic signal transduction units146. Enteroendocrine cells (EECs) in the small intestine are a major site of hormone production, and were reported to comprise 8 distinct sub-classes, traditionally classified by the primary hormone they produce11, 47, 48, such that cells expressing Sct, Cck, Gcg or GIP were traditionally termed S, I, L and K cells, respectively12. However, significant crossover between traditional subtypes has been observed12,22, such that the same hormone may be expressed by more than one type. Thus, a classification based on a single “marker” hormone may not represent the true diversity and function of EECs (Gribble and Reimann, 2016), and may limit the ability in follow up studies based on these genes.
Applicants identified a cluster of EECs in both the whole SI (FIG. 1b, 310 cells) and regional datasets (FIG. 10h, 239 cells) based on expression of known markers, including Chromogranin A (Chga) and B (Chgb), which this study confirmed as the two best markers for this group identified by the unbiased analysis (FIG. 11e), along with GDNF family receptor alpha-3 (Gfra3) as a novel and specific marker (FIG. 11e), for a total of 533 EECs (Methods). To define putative EEC subtypes ab initio, Applicants separately clustered these 533 cells, and distinguished 12 clusters (FIG. 3a, FIG. 11a), each supported by a distinct gene signature (FIG. 3b, Table 7, Methods). Four of the EEC groups expressed markers of EEC precursors (Neurog3, Neurod1, Sox4), while the other eight represented mature EEC subsets. A recent study of scRNA-seq of organoid derived EECs showed EEC heterogeneity but with fewer EEC subsets53.







TABLE 7





Summary of marker genes for enteroendocrine subsets




















Progenitor (early)
Progenitor (late)
Progenitor (mid)
Progenitor (A)
SAKD
SILA





Pycard
Tubb3
Fcgbp
Maged2
Sst
Cck


Oat
Neurod1
Tff3
Cdkn1a
Iapp
Parm1


Clca3b
Neurod2
Bcl2
Serpina1c
Hhex
Scg2


Cps1
Gadd45a
Aldob
Acsl1
Acot7
Tspan13


Dbi
Drap1
Gadd45g
Ceacam10
Rgs4
Cpn1


Prap1
Btbd17
Litaf
Zcchc12
BC048546
Crp


Ppp1r1b
Mrfap1
Sox4
Cxxc4
Arg1
Anpep


Hspe1
Cyth2
Slc39a2
Il11ra1
Asic5
0610011F06Rik


Mgst1
Mapk15
Tmsb10
Cdkn1c
Kcnk2
Gal


Gpx1
Vasp
Fuca1
Mboat4
Fam151a
Fars2


Pigr
Esd
Prom1
1500009L16Rik
Th
Hepacam2


Tkt
Trp53i11
Dll1
Krt18
Pdx1
Gpr119


Hspd1
Clta
Mfge8
Bambi
Fam46a
Gclm


C1qbp
Eif4a1
Hmgb3
Rgs17
Serpina1a
Tm4sf4


Cd74
Btg2
Top1
Arx
Hgfac
Agr3


Ccl25
Tubb5
Ddit4
Plb1
Tmem108
Gnai1


Mt1
Dbn1
Nek6
Fxyd2
Cd24a
Tm4sf5


Csrp2
Ypel3
Gpx2
Trp53i13
Rbpms
Sult1d1


Kcne3
Psmd10
Slc25a5
Necab2

Krt20


Cldn15
Fhl2
Pdha1
Serpina1d

Upp1


Slc12a2
Yipf4
Txndc5
Tuba1a

Nr4a2


Mrpl12
Cct2
Casp6
Gng4

Itm2b


Amica1
Rnase4
Eif4g2
Ghrl




Nop10
Krt7
Nme1
Card19




Tuba1b
Eif3l
Fubp1
Arhgap22




Mcm6
Prmt1
Llph
Fam183b




Pglyrp1
Npc2
Rps10
Nefm




Banf1
Gltscr2
Bok
Isl1




Aprt
Cdk2ap1
Vgll4
Akr1c19




Reg3g
Tsg101
Rnase1
Cd177




Idh3a
Eif3h
Rps4x
H1fx




2810417H13Rik
Jund
Rpl26
Capsl




Anp32b
Zfos1
Eef1g
Nefl




Tomm5
Mtch1
Acadsb
Nkx2-2




Phb2
Cdk4
Rps25
Serpina1e




Fgfbp1
Hpcal1
Lypd1





Sdc4
Hnrnpk
Hmgn1





Ncl
Fgd2
Rps26





Lypd8
Rph3al
Rps8





Ccnd2
Prdx2
Cd9





Ran
Crybb1
Shfm1





Dmbt1
Dact2
Rps5





Reg3b
Csnk1a1
Srsf2





Sdha
Calm2
Sap30





Chchd10
Eif3f
Hdac2





Aldh1b1
Marcksl1
Rplp0





Lgals9
Hspa8
Rps3





Atp5o
Tead2
Cdc14b





Snrpd2
Srsf6
Hnrnpab





Ociad2
Rcor2
Qsox1





Hmgb2
Adrm1
Rpl8





Hspa9
Eef2
Sypl





Prss32
H3f3a
Tubb2b





Tjp3
Krt8
Ywhaq





Ndufb9
Cd63






Lsm2
Psmc6






Mcm2
2700060E02Rik






Dtymk
Neurog3






Lsm4
Ppib






Nucks1
Tmem176b






Naa10
Btf3






Ranbp1
Uqcrc2






Nlrp6
Pcbp1






Cyc1
Tpm4






G3bp1
Naca






Cox7b
Pcbp2






Ube2c
Ooep






Cdca7
Pfdn5






Ndufv1
Psma7






Cenpa
Smarcd2






Rnf186
Sdcbp






Siva1
Pdap1






Cyba
Hn1






2700094K13Rik
Smim6






Dctpp1
Akr1c12






Cdca8
Cct4






Snrpd1
Cpt2






Alyref
Ftl1






Nhp2
Igsf8






Ldha
Commd3






Tsfm
Hsp90ab1






Mapk13
Ppp1r14b






Aqp1
Gadd45gip1






H2-Ab1
Rps21






Mif
Akr1c13






Mlec
Eif3k






Sri
Stard10






Hes1
Vwa5b2






Pmf1
Serbp1






Lsm3







Rnaseh2c







Marc2







Lyar







Ppa1







Tomm40







B2m







Plcb3







Uqcrc1







Cox5a







Timm10







Exosc5







Cct3







Aars







Mecr







Spc24







Epcam







Lmnb1







Prdx4







Gar1







Aadac







Snrpb







Kcnq1







Trim28







Cox6a1







Mettl1







Cox5b







Ybx1







Ndufs7







Acat1







Ifrd2







Hsd17b10







Psme2







Ascl2







Atp5h







Cebpb







Cldn3







Cdca3







Agmat







Snrpg







Anapc13







Eif3b







Pycrl







Atp5j







Cldn7







Fh1







Phb







Sdhb







Nxt1







Slc25a3







Myb







Cox7a2







H2-DMa







Vipr1







Fam195a







H2-Eb1







Sdsl







Mcm5







Cluh







Eif5a







Aimp2







Emg1







Rps27l







Mcm3







Srsf7







Uqcrq







Trap1







Tmem147







Atp5d







Rpl39







B4galnt1







Rcc2







Farsb







H2afx







Uqcr10







Ifngr1







Tyms







Hnrnpu







Ivns1abp







Atad3a







Tk1







Ifitm3







Klf5







Abhd11os







Gmnn







Kcnn4







Galk1







Ruvbl2







H2afv







Tfrc







H2afj







Atpif1







Prelid1







Slc39a5







Bdh1







Timm9







Noxo1







Bola3







Ndufa4







Pdss1







Txn2







Npm3







Rpl13







Ccnb2







Ccdc34







S100a10







Tmsb4x







Pa2g4







Rpsa







Cdk2ap2







Uqcr11







Birc5







Top2a







Anp32e







2200002D01Rik







Rpl12







Car9







Gjb1







Eef1d







Prdx6







Atp5j2







Ddx39







Rpl7







Txn1







Rps15







Rps16







Cox8a







Ndufa5







Aoc1







Mgam







Serinc3







Rfc3







Rrm1







Haus4







Stmn1







Rsl1d1







Rps19







Ccnd1







Gcat







Dhrs4







Atp5b







Fth1







Rplp1







Hnrnpa2b1







Pabpc1







Cox6c







Pebp1







Gm1123







Rpl37







Rpl18







Otc







Lig1







Vsig10







Atp5a1







Cks1b







Rpl34







Abhd11







Rplp2







Rps20







Shmt1







Gnb2l1







Dut







Nasp

















SIK
SIK-P
SIL-P
SIN
EC
EC Reg4






Gip
Car8
Pyy
Nts
Tac1
Reg4



Rbp2
Cdhr5
Gcg
Crip1
Vim
Afp



Pkib
Bdnf
Rnf130
Sct
Gch1
S100a1



Tpst1
Hexb
Nostrin
Adgrd1
Fev
Chga



Phlda1
Gatm
Gpbar1
Car4
Scn3a
Ambp



Acadl
Rnf32
Scin
Agr2
Slc25a35
Tpbg



Fabp5
Entpd5

Id3
Pdk3
Apoc3



Fam213a
Itm2c

4930539E08Rik
Slc38a11
Gstt1



Itpr1
Fam105a

Tppp3
Tmem158
Gstk1



Tmprss7
1700086L19Rik

Tnks1bp1
Cox7a2l
Rgs2



Fam167a
Il17re

S100a11
Igfbp3
Mapk14



Nrn1
Tmem163

Ece1
Mnx1
Apoa1



Gpx3
Gm14964

Tmem38a
Serpinb1a
Rab3b



Rhou
Scgn

Scg3
Fam204a
Cyp2d26



Bnip3
Scarb1

Fxyd5
Cyp4b1
Gsdmd



Rogdi
Prps1

Espn
Hmgn3
Serpinf2



Scp2
Pax6

Ffar1
Glud1
C1qa



Fabp1
Resp18

Dnajc12
Sepp1
Me2



Rbp4
Slc6a19

Gchfr
Tph1
Ucn3



Tspan7
1110032F04Rik

Uchl1
Pfn1
Ica1




Anxa6

Gcnt3
Gspt1
Ptprn




Anxa5

Nrp1
Gm43861
Upb1




1110017D15Rik

Rprml
Bax
Itpr3




Cib2

Banf2
Ddt
Psat1




Scg5

Qpct
Sec61b
Fxyd6




Abcc8

Myl7

Rpp25




Gmpr

Sis

Prodh2




Ffar4

Gucy2c

Gde1






Disp2

C1qtnf4






Rab37

Ndufv3






Bcam

Pcsk1








Tmem106a








Bex2








Rhoc








Trpa1








Slc18a1








Uqcc2








Ndufa2








Igfbp4








Ttr








Acvrl1








Atp6v1b2








Atp5e








Camk2n1








Lmx1a








Qdpr








Ssbp2








Rab3c








S100a13








Edf1








Chgb








Ddc








Ngfrap1








Comt








Minos1








Tmigd3








Tceb2








Atp5k








Pkdcc








Atp5g1








Gars








Rbp1





Significance cut-offs: FDR (Fisher's combined): 0.01, Log2 fold-change: 0.1, Fraction-expressing: 0.25






Applicants then compared this ab initio taxonomy to the canonical classification by the expression of the marker hormones in each cluster (FIG. 3c). Consistent with earlier reports22,49, several key hormones were expressed across multiple clusters rather than in a single group of cells. For example, Secretin (Sct), previously reported to be produced solely by S-cells11, was expressed by cells in all mature EEC clusters, albeit at varying levels (FIG. 3c). Similarly, Cholecystokinin (Cck), the canonical marker for I-cells49, was expressed in cells spanning five clusters. This surprisingly broad expression pattern of several hormones, particularly Sct and Cck, was reproducible and concordant in the high-coverage full-length scRNA-seq data, with excellent agreement in detection frequency across all GI hormones (FIG. 11b). In some cells, Cck was co-expressed with both glucagon (Gcg) and Ghrelin (Ghrl), the markers of L- and A-cells, respectively. Notably, Cck-expressing cells are a subset of those expressing Sct, and Gcg and Ghrl expression induces a further subdivision of the cells (FIG. 3c and FIG. 11c-d), which Applicants validated using smFISH (FIG. 3d).
Applicants placed each cluster of mature EECs in the new taxonomy (FIG. 3c and FIG. 1id) and labeled it by the expression of canonical hormones if over 50% of the cells in the subset express a particular hormone, using bootstrap resampling-based hierarchical clustering (FIG. 12a) and cell-cell correlations (FIG. 12b) to assess the relationships between subsets. For example, in this taxonomy the Sct+/Cck+/Gcg+/Ghrl+ subset—the components of which were traditionally termed S, I, L and A cells respectively12—is annotated with the label S-I-L-A (FIG. 3c), which Applicants subsequently validated (FIG. 3d). Within each cluster, the marker hormones are co-expressed in individual cells, and therefore generally do not partition into further subsets (FIG. 11c-d). In addition to the more broadly expressed hormones, several hormones are subset-specific (FIG. 3c and FIG. 12c). In particular, Galanin (Gal) is specific to SILA, Neurotensin (Nts) to SIN, Nesfatin-1 (Nucb2) to SA, and Amylin (Iapp) and Somatostatin (Sst) to SAKD. This taxonomy represents a “snapshot” of the subsets of post-mitotic EECs: although Applicants did not see evidence for transitional states, Applicants cannot rule out the possibility of cells transitioning between hormonal profiles, especially in light of the current number of EECs in the cell atlas.
Some EEC subsets are preferentially localized to specific regions of the small intestine. Specifically, SILA, expressing Ghrelin (Ghrl), the hunger hormone50, together with GCG, the incretin hormone51, are enriched in the duodenum (FDR<0.25, χ2 test, Methods), while SIL-P and SIK-P, both expressing the hormone Peptide YY, which reduces appetite upon feeding52, are found mainly in the ileum (FDR<0.1, χ2test) (FIG. 3e and FIG. 11a), consistent with the roles of these hormones in the regulation of appetite11.
Applicants note that a recent study53 used scRNA-seq of 145 organoid-derived EECs to identify seven subsets. The present taxonomy of 12 subsets from 533 in vivo cells includes all those mature identified subsets53, an additional three novel subsets (FIG. 12e, grey shading), including SIN, a particularly rare Nts-expressing subset, as well as a further sub-division of SIL and SIK cells that are enriched in the ileum, SIL-P and SIK-P.
Example 5—Two Sub-Types of Enterochromaffin Cells are Distinguished by Reg4 Expression
Mature enterochromaffin cells (EC), EECs that secrete serotonin, regulate gut motility and secretory reflexes54 and are implicated in diverse pathologies55, partition into two clusters in the taxonomy. Both are readily identified by the expression of two canonical EC markers: Preprotachykinin-1 (Tac1), a precursor for neurokinin A and substance P, and Tryptophan hydroxylase 1 (Tph1), the rate-limiting enzyme in the biosynthesis of serotonin56 (FIG. 3c and FIG. 11c-d). Comparing the gene signatures for the two clusters (FIG. 3b) highlighted Reg4 (regenerating islet-derived protein 4) and Afp as the top markers of one cluster (“EC-Reg4”), whereas Reg4 is barely detectable in the other cluster (“EC”) (FIG. 3c). Although a recent single-cell study23 suggested that Reg4 is a pan-enteroendocrine cell marker based on 238 cells from gut organoids, of the 7,216 cells Applicants profiled here, Reg4 is expressed in a subset of 35 out of 52 enterochromaffin cells (FIG. 3b-c and FIG. 11c-d), as well as in Paneth cells and in goblet cells (FIG. 12d). Applicants validated the partitioning of ECs by Reg4-specific expression in situ, validating the presence of two subsets of ECs (FIG. 3f).
As enteroendocrine cells play a central role in sensing luminal nutrients, Applicants examined the expression of genes encoding GPCRs in these cells, identifying those expressed significantly higher (FDR<0.25, Mann-Whitney U-test) in a given subset (FIG. 12f). Notably, the free fatty acid receptors 1 and 4 showed specific expression patterns. Ffar1 was highest in SIN cells, and also expressed by the Cck-expressing subsets previously collectively termed I-cells (STL-P, SILA and SIK-P), while Ffar4 was highest in the GIP-expressing subsets (SIK and SIK-P). These receptors are known to induce the expression of GIP and Gcg to maintain energy homeostasis51. Ffar2 was expressed by some progenitors and by EC cells, but notably absent from GIP-expressing cells, while the oleoylethanolamide receptor Gpr119, important for food intake and glucose homeostasis37, was expressed highest in SILA cells.
Example 6—Two Subgroups of Tuft Cells with Immune and Neuronal-Like Expression Programs
Tuft cells are the chemosensory cells of the gut and are enriched for taste-sensing molecules148. Tuft cells, a relatively poorly characterized epithelial cell type, were recently shown to play a key role in the T helper 2 (Th2) response to parasitic worm infection, through secretion of the Interleukin-25 (Il25), a potent chemoattractant for type II innate lymphoid cells14-16.
This study obtained sets of marker genes distinguishing the absorptive and secretory lineages and noticed that the known secretory lineage marker Cd24a (Sato et al., 2009) was indeed one of the specific markers for the secretory lineage (FIG. 2f). However, although Cd24a is broadly expressed by all secretory IECs, it was found to be expressed at a significantly higher level in tuft cells (FDR<0.05 Mann-Whitney U-test, FIG. 1C, FIG. 8F), which this study then confirmed at the protein level, observing a strong enrichment for tuft cells in a FACS sorted population of CD24+ high cells. This study therefore suggests that Hepacam2, a cell-surface marker, may be more useful to enrich for secretory cells without bias towards tuft cells (FIG. 8F).
A previous study21 defined a tuft cell signature based on expression profiles of a bulk population of cells isolated using the cell surface marker Trpm5. The bulk signature had both neuronal and inflammation related gene modules; these could in principle be explained by either co-expression in the same cells or in distinct sub-types.
To distinguish these possibilities, Applicants re-clustered the 166 cells in the tuft cell cluster (FIG. 1b, FIG. 7g), and found that the cells not only readily partitioned into progenitors (early and late) and mature tuft cells, but that the 84 mature tuft cells were further partitioned into two major sub-clusters (Methods), which Applicants termed Tuft-1 and Tuft-2 (FIG. 4a). Tuft-1 and Tuft-2 cells showed no significant distinction in spatial location along the SI (data not shown). Applicants confirmed the same sub-division by independent clustering of the 101 mature tuft cells (enriched by CD24a+ sorting) in the deeper, full length scRNA-seq dataset (FIG. 13a). These two datasets enabled us to define a consensus signature, of 30 and 74 specific markers for the Tuft-1 and Tuft-2 clusters respectively, identified independently in both the 3′ droplet and full-length datasets (FDR<0.01, Mann-Whitney U-test, Methods, FIG. 4b, FIG. 13b and Table 8).







TABLE 8







Summary of marker genes for tuft cell subsets












Tuft-1
Tuft-1
Tuft-1
Tuft-2
Tuft-2
Tuft-2


(plate)
(droplet)
(consensus)
(plate)
(droplet)
(consensus)





Nradd
Il13ra1
Nradd
Siglec5
Rac2
Rac2


Endod1
Ywhaq
Tppp3
Rac2
Matk
St6galnac6


Tppp3
Tsc22d1
Gga2
Ptprc
Nrgn
Tm4sf4


Gga2
Rgs13
Rbm38
St6galnac6
Siglecf
Ptgs1


Rbm38
Stx7
Ninj1
Tm4sf4
Alox5
Fcna


Ldhb
Ppp3ca
Gng13
Smpx
Cd300lf
Fbxl21


Slc44a2
Nebl
Nrep
Ptgs1
Ccdc28b
S100a1


Stoml1
Gng13
Akr1b10
C2
Trpm5
Spa17


BC016579
Skp1a
Inpp5j
Cpvl
Hck
Cd300lf


Rabl5
Rbm38
BC005624
Fcna
Ptgs1
Trim38


Cbr3
Nradd
Nkd1
Fbxl21
Tuba1a
Irf7


Ninj1
Calm2
Spon2
Ceacam2
Ptpn18
Plk2


Cnp
Tppp3
Vta1
S100a1
Tm4sf4
Krt23


Wdr6
Rnf128
Rgs2
Spa17
Ms4a8a
Tspan6


Gadd45a
Sh3bgrl
Zfhx3
Sucnr1
Sh2d6
Pigc


Gng13
Rab10
Stard5
Gde1
Krt23
Folr1


Usp11
Ctsc
Cirbp
Kcnj16
Folr1
Mlip


Mblac2
Nkd1
1810046K07Rik
AA467197
S100a1
Ptpn18


Pik3r3
Ppp1ca
Pde6d
Cd300lf
Ccnj
Basp1


Nrep
Cirbp
Fam195b
Trim38
Ptpn6
Plek


Akr1b10
Krcc1
Pou2f3
Vmn2r26
Reep5
Ms4a8a


Sphk2
Use1
0610040J01Rik
Gcnt1
Atp2a3
Ffar3


Ddah2
Ckap4
Cfl1
Irf7
Krt18
Tmem141


Haghl
Zfp428
Aamp
Plk2
Hebp1
Matk


Suv420h2
Nrep
Use1
Glyctk
Agt
Alox5


H2-L
Rsrp1
H3f3b
Krt23
Ffar3
Ccnj


Ulk1
Cetn2
Cyb5r4
Tmem116
H2-D1
S100a11


Atp4a
Bri3
Trappc3
Fam188a
Romo1
Gm4952


Gltpd1
Myo6
Runx1
Bmp2
Yipf1
Ncf2


Ift43
Vdac3
Pla2g4a
Ctsc
Ift172
Cfb


Uspl1
Chmp5

Tspan6
Ly6g6f
Cpne3


Mical1
Hsbp1l1

Slc25a20
9030624J02Rik
Sdcbp2


Homer3
Dpcd

Pigc
Basp1
Col15a1


Trafd1
Eif1b

Folr1
Mien1
Ly6g6f


Ldlrad4
Ube2d3

Mlip
Mlip
Man2a1


Pir
Pla2g4a

B4galt4
Tubb4b
Agt


Atp6v0c-ps2
St3gal6

Txndc16
Pnpla6
Nrgn


Anapc2
Bpgm

Ptpn18
Plk2
Snrnp25


Grpel2
Lima1

Ccdc23
Lman2l
Tmem245


Tanc2
Cby1

Capg
Tmem176a
Hck


Mta2
Dazap2

Ly6g6d
H2afj
Gimap1


Ankrd63
Cdc42se1

Basp1
Elovl1
Gprc5c


Exoc7
Nsfl1c

Abhd4
Col15a1
Coprs


Med27
Aamp

Plek
Tmem98
Stk40


Rmnd5a
Gdi2

Ms4a8a
Tspan6
Tuba1a


Gpm6b
Mff

Cwh43
Fbp2
Ttll10


Plscr3
Fkbp1a

Tm7sf2
Snrnp25
Tmem176a


Dcxr
Hpgds

Lect2
Fes
Tubb4b


Stau1
Scamp3

Ffar3
Fdps
Romo1


Inpp5j
Sub1

Adam22
Irf7
Fbp2


Bin3
Degs2

Oas1g
Ctsa
Dclk1


Ssh1
Wbp2

Slc2a1
S100a11
Tax1bp1


Ap1s2
Rnf5

Tmem141
Lmf1
Fes


Svil
Galk1

Gm17660
Gprc5c
Hebp1


Chd6
Med10

Suco
Sh2d7
Skap2


Gimap8
Tnfsf13os

Matk
Fbxl21
Clec4a1


Bloc1s2a
Ola1

Ccdc109b
Tmem245
Cox17


Zfp191
Rhoa

Alox5
Fcnaos
Mien1


Nbeal2
Psmd8

Acsl4
Car7
Car7


Plekhg5
Pla2g12a

Trim40
Aldh2
Reep5


Gtf2ird1
Mpg

Slc41a3
D17Wsu92e
Tmem80


Ogfr
Mtfr1l

Ccnj
Sdcbp2
Ccdc28b


Hmg20b
Fam96a

Rdx
Cox17
Krt18


Cdc42ep1
Trappc1

Rmdn1
Hypk
Ift172


Gna14
Srp14

Plekho2
Tmem80
Ptpn6


Zfp810
Cystm1

Cfi
Dyrk4
Pnpla6


Marveld2
Tcta

Car2
Ubl7
Isg15


Thtpa
Pnrc1

Apobec1
Fcna
Tmem57


BC005624
Ninj1

Mboat1
Tmem141
Abhd16a


Tcp11l2
Ube2l3

Ccdc68
Rtp4
1700112E06Rik


Shkbp1
Cryzl1

Smg7
Vav1
Map1a


Pcyox1l
Lpcat4

Rgs13
Man2a1
Shf


Tmem131
Rab3ip

Oas2
Trak1
H2-D1


Ssna1
Fam103a1

Rhoc
Gimap1
Lmf1


Nkd1
Zbtb20

Rnasel
Uba1


Ndufaf3
Ociad2

Pparg
S100a13


Zfp872
Cyb5r4

Gnai1
Gucy2c


Amz2
Rab18

Bmx
Sec14l1


Cyb561d1
H3f3a

Atp2b2
Atg101


Zfp444
Leprot

Dynlt1b
Ltc4s


Src
Rab14

Sept8
Lamtor4


Anxa11
Fam195b

Il17rb
Sfxn3


Pgm2l1
Lrrc42

Kalrn
Fam98c


Nsmce1
Akr1b10

Opn3
Map1a


Snapc3
Cyhr1

Dnase1l1
Stk40


Abi2
Cfl1

Ero1lb
Pigc


Smug1
Camk2d

Asl
Isg15


Slco3a1
Gm10384

Lrrc42
Pradc1


Myo10
Dcp1b

Ifitm1
Cpne3


Kcnn4
Acss2

Atp6v0c
Dclk1


Ehmt2
Prom1

Enpp4
Fip1l1


Snap47
Cutc

Samd9l
Plek


Snapin
Gng5

Abhd5
Arhgap1


Tas1r3
Dnaja2

S100a11
Pqlc1


Ssh2
Pold4

Fut2
Tax1bp1


Fn1
Dynlt3

Gm4952
Abcc3


Tchp
Prdx2

Ccrl1
1700112E06Rik


Nrbp2
Rbm39

Tmem74b
Snf8


Atxn7l1
Asah1

Enc1
Sez6l2


Kif3b
Trappc6b

Ncf2
Gm4952


Ppp2r3d
Tm2d1

Scd2
Zdhhc16


Atf7ip
1810046K07Rik

Il10rb
Rpp21


Adnp
Snx2

Kirrel3
Adcy5


Dnahc8
Cd24a

Gpr64
Slc4a2


Ctxn1
Trappc3

Hist2h2aa1
Tusc2


Tcf4
Zfhx3

Rhbdf1
Mrpl46


Cyth1
Trappc6a

Cfb
Clec4a1


Zscan21
Capza2

Gm14288
Csk


Dync1i2
Itfg1

A4galt
Cfb


Nlrc4
Dnaja1

Pmel
Kdm4a


Ttc1
Zfp410

Ifi27l1
Trim38


Afap1l2
Itpr2

Oas1a
Sdf4


Plod3
Pop7

Cpne3
Bst2


Utrn
Brk1

Rps6ka2
Ap2s1


Kdm2a
Sept7

Tmem246
Stat2


Etv4
Anxa4

Sdcbp2
1810037I17Rik


Maml1
Mast4

Col15a1
Coprs


Spon2
Tmx1

Ly6g6f
Pik3cg


Gata5
1700123020Rik

Man2a1
Plcg2


Tln1
Gstm7

Chat
Cd37


Akap8l
Stxbp3

Rgs22
Ttll10


F730043M19Rik
Dctn6

Pold4
Skap2


Arl10
Rassf6

Kctd13
Dmxl2


Vta1
Immp1l

Cdhr2
Mrpl41


Tbx3
Pnrc2

Apip
Tmem57


Rbm5
Sdcbp

Gabarapl2
St6galnac6


Gm6756
Sdhaf4

Gpcpd1
Cutal


Epb4.1l1
C2cd4b

Pcdh20
Shf


Il4ra
Arl2

D730039F16Rik
Lpp


Rgs2
Slc44a3

Agt
Ncf2


Pcdh1
Vapb

Nrgn
Ap1s1


Arid3b
H3f3b

Snrnp25
Abhd16a


Map1s
Pou2f3

Fam167a
Dalrd3


Ctnnal1
Inpp5j

Etohi1
Spa17


Acap3
Lpar6

Siae
Pde2a


Mboat2
Akirin2

Gstt1
Cyp51


Unc45a
Map1lc3b

Ndst1
Scand1


Zfhx3
Chmp3

Rhog
Trim31


Stard5
Fnta

Pot1a
Lrrc41


Hps5
Phpt1

Tmem245


Arrdc1
Commd7

Hck


Taf8
Syf2

Rab13


Rac3
Cdc42

Smyd1


Gnb2
Acot7

2810468N07Rik


Ehmt1
Mea1

Gimap1


Inpp5b
Vapa

Tmem219


Pam16
Ccdc109b

Gprc5c


Cdc25b
Pip5k1b

Slc6a8


Gfod1
Vta1

Coprs


B9d2
Ube2r2

Fam49a


Wdr85
Klf9

Uox


Atf6b
0610040J01Rik

Tmem121


Gatad2a
Ndfip2

Tmem241


Wdr13
Actr10

Mgll


Zfhx2
Manbal

Hrsp12


Ccdc92
Morf4l2

Tcta


Nfe2l3
Pigyl

Tmc5


Tead2
Runx1

1700011H14Rik


Rmnd5b
Rnf6

Mtmr11


Dock7
Ghitm

Neurl1a


Wnk2
Pim3

Stk40


Snapc2
Tank

Klhl28


Dixdc1
Nubp2

Nek7


Neu2
Lsm1

Ak7


Mcc
Zfand6

Tuba1a


Ythdf2
Uros

Slc16a3


Stx4a
Snapc5

Prkce


Flii
Frg1

Neu1


Mmp14
Malat1

Irs2


Hgs
Pla2g16

Tslp


Ptprf
9130230L23Rik

Ypel3


Puf60
Gga2

Ablim3


Aldh7a1
Tmem30b

Crip1


Prpf6
Ube2k

Gm14440


Gdpd5
Mocs2

Ppp1r3b


Gramd4
Slmo2

Ppt1


Mov10
Atp6v1g1

Cdhr5


Hipk3
Dnajb1

Ttll10


Mthfd1l
Stra6l

Fbxo9


Fam216a
Slc25a11

Gimap3


Rab4b
Smim8

1110032A03Rik


Sh3glb2
Tpgs2

Rbpms


Cdc14b
Bub3

Cadps2


Tmem63b
Rit1

Loh12cr1


Leng1
Hsbp1

Ccser2


Nab2
M6pr

Tmem176a


AW554918
Gemin7

Tubb4b


4931428F04Rik
Cpq

P2rx1


Ddx42
Jade1

Romo1


Cttn
BC004004

Chac2


Mtfmt
Sirt2

Ccbe1


Stox2
Tspan31

Lyn


Cirbp
Atg3

Bnip3


Gm8096
Bbs4

L1cam


Usf2
Wbscr22

Fbp2


Kcnh8
Rgs2

Wdfy2


Fam89b
Plaa

Nsf


Fundc1
Nudt14

Nfatc1


Arhgef2
Msi2

Rpl30


Myo7b
Dnlz

Necap1


1810046K07Rik
Akr1b3

Nlrx1


Afap1
Maf1

Ydjc


Gtdc1
Pde6d

Oasl2


Chd4
Stard5

Dpysl2


Dclk3
Phax

Parp4


C230052I12Rik
Slc23a3

Gm6644


2410018L13Rik
Prelid2

1700047I17Rik2


Arid2
Strbp

Fyb


Commd4
Pea15a

Gmpr


Pigv
Chn2

Enpp3


St5
Cmip

Nptn


Pde6d
Diablo

Serpini1


Traf7
Txndc9

Slc4a8


Fam195b
Alox5ap

Gprc5a


Ubn2
0610009L18Rik

Fabp1


Lzts2
Taf12

Gm14295


Mark2
Acer3

Dclk1


Pou2f3
Mpv17l2

Terf2


Csk
Nck1

Tax1bp1


Plekhm2
Tmbim1

Klf6


Abhd8
Metap2

Mn1


Dopey2
Hnrnpk

Pygl


Ppil2
Yif1b

Sema7a


Hdac6
Stat6

Chmp2a


Tmem158
Dctn2

Sh3kbp1


Vezt
Siah1a

Bicd1


Adora1
Spon2

Atp6v1d


Fhad1
Shisa5

Avpi1


Gripap1
Ppp1r35

Xaf1


Sptbn1
Arpc1b

Atp6v0d1


Tcea2
Ppp6c

Gm14436


Sugp2
BC005624

Sema5b


Efs


Chi3l1


Sbf1


Slc25a12


Lrrc16a


Fes


Nsd1


Fam177a


0610040J01Rik


Hebp1


Jup


Klf7


Cacnb3


Nudt8


Stub1


Tesk2


Mob3a


Inpp5d


Zdhhc8


Lrp12


Hmx2


Fam83d


Ywhab


Skap2


AI846148


Atg3


Tet1


Wdfy1


Rab1b


Hipk1


Hes6


Efhd2


Slc4a7


Krt222


2410004B18Rik


Trappc2


Rest


Lipo1


Abca7


Syne2


1110004F10Rik


Clec4a1


9230110C19Rik


Ptpra


Kdm6b


Ttll7


Gas8


Lyrm2


Cgn


Cox17


Tnrc18


Tm2d1


Taok2


Strip2


Gpsm1


Dock8


Setx


Sdf2


Patz1


Hyi


Esyt1


Gpr18


Junb


Cables2


Ntng2


Sertad1


Ncs1


Mien1


Ppm1m


Fam57a


Atxn2l


Ptpre


Arpc1a


1810058I24Rik


Smarce1


Car7


Tmem231


Lmtk2


Cish


Tnnt1


Agrn


Ypel5


Abcc5


Gtf2b


Plekhg2


Zdhhc20


Ssbp3


Mapre2


Sbk1


Sik1


2700086A05Rik


Erp29


Kdm5a


Tmem229a


Cfl1


Gas7


Ppp6r2


Rnasek


Jmy


Tuba4a


Oas1h


Ppp1r14c


Fgf12


Pacs2


Mau2


Pnpla3


Irf2bpl


Reep5


Ogdhl


Rbm4b


Trerf1


Tmem80


Lamtor5


Kctd15


Lmnb2


Capn1


Dync1h1


Ifnar2


Dpp3


Xrcc4


Aldh4a1


Tspan17


Wwc1


Hdac1


Zfp459


Ccdc28b


Pion


Tspan8


Strn4


Grina


Ppp2r5c


Fam46a


Stx8


4930539E08Rik


Wdr78


Casp3


Dsp


Adam1b


9030624G23Rik


Mxd1


Kifc2


Fdft1


Senp7


Kcns3


Aamp


Slc9a6


4931406H21Rik


Vamp4


Gtf2f1


Cd47


Oas1c


Slc52a3


Cachd1


Gm3002


Fis1


Apba3


Use1


Syne3


Kit


Krt18


Zdhhc17


Map1lc3a


Tmem9


Rusc1


H3f3b


Dctn3


Narf


Gnat3


Kcnh2


Homer1


Ddx17


Gngt2


Micall1


Slc39a13


Dnajb2


Rgs19


Ik


Emc2


Flt3l


Tusc3


Igfbp7


Vps53


Chdh


Gpr137b-ps


Pak1


Kif2a


Hoxa5


Ildr1


Rnf114


Limd2


Mlec


Gm10406


Rbm42


Rab11a


Kdm4d


Ift172


Fam50a


Tmem256


Irgq


6330407A03Rik


Irf5


Fbxo36


Cenpt


Ptpn6


Iqsec1


Exph5


Dvl3


Arl6


Figf


Stx7


Tmed1


Dcaf15


Znf512b


Lap3


Podxl2


Nav2


Cyb5r4


Lrrc57


Plekha6


Prox1


Trappc3


Pnpla6


Snn


Syap1


Zdhhc24


Itih5


Runx1


Rock2


Cd9912


Isg15


Zc3h11a


Tprg1


Gse1


Amdhd2


Cdx1


Unc13d


Camkk1


AI462493


Jag2


Ampd3


Arid4b


Gm14308


2310011J03Rik


Ell2


Rnf111


0610031J06Rik


Eif4h


Zdhhc9


Rraga


Zfp868


Dyrk1b


Gys1


Nfe2l1


Tmem57


Csrnp1


Hspb11


Cyld


Nebl


Tnip1


Fbxo25


Atp6v1e2


Gbp3


Tet3


Cdkl2


Pvrl1


Zdhhc12


Prpf38b


Gclm


Pla2g4a


Gm3317


Pfkfb3


Gm3494


Ubr4


4833418N02Rik


Ppp2r1a


Ube2j1


Polb


Htatsf1


Igsf8


Kif3a


Tmem223


Lca5


Tiam2


Taf9b


Sptan1


H2-Ke6


Zmym3


Bmyc


Shoc2


Mtmr7


Tnfrsf25


Abhd16a


Celf1


Itsn2


Map4k4


Atp6v0a1


Hyal2


Adra2a


Tjp3


Dcp1b


Morf4l1


Snx18


Ccdc115


Pxmp4


Phip


Smap1


Gclc


Cmip


Pcdhga5


Atp6v0b


Polr3g


Dnahc6


Pnn


1700112E06Rik


Fam129b


Cpm


Trio


Arhgap4


4931440P22Rik


Ccdc129


Lepre1


Fnta


Agpat1


Ccndbp1


Kank1


Itfg1


Pard6g


Map1a


Mapk1ip1l


Efnb2


Tmub2


Shf


Fgd6


H2-D1


Safb2


Tbcb


Bahd1


Phf1


Ajuba


Cry2


Pou2fl


Iqce


Pdlim5


Cript


Dnmt3a


Sema3b


Fcho2


Adh1


Trib2


Crot


Bptf


Eppk1


Ctnna1


B3gat3


2310035C23Rik


Arl8a


R3hdm4


Gadd45g





Alkbh7





Cib2





2010012005Rik





Cic





A630075F10Rik





Gm14420





Rabgef1





Lgals8





Lmf1





Bad





Cdipt





Kank3





Mtpn





Atp6v1e1





OTTMUSG00000016609





Myl6





Gfi1b





Pigyl





Ccdc126





Ocel1





Bloc1s1





Eml6





Kcnd3





Nfat5





Gm5617





Sos1





Mania





Acer3





Gm2382





Suox





Chuk





Coq10b





Dhcr24





Srpx2





Epb4.1l4b





Gemin7





Rab44





Elp5





Rasa2





Calml4





Slco4a1





Slc25a17





Arhgap5





Rbms3





Neat1





Nab1





Rdh14





1700030A11Rik





Tfpi2





Ccnc





Zfp428





B3gnt6





Ddt





Ostf1





Cdk11b





Tmem79





Gm14306





Vps13a





Fam3a





Clca5





Dcaf12





Mbd6





Gramd1b





Tbcc





Wsb2





Tmem8





B4galt6





Psd3





Marveld3





Synrg





Krcc1





Tshz1





Rogdi





Rap2a





Gm6249





Apc





Enpp5





Otud7b





Rilpl2





Stambpl1





Samd14





Ccdc104





Atp2b1





Phtf2





Ndrg1





Srp19





Tspyl1





B3galt5





Aldoc





Significance cut-offs: FDR (Fisher's combined): 0.01, Log2 fold-change: 0.25






The Tuft-2 cell signature is enriched for immune-related genes (FDR<0.001, FIG. 13c-d), whereas genes related to neurogenesis and neuronal development (e.g., Nradd, Ninj1, Plekhg5 and Nrep) are among the most specific markers for the Tuft-1 cluster (FIG. 13d). Irf7 is the only Tuft-2 specific TF and may be a target used for modulating activity of Tuft-2 cells. This supports the hypothesis that the previously reported inflammation and neuronal signatures in bulk data21 belonged to distinct tuft cell subsets. These two subsets may reflect dynamic states, transient stages of maturity, or two distinct bona-fide cell types.
As tuft cells were recently shown to be important for communication with gut-resident immune cells14-16, Applicants examined their expression of genes encoding epithelial cytokines. Both groups expressed Il25, consistent with recent findings14, but neither expressed Il33 (in both datasets) (FIG. 4c), which may be due to the low level of this transcript. However, the expression of thymic stromal lymphopoietin (TSLP), an important Th2 promoting cytokine13,57 was significantly higher in the Tuft-2 group (FDR<0.1, Mann-Whitney U-test) (FIG. 4c), a finding Applicants confirmed using smFISH and qPCR (FIG. 4d-e). TSLP expression by the Tuft-2 subset may, along with Il25, contribute to the induction of the Th2 response to intestinal parasites.
Finally, the Tuft-2 signature revealed that Ptprc, the gene encoding the pan-immune marker CD45, is expressed strongly and exclusively by Tuft-2 cells (FIG. 4f), a finding Applicants validated at the mRNA level in situ by co-FISH (FIG. 4g, top-left), at the protein level using FACS (FIG. 4g, top right) and by an immunofluorescence assay (IFA) (FIG. 4g lower panels and FIG. 13e). Finally, sorting for EpCAM+ CD45+ cells (n=3 mice) followed by 3′ droplet scRNA-seq of 332 cells, showed a strong enrichment for Tuft-2 cells (FIG. 4h and FIG. 13f). Applicants note that Applicants used a lenient sorting gate to ensure Applicants obtain sufficient numbers of these rare tuft cells, which led to a higher contamination rate of T cells, which Applicants removed using unsupervised clustering (T cell expression of Ptprc is ˜25% higher than in sorted CD45+ Tuft-2 cells). To Applicants knowledge, this is the first finding of CD45+ cells from a non-hematopoietic lineage, and highlights the challenges associated even with even well-established molecular markers of cell types.
Taken together, the data suggests that tuft cells are a population of two distinct sub-types; Tuft-1 cells, with neuron-like features that may transmit taste-chemosensory signals to enteric neurons (Westphalen et al., 2014) and Tuft-2 cells with immune-like features that in addition to the taste-chemosensory ability, may communicate with immune cells, as suggested before (Gerbe et al., 2016; Howitt et al., 2016; von Moltke et al., 2016) to boost type-2 immunity upon signals from the lumen.
The Tuft-1 and Tuft-2 signatures were also examined in cells obtained from the trachea. Applicants found that the respiratory Tuft cells included both subtypes of Tuft cells as in the gut, suggesting that they perform similar roles in the trachea (e.g., T helper 2 (Th2) response, ILC2 response). Applicants identified transcription factors that were exclusively expressed in trachea Tuft cells. The transcription factors expressed exclusively in Tuft cells included Etv1, Hmx2, Spib, Foxe1, Sox9, Pou2f3, Ascl2, Ehf and Tcf4 (FIG. 30). Applicants further analyzed genes specific to tuft cells in both the trachea and gut and found that tuft cells secreted IL-25 (FIG. 34, 35). Applicants also analyzed genes specific to the gut and trachea (FIG. 36).
Example 7—Identification and Characterization of Microfold (M) Cells In Vivo
Surprisingly, the Tuft-2 subset expressed several of the genes previously reported to be specific to microfold (M) cells17,58, including Rac2, Siglecf, and Gfi1b (Growth Factor Independent 1B Transcription Repressor), at a significantly higher mean level than Tuft-1 cells (p<1×10−5, Mann-Whitney U-test, FIG. 5a, FIG. 14a). M cells are derived from the common Lgr5+ stem cells of the intestinal epithelium17, but reside exclusively above Peyer's patches (PP) within a distinct flat epithelial tissue known as the follicle associated epithelia (FAE). The FAE comprises a small fraction of the total intestinal epithelium (<1%)18, and since M cells represent only a subset of the FAE, they were not detected in the initial atlas, as noted above (FIG. 1b). There are two alternative explanations for the observed overlap between Tuft-2 and M cell marker genes: (1) Tuft-2 cells are in fact rare M cells with an atypical location, that is, the previously proposed villous M cells59, or (2) Tuft-2 cells are indeed a subset of tuft cells, which nevertheless express some M cell-related genes.
To distinguish between these possibilities, Applicants used both ex vivo and in vivo strategies, to determine an M cell signature at the single-cell level. First, Applicants used an ex vivo model of M cell differentiation, analyzing 5,434 cells from small intestinal organoids treated with RANKL17 for 0, 3, and 6 days (FIG. 5b-c, FIG. 14b). One cluster of 378 cells (FIG. 5b) recovered by unsupervised clustering (Methods), was labeled as differentiated M cells by the expression of known M cell marker genes58, not expressed by Tuft-2 cells, including Gp2 and Tnfaip2 (M-see) (FIG. 14c-e). Based on this cluster, Applicants constructed signatures (FIG. 14i, Methods) of M cell specific genes and TFs in vitro (FIG. 14f-g, Table 9, Methods), highlighting several immune factors (e.g., Spib, Irf2, and Irf6).







TABLE 9







Summary of marker genes for Microfold (M) cells










In vivo
In vitro







Ccl20
Ccl9



Clu
Serpinb1a



Mfge8
Serpinb6a



Anxa5
Tnfaip2



Pglyrp1
1700011H14Rik



Ctsh
Ccl6



Serpinb6a
Ly6a



H2-M2
Anxa5



Gp2
Spib



Ubd
Ctsh



Lamp1
Fabp5



Cxcl16
Ccl20



Cyba
Pglyrp1



Scd1
Tmsb4x



1700011H14Rik
Rac2



Aif1
Dnase1



Ctsd
Smpdl3a



Tnfaip2
Far2os2



Far2os2
Rras2



Slc2a6
Nqo2



Adgrd1
Gjb2



Ncf4
1110046J04Rik



Rnf128
Npc2



Il4i1
Atp6v1c1



Far2
Marcksl1



BC021614
Psmb7



D630011A20Rik
Psg27



Vcam1
AI118078



Stx11
Brk1



Sdhaf1
Msln



Ces1b
Tnfrsf4



Itga3
Cd63



Msln
Rnf181



Scarb2
Sox8



Tnfrsf4
Pon2



Fam98a
Bcl2a1d



Tmsb4x
Rassf2



Nfkbia
Aif1



Rnase1
1700025G04Rik



Vamp5
C4bp



Gulo
Vamp8




Prr13




Bmp2




Rps6kl1




Degs2




9130008F23Rik




Il4i1




Gm5549




Npdc1




Gp2




H2-M2




Vamp5




Impa1




Gpa33




Cnp




Dapk2




Rasd1




Etfa




Mocs1




Slc2a6




Hars




Stk24




Fam131a




Snhg18




Pold1




Agps




Bcl2a1b




Zfp36l1




Btbd16




Mylk




Cpt2




Ahcyl2




Ier5







Significance cut-offs:



in vivo: FDR (Fisher's combined): 0.001, Log2 fold-change: 0.5



in vivo: FDR (max): 0.05, Log2 fold-change: 0.5






Next, to confirm the relevance of these signatures to M cells in vivo, Applicants profiled 4,700 EpCAM+ cells from FAE of WT and Gfi1b-GFP labeled knock-in mice, a known marker for both tuft and M cells17,60 (n=5 mice). A cluster of 18 cells (FIG. 5d, arrow; Methods), was enriched for known M cell markers (FDR<0.05, Mann-Whitney U-test), including Gp2, Ccl20, Tnfaip2, and Anxa5 (FIG. 5e). These cells also expressed high levels of the M cell signature genes derived from the in vitro data (p<10−4, Mann-Whitney U-test, FIG. 14h). Applicants then defined an in vivo signature of enriched markers and TFs (FIG. 5e-f, Methods). Notably, only one of the 7,216 cells in the sampling of the intestinal epithelium is positive for this M cell signature (data not shown), indicating that: (1) M cells are not readily obtained from scRNA-seq of epithelia without enrichment; (2) Peyer's patch M cells are extremely rare, and require specific FAE enrichment; the statistical model suggests that cells present at 0.07% or lower would be undetected with high (95%) probability (Methods); (3) Tuft-2 cells are not M cells, despite some genes expressed by both cell types; and (4) villous M cells are undetectable in the data. Applicants cannot rule out the possibility that Tuft-2 cells may have been previously erroneously termed “villous M cells”, because of the partial similarity in some of their features.
Example 8—Pathogen-Specific Recalibration of Cell Proportions and Cell States in Response to Bacterial and Helminth Infections
Immune and epithelial cell decisions to tolerate or elicit an immune response to specific gut pathogens play a key role in maintaining gut homeostasis2. Because the epithelial cells of the small intestine are generated in an ongoing, continuous and rapid process of differentiation from stem cells throughout life, it is likely that following infection with a pathogen, there are changes both in the relative composition of IEC sub-types and in the internal state of each type, as well as in global expression changes across multiple cell types. These three types of signals are challenging to distinguish in bulk analysis, whereas single-cell analysis can readily dissect each aspect.
Applicants therefore investigated the IEC responses to a common pathogenic bacterium, Salmonella enterica, which induces enteritis within hours61,62, and to the helminth Heligmosomoides polygyrus, a parasitic worm that damages the integrity of the small intestine and elicits a strong Th2 response63. Applicants profiled individual IECs using droplet-based 3′ scRNA-seq two days after Salmonella (n=2 mice, 1,770 cells) or 3 days (n=2 mice, 2,121 cells) and 10 days (n=2 mice, 2,711 cells) after H. polygyrus infections, as well as 3,240 cells from control mice (n=4 mice). Applicants profiled an additional 389 cells with the deeper, full-length scRNA-seq, which Applicants used to obtain high-confidence ‘consensus’ differentially expressed genes for all comparisons that are independent of cell-type.
First, Applicants investigated the global effects of infection with Salmonella. In infected IECs, 571 genes were up-regulated vs. control cells (FDR<0.25, likelihood-ratio test, FIG. 15a, top left) and these genes were enriched (FDR<0.001, hypergeometric test) for pathways involved in defense response to bacterium (FIG. 6a). Also up-regulated were genes involved in acute inflammatory programs such as the interferon-inducible GTPase (Igtp) and DNA-dependent activator of IFN-regulatory factors (Zbp1), or with a protective role in Salmonella infection, such as the anti-microbial lectins Reg3b and Reg3 g64,65 (FIG. 6b, top). In addition, Applicants identified a non-specific inflammatory response—a minority (112/571; 19%) of the genes up-regulated in response to Salmonella infection are also regulated in the same way in response to H. polygyrus (FDR<0.25, likelihood-ratio test), and are likely associated with a generalized acute stress response (FIG. 15a, middle panels). Indeed, genes known to be involved in stress responses such as Gpx2, Hspa1 and Hsph5 were among those up-regulated in response to both pathogens (FIGS. 15a and 10a). In particular, the invariant chain of MHC class II, Cd74, was also strongly induced (FDR<0.001, likelihood-ratio test) in both responses (FIG. 16a).
Second, Applicants identified cell-type-specific responses to Salmonella infection, most notably, an increase in the expression of both anti-microbial peptides and the mucosal pentraxin, Mptx2 (FIG. 1) in Paneth cells under infection (FIG. 15d). Comparing enterocytes in control and Salmonella-infected mice (424 vs. 705 cells) (FIG. 6e, top), Applicants found 40 enterocyte-specific genes significantly up-regulated (FDR<0.1, likelihood-ratio test), including the innate immune-related genes Tnfsf10 and Nlrp6. Among these cell-type-specific genes, 26 (65%) are induced in a Salmonella-specific manner (FIG. 6e, bottom, Methods), including several previously implicated in the response to Salmonella infection, such as Tgm266. Comparing single enterocytes in control and Salmonella-infected mice (424 vs. 705 cells) (FIG. 6h), this study found significant up-regulation of innate immune-related molecules including Clec2d, Nlrp6 and Smad4 and (FIG. 6h, left). this study further refined the list to 52 Salmonella-specific genes (Methods) and found several genes previously implicated in the response to Salmonella infection such as Tgm2, Nlrp6 and Casp8 (FIG. 6h, right) (Man et al., 2013; Rodenburg et al., 2007; Wlodarska et al., 2014). Thus, the dramatic elevation in the number of enterocytes together with the retuning of their intrinsic cell states suggests an unappreciated crucial role of these absorptive cells in anti-microbial defense. In addition, the pro-inflammatory apolipoproteins67 Serum Amyloid A1 and 2 (Saal and Saa2) were induced in the distal enterocytes, under Salmonella infection, with higher levels of Saa1 and Saa2 (FIG. 15a,c).
Notably, as a result of infection, some anti-microbial genes, that are enterocyte-specific in homeostatic conditions, are induced at two levels: (1) further induction in enterocytes; and (2) global induction in non-enterocyte cells, generating an overall elevated response of the tissue. Specifically, in control mice, expression of the Reg3 gene-family (Reg3a-g) was mainly restricted to absorptive enterocytes (Table 3-4). Upon Salmonella infection not only was their expression further elevated in absorptive enterocytes (FIG. 6b top, dots), but Reg3b and Reg3 g, largely undetectable in other cell types pre-infection, were up-regulated in all cell-types post-infection (FIG. 6b top, grey dots). Thus, the IEC response to Salmonella involves the induction in all cells of anti-microbial genes, including Clec2 h, Anpep, and Enpep, that are only expressed in enterocytes in homeostasis (FIG. 6b top, FIG. 15b).
Third, Applicants systematically distinguished the contribution of changes in cell intrinsic expression programs vs. shifts in cell composition. Applicants used unsupervised clustering to determine the proportion of each of the different IEC populations (FIG. 6d), visualized by tSNE embeddings (FIG. 6c). Applicants observed a dramatic shift in cell proportions following Salmonella infection (FIG. 6d; Methods), with a substantial increase in the frequency of mature absorptive enterocytes (from 13.1% on average in control to 21.7% in infection; FIG. 6d) and a significant reduction in the proportion of TA (52.9% to 18.3%) and stem (20.7% to 6.4%) cells. Applicants initially recovered a low number of Paneth cells (Methods), and thus analyzed an additional 2,029 cells from an additional experiment (droplet-based scRNA-seq; n=4, Salmonella-treated mice), and found a substantial increase in mature Paneth cell proportions (from 1.1% to 2.3%, FDR<0.01), in agreement with a previous study that showed more positive staining of Paneth cells in Salmonella infection68 (FIG. 15d-e). These results suggest that the IEC response to Salmonella infection includes the induction of specific differentiation towards absorptive enterocytes and Paneth cells, most likely to increase production of anti-microbial peptides.
Next, analyzing IECs during infection with H. polygyrus, Applicants found a distinct recalibration of cell composition and cell states than in Salmonella. There are 299 genes up-regulated in H. polygyrus infected vs. control mice, 187 of which (62%) were specific to the H. polygyrus response (FDR<0.25, likelihood-ratio test, FIG. 15a, bottom panels). These H. polygyrus-specific genes were enriched with inflammatory response molecules, including Dnaja1, Vcp, Noxa1 andPsmd6, the phospholipase Pla2 g4c (FIG. 15a, bottom right), and the tuft cell markers Acot7, Peal5a and Avil (FIG. 15a bottom panels). This again suggested a change in cell composition, which Applicants then tested by unsupervised clustering. Indeed, at ten days post infection, there is a striking increase in goblet cells—known to be important for the epithelial response to the parasite69 (on average, from 7.0% to 11.8%, FDR<1×10−5, Wald test, Methods), and a reduction in enterocyte proportions (15.3% to 4.9%, FDR<1×10−10, Wald test) (FIG. 6d). Tuft cell proportions were increased substantially at day three (1.9% to 6.3%, FDR<1×10−5, Wald test), with a further increase by day ten (to 8.5%, FDR<1×10−10, Wald test) (FIG. 6d). Within the tuft cell subset (409 cells overall, FIG. 16b-c) there was a significant elevation (17.2% to 43.0%, FDR<0.05, Wald test) in the proportion of immune-like Tuft-2 cells by day 10 (FIG. 6f), reflecting changes in tuft cell states along with the dynamic expansion in the overall tuft cell population in response to the parasite.
In addition to changes in cell proportions, within goblet cells there was a strong induction (FDR<1×10−5, likelihood-ratio test; FIG. 6g) of several genes previously implicated in anti-parasitic immunity, including RELMβ69 (Retnlb, FIG. 16d), but also in genes (e.g., Wars and Pnlipr2; FIG. 6g), previously reported to be expressed in response to parasitic infection70, but not known to be expressed by goblet cells. Further refining this gene set to those specific to the H. polygyrus pathogen revealed an up-regulation of genes related endoplasmic reticulum stress, specifically Ddit3, Ier3ip1 and Sft2d2, possibly involved in processing of secreted mucins to respond to the worm (FIG. 6i, right). Thus, H. polygyrus infection elicits shifts in both cell composition and cell state, with early expansion of tuft cells to initiate the Th2 response14, and later expansion of goblet cell numbers to help prevent attachment of the helminth to the epithelial barrier via secreted mucins71, along with an increase in the expression of key genes in the expanded goblet cells.
Table Legends
Table 2| Summary of single-cell RNAseq experiments. This table provides the number (after quality filtering, see Methods) of individual intestinal epithelial cells profiled in each of the in this study.
Table 3| Cell-type specific signature genes—droplet-based dataset. This table provides the lists of genes specific to each of the identified clusters of intestinal epithelial cells, identified using 3′ droplet-based scRNA-seq data (FIG. 1B).
Table 4|Cell-type specific signature genes—plate-based dataset. This table provides the lists of genes specific to each of the identified clusters of intestinal epithelial cells, identified using full-length plate-based scRNA-seq data (Extended Data FIG. 2A).
Table 5|Consensus cell-type specific signature genes—both datasets. This table provides high-confidence lists of genes specific to each subtype of intestinal epithelial cells in both 3′ droplet-based and full-length plate-based scRNA-seq datasets.
Table 6| Cell-type specific TFs and receptors. This table provides lists of genes annotated as either transcription factors (TFs), G protein-coupled receptors (GPCRs), or leucine-rich repeat (LRR) proteins, enriched in each subtype of intestinal epithelial cells in full-length plate-based scRNA-seq data.
Table 7| Enteroendocrine cell subset signature genes. This table provides the lists of genes specific to each of the identified clusters of enteroendocrine cells, identified using 3′ droplet-based scRNA-seq data.
Table 8|Consensus tuft cell subset signature genes. This table provides the lists of genes specific to each of the identified subsets of tuft cells, identified using both 3′ droplet-based and full-length plate-based scRNA-seq data.
Table 9| In vitro and in vivo M cell signature genes. This table provides the lists of genes specific to intestinal microfold (M) cells, using 3′ droplet-based scRNA-seq data from in vitro cells derived from RANKL-treated organoids, and in vivo cells derived from the follicle associated epithelia (FAE) of wild-type mice.
Table 10| Markers of proximal and distal Paneth cells. This table provides estimates of differential gene expression between two subsets of Paneth cells identified by clustering and interpreted (post-hoc) as derived from proximal and distal small intestine (FIG. 10).
Example 9—Discussion
The intestinal epithelium is the most diverse epithelial tissue in the body, composed of functionally and molecularly specialized subtypes. Here, Applicants dissected it into its different components using massively parallel scRNA-seq, analyzing a total of 53,193 IECs, to create a high-resolution single-cell atlas of the mouse intestinal epithelium, and reveal even further diversity than was previously appreciated. Using unsupervised analyses, Applicants identified and characterized the transcriptomes of the major differentiated epithelial cell-types: enterocyte, goblet, Paneth, enteroendocrine, tuft and microfold. Applicants also derived specific gene signatures for intestinal stem, transit-amplifying and various enterocyte precursor cells. For each major cell-type Applicants obtained specific markers, TFs and GPCRs and high-confidence consensus signatures from two complementary scRNA-seq methods (3′ and full-length).
The single-cell profiling of tens of thousands of intestinal epithelial cells revealed coherent cell-specific transcriptional programs, some revising predicted marker expression, which Applicants validated in situ and in prospectively isolated cells. This emphasized the utility of unsupervised profiling of tissues to define new cell-type gene signatures, rather than solely relying on previously annotated individual marker genes, which may lead to biased isolation of subtypes. For example, Applicants discovered and validated that tuft cells are composed of two subsets, one of which expresses neuron-related genes which might mediate interaction with the enteric nervous system, while the other expresses genes related to inflammation and immunity, including the immune-cell marker gene Ptprc (CD45). This CD45+ tuft population expresses the epithelial cytokine TSLP, which may represent an additional mechanism by which epithelial cells communicate with gut-resident immune cells. Further studies would be required to determine whether the Tuft-1 and Tuft-2 cells represent two different developmental fates, or alternative cell states. In another example, Applicants found that several known tuft cell markers are also expressed by M cells, which may have confounded studies based on those markers. Using single-cell profiling Applicants resolve this ambiguity, providing novel specific markers and TFs to distinguish these rare cells, which may enable further insights into M cell biology.
The large number of cells profiled allowed Applicants to assess heterogeneity even within rare subpopulations such as enteroendocrine cells (EECs). From 533 EECs extracted from 18,881 epithelial cells (Table 2), Applicants identified and characterized the transcriptomes of 12 subsets, 8 of which are mature. Interestingly, EECs were more abundant than expected and partitioned into two main groups, enterochromaffin (2 subsets) and Secretinhigh (6 subsets) cells (FIG. 3). The Reg4 gene, a previously proposed marker for all EECs23, was in fact expressed only in one of the groups of enterochromaffin cells. The in vivo sampling of EECs encompasses the subsets found in an organoid-derived EECs single-cell study53, and highlights three additional mature EEC subsets (FIG. 12E). Two of these subsets (SIL-P and SIK-P) are enriched in the ileum, while SILA were found mainly in the duodenum, consistent with the regulatory roles of the hormones Ghrelin—an appetite stimulant—and GLP-1 and PYY, which together act as an ‘ileal brake’, a feedback loop which limits gastric emptying as nutrients arrive in the distal gut11. Further, Applicants found that most EEC subsets express more than one GI hormone and defined a novel taxonomy reflecting each subset's unique hormonal expression profile. An open challenge is to understand the specific role of each of these novels subsets in the orchestration of appetite, gut motility, nutrient absorption, or in the onset and treatment of diseases, such as Type 2 diabetes and obesity.
Molecular Underpinning for the Integration of Lumen Signals by the Gut Epithelium
IECs play barrier roles, absorb nutrients, integrate and relay signals from the environment to the immune and enteric nervous systems12. The atlas resolves the cellular populations that are implicated in sensory pathways at unprecedented resolution. For example, Applicants found that two of the 10 most enterocyte-specific TFs were from the nuclear receptor (NR) family of proteins. These genes are crucial for sensing and metabolism of various substances. In particular, lipid homeostasis (Nrlh3), and sensing of endobiotic and xenobiotic substances, Nrli3.
Similarly, Applicants provide an enhanced map of the GPCRs expressed by all cells, and particularly by EEC subsets. Most notably, the important cannabinoid receptor Gpr11937 was enriched in the novel SILA subset (FDR<0.05, FIG. 12F), which co-expresses Ghrl and Gcg, genes encoding gut hormones that regulate appetite and satiety. Furthermore, several GPCRs enriched in EECs (FDR<0.05, FIG. 8d) may mediate communication with enteric neurons, including the metabotropic glutamate (Grm4) and acetylcholine (Chrm4) receptors. Additionally, the important neurotrophic cytokine brain-derived neurotrophic factor (Bdnf) was enriched in SIK-P cells (FDR<0.01, FIG. 3B), a possible additional EEC-neuron channel of communication. Tuft cells were also enriched for GPCR expression, supporting recent studies that they are specialized for chemosensory properties, especially taste sensing72. Indeed, the gene encoding taste receptor type 1 member 3 (Tas1r3) was expressed exclusively by tuft cells. Like EECs, tuft cells were enriched (FDR<0.05) for genes encoding GPCRs that sense nutrients, such as Ffar3 and Sucnr1 and for gamma-aminobutyric acid B (GABAB, Gabbr1) and dopamine (Drd3) receptors that may be involved in further crosstalk with enteric neurons.
The Adaptive Response of the Intestinal Epithelium to Pathogens Combines Cell Intrinsic and Cell Composition Changes
Although many studies have shown an expansion of goblet cells and recently tuft cells in response to parasites13-15, this analysis revealed that this dynamic restructuring of the epithelial barrier is specific to the identity of the individual pathogen and distinguished cell composition changes from changes in cell intrinsic programs. After infection with the parasitic worm H. polygyrus, there is, as reported, dramatic expansion of secretory cell types, initially an expansion of tuft cells, followed several days later by goblet cell metaplasia. While the overall Tuft cell population increased, the relative proportion of immune-like Tuft-2 subset was particularly expanded. In contrast, the pathogenic bacterium Salmonella enterica induced a strong expansion of absorptive enterocytes and Paneth cells. These dynamic shifts in epithelial composition constitute a generic response mechanism in which differentiation pathways are redirected to enhance the epithelial barrier under pathogenic insult.
These compositional changes are accompanied and enhanced by cell intrinsic changes to regulatory programs, both within specific cell types and across multiple cell types. During helminth infection, goblet cells induce the anti-parasitic molecules Retnlb, Wars and Pnliprp2. Upon Salmonella infection, Paneth cells not only increase in number, but also upregulate various genes encoding anti-microbial peptides (e.g., Lyz1, Defa5), and the mucosal pentraxin, Mptx2. Moreover, Applicants uncovered a novel epithelial cell response to Salmonella, where the expression of genes that are cell-type-specific in homeostatic conditions is broadened across multiple cell types during infection: the antimicrobial C-type lectins Reg3b and Reg3 g, known to be crucial for preventing attachment of bacteria to the epithelium73, are expressed only by enterocytes in normal conditions, but were globally up-regulated by all cells following Salmonella infection. This could only be distinguished by single-cell analysis.
In single-cell RNA sequencing there is a trade-off between sequencing fewer cells deeply and sequencing many cells at a lower coverage. This study pursued both directions simultaneously for maximal information capture, and showed that the very large cell numbers achievable with droplet-based methods enabled the discovery of extremely rare subtypes (Shekhar et al., 2016), while the high coverage (an average of more than 6,000 genes detected per cell) obtained by the plate-based data enabled the detection of less abundant mRNA molecules such as transcription factors, which frequently play important regulatory roles in gut function. Further, the high number of cells this study obtained from the rapidly differentiating intestinal epithelium constitutes a dense sampling of a dynamic process, and therefore provided a high level of ‘pseudo-temporal’ resolution. This enabled Applicants to profile gradual shifts in differentiation of the absorptive enterocytes, subsequently identifying both known and novel TFs such as Gata4 (Bosse et al., 2006) and Gata5 which are expressed coherently during differentiation toward proximal or distal mature enterocyte, respectively.
This study provides a detailed reference dataset and specific hypotheses for follow-up studies, including cell-type specific gene markers, TFs and GPCRs that may open the possibilities for novel clinical interventions in pathologies such as obesity, type-2 diabetes, and allergies. For example, the Tuft-2 cells, which secrete Th2-recruiting epithelial cytokines, may provide insight into mechanisms underlying food allergies. Furthermore, the characterization of epithelial differentiation dynamics in response to two enteric pathogens, may help find ways to manipulate epithelial cell differentiation to minimize gut pathologies, such as acute or chronic gut inflammation, identify cell-specific epithelial cell markers for restitution and inflammation resolution.
Understanding the development, differentiation and function of an organ, such as the intestine, requires the identification and characterization of all of its component cell types. In the small bowel, intestinal epithelial cells (IECs) sense and respond to microbial stimuli and noxious substances, provide crucial barrier function and participate in the coordination of immune responses. Here, this study profiled 24,423 individual IECs from mouse small intestine and intestinal organoid cultures. Taken together, the examples above demonstrate that using unsupervised clustering, Applicants defined specific gene signatures for major IEC lineages, including the identification of Mptx2, a mucosal pentraxin, as a novel Paneth cell marker responsive to Salmonella infection. In addition, this study identified unexpected diversity of rare hormone-secreting enteroendocrine populations, revealing co-expression programs of gut hormone genes, previously thought to represent different enteroendocrine subtypes, and constructed a novel hierarchical classification of these cells. this study also distinguished two subtypes of Dclk1-positive tuft cells, one of which (Tuft-2) expresses both the epithelial cytokine Tslp and the pan-immune cell marker Ptprc (CD45), which has not been previously associated with any non-hematopoietic cell type.
Finally, this study characterized how the intrinsic state and proportion of these cell types are reshaped in response to Salmonella enterica and Heligmosomoides polygyrus infections. Salmonella infection led to an increased number of Paneth cells and enterocytes, and a Paneth cell-specific up-regulation of both defensins and pentraxins, including Mptx1 and Mptx2. An absorptive enterocyte-specific antimicrobial program was broadly activated across all IEC types, demonstrating a previously uncharacterized cellular plasticity in response to pathogens. In contrast, H. polygyrus led to expansion of goblet and tuft cell populations. This increase in tuft cells was driven by an expansion of the Cd45+ Tuft-2 group. The comprehensive atlas highlights new markers and transcriptional programs, novel allocation of sensory molecules to cell types and organizational principles of gut homeostasis and physiology.
Example 10—Gut Atlas Analysis in Human Colon from Healthy Subjects
Applicants have generated a foundational resource in the healthy gut for: (1) Cell composition (i.e., changes in proportions of different cell types/states), (2) Cell intrinsic states (i.e., changes in gene expression within a cell type), (3) Cell-cell interactions (i.e., changes in cell-cell interaction mechanisms), and (4) the relevant cell types for each gene (e.g., GWAS genes).
Applicants used droplet-based scRNA-seq of colonoscopy samples from healthy individuals to generate the cell atlas. The samples were obtained from 10 healthy individuals (37,435 non-inflamed cells). The samples were small biopsies containing about <80,000 cells. The biopsies were fresh and dislocation and processing were performed by applicants.
FIG. 17 shows that clustering analysis partitioned cells by cell type in the healthy samples. FIGS. 18 and 19 show that the atlas uncovers almost all cell types and subtypes in the colon. Applicants identified the following cell types and subtypes in the colon: Plasma B cells, Class switching B cells, Follicular B cells, T cells, Macrophages, Dendritic cells, Mast cells, Cycling monocytes, Tolerogenic DCs, Neutrophils, Activated CD4 cells loFos, Activated CD4 cells hiFos, CD8 IELs, CD8 LP cells, Tregs, Memory T cells, NK cells, Cycling CD8 cells, Microvascular cells, Post-capillary venules, Vitamin metabolizing, Endothelial pericytes, Enterocytes, Tuft cells, Goblet 2, Absorptive TA 1, Secretory TA, Absorptive TA 2, Cycling TA, Goblet 1, Stem cells, Enteroendocrine, Glial cells, Inflammatory fibroblasts, Fibroblast pericytes, Myofibroblasts, Villus fibroblasts, Crypt fibroblasts (hiFos) and Crypt fibroblasts (loFos). Applicants identified markers specific for each cell type. Table 15 A-D shows the top 250 genes expressed in each cell type.
















TABLE 15A





Plasma_B_cells
Class_switching_B_cells
Follicular_B_cells
Microvascular_cells
Post-capillary_venules
Vitamin_metabolizing
Endothelial_pericytes
Enterocytes
Tuft_cells
Goblet_2







HERPUD1
IGLL5
CD79A
PRSS23
DARC
CD320
RGS5
RPL15
AZGP1
MUC2


IGJ
IGJ
MS4A1
RGCC
NPC2
RAMP2
HIGD1B
RPS2
LRMP
TFF1


SSR4
TMSB10
CD79B
PLVAP
CLDN5
CLDN5
CD320
RPL13
SH2D6
RPL13


SEC11C
CFL1
VPREB3
VWA1
CPE
PLVAP
PLVAP
RPS6
MARCKSL1
ZG16


XBP1
TMSB4X
TCL1A
PASK
MADCAM1
SLC9A3R2
CLDN5
GUCA2A
AVIL
RPL10


MZB1
PFN1
FCRLA
GNG11
CLU
GNG11
CRIP2
RPL10
BIK
RPL15


FKBP11
MYL6
CD37
CA4
DUSP23
IGFBP4
RAMP2
AQP8
SH2D7
RPS4X


DERL3
FTH1
CD19
CD36
JAM2
TXNIP
CAV1
RPL32
HCK
RPS2


SPCS2
GAPDH
SMIM14
CD320
PLVAP
ENPP2
ESAM
RPS4X
ANXA4
RPS18


TNFRSF17
ACTB
CST3
VWF
LY6E
CLEC14A
GNG11
RPS19
PTGS1
RPS19


CD79A
IGLL1
CD63
ENG
ECSCR
TMEM88
CD36
SLC26A3
ALOX5
RPL32


SSR3
TNFRSF17
LTB
RAMP2
SDCBP
ESAM
COX4I2
RPLP1
ANXA13
FCGBP


UBE2J1
CD79A
LIMD2
SLC9A3R2
TSPAN7
CRIP2
NDUFA4L2
RPS18
KRT18
RPL19


SPCS1
DERL3
CD22
ESAM
EGFL7
SPARCL1
IGFBP4
PLAC8
IL17RB
S100P


DNAJB9
MT-CO1
BLK
CRIP2
VWF
HLA-E
MGP
CEACAM7
TPM1
CEACAM5


EAF2
MZB1
LGALS3
GSN
GNG11
RAMP3
EGFL7
FXYD3
TRPM5
TSPAN1


FKBP2
SERF2
PTPRCAP
SPARCL1
RAMP2
CD59
TMEM88
KRT20
EIF1B
RPL11


MANF
AL928768.3
AL928768.3
FKBP1A
APLNR
CAV1
SPARCL1
FABP1
BMX
RPS9


PRDX4
ACTG1
HLA-DQA1
TMEM204
RAMP3
VAMP5
RBP7
PRAP1
HPGDS
RPS14


SDF2L1
RPL28
CD53
ITM2B
ITM2B
IFI27
IGFBP7
TSPAN1
POU2F3
FXYD3


SERP1
RPS24
BANK1
RBP5
CTNNAL1
JAM2
MYL9
CEACAM5
GNG13
RPL10A


AL928768.3
MT-CO3
RHOH
TM4SF18
IGFBP4
ECSCR
SLC9A3R2
SDCBP2
HTR3E
RPL35


SPCS3
ATP5E
S100A6
RAMP3
NNMT
SEPW1
TINAGL1
SRI
PSTPIP2
LYPD8


CYBA
COX4I1
GPR18
EGFL7
HLA-E
EGFL7
NOTCH3
MS4A12
SPIB
RPL12


WT1-AS
HLA-A
CORO1A
HSPG2
GIMAP7
BCAM
CLEC14A
PHGR1
PLCG2
RPS5


CRELD2
PPAPDC1B
BCAS4
CCDC85B
GPR126
GIMAP7
TXNIP
C19orf33
ELF3
MUC1


VIMP
GNG7
CXCR5
ECSCR
ICAM1
CD36
ENPP2
RPS8
MATK
ENTPD8


SEC61B
UBA52
CD74
TMEM88
HHEX
NPDC1
JAM2
RPS9
KRT8
RPLP1


PDIA6
ICAM3
SERPINA9
SDPR
GIMAP4
RBP7
SDPR
RPL10A
C11orf53
RPS8


HSP90B1
UQCR11
LRMP
VAMP5
TNFSF10
GSN
GIMAP7
CTD-2228K2.5
TFF3
RPL35A


GNG7
RPS12
FCGRT
BCAM
LINC01013
CYYR1
RAMP3
RPL35
EPCAM
RPL26


PPAPDC1B
SSR4
EAF2
CAV1
AC011526.1
SDPR
TM4SF1
MISP
RASSF6
CLDN4


CD27
S100A6
RGS13
MGP
CLEC14A
EFNA1
ECSCR
GUCA2B
RGS13
RPS13


FAM46C
PPDPF
CXCR4
EMCN
IGFBP7
ICAM2
HLA-E
RPS5
FYB
TFF3


PDIA4
RPL31
POU2AF1
ELTD1
NPDC1
TM4SF1
CYYR1
TMEM54
CRYM
REP15


ISG20
CHCHD2
SMARCB1
PLAT
NCOA7
EMCN
IFITM3
RPS7
PRSS3
FAM3D


PABPC4
BTF3
CD52
KDR
CAV1
IFITM3
SPARC
SLC51B
IGJ
RPS27A


TRAM1
SRP14
SPIB
CLEC14A
LMO2
MGP
A2M
RPL19
TREH
RPS3


ANKRD37
CD27
MGST3
HLA-E
SNCG
TSPAN7
GSN
RPL11
SPINT2
GNB2L1


RPL36AL
TOMM7
BLNK
IGFBP7
CTGF
FKBP1A
CALD1
CDHR5
IL13RA1
RPS7


C19orf10
PFDN5
HLA-DRA
FLT1
TM4SF1
IL3RA
HSPA1A
RPL5
NMU
RPS16


CCR10
MYL12B
CD72
PODXL
FAM213A
IFITM1
CAV2
RPL35A
SOX4
CLDN7


IGLL5
YBX1
POU2F2
SEPW1
SPARCL1
PODXL
CCDC85B
RPS13
DPYSL3
RPL6


HSPA5
EAF2
ACTR3
IGFBP4
CRIP2
IFITM2
VWF
CLDN7
ASCL2
RPLP2


ACTB
UBE2J1
FCRL2
HTRA1
ITM2A
TGFBR2
VAMP5
RPL12
LGALS4
RPS15


LMAN2
SRGN
HMGN1
SPARC
FAM167B
STOM
ZFP36
RPS23
HEPACAM2
RPS15A


MEI1
RPL30
CD40
CAV2
FKBP1A
PPA1
HLA-C
CEACAM1
LGALS1
MUC13


DUSP5
EIF3K
ARPC2
SLC14A1
ESAM
ENG
HSPB1
CA2
HOTAIRM1
RPLP0


SELK
NDUFA11
GGA2
AC011526.1
IFITM3
HES1
HLA-DRA
ANPEP
PLEKHB1
SDCBP2


UBC
CYTIP
EZR
SH3BP5
TMEM100
CD34
EGR1
LYPD8
CLDN4
RPL8


FCRL5
RPL23
HERPUD1
FAM167B
CCL14
VWF
TM4SF18
KRT8
PPAP2C
ELF3


CST3
TRAM1
NCF1
FAM213A
BCAM
HLA-C
IFI27
LINC01133
PPDPF
GDPD3


TXNDC11
ATP5G2
IRF8
SNCG
GIMAP1
RBP5
CSRP2
RPS3
PTPN18
NACA


UAP1
FAM46C
HLA-DPA1
GIMAP7
CD34
CAV2
JUNB
RPS12
OGDHL
RPS12


PIM2
TCEB2
HLA-DQB1
CDC37
IFI27
SLC14A1
NOSTRIN
RPLP0
MDK
RPL23A


CFL1
PTMA
HLA-DPB1
IFITM3
TGFBR2
PRSS23
FOS
RPL26
FXYD3
RPL5


SPAG4
ERLEC1
LAPTM5
RP11-536O18.2
CYBA
PLAT
CDH5
GNB2L1
OCIAD2
CLDN3


YPEL5
SH3BGRL3
UBE2J1
PPAP2A
RBP5
CDC37
RNASE1
SFN
RP11-93B14.5
PHGR1


PFN1
EDF1
HLA-DOB
TSC22D1
CYYR1
A2M
GADD45B
RPS15A
CLDN3
RPS23


S100A6
HM13
FCER2
IFITM2
ZNF385D
CCDC85B
IFITM2
RPL14
ESPL1
C19orf33


TPD52
RPS7
C12orf75
ICAM2
NRN1
TNFSF10
FRZB
RPS14
FABP1
GUCA2B


CHPF
KDELR1
SWAP70
PTRF
HLA-DRA
EPAS1
IER2
PRSS3
ALOX5AP
PLAC8


RP11-290F5.1
ARHGDIB
HMCES
EHD4
ADIRF
RNASE1
ENG
LGALS3
ANXA3
RPL4


HSPA1B
FKBP11
BTG1
NQO1
CD320
OAZ2
CTGF
RPL6
CD74
BCAS1


POU2AF1
PABPC4
P2RX5
CLDN5
CD59
SRP14
JUN
RPL4
FURIN
RPS6


JUN
SPCS3
LY86
CD59
SRPX
CTGF
ICAM2
RPS16
PPP1R1B
RPL13A


BTG2
RPL38
CYTIP
COL4A1
ENG
HLA-DRB1
BGN
RPS15
MT-CO3
TBX10


TXNDC15
COX6B1
METAP2
PPAP2B
CFI
GIMAP4
TPPP3
RPL23A
ANKS4B
TUBB2A


TSC22D3
ALDOA
CD180
HLA-C
HLA-A
HLA-DRA
FOSB
PTMA
HSPB1
TM4SF5


TMEM258
RPS11
AICDA
CXorf36
HSPB1
ELTD1
RBP5
PKIB
NCMAP
SMIM6


TMED10
CLIC1
CD9
NPDC1
HLA-DPB1
ITM2B
HLA-DPB1
RPS27A
DEFB1
VSIG2


MCL1
TPI1
LY9
ARHGAP29
PIM3
FAM107A
HES1
AMN
ZFP36
SERPINA1


TMSB10
TXNDC15
HLA-DRB1
ANGPT2
HLA-DRB5
AC011526.1
HLA-DRB1
RPL27A
CC2D1A
IFI27


TPST2
RPL10A
ANXA2
HSPB1
SEPW1
APP
HLA-DRB5
GPA33
COX5A
LGALS9B


ACTG1
CHST12
ISG20
CD34
SDPR
MPZL2
MGLL
GCNT3
MT-CO1
KRT20


NR4A1
NDUFA13
SEPW1
TM4SF1
ENPP2
IGFBP7
SLC14A1
PRDX6
EHF
ZG16B


S100A10
TRMT112
ARHGDIB
APP
NOSTRIN
TMEM204
SEPW1
AGPAT2
CALM2
MT-CO1


TNFRSF18
DPP7
HMGA1
BAALC
DNAJA1
GPR146
EPAS1
AOC1
SOX9
PTMA


ERLEC1
IFNAR2
TCEA1
C16orf80
PTRF
CD74
FKBP1A
SULT1A2
IFT172
SERINC2


NUCB2
RPL11
POLD4
EFNA1
KCTD12
FLT1
ITM2B
MEP1A
7SK
RPL14


TMSB4X
MYL12A
CD83
ACVRL1
IFITM2
C16orf80
SNCG
RPL8
ITM2C
TRIM31


RPN2
RPSA
BASP1
LXN
HLA-DRB1
ACVRL1
C8orf4
RPL31
CASP6
RPL24


SUB1
RPL26
STAG3
IGFBP3
MYCT1
FAM167B
SOCS3
SMIM22
EMP3
AMN


PNOC
ISG20
S100A11
CYYR1
GIMAP5
MMRN2
LDB2
TMIGD1
COX6C
TPSG1


SELM
ATP6V1G1
SNX29P2
MYL12A
CCDC85B
MGLL
ELTD1
KRT19
ATP1A1
FFAR4


SLAMF7
RPL27
TPD52
MGLL
CNN3
HLA-DPB1
PLAT
CA4
PHGR1
KLK1


IFNAR2
POU2AF1
IFI27
HLA-A
LMCD1
NOSTRIN
EMCN
CLDN3
CCDC115
TMEM54


DDOST
ALG5
ARPC3
HLA-DRA
KANK3
GIMAP1
ID3
SERINC2
GFI1B
RPL27A


MYL12B
PSMA7
HTR3A
STOM
CD74
BST2
GIMAP4
RPL13A
HSPA1A
RPS20


TNFRSF13B
RPL24
GCSAM
EGLN3
HLA-DPA1
HYAL2
PRSS23
PIGR
S100A11
CDHR5


FGF23
SLC25A3
PNOC
ROBO4
HLA-C
TIMP3
BST2
NEAT1
KIAA1324
CLTB


LMAN1
SEC62
E2F5
SPTBN1
CDH5
TM4SF18
CD59
RPL29
EPS8L3
RPL29


ANKRD28
CNPY2
CD27
ABI3
ADM5
HHEX
FAM167B
FTH1
NREP
CREB3L1


CD38
BST2
RAC2
HLX
NFKBIA
GIMAP5
TSC22D1
TST
HLA-DPB1
RPL3


ICAM3
TMEM230
AC023590.1
RASIP1
SPARC
RPLP1
HSPA1B
CLCA4
HLA-DRA
EPCAM


GAPDH
RPL37
STX7
HLA-B
PALMD
SLCO2A1
RGS16
RPL7A
HLA-DRB1
FOXA3


DNAJB11
CD63
LYL1
TGFBR2
CHCHD10
SNCG
PDGFRB
PPP1R14D
MYO1B
RPL31


ARF4
SLAMF7
TMSB10
S100A13
LPCAT4
FAM213A
ADIRF
MUC12
B2M
CAPN8


AC104699.1
LGALS1
UCP2
MMRN2
ERG
HLA-DPA1
GJA4
MUC13
GADD45B
GPA33


CDK2AP2
GYPC
IL32
IVNS1ABP
SH3BP5
HEY1
TGFBR2
TMEM171
KLK11
CFDP1


TMEM59
RPS9
HLA-DMA
CTGF
STXBP6
SOX17
KLF2
HIST1H1C
CLRN3
TMSB10


ALG5
NDUFA4
SELT
F2RL3
BST2
PTRF
MFGE8
CLDN4
ATP2A3
RPS25


C16orf74
COX5B
LAT2
ENPP2
CAV2
EMP2
APP
C2orf88
NDUFB4
RPS24


SRPRB
DUSP5
IFITM3
WWTR1
SMAD1
RPL12
PODXL
TRIM31
COX7A2
AQP8


CIRBP
RPS13
BFSP2
EXOC3L2
CLIC2
NKX2-3
TIMP3
MYO15B
S100A14
KRT18


FTH1
HNRNPDL
GDI2
B2M
IFIT1
SYNPO
HLA-A
ETHE1
EIF5
RPL30


TMED2
LRPAP1
HLA-DMB
NOTCH4
TPD52L1
SOCS3
BCAM
RPL18
PRDX2
FAM177B


RGS2
PARK7
HHEX
GABARAPL2
SOCS3
NRN1
SLC2A3
RPS25
CYB5A
RPL18


IGFBP7
MEI1
LGALS4
IFI27
GALNT15
RPLP0
DNAJA1
S100A6
C15orf48
LGALS4


RABAC1
RPL19
EPCAM
S100A16
HLA-DQA1
IFIT3
MCAM
RETSAT
CLDN7
RPL7A


CD74
RHEB
MZB1
HES1
CYP1B1
CDH5
SERPING1
RPS20
CHPT1
SPATS2L


SSR2
VIM
SIT1
GMFG
ICAM2
HLA-A
CD74
CES2
CKB
KRT8


ARHGDIB
RPL32
PLEKHF2
IL3RA
HSPA1A
IER2
SYNPO
CA1
COX7C
PRR15L


DNAJB1
COX7C
TNFRSF13B
GAS6
IRF1
TSC22D1
ISYNA1
RPL24
SLC25A6
PRSS3


CYTIP
COX6A1
RHOC
IDO1
FBLN2
RND1
COX7A1
RPL3
MAP7
DHRS9


ZBP1
PTPRCAP
OAZ1
COL4A2
HYAL2
KANK3
LHFP
RPL28
VSNL1
PIGR


HM13
SMARCB1
KRT18
MSN
EIF1
THBD
SRGN
C11orf86
MT-ND4
NEAT1


AMPD1
COMMD3
LCP1
FSCN1
SELP
NQO1
THBD
SPINT2
BUB3
PLA2G10


MYL12A
LSP1
HVCN1
HHEX
LIFR
C8orf4
EFNA1
RPL30
KRT19
EEF1D


RHOC
PSENEN
C15orf48
MYCT1
S100A16
LDB2
MMRN2
RPLP2
CCDC28B
RPL27


GSN
RPS25
KRT8
ACE
TCF4
ARHGAP29
HLA-DPA1
CYSTM1
SRI
SCNN1A


IFI27
ARL6IP4
ITM2B
TSPAN7
MPZL2
C10orf10
FAM107A
SLC26A2
SMIM22
FABP1


REEP5
EMC4
MBD4
EPAS1
YBX3
EHD4
IRF1
MT-CO1
FBP1
RPL28


TMEM208
ARPC3
BIK
FAM110D
EGR1
HSPB1
CLIC2
RPSA
H1F0
SMIM22


SDC1
ATP5O
TXN
C9orf3
ARL2
HLA-DRB5
PTRF
COL17A1
ALDH2
FAM101A


GLA
MT-ND4
CCND3
CALCRL
MTUS1
GABARAPL2
CYGB
S100A10
PAFAH1B3
FTL


TUBA1A
GMFG
DEF8
HLA-DRB1
B2M
NOTCH4
RPLP0
LINC00035
MAOB
FAU


EEF1D
SRPRB
RASGRP2
SOX18
SNHG7
GPR116
PPA1
MYH14
HLA-DRB5
HIST1H1C


KDELR2
ATP5B
MARCKSL1
FABP5
FAM110D
HEG1
MYCT1
EPCAM
HLA-DMA
PARM1


B4GALT3
NDUFA1
NEIL1
GALNT18
CALCRL
CLIC2
H3F3B
RPS24
ACTG1
CEACAM6


PDE4B
RPL37A
SUGCT
ITM2A
ELTD1
SRGN
B2M
C15orf48
MIEN1
CEACAM7


RGCC
RAC1
RP11-164H13.1
A2M
PIR
FABP5
SPINT2
NACA
MT-CYB
GSN


LGALS1
EIF3F
RFTN1
IFITM1
JUNB
NFIB
GPX3
HSD17B2
HOXB6
ARL14


RGS1
DNAJB11
ITM2C
IGFBP6
IL3RA
AIF1L
TSPAN7
SLC17A4
TIMP1
MISP


LGALS3
ATP5J
MT2A
NOSTRIN
RNASE1
ADAM15
COL18A1
TMSB10
GPX2
CA2


PDK1
MT-ATP6
TNFAIP8
JAM2
IL33
NOV
FLT1
RPL36
ZFHX3
GUCA2A


TMEM176B
RPS20
ZCCHC7
RNASE1
VGLL4
C9orf3
SOD3
EEF1D
CD9
MLPH


SH3BGRL3
MGAT1
LINC00926
MYL12B
IFIT3
S100A16
EIF1
SLC44A4
MALAT1
CEACAM1


IFITM3
CRELD2
AIM2
SLCO2A1
EFEMP1
SH3BP5
COL1A2
CDKN2B-AS1
RPL37A
SPINT2


KIAA0125
UBL5
STK17A
CALM1
AC116035.1
HLA-B
PPAP2B
LGALS4
NCK2
YBX1


MYL6
MT-CYB
CISD3
NES
KLF4
B2M
TCF4
IFI27
TAS1R3
RPL36


SRGN
SELM
CYB561A3
KANK3
TESC
PIK3R3
SERTAD1
PPDPF
PIK3CG
SCGB2A1


RP11-492E3.2
VAMP2
SLBP
ARHGAP18
EPCAM
PLLP
STOM
BTNL3
RBM38
C15orf48


TRIB1
OSTC
TMEM156
RND1
STOM
C10orf54
C16orf80
NPM1
LDHA
NAAA


CITED2
ICAM2
BACH2
FTH1
CD55
SPARC
HLA-DMA
BTNL8
COX5B
MT-ND2


ID2
EMP3
LMNA
CLIC2
RND1
CFI
APOLD1
ELF3
ESPN
RPSA


EVI2B
NACA
ATP1A1
LDB2
CDC42EP3
GAS6
NRN1
HN1
ESYT2
CLDN8


KRTCAP2
CALM2
GYPC
MPZL2
TIMP1
PPAP2A
HES4
POLD4
PSMD9
ASS1


BEX5
RPS3
RMI2
PEA15
HES1
LY6E
SOX17
ST14
ANXA2
S100A6


CISD2
NDUFS8
PPP1CC
MCAM
TSPAN4
SERTAD1
LGALS1
SLC6A8
MT-ND1
POLD4


SEPW1
COX7A2
UBE2N
DLL4
PLK2
TIE1
ZFP36L1
CLTB
TXN
MXD1


ANXA1
PLP2
AGR2
MFNG
ATP5G3
MYCT1
REM1
LAMB3
STMN1
PFDN5


RPN1
CCR10
PARP1
C8orf4
TXNIP
TMEM109
ID1
SLC51A
DEGS2
SLC25A6


EIF1
TPD52
MME
HLA-DPB1
HLA-DMA
CD93
NPDC1
CLDN23
PMM1
MYO15B


FOSB
SELT
HCLS1
BST2
BAG3
RPS2
AC011526.1
CDHR2
HOXA11-AS
MLLT3


HAX1
ZNF706
PABPC1
PTPRB
PDLIM4
GIMAP6
CFI
TMEM45B
IP6K2
TP53INP2


IL32
PIM2
IGLL5
TSPAN4
MGP
ID1
CYB5R3
TMEM37
TMEM176B
RPL18A


IFITM2
HINT1
RGS16
ACTN4
ID3
TAGLN2
EFHD1
CHP2
ZNHIT3
UBA52


TMED4
UAP1
CD1C
IPO11
PHGR1
ZFP36
OAZ2
GPRC5A
ATP5B
MT-CO2


SEMA4A
SNRPD2
RGS19
DUSP6
TIE1
S100A13
CD34
HPGD
IFITM2
ST3GAL4


RAB30
S100A10
PAX5
TEK
AGR2
CYBA
NES
CKB
RPL36
ITM2C


SLC17A9
SLC35B1
ETHE1
GUK1
SEMA6A
TACC1
SRP14
FCGBP
HMX2
ATP5G2


SLC38A5
REEP5
HLA-DQA2
ID3
HLA-B
LAP3
HHEX
AK1
TSC22D3
LMO7


CAPZB
TMEM66
DCK
CDH5
TAGLN2
CFLAR
TMEM204
ASS1
ACADSB
RPL34


PTPRCAP
ATRAID
ITSN2
IMP3
KRT222
HSPA1A
C1QTNF1
PRR15
S100A4
AGR2


H3F3B
SOD1
SH2B2
TBCD
TMEM176A
LMCD1
GABARAPL2
ITM2C
RHEB
AC009133.21


COPE
RPS23
SUSD3
CABP1
SORBS2
TINAGL1
COL3A1
TMPRSS2
SPINT1
SYTL2


WNT10A
GUK1
SRSF3
GIMAP1
ST8SIA4
DLL4
MYH9
YBX1
IMP4
RAB27A


TMED9
TMED4
LYN
JUP
IFI16
TNFRSF4
DNAJB1
S100A11
LSMD1
RPL37


CUTA
RPS21
SYPL1
TNFRSF4
LGALS4
PTP4A3
PHGR1
PRR13
ATPIF1
CKB


E2F5
DNAJC1
ARPC1B
ARHGDIB
GIMAP8
KDR
LGALS4
KRT18
ADH5
VILL


HSPA1A
NDUFB2
CTSD
PRX
KRT8
SPTBN1
ITGA7
DHRS11
H2AFJ
CA4


SELT
MT-ND5
IL16
GRB10
BAALC
HLX
HEY1
HNRNPA1
IGFBP2
LINC01133


HES1
NUCB2
ZFAND6
PCDH12
FAM107A
ROBO4
FAM222B
GNA11
RAB4A
RP11-294O2.2


EZR
PABPC1
PRPSAP2
NRP1
JUN
PTPRB
HSPG2
NDRG1
SPATS2L
S100A14


DUSP1
RPL12
MAP3K7CL
SRGN
S100A13
IFI6
GPR116
CCL15
AFAP1L2
MEP1A


RNU12
ATF4
S100A10
ERG
ZFP36
FAM110D
TACC1
RPL27
WFDC2
CYBA


PAIP2B
DNAJB9
PXK
NKX2-3
A2M
ATOH8
BBX
SPINT1
DNAJB1
MT-CO3


SPINK2
B4GALT3
RP11-960L18.1
CLIC4
DTL
APLNR
SH3BP5
DEFB1
SKAP2
PKIB


SLC35B1
HNRNPA1
CCR7
TUBA1B
EID1
LPAR6
C10orf10
CFDP1
HLA-DQB1
KCNK1


SMARCB1
NEDD8
LSM10
SLC25A6
PKP4
PRMT1
EHD2
DHRS9
ANXA5
MAST2


SEPP1
CISD2
LYPLA1
LAYN
CCL21
PALMD
TNS1
PFDN5
RPL31
EIF4A1


DNAJC1
KRTCAP2
DCAF12
TMEM255B
HLA-DQB1
COL15A1
FAM213A
PTPRH
PBXIP1
CLDN23


SEL1L
ERGIC2
CTSH
GIMAP4
LIMCH1
SEMA3G
LCN6
FLNB
COL27A1
HPGD


HSP90AA1
UQCRQ
TMEM243
LIMCH1
GADD45B
NDUFA12
PPP1R14A
ACAA2
MT-ND5
SMIM5


AC093818.1
CNBP
TFEB
THBD
CD9
RGS3
FAM110D
PRSS8
RAB25
MALAT1


HLA-A
LAMTOR4
AC079767.4
CD74
CXorf36
TMEM255B
RPLP1
RPS11
FRAT2
SPINT1


ICAM2
COX6C
UBE2G1
HLA-DPA1
HAPLN3
CHCHD10
SDCBP
RPL37
AOC1
MT-ATP6


TPI1
CD44
WIPF1
TSPAN12
VIM
COX4I1
SOX7
C10orf99
GSTP1
PRSS8


EMB
LMAN1
KIAA0125
CDC42EP1
ADCY4
CD151
GEM
RHOC
MT-CO2
CLCA4


QPCT
TMBIM4
HNRNPC
COX7A1
WARS
ARL2
EMP2
RPL34
RTN4
MT-ND4


SPATS2
CST3
FXYD3
SCARF1
PLAT
SLC25A6
LMO2
EIF4A1
TUBA1A
RPS3A


RHOH
C4orf3
ID2
TXNIP
ACVRL1
ID3
NEAT1
CDA
RPS27L
EEF2


APOE
EIF4A2
CBX3
SEMA3F
MEOX1
SCARF1
TIE1
BLOC1S1
CCDC14
ST14


MANEA
FXYD5
SNAP23
RHOA
CYB5A
GALNT18
IGFBP6
HHLA2
FUT3
MUC12


IRF4
NDUFB8
MOB1A
LDHB
INPP1
LIFR
COL4A1
AHCYL2
TP53I3
HIST1H2AC


ANXA2
AUP1
DBNL
SORBS2
LDB2
SWAP70
APLNR
LDHB
MCL1
RHOC


IFITM1
DDOST
DOK3
TACC1
IL1R1
RPL29
SEPP1
GDPD3
TSPO
RP11-665N17.4


JSRP1
GSTK1
PLCG2
ITGA6
TMEM176B
SEC14L1
PLK2
HRCT1
ZFP36L1
RPL37A


COMMD3
C19orf43
KRT19
KIFC3
ARL4A
RPS19
HYAL2
MT-CO2
CMTM8
MT-ND1


SRM
PRDX2
IGJ
LGALS4
CTHRC1
RPL10A
RPS29
FAM3D
PRDX5
TSPAN3


CXCL14
SKP1
SQRDL
TIE1
PRCP
HLA-DMA
RNASET2
ATP5G2
HES6
IGJ


SMDT1
A1BG
FCRL3
HLA-DRB5
IFIT2
RRAS
TAGLN2
FAM132A
PTMA
RPS11


MT-CO3
SAP18
RRAS2
PRDX1
TMEM173
LCN6
SLCO2A1
SLC9A3R1
NDUFB11
EIF1


RPS5
TMA7
CERS4
PELO
FAM198B
WARS
PKIG
PKP3
CHN2
KRT19


IL2RG
UBE2D3
OSER1
TP53I11
FABP1
EPHX1
KRT8
STAP2
TMEM63A
HSP90AB1


SRPR
DHRS7
LMO2
SERPINI1
GPR146
DUSP6
RPS2
SLC22A18
RASSF7
BEST2


ERGIC2
RPS15
TAGAP
PPA1
MLEC
JUNB
CXorf36
ESPN
VIL1
RASEF


PTMS
LGALS3
FTL
FAM101B
MMP28
PRKCDBP
LRRC32
MT-CO3
MT-ATP6
AOC1


PLP2
PSMB6
BTK
S100A6
SQSTM1
RPL18
RHOA
VIM
CERS6
SPDEF


OSTC
SDF2L1
ATP5I
PPFIBP1
KRT18
RALB
GIMAP1
TJP3
ID3
LGALS9C


CNPY2
CHID1
ANP32B
RPL12
SERTAD1
SORBS2
LIFR
PCK1
CDH17
NPM1


S100A4
ATP5G3
PTPRC
TMEM173
IFITM1
EIF1
HDAC7
CTSA
TMSB10
PCK1


SRP14
RBM39
RCSD1
ANKRD65
LPAR6
ALPL
TSPAN4
BSG
ARPC1B
PABPC1


PPIB
LAMP2
TUBB4B
PLXNA2
RASIP1
FOS
C10orf54
ARL14
IFI6
NLN


SIL1
ATP6V0E1
RPS4Y1
APLN
ALDH1A1
RPS5
CYBA
TSPAN8
FAM200B
SEPP1


GLRX
ITM2B
MLEC
CD93
MX1
TMEM173
IFIT3
ENTPD8
CDX2
VIPR1


CD69
EVI2B
GSTP1
ITGA1
PTPRB
CALM1
HEG1
CDH17
HOXB9
HNRNPA1


RPL28
EIF3H
CCNI
C10orf54
NKX2-3
KLF2
GIMAP5
MT-ND5
COX6A1
GPRIN2


SLC25A4
SEC11C
HLA-A
VAT1
PPP1R15A
VWA1
NQO1
CDKN2B
AP1M2
BTNL3


TMBIM6
UFM1
RP11-138I18.2
KLHDC8B
NEAT1
ADCY4
IL3RA
PEX26
RNF186
QSOX1


S100A11
OS9
NPM1
PHGR1
IGJ
NES
PTPRB
SLC25A6
RPS21
SMIM14


TNFRSF4
C11orf31
SGPP1
TINAGL1
MEIS2
ETS2
KANK3
SLC25A5
SHC1
BTNL8


LGALS4
ANXA7
HSH2D
CYBA
GIMAP6
MGAT1
IFITM1
GGT6
CD14
ITLN1


JTB
CALM1
BLVRB
ME3
SRGN
SERPING1
NKX2-3
LSR
DPP7
NEDD4L


RPL8
PSMB3
ORAI2
TNFSF10
CLDN7
SNX3
TMEM176B
NLN
LYZ
GPR153


THAP2
TPT1
TNFRSF17
SERPINE1
LAPTM4A
COX7A1
GPRC5B
RPL18A
SEPP1
TDP2


COTL1
TAPBP
ALOX5
RHOC
PLA1A
ACTN4
TCF21
APOBEC3B
PERP
CYSTM1


TIFA
CHPF
PTPN6
EPHX1
EPAS1
CARHSP1
NDUFA12
PABPC1
RNF24
SH3BGRL3


TXNIP
ERGIC3
ACTG1
NDUFA12
SLC41A3
ERG
ARID5B
EIF1
TBC1D2B
CDHR2


FCRLA
DERL2
GPSM3
PTMA
LAYN
RPS4X
RAC1
IL32
MACROD1
PTPRF


ENO1
HIGD2A
MTMR14
CCND1
ASRGL1
RAC1
TNFSF10
SULT1A1
MYO10
ISG20


CD151
15-Sep
FAM65B
GIMAP5
FOS
CYB5R3
EPHX1
LMO7
RPS11
LSR


BRSK1
ARPC2
TFF3
RPLP1
IFI6
LRRC32
PRKCDBP
CGN
IFITM3
FBXO32


ARPC1B
NDUFB4
KLHL5
GPX1
CSF2RB
IMP3
ITGA1
RPL37A
EPHB3
OASL


A2M
RPLP2
GRB2
RBP7
CSRP2
RNASET2
PLAU
S100A14
ASMTL
RPL23


AC104024.1
ST13
GNG7
KRT8
C10orf128
BTNL9
FAM162B
LLGL2
YPEL5
CYP3A5


LMTK3
JTB
CCDC69
SEC14L1
DDX5
RPL13
DUSP1
IFITM3
H2AFY2
SLC26A3


SSR1
ATP5D
CR2
CHCHD10
GBP2
YBX3
ACTN4
MVP
STK38
PRAP1


RNASET2
NUDT22
TMEM141
PKIG
IFI44L
HPCAL1
APOL3
CLCN2
JUNB
MT-ND5


COX5B
GNL3
DDX39A
PSMB5
TIMP3
RPS18
COL6A2
TPRN
PPAP2A
SLC44A4


SEC61A1
NDUFB11
SRGN
ARHGEF15
EIF4A2
ELK3
ROBO4
ACOX1
LACTB2
KCNK5


HSH2D
NHP2L1
MEF2C
SCARB1
EVA1C
KLF4
UBC
AKR1B10
TAGLN2
RASSF7


ATP5E
ARF1
HLA-DRB5
PRKCH
IDH2
PVRL2
IGJ
CA12
SMARCC1
H2AFJ


DCN
SEC61A1
LAMTOR4
MCF2L
RAB13
RHOC
WNT6
MT-ATP6
GAPDH
CA1


CHID1
CHMP2A
REL
GPR116
NEDD9
SNHG7
TMEM255B
MPST
AC005355.2
STARD10


MT-CO1
RPL14
KIAA0226L
DYNLL1
DNAJB4
CTNNBIP1
COL15A1
TSPAN3
TMPRSS2
CDH1


RP11-16E12.2
NPM1
PRDX5
HEG1
NR2F2
RPL28
ADAMTS1
FAU
C7orf55
STAP2


ERGIC3
ARMCX3
CCDC109B
OSBPL1A
CAPG
RASIP1
RPL10
PARK7
TSPAN13
STX19


TXNDC5
SRPR
PPDPF
ARL2
IPO11
HYAL1
PTMA
C1orf106
SNX3
PLAUR





















TABLE 15B







Absorptive_TA_1
Secretory_TA
Absorptive_TA_2
Cycling_TA
Goblet_1
Stem_cells





TXN
MT-ND1
FABP1
EPCAM
TFF3
B2M


GPX2
B2M
SELENBP1
LGALS4
KLK1
LEFTY1


MGST1
TFF3
CA2
MGST1
ITLN1
TMSB4X


EPCAM
MT-ATP6
LGALS4
AGR2
FCGBP
ASCL2


AGR2
PRDX5
C15orf48
C15orf48
AGR2
MT-ND4


C15orf48
MUC2
S100A14
GPX2
CLCA1
LGALS4


PPP1R1B
FCGBP
PHGR1
KRT8
LRRC26
SMOC2


LGALS4
KLK1
KRT19
CLDN7
RETNLB
PRDX5


HMGCS2
RPL36
ETHE1
CLDN3
MUC2
RGMB


TSPAN8
AGR2
FXYD3
PIGR
WFDC2
MT-CYB


C10orf99
PIGR
LGALS3
HLA-DPA1
SPINK1
FXYD3


UGT2B17
ITLN1
UQCRQ
PHGR1
SPINK4
GPX2


ATP5B
GPX2
PIGR
FXYD3
KRT18
CDCA7


CLDN7
ATP5G1
COX5B
TXN
REP15
MT-CO3


S100A14
MT-ND4
MT-ND1
ARHGDIB
ZG16
TSPAN8


PHGR1
EPCAM
MT-CO2
VIM
SERPINA1
PHGR1


ELF3
LGALS4
COX4I1
ELF3
TPSG1
MT-ND2


PIGR
ZG16
C10orf99
HLA-DPB1
LGALS4
MT-ND1


CDX1
MT1G
MT-CO3
BST2
ST6GALNAC1
EPCAM


MT1G
CLDN3
MT-ND4
TUBB4B
FAM3D
ELF3


CLDN3
FABP1
MT-ATP6
CD74
KRT8
PIGR


FABP1
PHGR1
MT1G
KRT18
EPCAM
HLA-C


FXYD3
KRT8
TST
S100A14
STARD10
MT-ATP6


KRT8
CLCA1
ATP5G3
MT1G
PHGR1
MT-ND3


COX5A
COX4I1
KRT8
ARPC1B
SMIM22
KRT8


ATP5G3
CLDN7
CA1
ATP5G1
FXYD3
MT-CO1


KRT18
H3F3B
TMEM54
HMGCS2
GMDS
PPP1R1B


PRDX5
FXYD3
CHCHD10
KRTCAP3
HEPACAM2
EPHB3


CYC1
MT-ND2
ATP5G1
CD9
RNASE1
SMIM22


RPLP0
RPS14
SLC26A2
HLA-DRB1
KRT19
KRT18


ATP5G1
MALAT1
TXN
PPP1R1B
MT-ND1
HSPB1


MT1E
IGJ
B2M
CLDN4
CLDN3
CLDN7


SLC25A5
KRT18
CLDN7
TSPAN8
VSIG2
CLDN4


TIMP1
CLDN4
CES2
HLA-DRA
C15orf48
RPS18


LEFTY1
RPL37A
COX7A2
SUCLG1
PIGR
C15orf48


FAM3D
RPS29
UQCR10
CDX1
CLDN7
HMGCS2


UQCRH
MT-CO2
COX6C
NUPR1
ANXA13
HLA-B


KLF5
RPS18
COX6B1
FAM3D
SPDEF
RPS24


CHCHD10
C15orf48
HMGCS2
CYC1
MT-CO3
RPS21


CLDN4
MT-CO3
AKR1C3
FABP1
TMEM141
CLDN3


LGALS3
TSPAN8
CKB
PRDX5
ANG
RPL36


SUCLG2
EIF1
EPCAM
SMIM22
COX6C
SPINK1


CD9
SPINK1
HSD11B2
LGALS1
ELF3
RPL37


TSPO
RPL35
AGR2
TMEM141
S100A14
MT-CO2


KRT19
SMIM22
SMIM22
TMEM54
HMGCS2
RPS6


SMIM22
SPINK4
MT-ND5
CKB
BEST2
SLC12A2


C19orf33
STARD10
AMN
CST3
MB
RPL37A


NXPE4
FAM3D
MGST1
NDUFAB1
FABP1
S100A14


B2M
MT-CYB
MT-CYB
C10orf99
CREB3L1
RPL31


SUCLG1
MT-ND3
COX8A
ITM2C
RPL36
RPL12


ATP5A1
RPS21
C19orf33
TMSB4X
GPX2
MT1G


ATP5F1
CD74
TMEM141
ARPC2
CLDN4
BST2


GAPDH
HLA-DPA1
COX6A1
HLA-DRB5
S100A6
ACTB


COX4I1
HLA-C
AKR7A3
SPINK1
RP11-234B24.2
MARCKSL1


COX5B
WFDC2
MT1E
PLP2
URAD
PDZK1IP1


RP11-519G16.5
ATP5I
GOLM1
SPINT2
TCEA3
MGST1


TMEM54
TMEM141
AKR1B10
HLA-DMA
TSPAN8
RNF186


ETHE1
ELF3
PRSS3
HLA-DMB
MT1G
GNB2L1


UQCRC2
RETNLB
CLDN3
MT1E
TSPAN1
RPS3


CA2
TIMP1
CISD3
COX5A
TMEM61
RPLP0


TMEM141
HMGCS2
ATP5D
ATP5B
RAP1GAP
ETS2


HLA-E
RPS3
MT-CO1
ECH1
C10orf99
HLA-A


CDX2
PPP1R1B
MT-ND2
TUBA1A
REG4
CD63


COX6C
RPS15
CHP2
IGJ
PRDX5
CST3


C1QBP
TMEM54
H3F3B
FXYD5
MT-ND4
ARHGDIB


RPSA
KRT19
KRT18
SELENBP1
CCL15
FAM3D


KRTCAP3
MT1E
NDUFA1
ETFB
UQCRH
MT-ND5


OLFM4
ZFP36
VSIG2
HLA-DQB1
H3F3B
CKB


UQCRFS1
RPL12
TIMP1
SRI
NANS
RPS4X


S100A10
KRTCAP3
COX7C
KRT19
NPDC1
GSN


ATP5C1
COX5B
FAM3D
KLF5
MT-ATP6
C10orf99


H3F3B
IGLL5
PDE4C
IGLL5
MT-CYB
FABP1


GSN
RPS9
EIF1
LGALS3
MT-CO2
ALDH1B1


MRPL12
MGST1
COX5A
HADH
IGJ
MT1E


CD74
C10orf99
LGALS1
CDX2
MT-ND2
TRABD2A


CKMT1B
RPL8
CD74
UQCRC1
IGFBP2
KLK1


SLC25A6
ITM2B
TSPAN1
SMAGP
SPINT2
SELENBP1


ARHGDIB
RPLP2
CLDN4
TIMP1
EIF1
STARD10


RPS2
CHCHD10
TSPAN8
ACTB
C2orf82
AGR2


MPC2
UBC
SLC22A18AS
LY6E
COX5A
RPL26


SELENBP1
HLA-DRB1
CYC1
COA3
IFI27
SPINT2


RPS24
COX6B1
MT-ND3
COTL1
HES6
ARPC1B


RPS18
ATP5D
ATPIF1
IGFBP2
COX5B
KRT19


MAOA
NDUFB11
UQCR11
ACADS
TIMP1
RPS5


RPL8
C19orf33
ELF3
PLA2G2A
CDC42EP5
RPS2


CKB
S100A14
SDCBP2
STARD10
FOXA3
RPL13


MPST
HLA-DRA
ATP5I
CES2
S100A4
TFF3


IGJ
RPS5
IGJ
TST
PPDPF
S100A11


TRABD2A
COX5A
CDX1
LEFTY1
ZG16B
MYL6


ATP5O
RPS12
TSPO
CKMT1B
MT-CO1
AQP1


RPS6
RPL13
SRI
ATP5G3
IL1R2
FERMT1


HINT1
ARHGDIB
UQCRC1
CISD3
TMEM176B
MT-ND4L


SPINK1
C2orf82
MGST3
ISG15
HSD11B2
RABAC1


HLA-DPA1
RPS8
S100A6
RARRES2
CD9
HLA-DPA1


ECH1
RPS2
MRPL41
MPC2
BTG1
RPL29


PHB
TCEA3
TCEA3
HLA-E
UQCR10
LY6E


CES2
HLA-DPB1
NDUFB9
ECHS1
IFT172
HLA-E


AKR1C3
LEFTY1
COX7B
CKMT1A
COX6B1
SLC25A6


CKMT1A
ACTB
ZFP36
UQCRQ
TPM1
TIMP1


PLA2G2A
LRRC26
ATP5J
GGH
ZFP36
RPL8


RPL5
MUC5B
ATP5B
TSPO
SERF2
CD74


UQCR10
NUPR1
SLC39A5
MPST
TSTA3
RPL35A


IGFBP2
MT-ND5
KRTCAP3
ATP5F1
MGST1
RPL10A


COX7C
CKB
NXPE4
COX4I1
TSPAN13
KRTCAP3


COX6B1
UQCR10
GPT
ATPIF1
C19orf33
UBB


LCN2
SELENBP1
MS4A12
CYCS
MT-ND3
RPS12


RPL7A
RPL27A
ANXA5
UQCRH
ATP5G3
KLF5


ZFP36
MT-CO1
ACADS
ZFP36
FAM195A
NOS2


RPS8
UQCRQ
SLPI
MACROD1
ITM2B
RPL5


CMBL
STRA13
PXMP2
COX5B
RAB25
OLFM4


FAM84A
RPL7A
NDUFB2
STAP2
FTL
SOX4


PEBP1
RPL32
FAM162A
RPLP0
CDX1
RPL32


S100A4
RPS19
DBI
RP11-519G16.5
STAP2
RPS23


STARD10
CISD3
ARHGDIB
COX6C
DNAJA1
SEPP1


HLA-C
TPSG1
PPP1R14D
RGS10
TMEM54
GUK1


IGFBP7
AMN
GPX2
SLC44A4
FABP2
COX5A


PPP1R14D
URAD
UQCRH
NANS
ATP5I
RPS8


HLA-DPB1
MT2A
TMEM45B
NDUFV1
CHCHD10
MLXIP


PDE4C
RPLP1
CYSTM1
RPS18
ARPC1B
CEACAM5


RPS3A
TSPO
MYO1A
B2M
TSTD1
RPS19


PCK1
COX6C
CDHR5
NBL1
UBC
QTRT1


GSTA1
RPL18
SLC44A4
ALDH2
PPP1R1B
IFITM3


RPL26
DUSP1
DHRS11
GNAI2
DDX5
STXBP6


STAP2
HERPUD1
ADIRF
C1QBP
ACTB
RPL14


RPS3
RPS6
PPP1R1B
S100A4
MLPH
CDX1


RPL10A
TMSB10
CKMT1B
MLEC
ETHE1
RPL30


SEPP1
RPSA
MT1M
SUCLG2
SH3BGRL3
RPS9


ATP5I
ARPC1B
HLA-C
MINOS1
KIAA1324
RHOC


FAM162A
DDX5
ITM2B
S100A10
KRT20
RPL7A


UQCRC1
ATP5G3
PKIB
OAZ1
HSPA1A
CDX2


TCEA3
UQCRH
USMG5
PSAP
STRA13
HLA-DRB1


CHP2
NDUFA1
FAM195A
ATP5I
IFITM2
IFI27


RPL31
ANXA5
FCGBP
TIMM13
CKB
RPS14


ATP5D
TIMM13
IFITM3
SEPP1
AC011523.2
IFITM2


RPL37A
HLA-B
MPC2
HSPD1
HLA-C
TXN


SRI
COX8A
S100A10
RPL36
UGT2B17
RPL34


HLA-DRB1
CDX1
MISP
UQCRFS1
ENTPD8
ISG15


SELK
HLA-E
STAP2
ATP5A1
COX4I1
HLA-DPB1


TSPAN1
SEPP1
MGAT4B
ANXA5
CST3
IGJ


RPS23
CDC42EP5
SULT1A1
HLA-C
RGCC
PFN1


SOCS3
SNX3
PYCARD
S100A6
B2M
AP003774.1


RAB25
CYC1
ATP1A1
SFN
RAB15
GPR160


MT1X
HLA-DRB5
DNAJA1
ATP5O
CD74
H3F3B


COX6A1
MRPL12
ZG16
AP1M2
NDUFA1
CD9


NACA
HLA-DMA
ASL
MT2A
MT1E
CDHR1


RPS29
IFITM2
NPM1
RAC2
ERI3
HLA-DRB5


GMDS
RPL28
MPST
RGCC
TST
HLA-DMA


COA3
RPL38
MUC4
GSN
ERN2
S100A4


UBC
RPS11
UBC
STRA13
TNNC2
NUPR1


RPL36
DNAJA1
SLC26A3
ATP5J2
NEURL1
RPL18


SPINT2
HLA-A
SLC51B
CA2
GSN
RPL27A


ITM2C
RPL37
URAD
PEBP1
LGALS3
RPS15


IFITM3
COX7C
HLA-B
TYMP
CAMK2N1
HLA-DRA


RPL13A
ETHE1
S100A4
PRDX2
SMAGP
RPS15A


DNPH1
HLA-DQB1
HLA-E
H3F3B
IFITM3
ANXA5


ISG15
MZT2B
CDH17
SQRDL
TSPO
RPL38


SLC25A3
LITAF
CKMT1A
GJB1
CAPN9
TMEM54


UGT2A3
ISG15
ANPEP
PBK
MALAT1
FXYD5


SLC39A5
TRABD2A
SLC25A5
RPL37A
CDX2
RPL24


RPL12
MZT2A
ABCC3
UCP2
TMEM176A
RPS29


RPL29
TSC22D3
UQCRFS1
TPM4
IGLL5
PSMB9


FAM195A
TSTD1
IGLL5
CHCHD10
SLC44A4
ARSE


URAD
ARPC2
DUSP1
TMEM98
TTC39A
RPSA


NDUFA10
ECI1
TRMT112
ADIRF
COX7B
RPL11


SQRDL
ETFB
IFITM2
DDT
OAZ1
CTSC


HSPD1
MPC2
SHD
AKR1B10
COX8A
EEF1B2


DDT
IGFBP2
JUNB
S100A16
JUNB
ARPC2


IFITM2
PLA2G2A
TSC22D3
PLEKHJ1
UQCRQ
CAPZB


NUPR1
TST
ATP5E
LGALS3BP
MARCKSL1
ZKSCAN1


TPI1
GSTP1
TMSB10
ARPC3
SCNN1A
TYMP


NOX1
HIST1H4C
TXNDC17
NOX1
LYPD8
KIAA1324


ACADS
SDCBP
HLA-DRA
EEF1B2
COX7A2
LRIG1


ATPIF1
DNPH1
SQRDL
FAM162A
CTD-2547H18.1
IMPDH2


TSC22D3
RPL31
CIRBP
RPS14
RASD1
GLTSCR2


TMSB10
SOCS3
SERINC2
GGCT
CIRBP
RNF43


RPS27A
S100A4
DDT
RPS8
KRTCAP3
RPS27A


ANXA5
PRDX2
LDHB
TCEA3
H1F0
ATP1A1


PRSS3
RP11-357H14.17
NDUFB7
GMDS
NXPE4
PSME2


TFF3
COX7B
CMBL
RPS6
RPS24
RPL23


GOLM1
HSPA1A
IFI27
ETHE1
RPL37A
RPS7


RPS15A
RARRES2
AOC1
LAMTOR4
PCBD1
DYNLL1


HLA-B
CLUH
RAB25
MT-ND1
YPEL5
RPLP2


MACROD1
RPLP0
KLF5
RPL8
HLA-E
LGR5


PXMP2
MPST
PCK1
SH3BGRL3
MUC1
OAZ1


TST
SPINT2
SPINT2
HLA-B
ITM2C
SOCS3


COX7A2
TXN
TCEB2
IMPDH2
ATP5G1
EIF3D


AP1M2
GSN
NDUFA2
TUFM
KCNMA1
SUCLG1


TUBB
UQCRC1
C2orf82
PXMP2
PRR15L
HSPA1A


IGLL5
CES2
HERPUD1
NDUFA10
RPL26
URAD


GJB1
RPL29
S100A16
LYZ
HLA-DRB1
PTGDR


EIF1
ATPIF1
GSN
PHB
CYC1
CHDH


ARPC1B
ST6GALNAC1
HNRNPA1
VIL1
AGR3
KCNN4


CISD3
MGAT4B
BCL2L15
NDUFA1
FFAR4
PSMA7


PKIB
MLEC
LAPTM4A
ACTR3
AMN
TAGLN2


GPR160
REP15
UGT2B17
HINT1
RPS29
C19orf33


MRPS33
IFI27
STARD10
RAB25
SCGB2A1
EPHB2


DCTPP1
FBL
EID1
IRF8
KLF5
ETHE1


AKR1B1
DNAJB1
NDUFB3
CHP2
DUSP1
PABPC1


CDH17
UQCR11
MRPL12
AKR1B1
DNAJC12
SELM


AKR7A3
RNASE1
ESRRA
FCGRT
MUC4
ITM2B


HSPA1A
NDUFS5
MT2A
RPS3
ATP5J2
IGLL5


RPS14
IMPA2
PNRC1
RPL26
RAB27A
MPST


PLP2
DDT
NDUFV1
RPL10A
COX7C
UQCRH


RGS10
MYL12A
GJB1
HOXB7
IL32
UBC


MT-CYB
RHOA
MYO1D
MAOA
PSAP
TDGF1


TKT
RPL11
NAP1L1
AMN
RP11-357H14.17
PPAP2C


MDH2
RPL10A
VIL1
TSPAN1
HLA-DRA
NQO1


ITM2B
NDUFB7
DDX5
MT1M
HSPA8
RARRES2


HLA-DRA
RAB25
TMC4
MRPL12
MUC5B
S100A6


EEF1B2
SUCLG1
NDUFS7
ITM2B
PLA2G10
HLA-DQB1


DUSP1
FAM195A
SOCS3
NPC2
MPC2
TSC22D3


PSMB9
MUC4
MAOA
NXPE4
DUSP2
CDKN1A


AMN
SFN
KRT20
UQCRC2
TRABD2A
TGIF1


AKR1B10
MT1X
PLCD3
SDC1
DYRK4
AP000344.3


FBL
GCHFR
SFN
ACAT1
KLK15
C10orf54


NDUFAB1
MUC1
ROMO1
IFITM2
LXN
SH3BGRL3


DBI
FKBP1A
SSR2
RPS21
NDUFB4
WNK2


CBLC
DCTPP1
CFTR
CENPW
BCAS1
PSAP


GNB2L1
RPS16
LDHD
H2AFZ
CREB3L4
AXIN2


NDUFV1
SLC44A4
HLA-A
DNPH1
MRPL27
MYC


CST3
PSAP
NDUFB10
LAD1
TYMP
RGCC


YBX1
TSPAN1
CD9
GADD45B
HSPA1B
LGALS3


MARCKSL1
CD9
SUCLG1
IGFBP7
CTSC
IFITM1


RPS7
NME1
C19orf70
TUBA1C
CLRN3
CYBA


TIMM13
NDUFS8
MINOS1
PRSS8
TXN
EPB41L4A-AS1


CYCS
C14orf2
MT1H
RPSA
PDZK1IP1
MYL12B


NDUFA9
ATP6V0E1
LAMTOR4
RPS12
CYBA
ZNF703


RPS5
CENPM
RNF186
RP11-357H14.17
HPCAL1
MYB


TUFM
UBE2D3
EIF4A1
CNN2
CMAS
ZFP36


ATP5G2
CA2
PLAC8
MRPS25
LINC00261
S100A16


RARRES3
JUNB
PLA2G10
SLIRP
NDUFA4
TMEM141


RPL32
LAPTM4A
SLC22A18
UGT2B17
GUCA2A
CA2


PPIA
S100A11
SELK
NDUFS8
SLC25A5
TMEM176B


CYSTM1
RPL26
PAPSS2
PPT1
KREMEN1
SMAGP


JUNB
PXMP2
HINT1
HSPA1A
PNRC1
ATP5G2


SLC44A4
CTSC
ATP5J2
MDH2
NEDD4L
PERP


DNAJB9
RPL19
SEPP1
PRDX4
DNAJB1
CFD


RNF186
VSIG2
MVP
ATP5D
AOC1
HSPA5


PSAP
NBL1
GIPC1
RPS29
ISG15
RPS11


RPL18
CENPV
HRCT1
COX6B1
FAM162A
S100A13


CASP6
ADIRF
MT1X
UQCR10
CKMT1A
RPS13


S100A13
CDHR1
HLA-DRB1
RPL7A
C9orf152
PTPRO


IMPDH2
ITM2C
NDRG1
SERINC2
ATP2C2
NACA


EEF2
TMSB4X
ID1
NDUFB7
S100A10
RPL15


RPL13
STAP2
PTMA
MRPL16
KLK3
RAB25


MTCH2
RAB7A
EEF1D
HERPUD1
C12orf57
RPS20


RPL14
GMDS
ITM2C
RPL13
SLC12A2
RHOA


RPL3
TMEM176B
PADI2
TPM1
DCTPP1
MYL12A


RPL11
FOS
NDUFB1
SH3YL1
TMSB10
COPE


RPS9
TRPM4
DPP7
HSD17B11
GADD45B
VAMP8















Enteroendocrine
Glial_cells
Inflammatory_fibroblasts
Fibroblast_pericytes







PCSK1N
CRYAB
VCAM1
RGS5



CRYBA2
ALDH1A1
NNMT
BGN



SCGN
GPM6B
LUM
CSRP2



CHGA
PLP1
SOD2
NDUFA4L2



PYY
SPP1
CCL2
MYL9



SCG5
S100B
TDO2
MFGE8



GCG
FXYD1
COL3A1
TINAGL1



FEV
PRNP
C1S
TSC22D1



MS4A8
PMP22
MFAP4
COX4I2



TTR
CLU
C1R
FRZB



CACNA1A
TUBA1A
MMP2
ADIRF



PRDX5
CD9
CTSK
TPPP3



HLA-C
MPZ
PDPN
HIGD1B



HOXB9
SPARC
FBLN1
COL18A1



FXYD3
NRXN1
DCN
GPX3



STARD10
DKK3
CTSC
SOD3



RAB26
CYR61
RARRES2
IGFBP7



B2M
LGI4
GPX3
NET1



LGALS4
MATN2
APOE
CALD1



PHGR1
TUBB2B
SELM
4-Sep



RAB3B
ANXA2
CALD1
TPM2



KRT18
PMEPA1
IFITM3
SERPINI1



MARCKSL1
PCSK2
TMEM176A
NOTCH3



MDK
PEBP1
CYGB
PGF



SLC29A4
GFRA3
DYNLT1
HES4



KRT8
CAPS
COL1A2
ACTA2



EPCAM
CALM2
ADAMDEC1
MGP



ELF3
MYOT
WARS
ISYNA1



SST
L1CAM
TMEM176B
PDGFRB



HLA-B
S100A1
COL6A2
SPARC



TMSB4X
COMT
CFD
FAM162B



ARX
CD59
GGT5
HSPB1



VIM
PLEKHB1
NDN
H2AFJ



CLDN3
TIMP3
FOXF1
BCAM



HLA-DPA1
CDH19
NINJ1
PLXDC1



C15orf48
SMIM5
PLAU
CD36



FABP1
TSPAN11
LAP3
CAV1



RPL37A
NTM
EMILIN1
DSTN



MLXIPL
C8orf4
IGFBP7
PRSS23



COX6C
CNN3
STMN2
REM1



C19orf77
MAL
CXCL14
LHFP



HLA-DRA
FIBIN
EPSTI1
COL4A2



NEUROD1
FBLN2
HAPLN3
RGS16



CPE
CCL2
CD63
LURAP1L



SMIM22
CBR1
GBP1
TPM1



TSPAN1
FGFBP2
SPARC
TAGLN



HLA-DRB1
ARHGAP15
COL1A1
EGR1



TFF3
LGALS1
PKIG
IFITM3



IGJ
JUN
LGALS1
HLA-C



HLA-DPB1
PRKCDBP
SERPING1
EHD2



CLDN4
SNCA
CFH
MEST



ITM2B
RPS6
DMKN
PKIG



SEPP1
IGFBP7
SERPINF1
LGALS1



IFITM3
NDRG2
PAQR5
STOM



RTN1
COL9A3
THY1
A2M



SPINK1
ST6GALNAC2
SOD3
STEAP4



LDHA
TTR
COL6A1
PTGIR



VWA5B2
TMEM176B
CNOT4
RPLP2



CD74
RPS2
LINC01082
PTK2



RPL36
FOS
TNFRSF1A
RBPMS



SOX4
AP1S2
PMP22
EPS8



SCT
WISP2
GSTT1
PPP1R14A



BEX2
HES1
SGCE
SRGN



ISL1
VIM
TPM2
COL3A1



ANXA5
RGS16
A2M
GEM



GSN
FEZ1
TFPI
CRIP2



RPS29
SORBS2
CLEC11A
ZFP36L1



S100A14
FCGR2B
FTH1
ARID5A



HOXB8
IFITM3
MFGE8
ARVCF



CHGB
RP4-792G4.2
SPON2
EPHX1



GUCY2C
RHOB
GBP4
HLA-A



FXYD5
TMEM176A
C2
ADAMTS1



CLDN7
ART3
SFTA1P
PRKCDBP



HLA-DMA
EGR1
LAPTM4A
MAP3K7CL



HLA-DRB5
RPL8
TIMP1
NDUFAF4



KRT19
TUBB2A
CDH11
C1R



PRDX2
PDLIM4
LY6E
CALM2



SPINT2
IL11RA
PLAT
C8orf4



EIF1
RPS19
CEBPB
SDC2



ETV1
ANXA5
APOL1
TCF21



HLA-E
SOCS3
PROCR
ESAM



QPCT
RPS18
TMEM205
HEYL



KIF12
PHLDA3
GADD45G
KNOP1



DDC
NRN1
EVA1A
EFHD1



LITAF
TSPAN15
ICAM1
SERPING1



TMEM141
MIA
FHL2
RCAN2



TMEM61
COL18A1
KLF6
C1QTNF1



MT-ND3
RPLP1
LGALS3BP
RBPMS2



COX5A
SPARCL1
RCN1
SERPINH1



IGLL5
TPT1
BST2
NDRG2



LY6E
C1orf198
CCL8
FXYD6



MPC2
SCCPDH
GALNT11
COL6A1



IFITM2
S100A10
IGFBP3
GPRC5C



UCP2
S100A4
ECM1
MAP1LC3A



NDUFB11
RPL11
CYR61
RERG



COX6B1
RASSF4
F3
GUCY1B3



HEPACAM2
TNFAIP6
HSD11B1
ASPN



HLA-A
SGCE
CEBPD
EPAS1



COX4I1
COL1A2
IGFBP6
CTSF



CXXC4
NNMT
EFEMP2
UBA2



KIAA1324
CADM4
SEPP1
GUCY1A3



TPH1
TAX1BP3
PRR24
RPS14



VAMP5
RPL19
COL18A1
LRRC32



ATP5G1
RPS3
NAB2
MSC



RPS9
TFAP2A
SCARA5
NR2F2



MT-ND4
RCAN1
TNFAIP6
LGALS3BP



SLC25A6
IER2
TNIP2
ANGPT2



MT-ND1
MYL9
TCF21
CD151



ERI3
RPS14
PRR16
SORBS3



ZFP36
GPNMB
IFI35
MCAM



S100A11
TUBA1B
PTGIR
COL1A2



RPS14
GPX3
BRCC3
GNG11



NPC2
FAM210B
EID1
PTMS



PCBD1
ID3
POSTN
MYH11



RPS21
CADM2
PSMA2
RNASET2



CKB
GATM
APOC1
RPLP1



ATP5G2
HSPB2
CXCL1
THY1



GPBAR1
RHOC
S100A13
TGFBI



SELENBP1
RPLP2
CD302
COL6A2



NDUFA3
RPL18
RBP1
ASAH1



SMIM6
NGFR
EMP3
PLOD2



RPS11
HSPA2
BSG
RARRES2



KLK1
ASPA
SPG20
EFEMP1



BAIAP3
FST
TNFRSF11B
SOCS3



RPS2
MARCKS
UBE2L6
RPS18



RPS18
KCNMB4
IL7
RPS19



RPL12
SBSPON
PSME2
LBH



MYL12A
PSAP
SCT
SELM



TM4SF5
OLFML2A
IL11
NEXN



CADPS
RPL10
SRGN
CDS2



C21orf58
SEPP1
IGJ
GADD45B



DNAJC12
C1S
ARID5B
COX7A1



CTSC
RPL13A
EDEM2
FKBP7



PPT1
CXXC5
PSMA4
HLA-B



RARRES1
S100A6
TAP2
CD248



RPS3
EMP2
IFI6
PTRF



DNAJA1
RPL13
FBLIM1
F2R



SNX3
MXRA8
COL5A2
MRVI1



NGFRAP1
SERPING1
FOSB
NFASC



ISG15
RPS4X
ATP5E
PPIL4



CDX1
RPL31
PCOLCE
STK16



RPL38
RPL28
COL14A1
SMDT1



C12orf75
SRGN
ETHE1
NF2



TAX1BP3
FGL2
CDK2AP2
ATF3



RPS8
TBCB
IFITM2
APOE



PPP1R1B
ENTPD2
ANXA5
FLNA



LYZ
SELM
TRIM47
TUBA1A



HMGCS2
PHLDA1
TSPAN4
RRAD



PAM
EID1
PDGFRA
TRIB2



PLA2G12A
NGFRAP1
ISG15
OAZ2



ACTB
ANGPTL7
CD276
RPL19



SPINK4
RPS8
ADM
HRC



IFITM1
RPL26
APH1A
HCFC1R1



COX8A
JUNB
IL34
HEY2



IGFBP2
SLITRK6
FILIP1L
C11orf96



TSTD1
RPS12
MAD2L2
LAPTM4A



LYPD8
RPL15
ADD3
RPL27A



RPSA
RPL12
TAGLN2
RPL11



C4orf48
SLC22A17
PHGR1
ARHGEF17



HLA-DQB1
RERG
SQSTM1
CACNA1H



GPX2
PCBP4
PLAC9
TGFB1I1



MLXIP
CADM1
MESDC2
COTL1



LAP3
RPS23
NR2F1
PLEKHA4



ATP5E
ATF3
SERPINH1
RPS13



HSPA1A
RPS27A
NUBP2
GULP1



AGR2
ITPR1
LAMA4
PARM1



TNNC1
LGALS3BP
CYB5R1
OLFM2



TPPP3
FSTL3
TSPAN9
RPS5



SOCS3
RPS5
SEC63
RASL12



MT-ATP6
FAU
DKK3
S100A10



QTRT1
RPL32
F10
RPS6



HERPUD1
ZFP36L1
AGT
ITGA7



ETFB
SOD1
COX5B
DOCK7



MRPL41
SERTAD1
BBIP1
ANGPT1



CD55
RPS16
TNIP1
CD74



PEMT
PCMT1
COTL1
CLMN



PRSS3
RARRES2
IFIT1
ENTPD3



C10orf54
ITGB1BP1
IFITM1
RPL36



CKMT1A
RPLP0
PTGDS
MAB21L2



TCEA3
CTNNAL1
CD40
ILK



TYMP
RPS20
ALDH1A3
COASY



S100A4
HSPA1A
ACP5
RPL28



PSMB9
YWHAE
NUPR1
MSRB3



RPL18
CST3
GSN
CYGB



RPS15
RPS9
OS9
PDE1A



MT1G
SLC15A3
MRFAP1
FHL2



RPL32
CLIC4
CLEC2B
CCL2



PIGR
DYNLL1
ARHGDIB
ZNF580



MT-CO3
RPS15A
GNG11
CASC3



CUTA
RPSA
NUMA1
SH3BGRL3



KIAA1456
MT2A
PPAP2B
HLA-F



CTSD
S100A16
LGALS4
TMEM98



RAC1
WDR86
SYPL1
RRAGA



QDPR
DLX2
FBN1
LINC00152



C19orf45
GSN
FABP1
LGI4



RPL13
LAMP1
TMEM119
MXRA8



WFDC2
ID4
MMP3
GPI



HSPB1
POLR2F
ATPIF1
10-Sep



RPL31
RXRG
S100A3
MYLK



CD59
SECISBP2L
C1RL
CCDC146



OCIAD2
RPS7
AKR1B1
PTP4A3



KIAA1377
TMOD2
HTRA3
NNT-AS1



CENPV
RPL6
NBL1
ARHGAP29



EMC10
SH3BGRL3
SLC9A3R2
FILIP1



PLAUR
DEPDC7
TYMP
SCN4B



DNAJB9
ERBB3
PUS3
FOS



RPL37
PON2
EZR
RPS15



EPHB3
STARD13
PRKCDBP
MOCS1



GADD45B
RPL23A
ANG
PPP1R15A



HIST1H4C
SCD
OLFML3
EPC1



SERINC2
GRAMD3
CXCL6
FXYD5



CTSS
AHNAK
GPX8
VIM



URAD
CDC42EP1
CPQ
SERTAD3



RGS2
IFIT3
CCL13
RPL8



NDUFA11
RPL27A
TNFRSF12A
ID3



ATP6AP2
RPL5
PGRMC1
HN1



NUDT16L1
C1R
PSMB9
EFEMP2



RPL27A
ST3GAL6
MDK
LSP1



NFASC
ANK3
PUSL1
C1QTNF2



RGS10
RBMS1
MYL9
HOXB-AS1



RPS12
RPS13
EPCAM
TMC4



NPDC1
PLSCR4
PTGES
PLEKHH2



RP11-279F6.1
MAPRE2
CAPG
C1S



GCHFR
CADM3
AGTRAP
C1orf54



RPS4X
IER3
VAMP5
IFIT1



SYT7
DST
CD320
IRF1



GRN
RPL4
RAB13
HSPA2



RASD1
PFN1
TLCD1
DDX5



CCDC24
RTN4
MEG3
CDK19



RPLP2
TIMP4
TMEM100
LIG1



UQCR11
TALDO1
RFK
CTDSP1



COX5B
SH3BGR
SAMD11
TYROBP



RHOA
FADS3
CTC-276P9.1
SDHD



ANG
PHLDB1
HOXA10
RPS3



RPL28
ZFYVE21
UGCG
PDLIM2



MT-CO2
IL32
CTSL
CYP4X1



ARPC1B
ST3GAL4
LEPROT
NUP85



ISYNA1
H3F3B
C12orf44
TPD52L2



TMEM54
TMEM59L
WFDC1
CARKD



COX7C
UBR4
ARHGAP24
CBWD1



TPM4
UBA52
CLDN3
SPRED1



GNG4
LHPP
TRPA1
MRPS6



PDZK1IP1
CTNNA1
HAPLN1
ISCA1



NDUFB4
ZNF428
TRAFD1
SLC25A4



WNK2
ARMCX1
INTS12
FRMD3



SAT1
CMTM5
TPST1
EBF1



ANXA2
TNFRSF12A
PAPPA
TIMP1



TIMM13
RPL29
FAM105A
LPL



UQCR10
RPS29
COPA
GNAI1



PRR15L
ARHGAP12
EHD2
RSBN1L





















TABLE 15C







Myofibroblasts
Villus_fibroblasts
Crypt_fibroblasts_(hiFos)
Crypt_fibroblasts_(loFos)
T_cells





ACTA2
NSG1
ADAMDEC1
CFD
DCN


TAGLN
F3
CFD
DCN
LUM


MYL9
FRZB
DCN
ADAMDEC1
CFD


TPM2
CXCL14
C1S
FBLN1
ADAMDEC1


PDLIM3
DMKN
LUM
LUM
C1R


ACTG2
VSTM2A
FBLN1
MFAP4
C1S


HHIP
POSTN
HAPLN1
C1R
FBLN1


SOSTDC1
BMP4
CCL8
APOE
TCF21


MYLK
ENHO
C1R
C1S
APOE


FHL1
PLAT
MFAP4
SOD3
COL3A1


HSD17B6
MMP2
APOE
TCF21
CXCL12


MYL6
EDNRB
CTSC
COL1A2
MFAP4


TPM1
HSD17B2
CCL2
ABCA8
GPX3


MYH11
COL6A1
COL1A2
COL3A1
HAPLN1


DSTN
COL6A2
TCF21
CTSC
CFH


CNN1
SDC2
COL3A1
CYGB
SERPINF1


NDUFA4
AGT
CYGB
CXCL12
COL1A2


TGFB1I1
TMEM176B
ABCA8
CXCL14
CCL2


NPNT
IGFBP3
SOD3
CTSK
PPAP2B


DCN
NBL1
STMN2
TMEM176B
PLAC9


PDLIM7
CYGB
CXCL14
GPX3
PTN


PRKCDBP
FENDRR
PROCR
RBP1
PTGDS


WFDC1
RARRES2
GPX3
PROCR
IGFBP7


CXCL14
FOXF1
CXCL12
COL6A2
PROCR


COL3A1
MFGE8
A2M
PLAC9
COL6A2


COL1A2
CAV1
RBP1
CCL8
CTSC


SMTN
ECM1
COL1A1
PTN
CXCL14


FLNA
TPM2
SERPINF1
IGFBP7
SOD3


HHIP-AS1
MFAP4
PTN
LINC01082
CYGB


C1S
PDGFRA
CCL13
CALD1
CCL13


SELM
COL3A1
TMEM176B
A2M
CCL8


PPIC
COL1A2
CTSK
TMEM176A
IFITM3


LUM
GPX3
LINC01082
COL1A1
PMP22


PPP1R14A
C1S
PPAP2B
SERPINF1
CCL11


ADAMDEC1
LGALS1
GSN
IFITM3
RARRES2


COL1A1
CALD1
CFH
CFH
GSN


TM4SF1
TMEM119
IGFBP7
ADH1B
CD2


COL6A2
FAM150B
CCL11
SERPING1
COL14A1


NBL1
WFDC1
CLEC11A
CCL2
ADH1B


NEXN
APLP2
ADH1B
CLEC11A
SCARA5


LGALS1
COL1A1
GGT5
HAPLN1
A2M


C1R
BMP5
PLAC9
GGT5
COL1A1


ILK
PDLIM1
SCARA5
RARRES2
FXYD1


KCNMB1
TMSB4X
VCAM1
SCARA5
DKK3


SPARC
SCPEP1
DKK3
CCL13
CALD1


CSRP1
PDGFD
COL6A2
LGALS3BP
CD3D


MFAP4
MMP11
PMP22
GSN
PPAP2A


CALD1
MMP1
TMEM176A
MMP2
ADAM28


IGFBP7
SPARC
SEPP1
DKK3
TMEM176B


LINC01082
TMEM176A
MATN2
CCL11
CLEC11A


HSPB1
IGFBP7
PPAP2A
PMP22
CTSK


APOE
PROCR
CYR61
PPAP2B
EFEMP1


POSTN
LGALS3BP
CALD1
HAAO
PCOLCE


APOC1
PPP1R14A
ADAM28
ADAM28
CD69


FBLN1
PKIG
RARRES2
CD63
EMILIN1


TMEM176B
IGFBP6
MMP2
PCOLCE
STMN2


SPARCL1
TRPA1
BMP4
BMP4
MMP2


CAV1
TIMP1
SERPING1
COL6A1
GGT5


LMOD1
MYL9
VIM
SEPP1
HAAO


AOC3
MRPS6
SGCE
SPON2
NDN


CFD
PCOLCE
EFEMP1
SPARC
SPON2


RBPMS
SLITRK6
PCOLCE
PPP1R14A
RBP1


TCEAL4
C1R
IFITM3
PPAP2A
CD52


IFITM3
IFITM3
ECM1
FHL2
THY1


TUBB6
TCF21
LTBP4
LGALS1
BMP4


MMP2
SERPINF1
PTGDS
PTGDS
VCAN


MXRA8
TGFBI
LAPTM4A
MFGE8
GNG11


CD151
REEP2
SPARC
EMILIN1
SCT


TCF21
SOX6
CD63
VIM
PPP1R14A


ACTN1
TSLP
COL6A1
PRKCDBP
ABCA8


PDIA5
CLEC11A
PPP1R14A
THY1
LAPTM4A


PMP22
INSC
SPON2
SELM
TMEM176A


EFEMP2
CTC-276P9.1
HAAO
GNG11
LGALS1


LGALS3BP
SRGN
FOS
LAPTM4A
LTB


CD9
RBP4
SNAI2
LTBP4
LINC01082


EMILIN1
LTBP4
NNMT
TIMP1
PAMR1


TUBA1A
PITX1
FHL2
STMN2
PLTP


GSN
LAPTM4A
GNG11
EFEMP2
IGFBP6


MRGPRF
EMILIN1
MEG3
SNAI2
NDUFA4L2


MFGE8
MAGED2
TM4SF1
ECM1
VIM


COL6A1
GLP2R
FABP4
SGCE
SELM


UBE2E3
LAMA4
EMILIN1
VCAM1
CIRBP


C9orf3
A2M
LGALS3BP
IL34
FABP4


PTMS
PROM1
EFEMP2
IGFBP6
S100A4


SERPINF1
RGS10
CXCL1
SPARCL1
QSOX1


JUNB
LHFP
LGALS1
NOVA1
RGCC


RCN1
BAMBI
PLAT
FBLN5
FBLN5


FXYD1
RBPMS
IGFBP6
NGFRAP1
PLAT


CES1
ANXA5
SOCS3
PLTP
MEG3


NUPR1
AKR1B1
TPM2
MATN2
SRGN


RARRES2
BSG
SMPDL3A
FXYD1
TIMP1


SRGN
PRR16
NDN
EDIL3
GSTT1


FN1
MAP1B
SELM
TPM2
EMID1


SDC2
GADD45G
FXYD1
SFTA1P
SERPING1


FOXF1
TSPAN4
C2
TSPAN4
CD3E


PCOLCE
S100A13
PLTP
MEG3
ANXA1


SERPING1
GLT8D2
VCAN
EPHX1
LTBP4


SCPEP1
HSPB1
NGFRAP1
QSOX1
CCL5


AC131025.8
C11orf96
QSOX1
MYL9
SPARCL1


SGCE
EFEMP2
SDC2
SRGN
MXRA8


MIR145
FGF9
EPHX1
TM4SF1
IFI27L2


CRYAB
EID1
GSTM3
EFEMP1
FN1


LTBP1
PTMS
SPARCL1
PLAT
GSTM3


CRIP2
COL5A1
TIMP1
OLFML3
MYL9


DUSP1
MXRA8
FHL1
GSTM3
PHGR1


CERCAM
FKBP10
SRGN
CCDC80
CD63


TPPP3
PTGDR2
COLEC11
DPT
SEPP1


SH3BGRL
CPE
EDIL3
RAB13
TPM2


VIM
SGCE
IL34
ITIH5
GATA3


CKB
TNC
PRKCDBP
NNMT
TFPI


NGFRAP1
TAGLN
C11orf96
SDC2
LEPROT


PTCH1
DCN
ARHGDIB
FSTL1
C16orf89


SOD3
TXNL1
FBLN5
LOXL1
SGCE


COL4A2
EMID1
SFTA1P
FABP4
LGALS3BP


LRRC17
CRISPLD2
EID1
S100A13
LCK


GNG11
SRPX2
FXYD6
COL14A1
DPT


CYBA
C1orf21
MYL9
NDN
SNAI2


RBP1
NDN
THY1
MXRA8
ZFP36L1


IER2
ISCU
LINC01116
UBE2E3
IL32


CPQ
CD9
TFPI
FHL1
TSC22D3


MAP1LC3A
ACP1
LOXL1
TAC3
LAMB1


BMP5
PALLD
MXRA8
IFITM2
MATN2


OSR1
F2R
IRF1
EID1
C6orf48


AKR7A2
BST2
PITX1
PITX1
SPARC


NDN
CPM
MFGE8
C2
CNBP


PKIG
SELM
UBE2E3
LRP1
NANS


S100A13
PTN
FGF7
NUPR1
FSTL1


HMG20B
WNT5B
SERPINH1
VKORC1
EEF1D


RP11-332H18.4
SERPING1
OLFML3
APOC1
AEBP1


CFH
RBP1
ARID5B
FKBP10
SERPINH1


GAS6
FBLN1
PPIC
FXYD6
NNMT


FOSB
NDUFA4L2
RAB13
LAMA4
WNT2B


LPP
PCDH18
CFL1
PPIC
C11orf96


PALLD
APOD
JUNB
DMKN
PDPN


TTLL7
KREMEN1
KCNS3
EMID1
GZMK


IGFBP5
TUBA1A
S100A13
NDUFA4L2
ELANE


LAPTM4A
ID1
CEBPD
PLAU
TRIM22


WLS
ADM
TSPAN4
FOXF1
CLEC14A


EDNRB
PRKCDBP
APOC1
FN1
PITX1


FAM127A
IFITM1
LAMA4
GLT8D2
SLC25A5


ARHGDIB
CXCL12
C6orf48
COL5A1
CXCL1


CSRP2
TSHZ2
ZFP36L1
CTC-276P9.1
COL6A3


TIMP2
LRRN4CL
GLT8D2
COLEC11
IDH2


MAMDC2
PTCH1
NDUFA4L2
CFL1
COLEC11


P2RY14
LAMB1
EMID1
EHD2
COX5A


S100A4
HHIP
CCL7
RBPMS
MXRA5


TRIP6
VIM
SRPX
COL18A1
EDIL3


SH3BGRL3
NNMT
TIMP3
SCPEP1
EFEMP2


CBR1
CIRBP
ANGPTL4
SMPDL3A
PPIC


MMP14
CAPZB
SCPEP1
WFDC1
TDO2


SEPW1
CD63
DPT
DUSP1
C4orf3


MFAP5
TGFB1I1
ADM
COX5A
VPS25


FENDRR
IL32
GADD45B
FOSB
FNDC1


CALU
PLK2
NUPR1
C6orf48
CYP7B1


TMEM176A
TBX2
LRP1
SERPINE2
SPRY1


CTSK
ANGPTL4
CYBA
FAM127A
PCDH7


C1QTNF2
PCSK6
RAB34
TMEM119
ZFP36L2


SNAI2
TSPAN2
PRNP
GSTM5
DMKN


COL4A1
WLS
EGR1
CPQ
ALDOA


CD63
AEBP1
ZFP36
RAB34
COL6A1


COX7A1
SCUBE2
PROS1
AKR1B1
HTRA3


LOXL2
LANCL2
ITIH5
CD81
PRKCDBP


CYB5R3
LOXL2
CD81
SLC9A3R2
CXCR4


FOS
FIP1L1
CIRBP
TNFAIP6
KRT8


IL32
RTN4
FOXF1
FILIP1L
KLRB1


RPL28
ADH5
CCDC80
VCAN
PHLDA1


CFL2
TM4SF1
NEGR1
TGFB1I1
FGF7


LTBP4
C7orf50
COX5A
COL15A1
LAMA4


EHD2
IL1R1
NOVA1
ATRAID
TAC3


ITM2C
EMP3
FN1
TFPI
COL18A1


STMN2
CYBA
CPQ
WNT2B
SPINT2


BSG
CAV2
ID3
SERPINH1
THNSL2


VCAN
TMEM100
FKBP10
PRNP
NEXN


LAMB1
MAP1LC3A
BST2
CTSF
RNASE1


MAP1B
IFI27
WFDC1
MDK
FXYD6


VCL
SEMA4D
TDO2
ACTA2
LOXL1


P2RX1
PXDN
DMKN
CST3
CD81


WNT2B
HAAO
COL5A2
KLF6
TMEM66


PARVA
NPY
COL14A1
TGFBI
CRIP2


S100A6
RGCC
PGRMC1
TIMP3
TIMP3


ECM1
SGCB
PHGR1
ABCA6
H3F3B


TCEAL1
FHL1
SH3BGRL3
FGF7
IRF1


LAMA4
TPBG
ANXA5
CYBRD1
ECM1


VKORC1
NUPR1
EHD2
MMP23B
IFI27


NME4
TBX3
TAC3
EVA1A
DDR2


TMEM98
RGS1
VASN
PTMS
SLC9A3R2


RPLP2
LEPROT
SLC25A5
TNFRSF1A
SGCA


TIMP1
GNAI1
AEBP1
C7
CD74


CD74
MSC
RBPMS
RP11-14N7.2
COL15A1


PPP1CC
PTX3
CCNI
RGCC
SFTA1P


A2M
ACTA2
CNBP
CDH11
CDK2AP2


CTSS
CD74
SPRY1
FGFR4
PTGER2


PTS
LRP1
SEC11C
BST2
FABP1


PPAP2A
TMEM98
PLAU
IGFBP5
TNFAIP3


TTC3
PLBD1
IFITM2
CXCL1
FGFR2


ADH5
CPQ
4-Sep
CP
FHL1


MCL1
VASN
GSTM5
C16orf89
KRT18


FAM105A
AMPD3
ABCA6
LINC01116
RND3


MAGED2
IGFBP5
FILIP1L
CIRBP
SCPEP1


NKX2-3
MXRA5
MT-ND2
SAMD11
MAPK10


RAB34
PHGR1
LEPROT
SAT1
LY6E


SGCA
STMN2
FSTL1
SH3BGRL3
CLEC2B


CCDC107
GULP1
MT-CO2
MIR497HG
FTH1


SERPINH1
CCDC68
TUBA1A
PHGR1
NUPR1


FILIP1L
SPON2
HES1
HTRA3
CD5


MINOS1
CH25H
CSF1
AEBP1
IL34


AEBP1
PLAU
CDK2AP2
TMEM9
EMP3


NEO1
MRVI1
CDH11
S100A4
NUDT16L1


EID1
CD151
PFN1
SCT
CCDC80


PDGFC
CNTFR
HTRA3
MXRA5
IL1R1


DCTN2
COL6A3
RND3
CNBP
EVL


CBR3
PDLIM4
HSPA1A
IFITM1
RP11-14N7.2


RCAN2
CYTL1
JUN
PDLIM3
CSF1


RERG
COL4A5
MT-ND4
KCNS3
GNAO1


CLEC11A
HMGB1
CST3
ISLR
LRP1


FSTL1
ST5
HINT1
HSPA8
ITIH5


C2
GADD45B
TNFAIP6
PFN1
MFGE8


FHL3
ID3
CHL1
BDH2
DUSP2


TGM2
CYR61
ADAMTS1
ELANE
FHL2


MORF4L2
CTSF
ACTA2
HINT1
TRAT1


TMEM47
CTSK
SERPINE2
WARS
FARP1


ISG20
ENPP6
C16orf89
COX7A1
MRPL23


ACTB
LUM
MYL12A
PAMR1
TM4SF1


CD99
HOXA10
RCN1
LY6E
LAMA2


EFEMP1
SERPINH1
IGJ
CRYAB
GZMA


ZYX
FILIP1L
TMEM98
MYL12A
PAM


SAMD11
SEC62
ELANE
SPRY1
RNASET2


SSPN
GPC1
MAMDC2
IL6ST
GPC6


RBBP7
ARPC1B
CTC-276P9.1
ANGPTL1
IL7R


CPED1
PDPN
CD302
GAS6
IFITM2


RGS10
TUSC3
PCDH18
NENF
FBN1


CREB3
RP11-332H18.4
FAM92A1
RUNX1T1
ACTA2


DDAH2
C12orf57
GRK5
CYBA
RARRES3


SEPP1
NOVA1
WNT2B
ANXA1
ADM


MIR143HG
WFS1
MDK
NEGR1
FKBP10


NENF
NGFRAP1
POSTN
CYCS
COL5A1


PITX1
CDH11
ISLR
COL6A3
CCDC127


COL6A3
OLFML3
EPHA7
SLC25A5
GAPDH


KANK2
ZFP36L1
ANGPTL1
PAM
MGST1


NUDT4
PDE1A
PHLDA1
IL32
VKORC1


ARHGEF25
ECHDC2
HSD11B1
CRIP2
HSD11B1


MMP23B
BRK1
FTH1
TDO2
DUSP23


THYN1
HLA-A
RGCC
COL4A2
CHCHD10


RGS1
TCF4
IL6ST
RGS1
SSBP3


ARPC1B
SEC11C
SMIM10
PCDH18
NGFRAP1


RCN3
COL4A6
TMEM150C
P4HA2
ARHGAP24


SQRDL
RCAN2
CTSF
CYB5R3
EID1


APCDD1
SCARB2
ATP6AP2
TSTD1
ID4


RP11-532F6.3
MMP14
AKR1B1
EEF1D
C11orf58


NDUFB9
SH3BGRL3
SVEP1
MT-CO2
CREG1
















Macrophages
Dendritic_cells
Mast_cells
Cycling_monocytes
Tolerogenic_DCs







FTL
CST3
TPSAB1
FTL
SNX3



C1QB
CLEC10A
VWA5A
PSAP
CPVL



C1QC
HLA-DPB1
LTC4S
MS4A6A
IDO1



PSAP
HLA-DPA1
C1orf186
GPX1
CST3



C1QA
HLA-DQB1
CPA3
AIF1
CLEC9A



CTSB
FCER1A
SLC18A2
C1QA
LGALS2



CD68
HLA-DQA1
HPGDS
C1QC
C1orf54



CTSD
HLA-DRA
MAOB
C1QB
HLA-DPB1



TYROBP
HLA-DRB1
HDC
CST3
DNASE1L3



SAT1
CD74
CLU
TYROBP
IRF8



LGMN
AIF1
NFKBIZ
IGSF6
HLA-DPA1



FCER1G
LST1
RP11-354E11.2
CD68
CD74



MS4A7
IL1B
SAMSN1
CTSB
HLA-DQB1



MS4A6A
LYZ
GATA2
DNASE1L3
LSP1



AIF1
CPVL
ANXA1
FCER1G
COTL1



ACP5
AMICA1
GLUL
MS4A7
HLA-DQA1



MS4A4A
HLA-DMA
FCER1A
MS4A4A
HLA-DRA



DNASE1L3
TYROBP
KRT1
NPC2
AIF1



GPX1
FCER1G
CAPG
LYZ
HLA-DQB2



IGSF6
SPI1
CTSG
IL1B
HLA-DRB1



FUCA1
MS4A6A
PPP1R15A
VSIG4
SPI1



FCGRT
HLA-DQB2
SLC45A3
LST1
LYZ



SEPP1
HLA-DMB
HPGD
SDS
HLA-DOB



HLA-DMB
CFP
HS3ST1
CTSD
HLA-DRB5



NPC2
HLA-DRB5
GMPR
GRN
HLA-DQA2



HLA-DPA1
IGSF6
KIT
CPVL
ACTB



STAB1
LGALS2
RGS13
FGL2
LST1



HLA-DQA1
PLAUR
CD9
SPI1
RGS10



HLA-DPB1
CD83
FCER1G
HLA-DPB1
BATF3



RNASET2
IFI30
NFKBIA
SAT1
CADM1



LST1
PLD4
BTK
CD74
MPEG1



LYZ
CD1C
HSP90AB1
HLA-DRB1
ASB2



HLA-DRA
MNDA
CD44
HLA-DQA1
C1orf162



CD14
COTL1
MITF
HLA-DPA1
PPT1



HLA-DMA
GPX1
SERPINB1
RNASE6
FGL2



GPNMB
HLA-DQA2
LMNA
FAM26F
S100A6



HLA-DRB1
ITGB2
ADRB2
PLAUR
HLA-DMB



PLA2G7
SGK1
VIM
CTSZ
BASP1



APOC1
GPR183
TYROBP
HLA-DRA
CD83



CD74
FGL2
SRGN
HLA-DRB5
KIAA0226L



SDS
C1orf162
IL1RL1
RNASET2
HLA-DMA



CTSS
SRGN
SDPR
PLA2G7
SGK1



LAPTM5
FAM26F
FAM46A
SEPP1
TMSB4X



CD163L1
LY86
BTG2
CD14
RGCC



RNASE6
RNASE6
ALOX5
HLA-DQB1
PLEK



VSIG4
RGS2
NSMCE1
STAB1
S100B



HLA-DQB1
DNASE1L3
CTNNBL1
HLA-DMA
SERPINF2



GRN
CTSH
MIR24-2
LAPTM5
ARPC2



ADORA3
CD1E
LEO1
CLEC10A
SMCO4



CTSZ
FCGR2B
SDCBP
ACP5
ITGB2



S100A11
MS4A7
PTGS1
HLA-DMB
HCK



SPI1
LAPTM5
LAT2
AP2S1
CST7



PLD3
SAT1
ALOX5AP
NCF4
UCP2



TREM2
CD1D
FTH1
S100A11
WDFY4



FOLR2
C1QA
DDX5
IGF1
CPNE3



CYBA
CXCL16
AC020571.3
A2M
TNNI2



CST3
ACTB
DNAJA1
CCL3
GLIPR1



RNASE1
RNASET2
BACE2
ITGB2
DUSP2



ATP6V1F
HCK
CD69
SLC7A7
PTPRE



CCL3
CACNA2D3
DUSP6
CD300A
RNASET2



SLC40A1
CORO1A
MLPH
LGMN
ARPC1B



LIPA
MPEG1
JUN
SLC40A1
LY86



GLUL
ARPC1B
IL1RAPL1
TYMP
SLAMF8



CSTB
VSIG4
SIGLEC8
C1orf162
SLAMF7



CPVL
BID
RAB27B
GLUL
C20orf27



ASAH1
STX11
LAT
RGS10
LIMD2



VAMP8
CTSS
UBB
VAMP8
FLT3



ATP6V0D2
FTL
ACOT7
SRGN
FAM49B



RENBP
SAMHD1
STMN1
P2RY6
PARVG



CREG1
GLIPR1
FXYD5
C1orf54
CORO1A



CLEC10A
CSF2RA
EGR2
MNDA
BID



FCGR2A
CD68
ALDH1A1
AMICA1
GCSAM



FAM26F
LSP1
NCOA4
IFI30
RAB32



RGS10
INSIG1
GCSAML
CTSH
FAM26F



TMSB4X
IL8
CD33
FCGRT
CD9



CTSL
NR4A3
STX3
CSF1R
LCP1



NCF4
ARPC3
SVOPL
FCGR2A
ARHGDIB



AP2S1
DUSP2
ATP6V0A2
TGFBI
CKS2



LY86
FAM110A
LAPTM4A
LGALS1
SUSD3



IGF1
CD33
HSP90AA1
MPEG1
PABPC1



HLA-DRB5
TMSB4X
CD63
GPR183
FKBP1B



FGL2
C1QC
ANKRD28
SERPINF1
GSTP1



AKR1B1
CD86
LAPTM5
TBXAS1
PPDPF



MALAT1
RGS10
EGR1
IL8
P2RY6



AMICA1
PHACTR1
ARL5B
CTSS
FCER1G



APOE
PPDPF
CATSPER1
APOC1
NAP1L1



IFI30
AOAH
HSPH1
RNF130
CD48



CD163
PYCARD
KLRG1
HCK
TYMP



ITGB2
PTPRE
CLIC1
ALOX5AP
LAPTM5



HLA-DQB2
ARHGDIB
TSC22D1
CD36
MT-ND2



S100A9
RNF130
S100A4
ADORA3
ID2



CD300A
PLEK
ATP6V1F
SIRPA
AMICA1



UCP2
TYMP
CTD-3203P2.2
CYBA
AIM2



CSF1R
GRN
SGK1
PLD3
CLNK



OAZ1
NCF4
RENBP
PDLIM1
LGALS3



GM2A
TBXAS1
PLIN2
RGS1
IFI27



PLAUR
C1QB
PTPN6
GPNMB
CSF2RA



NPL
ARRB2
ANXA2
CD4
VMO1



HCK
IFI27
FAM212A
RGS2
DUSP4



LILRB4
UCP2
FOSB
TIMP1
ID3



C1orf54
ARL5B
ASAH1
APOE
SAT1



C5AR1
DUSP1
HSPA8
OAZ1
TLR10



LGALS1
CD48
ASRGL1
VIM
TYROBP



RNF130
RHOG
LYL1
ATP6V0B
MIR142



CD209
RGS1
EIF4G2
CORO1A
GPR183



TTYH3
NR4A2
STXBP6
HLA-DQA2
TSPO



PRDX1
NCF2
TNFSF10
CREG1
MNDA



RAB42
HCLS1
GRAP2
HLA-DQB2
PFN1



IL1B
ARPC2
NFKBID
S100A9
LGALS1



FABP3
PILRA
CSF2RB
PPT1
GPX1



MPEG1
CD53
RAC2
LY86
HSPA1A



CD36
P2RY13
NR4A1
TXN
ACTG1



SLC7A7
CLEC4A
HSPA1B
EPCAM
CCND1



NINJ1
PPT1
H3F3B
LILRB4
CNN2



C3AR1
CHMP1B
SMYD3
FUCA1
LTB



CHMP1B
GPSM3
MPP1
FXYD5
SAMHD1



CAPG
ZNF385A
FAR2
GNAI2
NAAA



ADAP2
ATF3
LM04
ADAP2
ITM2C



OTOA
LITAF
SRSF5
CSF2RA
HCLS1



CFD
ZNF331
ARHGDIB
LGALS4
TACSTD2



HSD17B14
PARVG
EIF3D
NINJ1
PSMB9



CD83
MIR142
EGR3
ATP5G1
XCR1



LILRB5
NAMPT
CD82
FCGR1A
PLCD1



P2RY6
P2RY6
MYADM
EMP3
SERPINB9



CMKLR1
FAM49B
TESPA1
KRT18
TMEM176B



SERPINF1
FTH1
RASSF5
CAMK1
GMFG



CTSC
GAPT
CALB2
PHGR1
COX7A2



BLVRA
NPC2
BIRC3
CD163L1
CD99



TYMP
ITGB2-
HINT1
KRT8
PPM1J



TBXAS1
HLA-DOA
CD22
C3AR1
H2AFY



RGS1
CYBA
IL18
IFI27
PYCARD



CXCL16
OAZ1
HSPD1
S100A4
RGS1



CD86
PID1
STXBP2
RAB31
TMEM59



CD4
CCL3
MBOAT7
DAB2
SRGN



A2M
RILPL2
RGCC
ANXA1
ZYX



IL8
CXCR4
IER2
ATP6V1F
CLEC7A



C1orf162
CSF1R
MSRA
TUBB4B
NABP1



NAGK
ARL4C
JUNB
CD209
ZFP36L2



ATP6V0B
PDLIM1
BHLHE40
TFF3
ABI3



HLA-DQA2
IGJ
ARHGEF6
LSP1
MT-ND1



FTH1
NCF1
CST3
ARHGDIB
CD37



CAMK1
G0S2
DUSP10
UCP2
FNBP1



GPR34
HSPA1A
SCYL1
CXCL16
EVI2A



SLAMF8
VAMP8
RGS10
HBEGF
HAVCR2



S100A6
TNFSF13B
PRDX6
ZNF331
ARPC3



IL18BP
H2AFY
ACTG1
FCGR2B
CD63



CTSH
OLR1
CHST2
CTSC
HES1



ARHGDIB
HCST
CD37
RB1
KIAA1598



PLTP
MT-CYB
DDX3X
SRI
VAC14



COTL1
TMEM59
ESYT1
YWHAH
IGFBP7



ARL4C
CXorf21
CRBN
RENBP
TAP1



FPR3
CNPY3
SYTL2
SGK1
LDLRAD4



SRGN
EIF4A1
CTSD
CD163
ELOVL5



HMOX1
THEMIS2
HNRNPM
C5AR1
IL16



TNFSF13B
C20orf27
P2RY14
LILRB2
RGS19



CYBB
CD300A
CD83
COTL1
DUSP10



LAIR1
S100A11
SLC2A6
CLEC4A
PDLIM7



GLIPR1
YBX1
CKS2
TMSB4X
TWF2



ITM2B
LGALS1
ARHGAP18
LAIR1
CTSZ



YWHAH
IGFBP7
TIMP3
ASAH1
IFITM3



TGFBI
ANXA1
TMEM154
EEF2
CXCR4



HLA-DOA
PTPRC
CMA1
PLD4
COX5B



CCL4
AGPAT9
MALAT1
MAFB
VIM



DAB2
FCGR2A
RGS1
RPL24
SELPLG



EBI3
CTSZ
DNAJB1
FCER1A
CFL1



GATM
PPIF
FCGRT
PLTP
ATG3



ATOX1
DOK2
PFN1
TUBA1B
C12orf5



FCGR3A
MT-ND2
EXD3
RPS27A
PNMA1



ARPC3
GNA15
LIF
GMFG
APOL3



TNFAIP8L2
KRT18
GBE1
AXL
RAB31



ABI3
HERPUD1
CHORDC1
CLEC7A
MT-CYB



RHOG
HBEGF
GAPT
PRDX5
MYCL



RGS2
SCIMP
HSPE1
CD83
IFNGR1



CCL18
LCP1
ITM2B
HCST
GYPC



HN1
PTGS2
UBXN10
GNPDA1
GPSM3



RAC1
LIMD2
CNIH1
IGJ
PLEKHO1



TMEM176B
PMAIP1
SLC16A3
TUBB
LSM6



KRT8
PABPC1
GNPTAB
RPL31
MSL3



PYCARD
KDM6B
TSPO
DUSP1
UQCR10



PILRA
IL32
RPL28
RPL35A
LGALS4



LGALS4
FPR3
MAML1
P2RY13
CXCR3



SLCO2B1
PFN1
TUBA1B
CD9
CIITA



SMS
BSG
UBE3A
BLVRA
BCL2A1



CORO1A
GMFG
NFE2L2
GLIPR1
ROGDI



ZNF331
SLC31A2
SH3BGRL3
TNFSF13B
TGFBI



ARRB2
SNX10
ELF1
GATM
MIR4435-1HG



IFI27
SEPW1
PRKAR1A
OSM
CKLF



SIGLEC7
ZFP36
ENPP3
CLDN7
IGJ



GPR183
FOSB
GALNT6
NCF1
BST2



DOK2
KYNU
CCL2
CTSL
DGAT2



CLEC4A
RGS19
ACTR3
GM2A
NDUFB9



CECR1
PHGR1
TMEM66
LRRC25
COX6C



TMEM37
SDS
NCF4
C15orf48
MT-ND5



RHOC
AKIRIN2
BEX4
AKR1B1
KLF6



ANXA1
DSTN
BLVRA
RAB42
KRT18



PHGR1
VIM
SERP2
GSN
1-Mar



AP1B1
S100A4
TM6SF1
TREM2
EVI2B



NCF1
RB1
ITM2A
RPL34
CPPED1



GRB2
ARPC5
DHRS7
SLCO2B1
FERMT3



GAL3ST4
H3F3B
IFI27
ADAMDEC1
ST8SIA4



ID1
PAK1
2-Sep
TSPO
PTPRC



NINJ2
RAB32
CD84
TRPM2
GNAI2



SDSL
CSF3R
HSPA9
RPL18
ATP5J



CD63
GSN
FECH
RPL5
GPR137B



ABHD12
RAB31
PRDX5
H2AFZ
HSPA1B



GNPDA1
ID3
IFITM10
SDSL
RNASE6



CD81
TNFAIP8L2
HSPA1A
MT1E
AKIRIN2



LRRC25
SOD2
DLC1
FABP1
LITAF



YBX1
SLAMF8
HIF1A
ENG
TOMM34



GPSM3
CCL3L1
LYN
TNFAIP8L2
PTPRCAP



TFPT
LILRB2
DDX3Y
LIPA
AP1S2



MKNK1
PRDX5
ZEB2
NCF2
BSG



SLC15A3
ANXA5
RHOG
ARL4C
MT-ND4



BRI3
RABAC1
RBMX
MGST1
MCL1



ADAMDEC1
S100A6
CDK5
GPSM3
ACTR3



IL2RA
FCGRT
DDX39A
RAC1
CD40



IGJ
COX6C
TMSB10
CECR1
MT-ATP6



RB1
CD52
EIF1
ARPC1B
PPA1



MPP1
NGFRAP1
NEK6
PARVB
KCNMB1



SLC7A8
PLEKHO1
CSF2
CYBB
MAP4K1



TNFAIP2
RAB20
CSF1
VMO1
EPCAM



SCIMP
ITM2C
CXCL14
SLC16A3
MYADM



TFF3
CEBPD
PIK3R6
DOK2
CAP1



NCKAP1L
CD9
GPR65
TNFAIP2
SIGLEC10



FXYD3
CD151
RPS4Y1
ARRB2
CECR1



ARPC1B
NUDT1
VAV1
ATPIF1
ACTN1



SIGLEC1
CCDC88A
IL4R
COX5B
RAB7L1



TUBA1B
MAT2A
SELM
ITGB7
FAM110A



BSG
PRMT10
EVL
NAGK
LINC00152



EEF2
GCA
HNRNPA2B1
ATF3
INPP5D



BST2
LINC00936
BCL2A1
IL1RN
PHGR1



HCST
COX5B
RALB
SUCLG1
GRN



LGALS3
MCL1
CORO1A
HSD17B14
AC093673.5



MNDA
CARD9
RAB32
CD86
C12orf57



RAB20
REL
WDR45B
KRT19
PHACTR1



FCGR1A
BCL2A1
LINC00863
TTYH3
CD86



PTAFR
TUBA1B
ABCB8
ANXA5
S100A4



CD53
FGR
EIF2AK1
SOD2
DAPP1



HCLS1
ABI3
SAR1B
BST2
RHOG



LSP1
SOCS3
RHBDD2
CAPG
CYB5R3



AGR2
IFNGR1
DHRS9
FOLR2
C10orf128



C12orf57
JUNB
SEMA7A
PRDX2
RHOF



AOAH
GHRL
CCDC28A
STX11
KRT8



STMN1
MT-ND5
TRAPPC2P1
SNCA
ANXA6



GMFG
NAGK
IGFBP7
PTGS2
ITM2B



IRF8
CIITA
GPR35
CMKLR1
SCNM1



AXL
CPPED1
PAK1
ATP5B
PRDX5



MMP14
LGALS3BP
PARVB
RPL37A
CAT



C15orf48
KLF4
RARRES1
RASSF4
PTRHD1



TRPM2
WAS
IL5RA
S100A6
CD72






















TABLE 15D







Neutrophils
Activated_CD4_cells_loFos
Activated_CD4_cells_hiFos
CD8_IELs
CD8_LP_cells
Tregs





S100A9
RPLP1
IL32
CCL5
CCL5
IL32


SOD2
RPS3
ANXA1
CD7
IL32
CORO1B


IL1B
IL32
KLF6
GZMA
NKG7
BATF


PLAUR
RPL10
S100A4
NKG7
CCL4
TIGIT


LST1
RPS25
CD69
HOPX
GZMA
PFN1


AIF1
RPSA
DNAJA1
IL32
DUSP2
BTG1


SPI1
RPL32
HSPA8
CKLF
CD8A
CD3D


G0S2
ANXA1
CD3D
KLRC2
SH3BGRL3
ARHGDIB


LYZ
RPLP2
RPLP1
CD160
CST7
CREM


SAT1
RPL19
LTB
GZMB
CD8B
ICA1


FPR1
RPS19
CCL5
PTPRCAP
CD52
C9orf16


TYROBP
TPT1
CD52
TMIGD2
GZMK
DNPH1


FCER1G
RPS15A
ID2
HCST
ZFP36L2
TNFRSF4


SERPINA1
RPLP0
SH3BGRL3
EVL
HCST
CARD16


FTH1
RPL13
BTG1
CD52
HOPX
RAP1A


FCGR1A
RPL11
TNFAIP3
CD3D
PFN1
LTB


S100A8
RPL28
TNFRSF25
GNLY
TMSB4X
ARPC1B


IGSF6
RPS12
CALM1
CD3E
BTG1
CTLA4


CFP
RPL13A
TSC22D3
SH3BGRL3
GZMB
NDUFV2


IL1RN
RPL30
EIF1
RAC2
CD3D
FOXP3


HLA-DRA
RPS27A
TMEM66
CTSW
CD3E
PMVK


CTSS
RPL4
CD2
PHGR1
GZMH
PBXIP1


TYMP
RPS14
ZFP36L2
IGJ
CKLF
LCK


FAM26F
RPS6
RPS3
TMSB4X
MYL12A
CD63


HLA-DQB1
RPL23A
RPSA
GAPDH
CXCR4
BIRC3


FGL2
RPS2
CD3E
CORO1A
CFL1
PTPRCAP


CPVL
CD52
TMSB4X
ABI3
NR4A2
ITM2C


STX11
RPS18
RPS19
PRF1
B2M
UCP2


HLA-DRB1
RPL6
SRSF7
ACTB
ARHGDIB
IL2RG


CD14
LTB
HSP90AA1
CD3G
LYAR
AC017002.1


FTL
RPL27A
DUSP1
ARHGDIB
ANXA1
SRGN


HLA-DPB1
S100A4
MYL12A
SIRPG
RPL28
LGALS1


HLA-DQA1
RPL10A
ARHGDIB
LCK
CTSW
CD44


COTL1
RPL3
ACTB
ACTG1
TMEM66
CALM3


NCF2
RPS16
RPL28
RARRES3
C9orf142
DUSP4


HLA-DRB5
RPS5
CORO1A
PFN1
PSMB9
RGS1


LILRB2
IL7R
RPLP2
CD247
RPLP2
TNFRSF1B


APOBEC3A
RPL31
PFN1
STK17A
RPS3
MIR4435-1HG


EREG
RPL14
ABRACL
CAPG
PTPRCAP
LAIR2


C1orf162
UBA52
IL7R
TBC1D10C
LAG3
ICOS


S100A11
RPS15
LEPROTL1
XCL2
CORO1A
TNFRSF18


CDC42EP2
RPL18
RAC2
FABP1
HLA-B
HLA-A


PLEK
SH3BGRL3
B2M
ARPC2
GZMM
ACTB


MS4A7
RPS20
CD47
CD96
IFNG
SPOCK2


LY86
RPS13
IFITM3
C9orf142
TUBA4A
ANKRD12


HLA-DPA1
RPL27
APRT
LGALS4
ID2
EIF3H


IFI30
RPS8
HLA-DRA
FTH1
S100A4
GSTP1


HLA-DMB
EEF1B2
RPLP0
XCL1
RPS19
B2M


LGALS2
RPS23
IL2RG
CD8A
CD69
CORO1A


ITGB2
RPS4X
TPT1
1-Sep
CD7
CD27


C5AR1
RPL12
RPL10
CST3
ACTB
CCL5


SRGN
ARHGDIB
CD53
AC092580.4
HLA-A
LAT


CYBA
RPS3A
DNAJB1
CFL1
CD2
PKM


TIMP1
RPL35A
PTGER4
CST7
PSME1
PPP1R18


CD74
RPL5
ID3
CLIC1
ALOX5AP
ANXA1


CST3
RPL15
PPP2R5C
PPP1CA
RPL27A
EEF1D


CD36
RPL37
CD40LG
IL2RB
HSPA8
HINT1


TNFSF13B
RPL8
HLA-DPB1
ALOX5AP
LEPROTL1
IL10


MS4A6A
TMEM66
CKLF
TIGIT
SRGN
RAC2


BID
RPL34
RPS12
RPS19
HSPB1
ASB2


GBP1
CD3D
RPS27A
IGLL5
SRRT
LAG3


GLRX
RPL29
PHLDA1
PLEKHF1
RPS27A
FOS


NFKBIA
IGFBP7
RPL19
ACAP1
RPL30
ATP5L


MNDA
PTGER4
FTL
PTPN6
CXCR3
TBC1D4


CXCL10
RPL35
DRAP1
P2RY11
CALM1
COTL1


ACTB
CD3E
CD63
ID2
KLF6
RPL28


FCN1
RPS7
DEDD2
MYL12A
RPL13A
RHOH


IL8
TOMM7
GPSM3
FASLG
CREM
NINJ2


ARPC1B
CXCR4
DDX5
CYTIP
BIN1
RHOG


HLA-DQA2
MYL12A
1-Sep
KLRD1
RPL23A
GMFG


PILRA
RPL18A
UBE2D3
DRAP1
APRT
CST3


LILRB1
BTG1
CFL1
CD8B
RPS20
CD52


FGR
RPL9
GRN
CLIC3
HLA-C
PPP1R2


NINJ1
RPL7A
PSMB9
IFITM3
CYBA
UBE2D2


CD86
TMSB4X
TPM3
CXCR3
ABRACL
FYB


LINC00877
CD63
CD48
PPP1R18
TC2N
TNFRSF9


OAZ1
CORO1A
RPL14
RPS4Y1
1-Sep
PTTG1


TREM1
FAU
PDCL3
ACTR3
CD99
CD2


ASGR1
CD2
SAMSN1
GRN
EVL
TRAF3IP3


HLA-DMA
TNFAIP3
PSME2
RGL4
ICAM3
NTMT1


TNFAIP2
RPL36
RPS6
TPI1
IFITM3
RPS15A


ARPC3
CCL5
SRGN
COTL1
LCK
ADTRP


CAMK1
EEF1D
RPL32
TRAPPC1
C12orf75
CACYBP


S100A4
GPSM3
ALOX5AP
KLRC1
ARPC2
S100A4


CPPED1
LDHB
RPS20
HSPA1A
FYN
GPR183


RAB20
RPS9
ARHGDIA
CIB1
XCL1
JUN


RIPK2
LEPROTL1
SOCS1
PSMB10
PRF1
ENO1


CXCL9
PFN1
DDIT4
ITGA1
PSAP
UBC


LAP3
KLF6
MIR24-2
LAT2
ATP5E
TNIP2


ATP6V0B
CALM1
HLA-B
CD244
YPEL5
1-Sep


HCK
CD69
HLA-DRB1
ITGAE
DRAP1
EVL


GCA
APRT
PGK1
ENO1
MCL1
CXCR6


RP11-290F20.3
GLTSCR2
LAPTM4A
BCAS4
CRTAM
HSPA8


LILRB4
GPR183
FDX1
CDK2AP2
PPP1CA
TAPSAR1


CD37
RPL26
RPL27A
NFKBIA
RPLP1
GNB2L1


PRELID1
RPL36AL
RPS4X
PTMA
RPS15A
XRCC6


RNASET2
GIMAP7
CITED2
GIMAP7
GSTK1
CYTIP


GCH1
HSPB1
PSME1
RPLP2
TIMP1
CD37


CYBB
ABRACL
RAN
NPC2
CLIC1
RPL13A


NCF4
PSAP
MALAT1
ARPC1B
ID3
NSA2


IL23A
HLA-DPB1
H3F3B
VASP
TMA7
CD3E


RP11-701P16.5
RPL24
RPS15A
LSP1
PTPRC
HMGN1


SERPINB9
HLA-DRB1
FOSB
HERPUD1
PPP2R5C
TRAPPC4


MPEG1
PTPRCAP
CXCR4
RGCC
RGCC
TRAPPC1


CCL3
KLRB1
BCAS2
PTPN22
RNF167
SH2D1A


CFD
IFITM3
ALG13
CISH
MYL12B
TIMP1


UBE2D1
HLA-DPA1
LCK
MATK
PSME2
ARID5B


THEMIS2
FTL
RPL11
HSPA1B
HMOX2
SKAP1


STXBP2
EVL
CDC42SE2
SOCS3
RPL13
DOK2


ARRB2
APOE
RPL13
RPS3
CD59
SNRPB


GPX1
FXYD5
CACYBP
RPL13A
SAMSN1
ISG20


TIFAB
CD74
IDS
PSME1
RARRES3
TNFRSF14


CORO1A
HLA-DRA
GALM
PTPN7
TRAPPC1
FXYD5


DUSP2
DDX5
CD6
CD2
TAPBP
CDKN2A


TESC
RPS4Y1
CCL20
ASB2
SH3KBP1
RPL36AL


CD68
RGCC
RPS2
OSTF1
APOBEC3G
PCBP1


SPHK1
TC2N
RPL31
DOK2
GLIPR2
LAPTM5


KYNU
HSPA1A
UBE2D2
ITGB7
PSMB10
PTPN2


BCL2A1
CMPK1
IL4I1
MT-CO1
DHRS7
UXS1


GLUL
CD6
SLAMF1
CD59
RPL19
PMAIP1


BLVRA
IL2RG
FOS
TNFRSF18
TSC22D3
UGP2


KDM6B
SRGN
MGAT4A
RPLP1
MALAT1
9-Sep


NAMPT
NPM1
TRMT112
FCER1G
STK17A
ARF6


SLC31A2
TSC22D3
FAM96B
LAG3
DENND2D
CMC2


NUP214
PDCL3
IL12RB1
HSPB1
RPL31
LIMD2


ABI3
ZFP36L2
SVIP
ARL6IP5
ITM2A
PSME1


SELK
CD59
CCR6
WAS
CDK2AP2
LEPROTL1


PSAP
CFL1
RPL36AL
BUB3
MZT2A
TMSB4X


SAMSN1
SOCS1
PLP2
RGS1
RGS1
IGBP1


PPIF
NACA
CYCS
CD69
SOCS1
PYHIN1


ATF5
RPL38
TTC39C
SLC16A3
GUK1
BCAS2


AMICA1
CKLF
HLA-DPA1
HLA-DRA
GRAP2
PHLDA1


IGJ
CD37
NOP58
RPS3A
C19orf60
PRR13


ITM2C
SH2D2A
ENO1
PTTG1
TNFAIP3
ZNHIT1


YBX1
GNB2L1
MYADM
LDLRAD4
IL2RG
SOD1


ACSL1
IGJ
RPS25
CD53
DDX5
MAPK1IP1L


RNASE6
BTF3
HNRNPA0
PSAP
EEF1D
OSER1


ZFAND5
NPC2
JUN
PTGER2
RPS12
CASP4


GRN
CXCL14
YPEL5
SH3BP1
ARPC1B
RGCC


WAS
CCDC109B
PPP1R15A
CHMP4A
PTGER4
NAMPT


TNFAIP8
LGALS1
SERP1
IDH2
ZNF331
6-Sep


JUN
HSPA8
RPS14
RPS27A
BUB3
IDI1


ASGR2
CRIP1
EVL
LSM2
RBM8A
GBP2


CXCL2
HSPA1B
PSAP
EEF1A1
CAP1
SSU72


FCGR1B
CCR6
FAU
HCLS1
RPS18
COPE


LIMD2
RPS29
RPL13A
MYL12B
7-Sep
YWHAZ


DOK2
PFDN5
PTPRCAP
CRIP1
C19orf24
COMMD3


PFN1
TTC39C
TAGAP
PABPC1
HLA-DRB1
GLRX


LILRA2
RPS21
DHRS7
LYAR
FAM177A1
RBBP4


PYCARD
C9orf142
H2AFZ
FYN
CRIP1
PTPRC


ISG15
RPL37A
AMD1
BIN1
ABT1
HIGD2A


KMO
RPL17
CD74
RGS19
CXCL14
SMS


IL10
PABPC1
ODF2L
DEF6
RPS25
CCL20


CTSH
EIF1
SND1
RPL18A
SNRPB
ANP32A


CD48
GSTK1
OSTF1
IFI27
RPS2
NPM1


RTN1
ARL4A
ERP29
LCP1
EIF1
NAPA


IKZF1
YPEL5
PNP
HENMT1
CTSB
APOE


SH3BGRL3
CD7
ARL4A
CXCR6
BAX
RPL15


C19orf38
HCST
RPL30
RPL9
MFSD10
HSPB11


RIN3
LAPTM5
MRPL11
FCGRT
RPL36AL
ACTR3


PSME2
ITM2C
AATF
RPS10
RORA
CDC42SE2


HCLS1
ID2
RPL4
CCL4
SRSF7
TMEM66


CD83
FYB
MCL1
RPS27
HNRNPUL1
SNX5


AP1S2
TUBA4A
JUNB
APOBEC3G
COPE
CHCHD10


LCP1
1-Sep
BAZ1A
CD99
FTL
ICAM3


ITGAX
RPL22
EIF4A3
TPM3
C9orf78
JUNB


PKM
RORA
CREM
RPL17
PDCL3
LIMS1


CFL1
LAPTM4A
SRSF2
GYPC
TAGAP
EPSTI1


VAMP8
PLD3
RPS5
GSTK1
UBE2D3
C19orf43


IFNGR2
SNRPD2
MRPL34
IL2RG
C14orf1
PIM2


NPC2
METTL9
SNHG8
ATP5E
PLP2
LINC00152


RILPL2
B2M
C9orf142
GYG1
GPSM3
GTF3C6


GPBAR1
CACYBP
MTFP1
FOSB
UBE2L3
SOCS1


CSF1R
SAMSN1
RPS8
SASH3
GPR65
RPL11


OSM
HIGD2A
PRR5
C19orf53
ATP6V0E1
UFC1


CCRL2
ARPC1B
ACTG1
7-Sep
RAC2
TSC22D1


CLEC10A
9-Sep
CHCHD7
FXYD3
KLRD1
ARPC4


IL4I1
GPR171
RPS13
TSEN54
HLA-DRA
NUDT1


CD52
AES
PTGER2
COMMD8
EBP
ANAPC16


SYK
LAT
HSPE1
CYBA
RPSA
TPRKB


CHMP1B
ACTB
TC2N
PSME2
TMUB1
PHGR1


NLRP3
NKG7
SAP18
EGR1
RNF149
WDR1


HBEGF
DYNLT3
RORA
CD74
HLA-DQA1
RPS2


CCL3L1
TRAT1
CCDC109B
GZMM
GAPDH
CD58


IFI27
EIF3E
CDK2AP2
CAPN12
STUB1
DDX5


IL27
RARRES3
UBE2S
SPINT2
SNRPD2
RPS27A


ATP6V1F
OXNAD1
LAT
C9orf78
CSNK1D
SMCO4


ARHGDIB
SERPINB6
CD97
POLR3GL
HSPA1A
EEF2


TMSB10
SPINT2
GSTK1
PDLIM1
RPL14
LDHB


VSIG4
COMMD6
PSENEN
C9orf16
RALY
CUTA


ANXA2
HNRNPA1
LDHA
GUK1
RPL22L1
TNIP1


VASP
ACP5
EIF4A1
CCND2
FAM96B
SKP1


PPDPF
RAP1A
FUS
GPR68
TOMM7
ITM2A


ARL5B
RPSAP58
HNRNPUL1
TNFSF14
SNRPB2
HCLS1


MT-CYB
EEF1A1
C14orf166
RHOC
METTL5
HLA-G


GBP5
CREM
SPINT2
IL16
RPL32
GYPC


PSTPIP2
RCAN3
SURF4
SLC9A3R1
PNN
RPS3


GPR183
CD48
MZT2A
MIF
MBP
SH3BGRL3


HCAR2
SPOCK2
CXXC1
MT-CYB
CLDND1
RPS27L


SAMHD1
TNFSF13B
PCBP1
TTC1
GTF3A
GPX1


HAPLN3
EIF3H
RPS18
SAT1
ATF6B
SNRPB2


CAPG
SAT2
ANXA5
TSC22D4
CDC42SE2
AKIRIN2


EPSTI1
LYAR
HMGN1
LAPTM4A
ALG13
PSMD8


RNF130
PLP2
HCST
SCML4
RPS16
COX17


ID3
MZT2A
PSMA7
PTPN4
RBCK1
UBE2I


CREM
MGAT4A
LAPTM5
COMMD6
CD9
SELT


LITAF
SMDT1
TIMP1
HLA-DRB5
CD74
IL2RA


CXCL3
ANXA5
EML4
RP11-47L3.1
PHGR1
FAIM3


PLA2G7
ENO1
AMICA1
SSNA1
EPSTI1
GK


UBE2E2
TMEM14B
ICAM3
CASP4
CD53
NAA38


H2AFY
PSME1
IL17A
CTSB
OAZ1
PSMA2


UBXN11
CYCS
EIF1AX
ARPC3
PPP1R18
SNRPD2


RGS2
ATP5L
DYNLT3
APRT
RPS14
RPL24


RHOG
RBM3
IFITM2
RPL7
CSRNP1
PIK3IP1


CASP1
ICAM3
PRKCQ-AS1
VAMP8
BLVRB
ID3


CD274
ALOX5AP
RBL2
RPL4
RPL27
SLAMF1


HCAR3
C19orf24
HSP90B1
HLA-DQB1
RPL3
RPL18


LINC00936
GMFG
SNRPB
FAM173A
OXNAD1
UBXN1


TUBA1B
NEAT1
FERMT3
SLA2
NEDD8
FTL


IL18BP
EGR1
GHITM
GPR171
C11orf31
H2AFV


C12orf57
PTPRC
SELT
SAMSN1
HSPA9
CMTM7


EMG1
CD97
NFKBIA
PSMB9
TPM3
LINC00649


PTGS2
UBE2D2
RPS16
CD37
CHMP4A
RPL34


MYO1F
TXNIP
RPL22L1
ANAPC16
PSENEN
HSD17B10


NADK
GTF3A
ISG20
RPL21
MLX
BAX


RABAC1
RPS11
SNRPD2
RPSA
RPL34
CLPP


A2M
PPP2R5C
IFI35
HMOX2
CIB1
HSPA1B


GDI2
SNRPG
CASP1
LSM10
OCIAD2
CD7


GLIPR1
TMA7
NPC2
PSMD13
SLC38A1
RPS25


HSPB1
RPL22L1
SLC1A5
PGK1
TADA3
HLA-DRA


DSTN
IFITM2
TRAPPC1
TYROBP
IDH2
ZNF706


NMI
DUSP2
TUBA4A
HLA-DRB1
GHITM
RPL12


CD9
RPL23
MAX
C11orf48
NHP2L1
DCXR


DUSP1
SCML4
UXT
RPL28
ATP5D
CUL9


MCL1
C19orf53
HSPB1
GGA1
ACADVL
RNF213


BSG
GGA1
RBM3
EEF1D
ATP8A1
ZC2HC1A


MT-ND3
MZB1
RAB8A
LAPTM5
GATA3
ALDOA


RNF19B
ARHGEF1
TAPBP
GPR34
NAA50
G3BP2


GLIPR2
HERPUD1
RPL23A
TSTA3
AKR1A1
HCST


PSMB9
JUN
EMP3
BANF1
CD97
CIB1


GAPT
PHLDA1
UBA52
CDIP1
MZB1
PDCL3


NAGK
PRKCQ-AS1
CRIP1
C11orf31
MEA1
SSBP1


C10orf54
ZFAS1
NR4A2
STXBP2
PSMB8
CCT7


CTSB
EEF2
TAP1
RPL22
MYEOV2
HPRT1


CD53
COTL1
SS18L2
CALM1
UBE2I
PTGES3


CSF3R
TRAPPC6A
FLT3LG
RPL36A
ZFP36
MRPS35


SCIMP
CTSD
GPR183
NUDT14
FIBP
SRP19


MT-ND4
NDUFS5
IRF1
IRF2
C14orf166
HSP90B1


PSMA4
CLIC1
CXCR3
MRPL46
SIGIRR
BUB3


HCST
RPS24
PPP6C
YPEL5
RPL10
BTG3















Memory_T_cells
NK_cells
Cycling_CD8_cells
Inflammatory_CD2_DCs







LDHB
NKG7
CD3D
LST1



RPL11
TYROBP
CD3E
IL4I1



CCR7
FCER1G
NKG7
KRT86



RPS12
XCL2
CD2
LTB



RPL32
CTSW
CCL5
FXYD5



RPS3
XCL1
CD7
ALDOC



RPL19
CLIC3
IL32
KRT81



RPLP2
IL2RB
GZMA
ID2



RPL13
GZMB
CST3
LTA4H



RPS15A
CCL4
ITM2A
NFKBIA



RPS14
GSTP1
TUBB4B
ZFP36L1



RPL23A
KLRC1
CTSW
CASP3



RPL31
MATK
PTPRCAP
TNFRSF25



RPSA
APOBEC3G
GZMB
HSPA8



RPS4X
CST7
VIM
MIR24-2



RPL18
GZMA
CD8A
LIF



RPS6
GNLY
CD8B
TYROBP



RPS13
GZMK
B2M
DUSP1



RPL28
CD7
CD96
NXT1



RPL27A
KLRD1
AC092580.4
HNRNPA0



RPS2
HCST
SH3BGRL3
MPG



RPS25
EIF3G
CD3G
HMGN3



LTB
PFN1
RGL4
CXCR4



RPS18
PRF1
LGALS1
NR4A1



RPL30
FGR
FCGRT
CSF2



RPL4
KRT81
HCST
PRMT10



RPS9
HOPX
PLA2G16
CD83



RPL35A
CAPG
TMIGD2
DNAJA1



RPS27A
CCL3
IFNG
H2AFY



RPS8
KLRF1
RAC2
SRSF2



RPL10A
MAP3K8
GYPC
TMIGD2



GNB2L1
SRGN
SPINT2
OTUD5



CD63
IFITM2
LGALS4
CD300LF



RPS23
CD3D
HLA-DRA
SPINK2



RPS20
STK17A
CD69
TPT1



RPL14
FAM177A1
LY6E
TLE1



RPL36
PTP4A1
LDHA
DLL1



RPL37
ITGB2
ARHGDIB
PTGDR



RPL13A
CCL5
GIMAP7
NCOA7



IL32
BTG1
SRGN
CD52



RPL27
NR4A2
TBC1D10C
AMICA1



PABPC1
APMAP
CD52
MAFF



RPL26
DUSP2
RPL8
BIRC3



RPL8
PTGDR
EPCAM
JUNB



SELL
GZMH
RARRES3
TOX2



RPS21
CORO1A
CD9
DRAP1



RPL5
KRT86
H2AFZ
CD69



RPS15
CD160
ATPIF1
IL23R



RPL10
LAT2
MSN
ARL4A



LGALS1
ID2
APOBEC3G
TCIRG1



RPS16
MIB2
GZMM
UBB



BTG1
ALOX5AP
SLC9A3R1
IER2



RPL34
BCO2
CDKN2A
CAT



RPL29
NCR3
COX5B
EIF1



RPL12
ARPC5L
C15orf48
AREG



TMEM66
MYL12A
ICAM3
FOSB



FXYD5
FTL
TXN
ZFP36



ARHGDIB
CD97
CD37
TCP1



RPS5
PPP1R2
SKAP1
CD164



RPLP1
CD247
PIM1
DDX3X



RPS7
GUPR2
SLA2
METTL9



EEF1B2
CLIC1
TRAT1
ZNF75A



RPS19
SLC35E1
CXCR3
C16orf91



CD52
7-Sep
TCEA2
NR4A2



FAU
CHST12
PRKCH
TNFRSF18



RPL7A
CDC42SE1
ATP5B
MAP3K8



GLTSCR2
C20orf24
EMP3
TEX30



NOSIP
LSP1
MARCKSL1
BZW1



NPM1
SAMD3
HLA-DRB1
H3F3B



LEF1
PTPRCAP
HLA-B
DDX18



RPL6
HSPB1
PEBP1
MRPL18



ZFP36L2
ABHD17A
TRAF3IP3
PRPF6



RPL15
RGCC
ATP5G1
PRAM1



EIF3E
CD44
RPL37A
SLC43A2



TCF7
MAPK1
HLA-DPB1
RAN



HINT1
LDLRAD4
PRF1
FCER1G



RPS29
ACTB
HOPX
MGAT4A



RPLP0
EVL
GSTP1
SLC25A39



UBA52
TMIGD2
PDLIM7
NFKBIZ



LEPROTL1
MRPL3
CST7
BLVRA



RPL22
GZMM
GRN
FOS



RPL38
ZFP36L2
HLA-DPA1
RNASET2



ITM2C
NUDT14
IFITM2
IL2RG



HSPA1A
TESC
LCK
EIF4A1



RPL3
SH2D1B
EVL
LINC00299



TRAT1
CHD2
GZMK
EMP3



EEF1D
FAM49B
SIRPG
DNAJB1



EEF2
VDAC1
LGALS3
IL7R



BTF3
BIN2
NANS
BST2



LGALS3
ARHGDIA
CD74
CREM



SMDT1
CDHR1
CYC1
SLC16A3



PFDN5
SIGIRR
AGR2
KIAA1324



TOMM7
VPS37B
HLA-C
UNC93B1



HNRNPA1
TNFRSF18
SH2D1A
ENO1



EIF3F
GRK6
LAPTM5
SKIL



CCDC109B
DUSP1
SLC25A5
RNF139



PTPRCAP
ZFP36
CORO1A
HSP90AA1



CD3D
SELM
HLA-A
BEX2



CD37
IDS
HSPD1
TMEM243



RPL23
PRDX1
RPL36
DDIT4



RPS3A
RHOF
IL2RB
RBM39



PSAP
LGALS3
LSP1
SIK1



GIMAP7
CFL1
TSPAN5
PSMD13



RPL24
CMC1
GCHFR
RASD1



LIMD2
RNF113A
ATP5G3
AQP3



RPL37A
IL2RG
HIST1H4C
MED30



RPL9
TIMM8B
GPX2
HHEX



RPS11
FASLG
RPL38
ZNF331



TRAF3IP3
TMSB4X
SAMSN1
BTG2



RPS24
SRSF5
COX5A
RPL22L1



PASK
LAMTOR5
HN1
NCR3



TPT1
AKNA
HLA-DQA1
MYADM



NACA
USF2
ATP5O
LPXN



CORO1A
RAC2
UQCR10
RBPJ



COX7C
NDUFB8
UBE2C
UBE2S



IFITM3
SDHC
GMFG
DPAGT1



EIF3H
RANGRF
GNG2
NHP2



CXCR4
KLRB1
FYN
CYCS



ANAPC5
PSMB2
HES1
PRR5



RPL18A
SLC16A3
GNLY
CCT4



CD7
RIN3
ID2
HMGN1



DENND2D
RBM38
UQCRQ
BCAS2



MZT2A
IDI1
XCL1
BTG1



RPL35
UBXN2B
HLA-DMA
MAP2K1



FAIM3
LINC00667
RPS29
CXXC5



OCIAD2
CST3
ANXA1
ATG4B



GPSM3
PPP5C
RGCC
SFPQ



C6orf48
ID3
CCNB1
SRGN



UBB
DRAP1
BST2
NPC2



RNF138
NFKBIA
ATP5A1
TUBA4A



CYTH1
CCNL1
CLEC2D
CALM1



SERPINB6
NXT1
ECHS1
TXK



CCL5
ARID5A
CCNB2
SPTLC2



FAM177A1
AGTRAP
ATP5J2
ANP32A



DCXR
ARHGDIB
TNFRSF18
CCR6



NUCB1
CBX3
CKS2
PROSC



DAP3
TCEB2
NCAPH
TXNL1



P4HB
RPS3
RPL5
TRAF4



FYB
ZNF814
RAC1
HSP90AB1



HLA-DPA1
LINC00996
ARPC1B
SRSF5



FOSB
PSMA7
ABI3
SLA



6-Sep
CD69
GPX1
RNF19B



CHI3L2
YPEL3
MT1G
COL9A2



ID3
APRT
CYTIP
NFKB1



CST3
HMGN1
SURF4
PPP2CA



ARPC1B
CPNE1
CTSH
NAP1L1



JUNB
IGFBP7
FXYD5
SRP9



RARRES3
PPP1CA
PFKP
BEX4



BEX2
YWHAZ
CRIP1
TMEM123



ICOS
EBP
ZAP70
TUBB4B



HLA-DRB1
MIR24-2
ICOS
LGALS3BP



IFITM1
ZNF331
MT1E
TNF



PFN1
GCHFR
RPL12
HSPD1



CD3E
TRAPPC1
RORA
SAMD10



TBC1D10C
DDIT4
IL2RG
CSTB



RNASET2
GRB2
IFI16
CRIP1



EIF2S3
OCIAD2
ETFB
CD47



CASP8
MPG
UPP1
EMC10



FXN
RALA
ATP5J
SACM1L



CYLD
C19orf25
S100A4
ANXA5



SC5D
SNRPA1
PSMB9
IFI44L



LMNA
BUB3
STK17A
CAPG



MAL
PLAC8
RPS14
FAM213B



AIM1
PDCD4
KRT19
EIF3D



TMSB4X
SLC25A39
TIMM13
EIF4G2



MRPL16
RPL7L1
CD247
ERBB2IP



COTL1
NSMCE1
RPS4Y1
ARF1



CRLF3
BUD31
RPL7A
PARL



H2AFV
PAPOLA
TMSB4X
HSPA5



AAK1
CALM1
RPS18
ZFAS1



SLC2A3
MRPS11
GZMH
GSN



CTSD
C1orf162
SRI
WDR45B



AC013264.2
PSME2
PIGR
TPM3



TMSB10
LDHB
KRT18
SERTAD2



1-Sep
CD59
COX4I1
CA13



IGBP1
RBCK1
CCL4
AKAP17A



RPS4Y1
GRN
CENPW
CUTA



ZFP36
BCAP31
STOM
CDC42SE1



COMMD6
AIM1
CREM
PRKAR1A



DNMT1
TGFB1
PTTG1
EPS8L2



GIMAP4
TIPARP
RPL35A
H2AFX



CXCL14
MYO1F
FKBP11
CXCL2



YPEL5
SF3B2
PHB
ALG13



WHSC1L1
NDUFS8
PTGER2
SNRPB



ZNF331
RTN3
SUCLG1
B3GALT5



CD27
NDUFA3
ACAP1
NRBP1



GSTK1
PPP1R14B
AIP
AUP1



SSU72
PTMA
PLEKHF1
GPATCH3



HLA-DPB1
SH2D1A
UQCR11
TRIAP1



SPOCK2
PIGX
HSPE1
SF1



TIMP1
PTPN4
TPM1
GPR65



SLC25A6
JAK1
HINT1
VEZT



C1orf228
IRF8
DBI
PCBP1



LCK
TADA3
CCND3
NR1H2



NAA38
HSPE1
ITK
FKBP3



DCK
GGA1
RPS6
TNFRSF4



GPR18
RTCA
CLDN7
CD3E



CTSC
TLN1
CD53
GPR68



HERPUD1
TRMT2A
EEF2
TNFSF4



TPI1
PSTPIP1
SH2D2A
H2AFZ



RGCC
GGNBP2
COX6B1
PSME1



LINC00861
NHP2
HMGA1
JMY



CD59
PSD4
RNF187
NUP54



EVL
RTFDC1
NDUFA11
XCL1



CORO1B
PSMD6
HMGN1
GNA15



CYCS
DCXR
STMN1
LTC4S



ZNHIT3
TSPAN32
UQCRC2
TXNDC17



TOMM20
CUTC
LAT
GATA3



TUBA4A
ATP6V1G1
HLA-DQB1
N4BP2L2



9-Sep
IFITM1
AK2
CTSH



DGUOK
C19orf66
WDR54
SLC39A4



LYRM4
PPP1R18
RPS24
PER1



FTL
TMEM14C
TSC22D3
AC022182.3



JUN
ALKBH2
MTPN
HCST



LAT
POLR2L
MYO1G
PCDH9



PIK3IP1
METRNL
KLRG1
TPI1



OAZ2
SERBP1
NRM
RP11-425D10.10



FOS
C9orf16
FOSL2
SERTAD1



HSPB1
BHLHE40
1-Sep
KIT



AES
TSC22D4
PRDX5
ERGIC3



COX4I1
S100A6
SSBP4
ZNF814



LRRFIP1
RBM39
RPS13
FOSL2



RAB1A
RNF125
GIMAP1
LYPLA2



MALAT1
MAFF
MPC2
SIVA1



RILPL2
SLC9A3R1
COX7C
JTB



HLA-DRA
STXBP2
GMNN
ANP32E



GTF3A
CLDND1
GNB2L1
RNFT1



ABHD14B
AP2M1
PABPC1
EIF5



ACP5
PSMD8
HLA-DRB5
BAD



CHCHD7
VAPA
FAM162A
PNP



RAN
SOCS1
OASL
HNRNPK



CD74
HNRNPA2B1
OSTF1
MGMT



VAMP2
DHRS7
DOK2
TCTN3



APRT
AKIRIN2
C1QBP
C6orf57



C19orf43
COA5
LIMD2
PCNP



TSC22D4
COMMD6
CD160
TP53I13



BRMS1
ATG12
TUBB
C3orf17



RASAL3
ARPC2
RPL24
MRPS15



NDUFS6
KLHDC4
DDT
GPX7



NPC2
LRRFIP1
GTPBP1
CASP6



LSM14A
APOBR
ATP2B4
HLA-B



CCDC104
ETF1
NDUFC1
CRTC2



WDR82
CASP4
RAB27A
MBOAT7



MEA1
CASP3
PRDX3
TNFAIP3



GADD45B
CD53
CXCR6
DCAF11



ANXA2
U2AF1
RPL23
SRSF7



CD28
TSEN15
CDC20
PPP1R11



FCGRT
AOAH
NASP
ZNF207



LINC00649
HLA-DPA1
RHOF
FURIN



PHGR1
OBFC1
CDKN3
WDR83OS










Applicants were able to determine the cell of origin for genes associated with disease by genome wide association (GWAS) (e.g., IBD). Applicants show heatmaps for GWAS genes expressed in each cell type (FIGS. 20-24). Applicants show a heatmap for G-protein coupled receptors (GPCR), genes involved in cell-cell interactions, and in epithelial cells in the gut cell types. (FIGS. 21, 22 and 23). Key genes are highlighted in FIG. 23. FIG. 24 shows that genes associated with other disease indications can be localized to specific cell types in the atlas.
Applicants also show that the atlas may be used to determine cell-cell interaction mechanisms within the gut (FIG. 25). Finally, Applicants show that fibroblasts that support the stem cell niche can be identified using the atlas (FIG. 26).
Example 11—the Tuft Cell is Dynamically Maintained by the Stem Cell Lineage
Using a cell lineage system Applicants show that tuft cells are maintained by basal stem cells in the trachea and not club cells (FIG. 31-33).
Example 12—Materials and Methods for Gut
Mice
All mouse work was performed in accordance with the Institutional Animal Care and Use Committees (IACUC) and relevant guidelines at the Broad Institute and MIT, with protocols 0055-05-15 and 0612-058-15. Seven to ten weeks old female or male C57BL/6J wild-type, Lgr5-EGFP-IRES-CreERT2 (Lgr5-GFP), MHCII-KO, Foxp3-DTR, B6 Nude and TCRβ-KO mice, obtained from the Jackson Laboratory (Bar Harbor, ME) or Gfi1beGFP/+ (Gfi1b-GFP) were housed under specific-pathogen-free (SPF) conditions at the Broad Institute, MIT or at the Harvard T. H. Chan School of Public Health animal facilities. MHCII-EGFP was obtained from Hidde Ploegh's lab and Lgr5-tdTomato-MHCII-EGFP and H2-Ablfl/fl-Villin-CreERT2 (MHCIIDgut) mice were crossed for this study. All mice were housed under specific-pathogen-free (SPF) conditions at either the Broad Institute or MIT animal facilities; infection experiments were conducted at the laboratory of Dr. HN Shi, maintained under specific pathogen-free conditions at Massachusetts General Hospital (Charlestown, MA), with protocol 2003N000158. BrdU and EDU incorporation: EdU was injected intraperitoneally (IP) into Lgr5-GFP mice at 100 mg kg−1 for 2 or 4 hours before tissue collection.
Salmonella enterica and H. polygyrus infection. C57BL/6J mice (Jackson Laboratory) were infected with 200 third-stage larvae of H. polygyrus or 108 Salmonella enterica at the laboratory of Dr. HN Shi, maintained under specific pathogen-free conditions at Massachusetts General Hospital (Charlestown, MA), with protocol 2003N000158. H. polygyrus was propagated as previously described76. Mice were sacrificed 3 and 10 days after H. polygyrus infection. For the MHCII blocking experiment, mice infected with H. polygyrus were injected with 500 g of blocking anti-mouse MHCII antibody (BioXCell) or Rat IgG2b isotype control (BioXCell) one-day prior to and for 2 consecutive days after H. polygyrus infection. For Salmonella enterica, mice were infected with a naturally streptomycin-resistant SL1344 strain of S. typhimurium (108 cells) as described76 and were sacrificed 48 hours after infection.
Foxp3-DTR. Foxp3 and wild-type C57BL/6J mice were injected intraperitoneally with diphtheria toxin (DT) at 22.5 ng/g body weight every other day for one week and then sacrificed.
MHCII deletion in intestinal epithelial cells. Cre activity was induced in 7-10 weeks old mice by intraperitoneal injection (IP) of Tamoxifen (SIGMA), diluted in corn oil, 4 mg per injection, 3 times, every other day. Mice were sacrificed 10 days after the first injection.
Cell Dissociation and Crypt Isolation
Crypt isolation. The small intestine of C57BL/6J wild-type, Lgr5-GFP or Gfi1b-GFP mice was isolated and rinsed in cold PBS. For all mice, crypts were isolated from the whole small intestine or the duodenum, jejunum and ileum compartment to account for regional distribution of Lgr5+ stem cells. The small intestine was extracted and rinsed in cold PBS. The tissue was opened longitudinally and sliced into small fragments roughly 0.2 cm long. The tissue was incubated in 20 mM EDTA-PBS on ice for 90 min, while shaking every 30 min. The tissue was then shaken vigorously and the supernatant was collected as fraction 1 in a new conical tube. The tissue was incubated in fresh EDTA-PBS and a new fraction was collected every 30 min. Fractions were collected until the supernatant consistent almost entirely of crypts. The final fraction (enriched for crypts) was washed twice in PBS, centrifuged at 300 g for 3 min, and dissociated with TrypLE express (Invitrogen) for 1 min at 37° C. The single cell suspension was then passed through a 40 μm filter and stained for FACS sorting for either scRNA-seq method (below) or used for organoid culture.
FAE isolation. Epithelial cells from the follicle associated epithelium were isolated by extracting small sections (0.5 cm) containing Peyer's patches from the small intestine of C57Bl/6J or Gfi1beGFP/+ mice.
Immune cell isolation. Immune cells from the Lamina Propria were isolated enzymatically by incubating the small intestine with Liberase™ (100 ug/mL, Sigma) and DNaseI (100 ug/mL, Sigma) for 30 min at 37° C. Immune cells were also isolated from the mesenteric lymph nodes (mLN). Cells were then incubated with CD3, CD4, CD45, or CD11b FACS-labeled antibodies and sorted for scRNA-seq.
Cell Sorting
For plate-based scRNA-seq experiments, a fluorescence-activated cell sorting (FACS) machine (Astrios) was used to sort a single cell into each well of a 96-well PCR plate containing 5 μl of TCL buffer with 1% 2-mercaptoethanol. For EpCAM+ isolation, cells were stained for 7AAD− (Life Technologies), CD45− (eBioscience), CD31− (eBioscience), Ter119− (eBioscience), EpCAM+ (eBioscience), and for specific epithelial cells Applicants also stained for CD24+/− (eBioscience) and c-Kit+/− (eBioscience). To enrich for specific IEC populations, cells were isolated from Lgr5-GFP mice, stained with the antibodies mentioned above and gated on GFP-high (stem cells), GFP-low (TAs), GFP−/CD24+/c-Kit+/− (secretory lineages) or GFP−/CD24−/EpCAM+ (epithelial cells). For Tuft-2 isolation, epithelial cells from 3 different mice were stained as above only this time Applicants used EpCAM+/CD45+ and sorted 2000 single cells. A population control of 200 cells was sorted into one well and a no-cell control was sorted into another well. After sorting, the plate was sealed tightly with a Microseal F and centrifuged at 800 g for 1 min. The plate was immediately frozen on dry ice and kept at −80° C. until ready for the lysate cleanup. Bulk population cells were sorted into an Eppendorf tube containing 100 μl solution of TCL with 1% 2-mercaptoethanol and stored at −80° C.
For droplet-based scRNA-seq, cells were sorted with the same parameters as described for plate-based scRNA-seq, but were sorted into an Eppendorf tube containing 50 μl of 0.4% BSA-PBS and stored on ice until proceeding to the GemCode Single Cell Platform or the Chromium Single Cell 3′ Library.
Plate-Based scRNA-Seq
Single cells: Libraries were prepared using a modified SMART-Seq2 protocol as previously reported32. Briefly, RNA lysate cleanup was preformed using RNAClean XP beads (Agencourt) followed by reverse transcription with Maxima Reverse Transcriptase (Life Technologies) and whole transcription amplification (WTA) with KAPA HotStart HIFI 2× ReadyMix (Kapa Biosystems) for 21 cycles. WTA products were purified with Ampure XP beads (Beckman Coulter), quantified with Qubit dsDNA HS Assay Kit (ThermoFisher), and assessed with a high sensitivity DNA chip (Agilent). RNA-seq libraries were constructed from purified WTA products using Nextera XT DNA Library Preparation Kit (Illumina). On each plate, the population and no-cell controls were processed using the same method as the single cells. The libraries were sequenced on an Illumina NextSeq 500.
Bulk samples: Bulk population samples were processed by extracting RNA with RNeasy Plus Micro Kit (Qiagen) per the manufacturer's recommendations, and then proceeding with the modified SMART-Seq2 protocol following lysate cleanup, as described above.
Droplet-Based scRNA-Seq
Single cells were processed through the GemCode Single Cell Platform using the GemCode Gel Bead, Chip and Library Kits (10× Genomics, Pleasanton, CA), or the Chromium Single Cell 3′ Library, Gel Bead and Chip Kits (10× Genomics, Pleasanton, CA), following the manufacturer's protocol. Briefly, single cells were sorted into 0.4% BSA-PBS. An input of 6,000 cells was added to each channel of a chip with a recovery rate of 1,500 cells. The cells were then partitioned into Gel Beads in Emulsion (GEMs) in the GemCode instrument, where cell lysis and barcoded reverse transcription of RNA occurred, followed by amplification, shearing and 5′ adaptor and sample index attachment. Libraries were sequenced on an Illumina NextSeq 500.
Div-Seq
Lgr5-GFP mice were intraperitoneally (IP) injected with 100 mg kg, EdU (Click-iT Plus EdU Pacific Blue Flow Cytometry Assay Kit, Thermo Fisher Scientific) for 2 hours and then sacrificed. Crypts were isolated as described above and Lrg5hi cells were FACS sorted into PBS, spun down to remove the supernatant, flash frozen and stored in −80° C. Nuclei were then isolated using EZ Prep NUC-101 (Sigma) per manufacturer's recommendation, and then incubated in the Click-iT Cocktail per manufacturer's recommendations for 30 min, washed in 1% BSA-PBS and counterstained with Vybrant DyeCycle Ruby stain (Thermo Fisher Scientific) for 15 min. Nuclei were then individually sorted into the wells of 96 well plates with TCL+1% 2-mercaptoethanol as described before14 using FACS, based on positive Ruby and either EdUhigh or EdUlow. Plate-based single nucleus RNA-seq (snRNA-Seq) was then performed as described above for scRNA-seq.
Immunofluorescence and Single-Molecule Fluorescence In Situ Hybridization (smFISH)
Immunofluorescence (IFA) and immunohistochemistry (IHC): Staining of small intestinal tissues was conducted as described13. Briefly, tissues were fixed for 14 hours in formalin, embedded in paraffin and cut into 5 μm thick sections. Sections were deparaffinized with standard techniques, incubated with primary antibodies overnight at 4° C. and then with secondary antibodies at RT for 30 min. Slides were mounted with Slowfade Mountant+DAPI (Life Technologies, S36964) and sealed.
Single-molecule fluorescence in situ hybridization (smFISH): RNAScope Multiplex Fluorescent Kit (Advanced Cell Diagnostics) was used per manufacturer's recommendations with the following alterations. Target Retrieval boiling time was adjusted to 12 minutes and incubation with Protease IV at 40° C. was adjusted to 8 minutes. Slides were mounted with Slowfade Mountant+DAPI (Life Technologies, S36964) and sealed.
Combined IFA and smFISH was implemented by first performing smFISH as described above, with the following changes. After Amp 4, tissue sections were washed in washing buffer, incubated with primary antibodies overnight at 4° C., washed in 1×TBST 3 times and then incubated with secondary antibodies for 30 min at room temperature. Slides were mounted with Slowfade Mountant+DAPI (Life Technologies, S36964) and sealed.
Antibodies and Probes
Antibodies usedforIFA: rabbit anti-DCLK1 (1:200, Abcam ab31704), rat anti-CD45 (1:100, Biolegend 30-F11), goat anti-ChgA (1:100, Santa Cruz Sc-1488), mouse anti-E-cadherin (1:100, BD Biosciences 610181), rabbit anti-RELMP (1:200, Peprotech® 500-p215), rat anti-Lysozyme (1:200, Dako A0099) and anti-mouse I-A/I-E (1:100, Biolegend 107601). Alexa Fluor 488-, 594-, and 647-conjugated secondary antibodies were used and obtained from Life Technologies.
Probes used for single-molecule RNAscope (Advanced Cell Diagnostics): Cck (C1), Ghrl (C2), GCG (C3), Tph1 (C1), Reg4 (C2), TSLP (C1), Ptprc (C1) andMptx2 (C1). Probes used for single-molecule RNAscope (Advanced Cell Diagnostics): Lgr5 (C1, C3), Cyp2e1 (C2), Psrc1 (C1), Fgfr4 (C2), Cenpf (C3), mKi67 (C1, C3).
Th Cell Polarization In Vitro
CD4+naîve (CD44loCD62L+ CD25−) T cells were isolated from spleen and lymph nodes of 7-10 weeks old C57BL/6J mice using flow cytometry cell sorting. The purity of isolated T cell populations routinely exceeded 98%. Naï{umlaut over (v)}e T cells were stimulated with plate-bound anti-CD3 (145-2C11, 1 mg/ml) and anti-CD28 (PV-1, 1 mg/ml) and polarizing cytokines (Th1: 4 ng/ml IL-12; Th2: 4 ng/ml IL-4; Th17: 10 ng/ml IL-6, 2 ng/ml TGF-β1; iTreg: 5 ng/ml TGF-β1; all cytokines from R&D).
Intestinal Organoid Cultures
Organoid cultures. Following crypt isolation from the whole small intestine142, the single cell suspension was resuspended in Matrigel® (BD Bioscience) with 1 μM Jagged-1 peptide (Ana-Spec). Roughly 300 crypts embedded in 25 μl of Matrigel® were seeded onto each well of a 24-well plate. Once solidified, the Matrigel® was incubated in 600 μl culture medium (Advanced DMEM/F12, Invitrogen) with streptomycin/penicillin and glutamax and supplemented with EGF (100 ng/mL, Peprotech®), R-Spondin-1 (600 ng/mL, R&D), Noggin (100 ng/mL, Peprotech®), Y-276432 dihydrochloride monohydrate (10 μM, Tocris™)), N-acetyl-1-cysteine (1 μM, Sigma-Aldrich), N2 (1×, Life Technologies), B27 (1×, Life Technologies) and Wnt3A (25 ng/mL, R&D Systems). Fresh media was replaced on day 3, and organoids were passaged by dissociation with TrypLE and resuspended in new Matrigel® on day 6 with a 1:3 split ratio. For selected experiments, organoids were additionally treated with RANKL (100 ng/mL, Biolegends). For T helper cell co-culture experiments, organoids were cultured with Th1, Th2, Th17 or iTregs. Roughly 10,000 T helper cells were added to each well of 500 organoids and were supplemented either to the medium or suspended in the Matrigel®. Treated organoids were dissociated and subjected to scRNA-seq using both methods.
Cytokine treated organoids. Organoids were additionally treated with 0.5U/ml IFNγ, 20 ng/ml IL-13, 20 ng/ml IL-17A or 10 ng/ml IL-10 in the culture medium for 3 days. Re-seeding after cytokine treatment. 500 organoids/well were treated with cytokines, as in the cytokine treated organoids above, collected after 3 days and then re-seeded at 500 organoids/well in media without cytokines. Each day, images were taken at 2× magnification and quantification of organoids number was performed with the ImageJ software.
Two-Photon Intra-Vital Microscopy (2P-IVM) of T Cells and ISCs
To generate gut-homing T cells visualized by 2P-IVM, a combination of modified protocols143,144 was used. CD4+ T cells were isolated from spleen, pLN and mLN from β-actin-RFP mice using a MACS CD4 T cell positive-selection kit (Miltenyi clone L3T4) following the manufacturer's instructions. Plates were pre-treated with 5 ug/mL anti-CD3 (clone 145-2C11) and 1 ug/mL anti-CD28 (clone 37.51) and 1 Ř106 CD4+ T cells were added to each well for a final volume of 2.5 mL in complete RPMI1640 media supplemented with all-trans Retinoic Acid (100 nM, Sigma R2625). The T cells were cultured for 96 hours before replacing half of the volume with fresh media containing 20U/mL of rIL-2 and then cultured for another 48 hours. Before adoptive transfer into Lgr5-GFP hosts, the gut-homing phenotype was validated with flow cytometry for α4β7 and CCR9 expression. 1 Ř107 cells were then transferred into recipient mice for two hours, and treated with 20 ug of anti-CD3 (clone 2C11). 2P-IVM was performed 72 hours following transfer. The small intestine was surgically exposed through a laparotomy incision. Anesthetized mice were placed on a custom-built stage with a loop of the intact small intestine fixed to a temperature-controlled metallic support to facilitate exposure of the serosal aspect to a water-immersion 20× objective (0.95 numerical aperture) of an upright microscope (Prairie Technologies). A Mai Tai Ti:sapphire laser (Spectra-Physics) was tuned between 870 nm and 900 nm for multiphoton excitation and second-harmonic generation. For dynamic analysis of cell interaction in four dimensions, several X/Y sections (512×512) with Z spacing ranging from 2 μm to 4 μm were acquired every 15-20 seconds with an electronic zoom varying from 1× to 3×. Emitted light and second harmonic signals were directed through 450/80-nm, 525/50-nm and 630/120-nm bandpass filters and detected with non-descanned detectors. Post-acquisition image analysis, volume-rendering and four-dimensional time-lapse videos were performed using Imaris software (Bitplane scientific software).
Analysis
Pre-processing of droplet (10×) scRNA-seq data. Demultiplexing, alignment to the mm10 transcriptome and UMI-collapsing were performed using the Cellranger toolkit (version 1.0.1) provided by 10× Genomics. For each cell, Applicants quantified the number of genes for which at least one read was mapped, and then excluded all cells with either fewer than 800 detected genes. Expression values Ei,j for gene i in cell j were calculated by dividing UMI count values for gene i by the sum of the UMI counts in cellj, to normalize for differences in coverage, and then multiplying by 10,000 to create TPM-like values, and finally calculating log2(TPM+1) values. Batch correction was performed using ComBat78 as implemented in the R package sva79, using the default parametric adjustment mode. The output was a corrected expression matrix, which was used as input to further analysis.
Selection of variable genes was performed by fitting a generalized linear model to the relationship between the squared co-efficient of variation (CV) and the mean expression level in log/log space, and selecting genes that significantly deviated (P<0.05) from the fitted curve, as previously described80.
Pre-processing of SMART-Seq2 scRNA-seq data. BAM files were converted to merged, demultiplexed FASTQs using the Illumina provided Bcl2Fastq software package v2.17.1.14. Paired-end reads were mapped to the UCSC hgl9 human transcriptome using Bowtie81 with parameters “-q --phred33-quals-n 1-e 99999999-1 25-I1-X 2000-a -m 15-S -p 6”, which allows alignment of sequences with one mismatch. Expression levels of genes were quantified as using transcript-per-million (TPM) values calculated by RSEM82 v1.2.3 in paired-end mode. For each cell, Applicants quantified the number of genes for which at least one read was mapped, and then excluded all cells with either fewer than 3,000 detected genes or a transcriptome-mapping of less than 40%.
Selection of variable genes was performed by fitting a generalized linear model to the relationship between the squared coefficient of variation (CV) and the mean expression level in log/log space, and selecting genes that significantly deviated (p<0.05) from the fitted curve, as previously described80.
Dimensionality reduction usingPCA and tSNE. Applicants restricted the expression matrix to the subsets of variable genes and high quality cells noted above, and values were centered and scaled before input to PCA, which was implemented using the R function ‘prcomp’ from the ‘stats’ package for the SMART-seq2 dataset. For the droplet dataset, Applicants used a randomized approximation to PCA, implemented using the ‘rpca’ function from the ‘rsvd’ R package, with the parameter k set to 100. This low-rank approximation was used as it is several orders of magnitude faster to compute for very wide matrices. Given that many principal components (PCs) explain very little of the variance, the signal to noise ratio can be substantially improved by selecting a subset of n ‘significant’ PCs. After PCA, significant PCs were identified using the permutation test described in83, implemented using the ‘permutationPA’ function from the ‘jackstraw’ R package. This test identified 13 and 15 significant PCs in the 10× and SMART-Seq2 datasets of FIG. 1, respectively. Only scores from these significant PCs were used as the input to further analysis.
For visualization, the dimensionality of the datasets was further reduced using the ‘Barnes-hut’ approximate version of the t-distributed stochastic neighbor embedding (tSNE)84,85. This was implemented using the ‘Rtsne’ function from the ‘Rtsne’ R package using 20,000 iterations and a perplexity setting that ranged from 10 to 30 depending on the size of the dataset. Scores from the first n PCs were used as the input to tSNE, where n was determined for each dataset using the permutation test described above.
Identifying cell differentiation trajectories using diffusion maps. Prior to running diffusion-map dimensionality reduction Applicants selected highly variable genes in the data as follows. Applicants first fit a null model for baseline cell-cell gene expression variability in the data based on a power-law relationship between coefficient of variation (CV) and the mean of the UMI-counts of all the expressed genes, similar to86. Next, Applicants calculated for each gene the difference between the value of its observed CV and that expected by the null model (CVdiff). The histogram of CVdiff exhibited a “fat tail”. Applicants calculated the mean μ and standard deviation a of this distribution, and selected all genes with CVdiff>μ+1.67σ, yielding 761 genes that were used for further analysis.
Applicants performed dimensionality reduction using the diffusion map approach40. Briefly, a cell-cell transition matrix was computed using the Gaussian kernel where the kernel width was adjusted to the local neighborhood of each cell, following87. This matrix was converted to a Markovian matrix after normalization. The right eigenvectors vi(i=0, 1, 2, 3, . . . ) of this matrix were computed and sorted in the order of decreasing eigenvalues λi(i=0, 1, 2, 3, . . . ) after excluding the top eigenvector v0, corresponding to λ0=1 (which reflects the normalization constraint of the Markovian matrix). The remaining eigenvectors vi(i=1,2 . . . ) define the diffusion map embedding and are referred to as diffusion components (DCk(k=1, 2, . . . )). Applicants noticed a spectral gap between the λ4 and the λ5, and hence retained DC1-DC4.
Removing contaminating immune cells and doublets. Although cells were sorted prior to sequencing using EpCAM, a small number of contaminating immune cells were observed in the 10× dataset. These 264 cells were removed by an initial round of unsupervised clustering (density-based clustering of the tSNE map using ‘dbscan’88 from the R package ‘fpc’) as they formed an extremely distinct cluster. In the case of the SMART-Seq2 dataset, several cells were outliers in terms of library complexity, which could possibly correspond to more than one individual cell per sequencing library or ‘doublets’. These cells were then removed by calculating the top quantile 1% of the distribution of genes detected per cell and removing any cells in this quantile.
Cluster analysis (e.g., k-NNgraph based clustering). To cluster single cells by their expression, Applicants used an unsupervised clustering approach, based on the Infomap graph-clustering algorithm25, following approaches recently described for single-cell CyTOF data89 and scRNA-seq26. Briefly, Applicants constructed a k-nearest-neighbor (k-NN) graph on the data using, for each pair of cells, the Euclidean distance between the scores of significant PCs to identify k nearest neighbors. The parameter k was chosen to be consistent with the size of the dataset.
Specifically, k was set to 200 and 80 for the droplet dataset of 7,216 cells (FIG. 1A), the SMART-Seq2 dataset of 1,522 cells (FIG. 8A). RANKL-treated organoids contained 5434 cells and k was set to 200, while the Salmonella and H. polygyrus dataset contained 9842 cells and k was set to 500. For cluster analyses within celltypes, specifically the EEC and tuft cell subsets, Applicants used the Pearson correlation distance instead of Euclidean, and set k=15, k=30 and k=40 for the enteroendocrine subtypes (533 cells), and 166 and 102 tuft cells in the 10× and SMART-Seq2 datasets respectively.
The nearest neighbor graph was computed using the function ‘nng’ from the R package ‘cccd’. The k-NN graph was then used as the input to Infomap25, implemented using the ‘infomap.community’ function from the ‘igraph’ R package.
Detected clusters were mapped to cell-types or intermediate states using known markers for intestinal epithelial cell subtypes. (7 FIGS. 7E and 7 FIG. 8A). In the case of the enteroendocrine cell (EEC) sub-analysis (FIG. 3), any group of EEC progenitor clusters with average pairwise correlations between significant PC scores r>0.85 was merged, resulting in 4 clusters, which were annotated as Prog. (a) based on high levels of Ghrl and Prog. (early), (mid) and (late)—based on decreasing levels of stem (Slc12a2, Ascl2, Axin2) and cell-cycle genes and increasing levels of known EEC regulatory factors (Neurod1, Neurod2 and Neurog3) from early to late (FIG. 11C). For the SMART-Seq2 dataset, two clusters expressing high levels of stem cell marker genes (FIG. 8A) were merged to form a ‘Stem’ cluster and two other clusters were merged to form a ‘TA’ cluster.
For the cluster analysis of the follicle-associated epithelium (FAE) dataset of 4700 cells, the M cells were exceedingly rare (0.38%), and therefore the ‘ClusterDP’ method90 was used to identify them, as it empirically performed better than the kNN-graph algorithm on this dataset containing such a rare subgroup. As with the kNN methods, ClusterDP was run using significant (p<0.05) PC scores (19 in this case) as input, and was implemented using the ‘findClusters’ and ‘densityClust’ functions from the ‘densityClust’ R package using parameters rho=1.1 and delta=0.25.
Detected clusters were annotated by cell types or states using known markers for IEC subtypes. Specifically, for each known epithelial type Applicants selected five canonical marker genes (e.g., Lgr5, Ascl2, Slc12a2, Axin2 and Olfm4 for stem cells, or Lyz1, Defa17, Defa22, Defa24 and Ang4 for Paneth cells), and scored all clusters for their expression (see below for signature scoring procedure).
Extracting rare cell-types for further analysis. The initial clustering of the whole-gut dataset (7,216 cells, FIG. 1B) showed a cluster of 310 EECs and 166 tuft cells. The tuft cells were taken ‘as is’ for the sub-analysis (FIG. 4A-B), while the EECs were combined with a second cluster of 239 EECs identified in the regional dataset (FIG. 10H) for a total of 533 EECs. A group of 16 cells co-expressed EEC markers Chga, Chgb with markers of Paneth cells including Lyz1, Defa5 and Defa22, and were therefore interpreted as doublets, and removed from the analysis, leaving 533 EECs, which were the basis for the analysis in FIG. 3. To compare expression profiles of enterocytes from proximal and distal small intestine (FIG. 10I), the 1,041 enterocytes identified from 11,665 cells in the regional dataset (FIG. 10H) were used.
Defining cell-type signatures. To identify maximally specific genes for cell-types, Applicants ran differential expression tests between each pair of clusters for all possible pairwise comparisons. Then, for a given cluster, putative signature genes were filtered using the maximum FDR Q-value and ranked by the minimum log 2 fold-change. The minimum fold-change and maximum Q-value represent the weakest effect-size across all pairwise comparisons, therefore this a stringent criterion. Cell-type signature genes shown in (FIG. 1C, FIG. 14H, and Tables 3-5 and 9) were obtained using a maximum FDR of 0.05 and a minimum log 2 fold-change of 0.5.
In the case of signature genes for subtypes within cell-types (FIG. 3B, FIG. 4B and FIG. 13B), an aggregate p-value (across the pairwise tests) for enrichment was computed using Fisher's method—a more lenient criterion than simply taking the maximum p-value—and a maximum FDR Q-value of 0.01 was used, along with a cutoff of minimum log 2 fold-change of 0.25 for tuft cell subsets (FIG. 4B, FIG. 13B and Table 8) and 0.1 for enteroendocrine subsets (FIG. 3B, Table 7). Due to low cell numbers (n=18), this Fisherp-value was also used for the in vivo M cell signature, with an FDR cutoff of 0.001 (FIG. 5D), Table 9). Marker genes were ranked by minimum log 2 fold-change. Differential expression tests were carried out using the Mann-Whitney U-test (also known as the Wilcoxon rank-sum test) implemented using the R function ‘wilcox.test’. For the infection experiments (FIG. 6), Applicants used a two part ‘hurdle’-model to control for both technical quality and mouse-to-mouse variation. This was implemented using the R package MAST91, and p-values for differential expression were computed using the likelihood-ratio test. Multiple hypothesis testing correction was performed by controlling the false discovery rate92 using the R function p.adjust.
Gene sets associated with G1/S and G2/M phases of the cell-cycle were downloaded from cell.com/cms/attachment/2051395126/2059328514/mmc2.xlsx [Macosko 2015]. A set of cell-cycle genes to assess overall proliferation (see below for scoring procedure) was defined as the union of the G1/S and G2/M sets.
Scoring cells using signature gene sets. To obtain a score for a specific set of n genes in a given cell, a ‘background’ gene set was defined to control for differences in sequencing coverage and library complexity between cells in a manner similar to29. The background gene set was selected to be similar to the genes of interest in terms of expression level. Specifically, the 10n nearest neighbors in the 2-D space defined by mean expression and detection frequency across all cells were selected. The signature score for that cell was then defined as the mean expression of the n signature genes in that cell, minus the mean expression of the IOn background genes in that cell.
Estimates of cell type samplingfrequencies. For each cell-type the probability of observing at least n cells in a sample of size k is modeled using the cumulative distribution function of a negative binomial NBcdf(k, n, p), where p is the relative abundance of this cell type. For m cell types with the same parameter p the overall probability of seeing each type at least n times is NBcdf(k; n, p){circumflex over ( )}m. Such analysis can now be performed with user specified parameters at satijalab.org/howmanycells.
EEC dendrogram. Average expression vectors were calculated for all 12 EEC subset clusters, using log2(TPM+1) values, and restricted to the subset of 1,361 genes identified as significantly variable between EEC susbsets (p<0.05), as described above. The average expression vectors including these genes were hierarchically clustered using the R package pvclust (Spearman distance, ward.D2 clustering method), which provides bootstrap confidence estimates on every dendrogram node, as an empirical p-value over 100,000 trials (FIG. 12A).
Cell-type specific TFs, GPCRs and LRRs. A list of all genes identified as acting as transcription factors in mice was obtained from AnimalTFDB 93, downloaded from: bioguo.org/AnimalTFDB/BrowseAllTF.php?spe=Mus_musculus. The set of G-protein coupled receptors (GPCRs) was obtained from the UniProt database, downloaded from: uniprot.org/uniprot/?query=family %3A %22 g+protein+coupled+receptor %22+AND+organis m %3A %22Mouse+%5B10090%5D %22+AND+reviewed %3Ayes&sort=score. Functional annotations for each protein (FIG. 8D) were obtained from the The British Pharmacological Society (BPS) and the International Union of Basic and Clinical Pharmacology (IUPHAR) data, downloaded from: guidetopharmacology.org/GRAC/GPCRListForward?class=A. The list of leucine-rich repeat proteins (LRRs) was taken from 94. To map from human to mouse gene names, human and mouse orthologs were downloaded from Ensembl (latest release 86, ensembl.org/biomart/martview), and human and mouse gene synonyms from NCBI (ftp.ncbi.nlm.nih.gov/gene/DATA/GENE_INFO/Mammalia/). For each human LRR gene, all human synonyms were mapped to the orthologous gene in mouse using the ortholog list, and mouse gene names were mapped to those in the single-cell data using the synonym list.
Cell-type enriched TFs, GPCRs and LRRs were then identified by intersecting the list of genes enriched in to each cell type with the lists of TFs, GPCRs and LRRs defined above. Cell-type enriched genes were defined using the SMART-Seq2 dataset, as those with a minimum log 2 fold-change of 0 and a maximum FDR of 0.5, retaining a maximum of 10 genes per cell type in FIG. 1F, FIG. 1G, and FIG. 8E, while complete lists are provided in Table 6. In addition, a more extensive panel of cell-type specific GPCRs was identified (FIG. 8D) by selecting a more lenient threshold. This was achieved by comparing each cell-type to all other cells, instead of the pairwise comparisons described in the previous section, and selecting all GPCR genes differentially expressed (FDR<0.001).
Testing for changes in cell type proportions. Applicants model the detected number of each cell-type in each analyzed mouse as a random count variable using a Poisson process. The rate of detection is then modeled by providing the total number of cells profiled in a given mouse as an offset variable, while the condition of each mouse (treatment or control) was provided as a covariate. The model was fit using the R command ‘glm’ from the ‘stats’ package. The p-value for the significance of the effect produced by the treatment was then assessed using a Wald test on the regression coefficient.
In the case of the assessment of the significance of spatial distributions of enteroendocrine (EEC) subsets (FIG. 3E), the comparison involved more than two groups. In particular, the null hypothesis was that the proportion of each EEC subset detected in the three intestinal regions (duodenum, jejunum, and ileum) was equal. To test this hypothesis Applicants used analysis of variance (ANOVA) with a χ2-test on the Poisson model fit described above, implemented using the ‘anova’ function from the ‘stats’ package.
Specifically, given that m and n total cells (of all cell types) are sequenced in a treatment and control condition respectively, Applicants test, for a given cell type, whether the number of k and q of observed cells of type C in total and treatment condition respectively, significantly deviates from a null model given by the hypergeometric distribution. The probability of observing these values was calculated using the R function ‘phyper’ from the ‘stats’ package, using the command:

    
    
        P=phyper(q, k, m, n)
        and was reported as a hypergeometric p-value.
    
    


Gene set enrichment and GO analysis. GO analysis was performed using the ‘goseq’ R package95, using significantly differentially expressed genes (FDR<0.05) as target genes, and all genes expressed with log2(TPM+1) >3 in at least 10 cells as background.
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Example 13—an Atlas of the Airway Epithelial Hierarchy Reveals CFTR-Expressing Ionocytes
INTRODUCTION
The airways are responsible for conducting oxygen from the atmosphere to the distal gas-exchanging alveoli, and are the locus of major diseases including asthma, COPD, and cystic fibrosis. The cells of epithelium include basal stem cells, secretory club cells, and ciliated cells that sweep debris out of the airway1. Rare cell types such as neuroendocrine (NE), goblet, and tuft cells have only recently been investigated, and their functions remain poorly understood. Diseases of the airway occur at distinct sites along the respiratory tree. Such localized disease presentations have been attributed to physical factors governing the deposition of inhaled particulates, toxins, smoke and allergens to particular regions of the airway2. An open question is whether disease heterogeneity is similarly a reflection of intrinsic cellular heterogeneity along the airway tree. Single-cell RNA-seq (scRNA-seq) opens the way to address these questions. Early scRNA-seq studies such as LungMAP3 have demonstrated the ability to probe epithelial cell-type diversity and lineage hierarchy4 in the developing lung. Here, Applicants combined massively-parallel single-cell RNA-seq and in vivo lineage tracing to study the cellular composition and hierarchy of the adult murine tracheal airway epithelium. Applicants now demonstrate that a finer comprehensive taxonomy of the cellular composition of this airway epithelium and its developmental hierarchies identifies new cell types, new developmental paths, and reframes the understanding of both cystic fibrosis, a prototypical Mendelian disease, and a complex multigenic disease like asthma.
Results
A Single-Cell Census Reveals New Disease-Associated Cell Types
Applicants initially profiled 7,491 high quality individual airway EpCAM+ epithelial cells from the tracheas of either C57BL/6 wild type mice (n=4) or FOXJ1-GFP ciliated cell reporter mice (n=2; Methods), using two complementary single-cell approaches: massively parallel droplet-based 3′ scRNA-seq (k=7,193 cells) and deeper, full-length scRNA-seq (k=301 cells) (FIG. 37A, Methods, FIG. 43A,C).
Applicants partitioned the cells into seven distinct clusters5,6 (FIG. U37B-D, Methods), annotated post-hoc by the expression of known marker genes (FIG. 43D). Each cluster mapped one-to-one with a distinct cell type: three known major cell types (basal, club, and ciliated), three known rare cell types (tuft, NE, and goblet cells), and one additional cluster (FIG. 37B), whose expression signature was distinct from any known airway epithelial cell type, suggesting that this may be a previously unrecognized population. Applicants termed this cell type the pulmonary ionocyte, because of a conserved expression pattern with ionocytes, specialized cells that function to regulate ion transport and pH in freshwater fish skin and gill epithelia, Xenopus skin, and the mammalian kidney and epididymis. All clusters contained cells from all mice (n=6, FIG. 43B), except for the goblet cell cluster (five of six mice) and the unannotated cluster of exceedingly rare (0.31%) cells (four of six mice). Applicants validated the assignment of each cell type cluster with high quality full-length scRNA-Seq7 of 301 EpCAM+ CD45− epithelial cells from tracheas of C57BL/6 wild-type mice (n=3; FIGS. 44 and 45A,B) obtained from either proximal (cartilage 1-4) or distal (cartilage 9-12) tracheal segments (FIG. 37A, FIG. 44D, top left). Applicants did not detect a distinct goblet cell cluster in this small dataset, consistent with their low frequency (0.85% of epithelial cells, FIGS. 44D and 45A,B).
Applicants associated novel functions with rare cell types and highlighted new cell type-specific transcription factors (TFs) by defining cell type-specific expression signatures that are congruent between the two datasets (FDR<0.01, likelihood-ratio test, FIG. 37D,E, FIG. 45B). Basal cell type-specific TFs (FIG. 37E) included the canonical TF Trp63, as well as Klf4, Klf5, and K1f10, a family known to regulate proliferation and differentiation in epithelia8. In club cells, Applicants identify Nfia and Eaf2, which are the first TFs specifically associated with this cell type. Nfia is associated with the regulation of Notch signaling, which is required for club cell identity and maintenance9. Ascl1, Ascl2, and Ascl3, which are also associated with Notch signaling10,11, are specifically enriched the rare NE, tuft, and ionocyte cells, respectively (FDR<0.0001, likelihood-ratio test). Tuft cells also expressed the known intestinal tuft cell TF Pou2f312 along with novel TFs Foxe1 and Etv1. Ionocytes were marked by the expression of Foxi113, whose ortholog is associated with ionocytes in Xenopus, as well as Foxi2. Finally, goblet cells specifically express the known goblet cell regulator Spdef as well as Foxq1, which is essential for mucin gene expression and granule content in gastric epithelia14.
Some cell type-specific signature genes have previously been identified as risk genes in Genome-Wide Association Studies (GWAS) of asthma15 (Methods, FIG. 45C-E). For example, the asthma-associated genes Cdhr3 and Rgs1315 are specifically expressed in ciliated and tuft cells, respectively (FIG. 45C-E). Cdhr3 encodes a rhinovirus receptor and is associated with severe childhood asthma exacerbations16,17, suggesting that exacerbations may be precipitated by rhinovirus infection of ciliated cells. Rgs13 was associated with asthma and IgE-mediated mast cell degranulation18; its specific expression in tuft cells (FIG. 45C-E), which play an immunomodulatory role in the intestines12, 19, 20 suggests that they may also participate in driving asthmatic inflammation.
Some cell type-specific expression programs also vary along the proximodistal axis of the airway tree, mirroring the distribution of airway pathologies. In mouse, mucous metaplasia (an excess of mucus-producing goblet cells) occurs more prominently in the distal versus proximal trachea, and is the identifying epithelial pathology of asthma21. Notch signaling is required for this mucous metaplasia in mouse models22. Applicants found that 105 genes are differentially expressed (FDR<0.05, Mann-Whitney U-test) between club cells of the proximal versus distal trachea (FIG. 38G). In particular, Muc5b23,24, Notch222, and Il13ra125, are all more prevalent in distal club cells, and all play known roles in mucous metaplasia. Indeed, when Applicants induced mucous metaplasia using recombinant murine IL-13 (rIL-13) in cultured proximal and distal airway epithelium, Applicants found a much higher induction of goblet cell differentiation in the distal epithelium, consistent with the increased expression of Il13ra1 in distal club cells (FIG. 38H,I, p<0.001, likelihood-ratio test).
A Novel Cell Population Organized in “Hillocks”
Cellular differentiation during adult tissue homeostasis in the trachea is an ongoing, asynchronous process. Applicants inferred trajectories of cell differentiation from pseudo-ordered putative transitional cells between the three common cell types (FIG. 38A,B, FIG. 46 and FIG. 47) using diffusion maps26,27. Applicants predicted which cells are in transition using curve fitting (Methods) and characterized gene programs and TFs that vary coherently along these trajectories (p<0.001, permutation test, Methods, FIG. 47). For example, the expression of the novel club cell TF Nfia diminished while the ciliated cell TF Foxj1 increased along the inferred trajectory from club to ciliated cells (FIG. 47H).
Surprisingly, the diffusion map revealed not only the canonical lineage path wherein basal cells produce club cells directly (DC1 and 2, k-555 cells, FIG. 38A,B), but also another path by which basal cells transition into club cells. This path was populated by novel transitional cells demarcated by the unique expression of Krt13 and Krt4 (FDR<10−5, likelihood-ratio test), two characteristic markers of squamous epithelia (DC2 and 3, k=1,908 cells, 38A,B). Conversely, Krt8, a prior marker of basal to luminal cell differentiation28, is broadly expressed, and does not uniquely identify the novel cells (FIG. 46A, bottom right). Applicants did not detect any cells on a direct trajectory from basal to ciliated (38A,B), supporting previous reports that club cells are the primary source of ciliated cells during homeostasis1,28.
Remarkably, the majority of Krt13+ cells are confined to discrete structures comprised of contiguous groups of stratified cells with cuboidal morphology. Unlike the majority of the pseudostratified epithelium, these structures possess no ciliated cells (FIG. 38C). Scgb1a1+Krt13+ club cells are located at the luminal surface (FIG. 38D,F). Additionally, there are scattered rare Krt13+ cells throughout the epithelium (data not shown). Trp63+Krt13+ cells are located in both the basal and intermediate strata of the structures, appearing as multiple layers of Trp63+ cells. This pattern of layered Trp63+ cells is not seen outside of the Krt13+ regions. Applicants term these small mounds of distinct presumptive progenitors and their Krt13+ club cell progeny “hillocks”. The graded decrease of Trp63 expression and the graded increase of Scgb1a1 expression in cells along the basal to luminal axis of the hillock parallel the transition of Krt13+ basal cells into Krt13+ club cells, as predicted by the pseudo-ordering analysis (FIG. 38A).
To examine turnover in these unique progenitors, Applicants administered the thymidine analog 5-ethynyl-2′-deoxyuridine (EdU) to wild-type mice to label proliferating cells (Methods). The distribution of replicating cells varied across hillocks. In aggregate, Applicants found that 7.7% (95% CI [4.8%, 10.5%]) of Krt13+ cells were EdU+ vs. 2.4% (95% CI [1.8%, 3.1%]) of Krt13− cells in the neighboring pseudostratified epithelium (FIG. 46B, p<0.0001, likelihood-ratio test, n=4 mice). Thus, the topologically distinct hillocks represent discrete zones of unique cells that replicate faster than the adjoining pseudostratified epithelium (FIG. 38E,F).
Although club cells are known to dedifferentiate into stem cells following basal cell injury29, Applicants did not find evidence of appreciable club cell dedifferentiation in hillocks under homeostatic conditions. Applicants generated an Scgb1a1-CreER LSL-tdTomato mouse strain to label hillock club cells and their progeny. After 8 weeks of homeostatic turnover, the hillock club cell lineage label was actually diluted from an initial 10.17% of all Krt13+ club cells to only 5.12% of Krt13+ club cells (FIG. 46B), consistent with ongoing cell turnover rather than dedifferentiation. This supports a model in which Trp63+Krt13+ hillock progenitor cells rapidly produce hillock club cells that are then lost.
Krt13+ hillock cells express unique gene modules associated with immunomodulation, squamous differentiation, and barrier function (FIG. 46D,E). Genes involved in squamous differentiation and the regulation of cellular adhesion and differentiation in squamous epithelia30-32 include Ecm1, S100a11, and Cldn3 (FIG. 46D). Immune modulatory genes with asthma related functions33, 34, 35 include Anxa1 and Lgals3 (FDR<10−10 likelihood-ratio test). Overall, hillocks have attributes that normally would be predicted to play a role in regenerating epithelium: rapid turnover to replace damaged cells, squamous differentiation to enhance barrier function, and immunomodulation.
High Resolution Lineage Tracing Incorporating Cellular Dynamics with Pulse-Seq
During homeostatic turnover, basal stem cells self-renew and generate club cell progenitors, which in turn generate terminally differentiated ciliated cells1,36. However, the source of rare cells is unknown. The tuft cell lineage hierarchy has not been directly assessed in the airway epithelium, but it has been suggested that Gnat3+ tuft cells in the trachea are static because they do not appreciably label with BrdU pulses37. Moreover, although prior lineage tracing has shown that Pgp9.5+ NE cells are derived from basal cells during a 6 month lineage trace28, no specific progenitor cell was identified as the immediate parent of mature NE cells.
Applicants developed a novel assay, Pulse-Seq, to monitor the generation of rare tuft cells, NE cells, and ionocytes (FIG. 39A). Pulse-Seq combines scRNA-seq and in vivo genetic lineage tracing of stem cells so that labeling of all their progeny can be monitored over a time course in the steady-state tracheal epithelium. Applicants crossed a basal cell-specific tamoxifen-inducible CreER driver to a reporter strain such that lineage-labeled basal stem cells and their subsequently labeled progeny will express membrane-localized eGFP (mG), while non-lineage-labeled cells will express membrane-localized tdTomato (mT) (Krt5-CreER/LSL-mT/mG) (Methods). Following tamoxifen-induced basal cell labeling, Applicants profiled 66,265 high quality labeled (mG+) and unlabeled (mT+) cells by scRNA-seq at day 0, 30, and 60 of homeostatic turnover (Methods; n=9 mice, 3 per time point). Applicants identified groups of cells corresponding to each of the seven epithelial cell types (Methods) and an additional group of proliferating cells, predominantly basal (FIG. 39B and FIG. 48B). For each subset, Applicants directly calculated the fraction of lineage-labeled cells at each time point (FIG. 39C,D). Applicants then used quantile regression to estimate the daily rate of change of this fraction, thereby estimating new cell generation (or the fraction of each cell-type that is produced from basal cells each day) (Methods, FIG. 39E, FIG. 48C). Confirming the specificity of the basal cell trace, at time point 0, 64.2% of the cells in the basal cluster are labeled, all non-basal cells were labeled at less than 3.3%, including less than 1.8% of the goblet, tuft, NE, and ionocyte cells (n=3 mice, FIG. 39C,D).
Cells that are not direct basal cell progeny would be expected to be labeled at low frequencies at early time points relative to those that arise directly. Indeed, in prior basal stem cell lineage traces, club cells were labeled earlier than ciliated cells. Subsequent club cell lineage traces confirmed that ciliated cells are produced from club cells in the steady-state epithelium, underscoring why basal cell lineage traces appear late in the ciliated cell population1,36. Using Pulse-Seq, Applicants showed that the fraction of labeled basal cells did not significantly change over the time course, consistent with the behavior of a self-renewing cell population (FIG. 48B). However, the fractions of labeled tuft, NE and ionocyte cells were substantially increased at day 30 and had further risen at day 60 (FIG. 39D). These rates are consistent with the club cell population at day 30 and 60 (FIG. 39D,E), suggesting that the rare cells are similarly immediate descendants of basal cells.
Applicants validated the result that basal cells are the direct parents of tuft cells using conventional in vivo lineage tracing of both basal and club cells separately along with subsequent in situ detection of tuft cells. Over a 30-day basal cell lineage trace (with Krt5-CreER/LSL-tdTomato mice), the proportion of lineage-labeled tuft cells dramatically increased (FIG. 39F), whereas club cell lineage tracing (with Scgb1a1-CreER/LSL-tdTomato mice) labeled only a modest fraction of Gnat3+ tuft cells (FIG. 48D). Thus, basal cells are the predominant source of tuft cells (FIG. 39G), while club cells may provide a minor pathway of their differentiation. Applicants similarly verify that club cells do not substantially contribute to the ionocyte or NE cell populations following club cell lineage tracing (less than 3% labeling of each, FIG. 48E,F). While the fraction of labeled goblet and ciliated cells increased over time (p<0.05 in both cases, likelihood-ratio test), fewer cells were labeled by day 30 than for other cell types (FIG. 39D), and the rate of appearance of label within goblet cells was as low as that for ciliated cells (FIG. 39E). This is consistent with a model in which goblet cells are produced from club cells (FIG. 39G).
Finally, Applicants investigated the lineage of hillock-associated club cells identified by clustering of club cells (FIG. 49A,B, Methods). The fraction of labeled hillock-specific club cells increased more rapidly than the fraction of total labeled club cells (p<0.01, rank test, FIG. 49C-E) (compare FIG. 39E and FIG. 49D). This is consistent with the frequent EdU labeling observed within hillocks (FIG. 38E).
Distinct Types of Tuft and Goblet Cells
Applicants next tested if each of the rare cell populations (tuft, NE, goblet, ionocytes) are comprised of distinct subsets, by re-clustering the cells of each rare cell type from both droplet-based datasets combined (FIG. 37B and FIG. 39B, n=15 mice). The 892 tuft cells and 468 goblet cells each partitioned into three clusters, whereas neither the 276 ionocytes nor the 726 NE cells further partitioned (data not shown), as the latter do in the intestine.
The entire tuft cell population expressed a greater number of specific GPCRs than any other cell type (FDR<0.001 likelihood-ratio test, FIG. 50A), suggestive of a sensory specialization. These included Adora1 (involved in the regulation of respiratory rate in response to hypoxia38), Gpr64 (mediation of fluid exchange in the epididymis39), and the taste receptor cell transducer Gpr11340. They also express the alarmins 1125 and Tslp (FDR<10−10, FIG. 50C), possibly linking their sensory function to the initiation of type-2 immunity in the airway, paralleling the gut12, 19, 20. Tuft cells possess unique lateral cytoplasmic extensions that traverse several cell diameters (FIG. 50D), perhaps extending their chemosensory span.
Applicants found one cluster of immature tuft cells and two clusters of mature tuft cells (FIG. 50E), which Applicants term tuft-1 and tuft-2 (FIG. 40). Cells in both the tuft-1 and tuft-2 clusters express the known tuft cell marker Trpm5, while the immature cells display low Trpm5 expression (FIG. 40A). The tuft-1 subset expresses genes that suggest a more prominent chemosensory function: elements of the taste transduction pathway (Gnb3, Gng13, Atp1b1, Fxyd641), many type II taste receptors including those implicated in airway sensing of gram-negative bacterial infection (Tas2R38)42,43 and regulation of breathing (Tas2R105, Tas2R108)44-46, and the type I taste receptor Tas1r3 (FIG. 40F). Conversely, tuft-2 cells are associated with expression of inflammation, asthma and allergy-related genes (FIG. 40B-D,F) including Mgst3 and Alox5ap, both which are necessary for leukotriene biosynthesis47,48 (FDR<0.05, hypergeometric test, FIG. 40B-D). They are also enriched, as in the gut, for the immune-cell associated Ptprc (CD45, FDR<0.1). Interestingly canonical tuft cell lineage TFs are specifically associated with the respective subsets, such as Pou2f3 (tuft-1) and Gfi1b, Spib, Sox9 (tuft-2, FDR<0.01, FIG. 40G).
Goblet cells also partitioned into one putative immature cell subset and two mature cell subsets, goblet-1 and goblet-2 (FIG. 40H and FIG. 50F-I). The most highly enriched marker across the entire goblet cell cluster was Gp2 (FIG. 37D and FIG. 43D), an M cell-specific marker in the intestinal epithelium, which binds pathogenic enterobacteria and initiates a mucosal immune response49. Goblet-1 cells are enriched for the expression of genes encoding key mucosal proteins (e.g, Tff1, Tff2, Muc5b23, FDR<0.001, likelihood-ratio test, FIG. 50G-I) and regulators of mucus secretion (e.g., Lmanll, P2rx450-52, FDR<0.1, likelihood-ratio test). Applicants validated co-expression of Tff2 and Muc5ac in goblet-1 cells by antibody staining (FIG. 50I). Goblet-2 cells are distinguished by higher expression of Dccpl, Dccp2, andDccp3 (FIG. 50H), orthologs of the lectin-like secreted protein ZG16B53, which physically aggregates bacteria54 and of Lipf, a secreted gastric lipase that hydrolyses triglycerides (FIG. 40H and FIG. 50G,H). Applicants validated that Tff2 and Lipf are unique markers of the goblet-1 and goblet-2 cells, respectively (FIG. 40H).
The Foxi1+ pulmonary ionocyte expresses CFTR in mouse and human
Foxi1+ ionocytes are a new cell population, observed as a cluster of 26 cells in the initial dataset, and confirmed independently as a 276 cell cluster in the larger Pulse-Seq dataset. Applicants validated the presence of ionocytes using a transgenic Foxi1-GFP reporter mouse strain55. Foxi1-GFP+ cells co-labeled with anti-Foxi1 antibody, confirming the fidelity of the reporter line (FIG. 51A, left column). Ionocytes are labeled by the canonical airway markers Sox2 and Ttf1 (Nkx2-1) but are not labeled by the cell-type specific markers of any of the known airway epithelial cell types, confirming their distinct identity (FIG. 51A). Cell counting in three formalin-fixed whole-mounted tracheas (FIG. 51B, Methods), showed 1,038±501 ionocytes per trachea on average (˜1% of all epithelial cells in the mouse trachea; compared to 0.36-0.42% detection by scRNA-seq).
Based on their expression signature, pulmonary ionocytes resemble evolutionarily conserved V-ATPase-rich ionocytes in other organisms, where Foxi1 orthologs specify cell identity and regulate V-ATPase expression. In the multiciliated skin of Xenopus, ionocytes are specified by Foxi113. Foxi3a and Foxi3b identify mitochondria-rich skin ionocytes in zebrafish56. Mammalian Foxi1 regulates V-ATPase in specialized cells of the inner ear, kidney, and epididymis that regulate ion transport and fluid pH57,58. Pulmonary ionocytes are similarly enriched in the expression of V-ATPase subunits Atp6v1c2 and Atp6v0d2 (FDR<0.0005, likelihood-ratio test, FIG. 41B top row, FIG. 45B) and are uniquely marked by an anti-ATP6v0d2 antibody (FIG. 41B, top row). Like tuft cells37 (FIG. 50B) and zebrafish ionocytes59, pulmonary ionocytes extend lateral processes some 10 μm-20 μm away from their cell bodies, contacting several additional epithelial cells beyond their immediate neighbors, as well as the basement membrane (FIG. 51C). Applicants speculate these processes may be involved in chemosensation and cell-to-cell communication.
Strikingly, the pulmonary ionocyte is specifically enriched for the expression of cystic fibrosis transmembrane conductance regulator (Cftr) mRNA (FDR<0.005 and FDR<10−10 in the initial and Pulse-Seq dataset respectively, likelihood-ratio test, FIG. 41A,C, FIG. 45B), accounting, on average, for 55% of detected Cftr transcripts across all single cells profiled from the mouse tracheal epithelium, despite the fact that ionocytes comprise, on average, only 0.39% of the cells analyzed. Applicants confirmed the specific enrichment of Cftr in ionocytes by qRT-PCR of prospectively isolated populations of primary ionocytes (Foxi1-GFP) vs. ciliated cells (Foxj1-GFP) or bulk EpCAM+ epithelial cells (FIG. 41D), and at the protein level by Cftr staining of Foxi1-GFP+ cells in situ (FIG. 41B, bottom row).
Of note, Foxi1-GFP+ cells are detected in murine submucosal glands (FIG. 51D), which are implicated in the pathogenesis of cystic fibrosis60 and have the highest levels of CFTR expression in human airways61. Ionocytes also specifically express Cochlin (Coch), a secreted protein that promotes antibacterial innate immunity against Pseudomonas aeruginosa and Staphylococcus aureus, the two most prominent pathogens in CF lung disease62. Deletion of Foxi1 in the mouse results in defective acidification of the epididymal lumen and male infertility63, resembling the reduced fertility phenotype observed clinically in CF.
Analysis of epithelial cells derived from Foxi1 knockout (Foxi1-KO) mice shows that Foxi1 is required for the expression of the ionocyte TF Ascl3, and the majority of Cftr expression (FIG. 41E). Thus, loss of Foxi1 causes either loss of the ionocyte itself or a significant alteration in its transcriptional state. In contrast, epithelial cells derived from the Ascl3 knockout mouse displayed only moderately reduced Foxi1 and Cftr expression (FIG. 51E).
Ionocytes Regulate Epithelial Surface Physiology
Both the amount and viscosity of mucus in the airway surface liquid (ASL) is tightly regulated and this process is necessary for effective mucociliary clearance of debris and pathogens and is disturbed in diseases such as CF64,65. Several functional assays show that the loss of Foxi1 in mouse airway epithelium alters physiologic parameters that govern mucus clearance. Applicants assessed ASL depth, mucus viscosity, and ciliary beat frequency in the murine airway epithelium of Foxi1-KO with live imaging by micro-optical coherence tomography (μOCT) and particle tracking microrheology64,66 (Methods). Strikingly, Foxi1-KO epithelia had increased optical density of airway mucus (FIG. 51F) and increased effective viscosity compared to wild type littermates (1.56+/−0.3 cP WT, 8.78+/−3.2 KO, p<0.0001 Mann Whitney U-test, FIG. 41F, left). Though modest in magnitude, these results are consistent with the increased mucus viscosity seen in animal models of cystic fibrosis64,67. Indeed, the changes in mucus viscosity are in line with those observed in primary human bronchial epithelial cells of CF patients as compared to normal individuals64,68. Ciliary beat frequency (CBF) significantly increased in the Foxi1-KO epithelium (FIG. 41F, right, 8.54+/−0.8 and 11.16+/−1.2 Hz in WT and KO, p<0.001, Mann Whitney U-test), consistent with mechanical feedback elicited by increased airway mucus viscosity69. In this model, as mechanical load increases, CBF increases until a failure threshold is reached. As with murine Cftr knockout models, neither depth (FIG. 52A) nor pH (FIG. 52B) of the ASL was significantly altered in Foxi1-KO epithelial cultures (Methods). In Cftr knockout models this lack of alteration in pH is attributed to low expression of Na+/K+ adenosine triphosphatase70 and ASL depth is preserved through high compensatory upregulation of CaCC expression71, as is also observed in murine Cftr−/− excised trachea (data not shown).
Applicants also tested whether Foxi1-KO epithelia display abnormal forskolin-induced and CFTR inhibitor (CFTRinh-172)-blocked equivalent currents (AIeq) in measurements with the transepithelial current clamp system72 (Methods). Paradoxically, Foxi1-KO mouse epithelium displayed increases in CFTRinh-172-sensitive forskolin-induced currents under asymmetrical chloride (FIG. 51G,H). The reason for increased chloride currents in the setting of reduced Cftr expression of Foxi1-KO epithelium remains unclear, although cross-talk between cAMP and Ca2+ pathways in mouse airways has been suggested to be partially responsible for a compensatory activation of forskolin-inducible currents in CF mouse airway epithelia73. The relevance of these findings and whether other non-ionocyte cell types contribute to Cftr currents in mouse airway epithelia in the setting of Foxi1 loss remain to be determined.
Since the ferret represents a more faithful model of human cystic fibrosis74, 75, Applicants investigated the role of Foxi1 in regulating CFTR. CRISPR/dCas9VP64/p65-mediated transcriptional activation of Foxi1 increased airway epithelial expression of Cftr and other ionocyte genes as assessed by qRT-PCR (Methods, FIG. 41G). Importantly, Applicants found that ferret epithelial cultures subjected to Foxi1 transcriptional activation displayed significantly increased forskolin-induced ΔIsc and CFTR (GlyH101) inhibitor ΔIsc relative to mock-transfected controls (FIG. 41H, FIG. 52C). Thus, Foxi1 regulates CFTR expression and function in ferret airway epithelium.
The Pulmonary Ionocyte is the Predominant CFTR Expressing Cell in Human Airways
Human pulmonary ionocytes are the major source of CFTR in the airway epithelium as assessed by scRNA-Seq of healthy human lung from transplant material (FIG. 41J,K) and by RNA fluorescent in situ hybridization (RNA-FISH, Methods) of FOXI1 and CFTR in human bronchial airway epithelium (FIG. 41I). Among 78,217 cells from 5 regions along the airways of human lung (AT, AW, JR, AR, MS, J W et al, unpublished data), 765 ionocytes are detected by unsupervised clustering (FIG. 41J, left), with specific expression of FOXI1, ASCL3 and CFTR (FDR<10−10, likelihood-ratio test, FIG. 41J,K), and a 14 gene cross-species consensus signature (FDR<10−5, FIG. 41K). Ionocytes are detected at approximately the same fraction (0.5-1.5%) along the proximodistal axis from the carina to the secondary bronchus. As in mouse, FOXI1 expression is specific to ionocytes (FIG. 41J, middle), and CFTR is highly expressed in those cells (FIG. 41K, middle). Applicants do note however, that much lower expression is detected in a modest portion of some club and basal cells (FIG. 41J, right). In an accompanying study, FOXI1 transcriptional activation in human airway epithelial cultures results in increased ionocyte differentiation. Additionally, numbers of human ionocytes correlated with forskolin-induced CFTR(inh)-172 inhibitable short-circuit currents (Wingert et al.).
Taken together, these results identify the ionocyte as a novel rare airway epithelial cell type with unique morphology, expression profile, and role in regulating airway epithelial surface physiology. Though the loss of pulmonary ionocytes alters physiologic parameters that are also aberrant in cystic fibrosis, defining the role of the ionocyte in cystic fibrosis or any other airways disease requires future study.
DISCUSSION
Applicants combined scRNA-seq and genetic lineage tracing to generate a revised hierarchy of the murine tracheal epithelium that includes a new cell type, the ionocyte, new subclasses of tuft and goblet cells, new transitional cells, a new structure (hillocks). Applicants also show that the basal cell is the direct parent cell of club, tuft, NE, and ionocyte cells (FIG. 42). An accompanying manuscript, using related but distinct models and computational approaches including murine tracheal regeneration models, identified similar cell types including the pulmonary ionocyte (Wingert et al.). The use of Pulse-Seq allowed Applicants to assess differentiation dynamics across multiple cell types and subtypes in a complex new lineage tree in a single internally controlled experiment. Surprisingly, Applicants show that ionocytes, NE cells, and tuft cells appear at approximately the same rate as club cells. Within hillocks, cells appear even more rapidly and are associated with squamous, barrier, and immunomodulatory features. However, their actual function and origin is mysterious.
Have Applicants catalogued the full range of biologically relevant epithelial cell types?Statistical modeling76 suggests that with 66,000 cells Applicants should have detected any discrete cell type that comprises more than 0.035% of the total cell population with 99% confidence. As a caveat, the model assumes that one recovers cells in their correct in vivo proportions, but Applicants note some cell populations may require special dissociation conditions76. Importantly, injury and disease are likely to induce plasticity, thereby revealing new lineage paths. Indeed, cell states may change with disease and cell types not evident in the homeostatic epithelium may make an appearance.
The cell census allows one to reconstruct a hypothetical new cellular narrative of lung disease (FIG. 42). Disease genes associated with common diseases with complex genetic architecture, such as asthma, or with rare Mendelian genes, such as CF, can now be associated with particular cell types and subtypes. Generating comprehensive cell atlases of the healthy and diseased human lung and airways are a critical next step77. Lineage relationships, cell types, and cell type functionality may all be different in mouse and human. Indeed, Applicants focused on the murine trachea and even here Applicants have shown functional variation along this short anatomic span. As the human respiratory tree is so large, it will be important to sample single cells along its length.
Materials and Methods for Trachea
EXPERIMENTAL METHODS
Mouse models. The MGH Subcommittee on Research Animal Care approved animal protocols in accordance with NIH guidelines. Krt5-creER80 and Scgb1a1-creER36 mice were described previously. Foxi1-eGFP mice were purchased from GENSAT. C57BL/6J mice (stock no. 000664), LSL-mT/mG mice (mouse stock no. 007676), and LSL-tdTomato (stock no. 007914), Ascl3-EGFP-Cre mice (stock no. 021794), and Foxi1-KO mice (stock no. 024173) were purchased from the Jackson Laboratory. To label basal cells and secretory cells for in vivo lineage traces, Applicants administered tamoxifen by intraperitoneal injection (3 mg per 20 g body weight) three times every 48 hours to induce the Cre-mediated excision of a stop codon and subsequent expression of tdTomato. For Pulse-Seq experiments Applicants administered tamoxifen by intraperitoneal injection (2 mg per 20 g body weight) three times every 24 hours to induce the Cre-mediated excision of a stop codon and subsequent expression GFP. To label proliferating cells, Applicants administered 5-ethynyl-2′-deoxyuridine (EdU) per 25 g mouse by intraperitoneal injection (2 mg per 20 g body weight). 6-12-week-old mice were used for all experiments. Male C57BL/6 mice were used for the full length and initial 3′ scRNA-seq experiments. Both male and female mice were used for lineage tracing and ‘Pulse-Seq’ experiments. Applicants used three mice for each lineage time point.
Immunofluorescence, microscopy and cell counting. Tracheae were dissected and fixed in 4% PFA for 2 h at 4° C. followed by two washes in PBS, and then embedded in OCT. Cryosections (6 μm) were treated for epitope retrieval with 10 mM citrate buffer at 95° C. for 10-15 minutes, permeabilized with 0.1% Triton X-100 in PBS, blocked in 1% BSA for 30 min at room temperature (27° C.), incubated with primary antibodies for 1 hour at room temperature, washed, incubated with appropriate secondary antibodies diluted in blocking buffer for 1 h at room temperature, washed and counterstained with DAPI.
In the case of whole mount trachea stains, tracheas were longitudinally re-sectioned along the posterior membrane, permeabilized with 0.3% Triton X-100 in PBS, blocked in 0.3% BSA and 0.3% Triton X-100 for 120 min at 37° C. on an orbital shaker, incubated with primary antibodies for 12 hours at 37° C. (again on an orbital shaker), washed in 0.3% Triton X-100 in PBS, incubated with appropriate secondary antibodies diluted in blocking buffer for 1 h at 37° C. temperature, washed in 0.3% Triton X-100 in PBS and counterstained with Hoechst 33342. They were then mounted on a slide between two magnets to ensure flat imaging surface.
The following primary antibodies were used: rabbit anti-Atp6v0d2 (1/300; pa5-44359, Thermo), goat anti-CC10 (aka Scgb1a1, 1:500; SC-9772, Santa Cruz), rabbit anti-CFTR (1:100; ACL-006, Alomone), mouse anti-Chromogranin A (1/500; sc-393941, Santa Cruz), rat anti-Cochlin (1/500; MABF267, Millipore), goat anti-FLAP (aka Alox5ap, 1:500; NB300-891, Novus), goat anti-Foxi1 (1:250; ab20454, Abcam), chicken anti-GFP (1:500; GFP-1020, Aves Labs), rabbit anti-Gnat3 (1/300; sc-395, Santa Cruz), rabbit anti-Gng13 (1:500; ab126562, Abcam), rabbit anti-Krt13 (1/500; ab92551, Abcam), goat anti-Krt13 (1/500; ab79279, Abcam), goat anti-Lipf (1:100; MBS421137, mybiosource.com), mouse anti-Muc5ac (1/500; mal-38223, Thermo), mouse anti-Muc5ac (1/500; mal-38223, Thermo), mouse anti-p63 (1:250; gtx102425, GeneTex), rabbit anti-Tff2 (1/500; 13681-1-AP, ProteinTech), rabbit anti-Trpm5 (1:500; ACC-045, Alamone), mouse anti-tubulin, acetylated (1:100; T6793, Sigma). All secondary antibodies were Alexa Fluor conjugates (488, 594 and 647) and used at 1:500 dilution (Life Technologies).
EdU was stained in fixed sections alongside the above antibody stains as previously described81.
Confocal images for both slides and whole mount tracheas were obtained with an Olympus FV10i confocal laser-scanning microscope with a 60× oil objective. Cells were manually counted based on immunofluorescence staining of markers for each of the respective cell types. Cartilage rings (1 to 12) were used as reference points in all the tracheal samples to count specific cell types on the basis of immunostaining. Serial sections were stained for the antibodies tested and randomly selected slides were used for cell counting.
Cell dissociation and FACS. Airway epithelial cells from trachea were dissociated using papain solution. For whole trachea sorting, longitudinal halves of the trachea were cut into five pieces and incubated in papain dissociation solution and incubated at 37° C. for 2 h. For proximal-distal cell sorting, proximal (cartilage 1-4) and distal (cartilage 9-12) trachea regions were dissected and dissociated by papain independently. After incubation, dissociated tissues were passed through a cell strainer and centrifuged and pelleted at 500 g for 5 min. Cell pellets were dispersed and incubated with Ovo-mucoid protease inhibitor (Worthington biochemical Corporation, cat. no. LK003182) to inactivate residual papain activity by incubating on a rocker at 4° C. for 20 min. Cells were then pelleted and stained with EpCAM-PECy7 (1:50; 25-5791-80, eBioscience) and CD45, CD81, or basis of GFP expression for 30 min in 2.5% FBS in PBS on ice. After washing, cells were sorted by fluoresence (antibody staining and/or GFP) on a BD FACS Aria (BD Biosciences) using FACS Diva software and analysis was performed using FlowJo (version 10) software.
For plate-based scRNA-seq, single cells were sorted into each well of a 96-well PCR plate containing 5 μl of TCL buffer with 1% 2-mercaptoenthanol. In addition, a population control of 200 cells was sorted into one well and a no-cell control was sorted into another well. After sorting, the plate was sealed with a Microseal F, centrifuged at 800 g for 1 minute and immediately frozen on dry ice. Plates were stored at −80° C. until lysate cleanup.
For droplet-based scRNA-seq, cells were sorted into an Eppendorf tube containing 50 μl of 0.4% BSA-PBS and stored on ice until proceeding to the GemCode Single Cell Platform.
Plate-based scRNA-seq. Single cells were processed using a modified SMART-Seq2 protocol as previously described4. Briefly, RNAClean XP beads (Agencourt) were used for RNA lysate cleanup, followed by reverse transcription using Maxima Reverse Transcriptase (Life Technologies), whole transcription amplification (WTA) with KAPA HotStart HIFI 2× ReadyMix (Kapa Biosystems) for 21 cycles and purification using AMPure XP beads (Agencourt). WTA products were quantified with Qubit dsDNA HS Assay Kit (ThermoFisher), visualized with high sensitivity DNA Analysis Kit (Agilent) and libraries were constructed using Nextera XT DNA Library Preparation Kit (Illumina). Population and no-cell controls were processed with the same methods as singe cells. Libraries were sequenced on an Illumina NextSeq 500.
Droplet-based scRNA-seq. Single cells were processed through the GemCode Single Cell Platform per manufacturer's recommendations using the GemCode Gel Bead, Chip and Library Kits (10× Genomics, Pleasanton, CA). Briefly, single cells were partitioned into Gel Beads in Emulsion (GEMs) in the GemCode instrument with cell lysis and barcoded reverse transcription of RNA, followed by amplification, shearing and 5′ adaptor and sample index attachment. An input of 6,000 single cells was added to each channel with a recovery rate of roughly 1,500 cells. Libraries were sequenced on an Illumina Nextseq 500.
qRT-PCR. FACS isolated cells were sorted into 150 μl TRIzol LS (ThermoFisher Scientific), while ALI culture membranes were submerged in 300 μl of standard TRIzol solution (ThermoFisher Scientific). A standard chloroform extraction was performed followed by an RNeasy column-based RNA purification (Qiagen) according to manufacturer's instructions. 1 μg (when possible, otherwise 100 ng) of RNA was converted to cDNA using SuperScript VILO kit with additional ezDNase treatment according to manufacturer's instructions (ThermoFisher Scientific). qRT-PCR was performed using 0.5 μl of cDNA, predesigned TaqMan probes, and TaqMan Fast Advanced Master Mix (ThermoFisher Scientific), assayed on a LightCycler 480 in 384 well format (Roche). Assays were run in parallel with the loading controls Hprt and Ubc, previously validated to remain constant in the tested assay conditions. Subsequent experiments using ferret epithelial cells were performed using the same methodology.
Single-molecule fluorescence in situ hybridization (smFISH). Intact primary human bronchus was obtained through the New England Organ Bank. Segments of bronchus were flash frozen by immersion in liquid nitrogen and embedded in OCT and 4 uM sections were collected. RNAScope Multiplex Fluorescent Kit (Advanced Cell Diagnostics) was used per manufacturer's recommendations, and confocal imaging was carried out as described above.
Transwell cultures. Cells were cultured and expanded in complete SAGM (small airway epithelial cell growth medium; Lonza, CC-3118) containing TGF-β/BMP4/WNT antagonist cocktails and 5 μM Rock inhibitor Y-27632 (Selleckbio, S1049). To initiate air-liquid interface (ALI) cultures, airway basal stem cells were dissociated from mouse tracheas and seeded onto transwell membranes. After reaching confluence, media was removed from the upper chamber. Mucociliary differentiation was performed with PneumaCult-ALI Medium (StemCell, 05001). Differentiation of airway basal stem cells on an air-liquid interface was followed by directly visualizing beating cilia in real time after 10-14 days.
Once air-liquid cultures were fully differentiated, as indicated by beating cilia, treatment cultures were supplemented with 10 ng/mL of recombinant murine IL-13 (Peprotech®-stock diluted in water and used fresh) diluted in PneumaCult-ALI Medium, while control cultures received an equal volume of water for 72 hours. After treatment, whole ALI wells were fixed in 4% PFA, immunostained in whole mount using the same buffers and imaged with a confocal microscope as described above.
Airway surface physiologic parameters. Epithelia derived from Foxi1-KO mice (wild type, heterozygous knockout, and homozygous knockout genotypes) were grown as ALI cultures in transwells as described above and OCT, particle-tracking microrheology, airway surface pH measurements, and equivalent current (Ieq) assays were used to characterize their physiological parameters as described below.
μOCT methodologies have been used as previously described64, 66, 69. Briefly, Airway Surface Liquid (ASL) depth and ciliary beat frequency (CBF) were directly assessed via cross-sectional images of the airway epithelium with high resolution (<1 μM) and high acquisition speed (20,480 Hz line rate resulting in 40 frames/s at 512 line/frame). Quantitative analysis of images was performed in ImageJ82. To establish CBF, custom code in Matlab (Mathworks, Natick, MA) was used to quantify Fourier analysis of the reflectance of beating cilia. ASL depth was characterized directly by geometric measurement of the respective layers.
Particle-tracking microrheology was used to measure mucous viscosity following the methods detailed in Birket et al.68 
Airway surface pH was measured by use of a small probe as described in Birket et al.65 
Equivalent current (Ieq) assay on mouse ALI was carried out as described in Mou et al.72 with these changes: benzamil was used at 20 uM and CFTR activation was done only with 10 uM forskolin.
Transcriptional activation of Foxi1 in ferret basal cell cultures. Lentivirus production and transduction. HEK 293T cells were cultured in 10% FBS, 1% penicillin/streptomycin DMEM. Cells were seeded at ˜30% confluency, and then were transfected the next day at ˜90% confluency. For each flask, 22 μg of plasmid containing the vector of pLent-dCas9-VP64 Blast or pLent-MS2-p65-HSF1 Hygromycin, 16 g of psPAX2, and 7 μg pMD2 (VSV-G) were transfected using calcium phosphate buffer. The next day after transfection, culture medium was removed and replaced with 2% FBS-DMEM medium and incubated for 24 h. Lentivirus supernatant was harvested 48 h after transfection, and the supernatant was centrifuged at 5000 rpm for 5 min. Lentivirus was filtered with a 0.45 μm PVDF filter, concentrated by Lenti×concentrator (Takara), aliquoted and stored at 80° C. Ferret basal cells were cultured in Pneumacult-Ex with medium supplemented with Pneumacult-Ex and supplemented with hydrocortisone and 1% penicillin/streptomycin and passaged at a 1:5 ratio. Cells were incubated with lentivirus for 24 h in growth media. At 72 h selection was initiated (10 μg/mL Blasticidin, 50 μg/mL Hygromycin). Selection was performed for 14 days for Hygromycin and Blasticidin with media changes every 24 h83.
To generate sgRNA for transcriptional activation of Foxi1 in ferret cells, gBlocks were synthesized from IDT and included all components necessary for small guide (sg)RNA production, namely: T7 promoter, Foxi1 target specific sequence, guide RNA scaffold, MS2 binding loop and termination signal. gBlocks were PCR amplified and gel purified. PCR products were used as the template for in vitro transcription using MEGAshortscript T7 kit (Ambion). All sgRNAs were purified using MegaClear Kit (Ambion) and eluted in RNase-free water.
Foxi1 sgRNA was reverse transfected using Lipofectamine RNAiMAX Transfection Reagent (Life Science) into ferret basal cells that stably expresses dCas9-VP64 fusion protein and MS2-p65-HSF1 fusion protein. For the 0.33-cm2 ALI inserts, (1 μg) sgRNA and Lipofectamine RNAiMAX was diluted in 50 μl of Opti-MEM. The solution was gently mixed, dispensed into insert and incubated for 20-30 min at room temperature. Next, 300,000 cells were suspended in 150 μl pneumacult-Ex plus medium and incubated for 24 h at 37° C. in a 5% CO2 incubator.
Short circuit current measurements of CFTR-mediated chloride transport in ferret. Polarized ferret basal cells with activated Foxi1 expression as well as matched mock transfection controls (without DNA) were grown in ALI, and after three weeks short-circuit current (Isc) measurements were performed as previously described84. The basolateral chamber was filled with high-chloride HEPES-buffered Ringer's solution (135 mM NaCl, 1.2 mM CaCl2), 1.2 mM MgCl2, 2.4 mM KH2PO4, 0.2 mM K2IPO4, 5 mM HEPES, pH 7.4). The apical chamber received a low-chloride HEPES-buffered Ringer's solution containing a 135-mM sodium gluconate substitution for NaCl. Isc was recorded using Acquire & Analyze software (Physiologic Instruments) after clamping the transepithelial voltage to zero. The following antagonists and agonists were sequentially added into the apical chamber: amiloride (100 μM) to block ENaC channels, apical DIDS (100 μM) to block calcium-activated chloride channels, forskolin (10 μM) and IBMX (100 μM) to activate CFTR, and GlyH101 (500 μM) to block CFTR.
Computational Methods
Pre-processing of 3′ droplet-based scRNA-seq data. Demultiplexing, alignment to the mm10 transcriptome and UMI-collapsing were performed using the Cellranger toolkit (version 1.0.1, 10× Genomics). For each cell, Applicants quantified the number of genes for which at least one read was mapped, and then excluded all cells with fewer than 1,000 detected genes. Expression values Ei,j for gene i in cell j were calculated by dividing UMI count values for gene i by the sum of the UMI counts in cell j, to normalize for differences in coverage, and then multiplying by 10,000 to create TPM-like values, and finally calculating log2(TPM+1) values.
Selection of variable genes was performed by fitting a generalized linear model to the relationship between the squared co-efficient of variation (CV) and the mean expression level in log/log space, and selecting genes that significantly deviated (p<0.05) from the fitted curve, as previously described85.
Both prior knowledge and this data show that different cell types have dramatically differing abundances in the trachea. For example, 3,845 of the 7,193 cells (53.5%) in the droplet-based dataset were eventually identified as basal cells, while only 26 were ionocytes (0.4%). This makes conventional batch correction difficult, as, due to random sampling effects, some batches may have very few (or even zero) of the rarest cells (FIG. 43b). To avoid this problem and simultaneously identify maximally discriminative genes, Applicants performed an initial round of clustering on the set of variable genes described above, and identified a set of 1,380 cell type-specific genes (FDR<0.01), with a minimum log 2 fold-change of 0.25. In addition, Applicants performed batch correction within each identified cluster, which contained only transcriptionally similar cells, ameliorating problems with differences in abundance. Batch correction was performed (only on these 1,380 genes) using ComBat86 as implemented in the R package sva87 using the default parametric adjustment mode. The output was a corrected expression matrix, which was used as input to further analysis.
Pre-processing of plate-based scRNA-seq data. BAM files were converted to merged, de-multiplexed FASTQs using the Illumina Bcl2Fastq software package v2.17.1.14. Paired-end reads were mapped to the UCSC mm10 mouse transcriptome using Bowtie88 with parameters “-q --phred33-quals-n 1-e 99999999-1 25-I 1-X 2000-a -m 15-S -p 6”, which allows alignment of sequences with one mismatch. Expression levels of genes were quantified as transcript-per-million (TPM) values by RSEM89 v1.2.3 in paired-end mode. For each cell, Applicants determined the number of genes for which at least one read was mapped, and then excluded all cells with fewer than 2,000 detected genes. Applicants then identified highly variable genes as described above.
Dimensionality reduction by PCA and tSNE. Applicants restricted the expression matrix to the subsets of variable genes and high-quality cells noted above, and values were centered and scaled before input to PCA, which was implemented using the R function ‘prcomp’ from the ‘stats’ package for the plate-based dataset. For the droplet-based dataset, Applicants used a randomized approximation to PCA, implemented using the ‘rpca’ function from the ‘rsvd’ R package, with the parameter k set to 100. This low-rank approximation is several orders of magnitude faster to compute for very wide matrices. After PCA, significant PCs were identified using a permutation test as previously described90, implemented using the ‘permutationPA’ function from the ‘jackstraw’ R package. Because of the presence of extremely rare cells in the droplet-based dataset (as described above), Applicants used scores from 10 significant PCs using scaled data, and 7 significant PCs using unscaled data. Only scores from these significant PCs were used as the input to further analysis.
For visualization purposes only (and not for clustering), dimensionality was further reduced using the Barnes-Hut approximate version of the t-distributed stochastic neighbor embedding (tSNE)91,92. This was implemented using the ‘Rtsne’ function from the ‘Rtsne’ R package using 20,000 iterations and a perplexity setting of 10 and 75 for plate- and droplet-based respectively. Scores from the first n PCs were used as the input to tSNE, where n was 11 and 12 for plate- and droplet-based data, respectively, determined using the permutation test described above.
Excluding immune, mesenchymal cells and suspected doublets. Although cells were sorted using EpCAM prior to scRNA-seq, 1,873 contaminating cells were observed in the initial droplet dataset, and were comprised of. 91 endothelial cells expressing Egf17, Sh3g13 and Esam, 229 macrophages expressing MHCII (H2-Ab1, H2-Aa, Cd74), C1qa, and Cd68, and 1,553 fibroblasts expressing high levels of collagens (Col1a1, Col1a2, and Col3a1). Each of these cell populations was identified by an initial round of unsupervised clustering (density-based clustering of the tSNE map using ‘dbscan’55 from the R package ‘fpc’) as they formed extremely distinct clusters, and then removed. In the case of the Pulse-Seq dataset, the initial clustering step removed a total of 532 dendritic cells identified by high expression of Ptprc and Cd83. In addition, 20 other cells were outliers in terms of library complexity, which could possibly correspond to more than one individual cell per sequencing library, or ‘doublets’. As a conservative precaution, Applicants removed these 20 possible doublet cells with over 3,700 genes detected per cell.
kNN-graph based clustering. To cluster single cells by their expression profiles, Applicants used unsupervised clustering, based on the Infomap community-detection algorithm6, following approaches recently described for single-cell CyTOF data93 and scRNA-seq5. Applicants constructed a k nearest-neighbor (k-NN) graph using, for each pair of cells, the Euclidean distance between the scores of significant PCs as the metric.
The number k of nearest neighbors was chosen in a manner roughly consistent with the size of the dataset, and set to 25 and 150 for plate- and droplet-based data respectively. For sub-clustering of rare cell subsets, Applicants used k=100, 50, 50 and 20 for tuft cells, neuroendocrine cells, ionocytes and goblet cells respectively. The k-NN graph was computed using the function ‘nng’ from the R package ‘cccd’ and was then used as the input to Infomap6, implemented using the ‘infomap.community’ function from the ‘igraph’ R package.
Detected clusters were mapped to cell-types using known markers for tracheal epithelial subsets. In particular, because of the large proportion of basal and club cells, multiple clusters expressed high levels of markers for these two types. Accordingly, Applicants merged nine clusters expressing the basal gene score above a median log2(TPM+1) >0, and seven clusters expressing the club gene score above median log2(TPM+1) >1. Calculation of a ciliated cell gene score showed only a single cluster with non-zero median expression, so no further merging was performed. This resulted in seven clusters, each corresponding 1 to 1 with a known airway epithelial cell type, with the exception of the ionocyte cluster, which Applicants show represents a novel subset.
Rare cells (tuft, neuroendocrine, ionocyte and goblet) were sub-clustered to examine possible heterogeneity of mature types (FIG. 40 and FIG. 50). In each case, cells annotated as each type from the initial 3′ droplet-based dataset (FIG. 37B and FIG. 43D) were combined with the corresponding cells from the Pulse-Seq dataset (FIG. 39B and FIG. 48A) before sub-clustering. In the case of goblet cells, sub-clustering the combined 468 goblet cells (k=20, above) partitioned the data into 7 groups, two of which expressed the novel goblet cell marker Gp2 (FIG. 37D) at high levels (median log2(TPM+1) >1). These two groups were annotated as mature goblet-1 and goblet-2 cells (FIG. 50F-J), while the five groups were merged and annotated as immature goblet cells.
Differential expression and cell-type signatures. To identify maximally specific genes for cell-types, Applicants performed differential expression tests between each pair of clusters for all possible pairwise comparisons. Then, for a given cluster, putative signature genes were filtered using the maximum FDR Q-value and ranked by the minimum log 2 fold-change (across the comparisons). This is a stringent criterion because the minimum fold-change and maximum Q-value represent the weakest effect-size across all pairwise comparisons. Cell-type signature genes for the initial droplet based scRNA-seq data (FIG. 37C) were obtained using a maximum FDR of 0.05 and a minimum log 2 fold-change of 0.5.
Where less cells were available, as is the case of full-length plate-based scRNA-seq data (FIG. 45B) or for subtypes within cell-types (FIG. 39C, FIG. 50C), a combined p-value across the pairwise tests for enrichment was computed using Fisher's method (a more lenient criterion) and a maximum FDR Q-value of 0.001 was used, along with a cutoff of minimum log 2 fold-change of 0.1 for tuft and goblet cell subsets (FIG. 39C, FIG. 50C). Larger clusters (basal, club, ciliated cells) were down-sampled to 1,000 cells for the pairwise comparisons. Marker genes were ranked by minimum log 2 fold-change. Differential expression tests were carried using a two part ‘hurdle’ model to control for both technical quality and mouse-to-mouse variation. This was implemented using the R package MAST64, and p-values for differential expression were computed using the likelihood-ratio test. Multiple hypothesis testing correction was performed by controlling the false discovery rate65 using the R function ‘p.adjust’.
Assigning cell-type specific TFs, GPCRs and genes associated with asthma. A list of all genes annotated as transcription factors in mice was obtained from AnimalTFDB94, downloaded from:

    
    
        bioguo.org/AnimalTFDB/BrowseAllTF.php?spe=Mus_musculus.
    
    


The set of G-protein coupled receptors (GPCRs) was obtained from the UniProt database, downloaded from:

    
    
        uniprot.org/uniprot/?query=family %3A %22 g+protein+coupled+receptor %22+AN D+organism %3A %22Mouse+%5B10090%5D %22+AND+reviewed %3Ayes&sort=score. To map from human to mouse gene names, human and mouse orthologs were downloaded from Ensembl latest release 86 at:
        ensembl.org/biomart/martview, and human and mouse gene synonyms from: NCBI (ftp.ncbi.nlm.nih.gov/gene/DATA/GENE_INFO/Mammalia/).
    
    


Cell-type enriched TFs and GPCRs were then identified by intersecting the list of genes enriched in to each cell type with the lists of TFs and GPCRs defined above. Cell-type enriched TFs (FIG. 37E) and GPCRs (FIG. 50A) were defined using the 3′ droplet-based and full-length plate-based datasets, respectively, as those with a minimum log 2 fold-change of 0.1 and a maximum FDR of 0.001, retaining a maximum of 10 genes per cell type in FIG. 37E.
Gene set or pathway enrichment analysis. GO analysis of enriched pathways in Krt13+ hillocks (FIG. 45D) was performed using the ‘goseq’ R package95, using significantly differentially expressed genes (FDR<0.05) as target genes, and all genes expressed with log2(TPM+1) >3 in at least 10 cells as background. For pathway and gene sets, Applicants used a version of MSigDB78 with mouse orthologs, downloaded from: bioinfwehi.edu.au/software/MSigDB/. Association of principal components with cell-types (FIG. 49A,B) was computed using the Gene Set Enrichment Analysis (GSEA) algorithm95 implemented using the ‘fgsea’ package in R. Genes that are involved in leukotriene biosynthesis and taste transduction pathways (FIG. 40F and FIG. 50B) were identified using KEGG and GO pathways. Specifically, genes in KEGG pathway 00590 (arachidonic acid metabolism) or GO terms 0019370 (leukotriene biosynthetic process) or 0061737 (leukotriene signaling pathway) were annotated as leukotriene synthesis-associated, while genes in KEGG pathway 04742 (taste transduction) were annotated as taste transduction-associated.
Statistical analysis of proximodistal mucous metaplasia. For the analysis in FIG. 38H,I, the extent of goblet cell hyperplasia was assessed using counts of Muc5ac+ goblet cells, normalized to counts of GFP+ ciliated cells. To quantify differences in the count values between the samples in different conditions (n=6, Foxj 1-GFP mice), Applicants fit a negative binomial regression using the ‘glm.nb’ function from the ‘MASS’ package in R. Pairwise comparisons between means for each condition were computed using post hoc tests and p-values were adjusted for multiple comparisons using Tukey's HSD, implemented using the function ‘pairs’ from the ‘emeans’ package in R.
Lineage inference using diffusion maps. Applicants restricted the analysis to the 6,848 cells in basal, club or ciliated cell clusters (95.2% of the 7,193 cells in the initial droplet dataset), since it was unlikely that rare cells (e.g., NE, tuft, goblet, and ionocyte cells) in transitional states will be sufficiently densely sampled. Next, Applicants selected highly variable genes among these three cell subsets as described above, and performed dimensionality reduction using the diffusion map approach96. Briefly, a cell-cell transition matrix was computed using the Gaussian kernel where the kernel width was adjusted to the local neighborhood of each cell, following the approach of Haghverdi et al.97. This matrix was converted to a Markovian matrix after normalization. The right eigenvectors vi(i=0, 1, 2, 3, . . . ) of this matrix were computed and sorted in the order of decreasing eigenvalues λi(i=0, 1, 2, 3, . . . ), after excluding the top eigenvector v0, corresponding to λ0=1 (which reflects the normalization constraint of the Markovian matrix). The remaining eigenvectors vi(i=1, 2 . . . ) define the diffusion map embedding and are referred to as diffusion components (DCk(k=1, 2, . . . )). Applicants noticed a spectral gap between the λ3 and the λ4, and hence retained DC1-DC3 for further analysis.
To extract the edges of this manifold, along which cells transition between states (FIG. 38A), Applicants fit a convex hull using the ‘convhulln’ from the ‘geometry’ R package. To identify edge-associated cells, any cell within d<0.1 of an edge of the convex hull (where d is the Euclidean distance in diffusion-space) is assigned to that edge.
To identify cells associated with the Krt4+Krt13+ population, Applicants used unsupervised Partitioning Around Medoids (PAM) clustering of the cells in diffusion space with the parameter k=4. Edge-association of genes (or TFs) was computed as the autocorrelation (lag=25), implemented using the ‘acf’ function from the ‘stats’ R package. Empirical p-values for each edge-associated gene were assessed using a permutation test (1,000 bootstrap iterations), using the autocorrelation value as the test statistic.
Genes were placed in pseudotemporal order by splitting the interval into 30 bins from ‘early’ to ‘late’, and assigning each gene the bin with the highest mean expression. These data were smoothed using loess regression and then visualized as heatmaps (FIG. 47).
Pulse-Seq data analysis. For the much larger Pulse-Seq dataset (˜66,700 cells), Applicants used a very similar, but more scalable, analysis pipeline, with the following modifications. Alignment and UMI collapsing was performing using the Cellranger toolkit (version 1.3.1, 10× Genomics). Log2(TPM+1) expression values were computed using Rcpp-based function in the R package ‘Seurat’ (v2.2). Applicants also used an improved method of identifying variable genes. Rather than fitting the mean-CV2 relationship, a logistic regression was fit to the cellular detection fraction (often referred to as α), using the total number of UMIs per cell as a predictor. Outliers from this curve are genes that are expressed in a lower fraction of cells than would be expected given the total number of UMIs mapping to that gene, i.e., cell-type or state specific genes. Applicants used a threshold of deviance<−0.25, producing a set of 708 variable genes. Applicants restricted the expression matrix to this subset of variable genes and values were centered and scaled—while ‘regressing out’98 technical factors (number of genes detected per cell, number of UMIs detected per cell and cell-cycle score) using the ‘ScaleData’ function before input to PCA, implemented using ‘RunPCA’ in Seurat. After PCA, significant PCs were identified using the knee in the scree plot, which identified 10 significant PCs. Only scores from these significant PCs were used as the input to nearest-neighbor based clustering and tSNE, implemented using the ‘FindClusters’ (resolution parameter r=1) and ‘RunTSNE’ (perplexity p=25) methods respectively from the ‘Seurat’ package.
Once again due to their abundance, the populous basal, club and ciliated cells were spread across several clusters, which were merged using the strategy described above: 19 clusters expressing the basal score above mean log2(TPM+1)>0, 12 expressing the club score above mean log2(TPM+1) >−0.1, and 2 clusters expressing the ciliated signature above were merged to construct the basal, club and ciliated subsets, respectively. Goblet cells were not immediately associated with a specific cluster, however, cluster 13 (one of those merged into the club cluster) expressed significantly elevated levels of goblet markers Tff2 and Gp2 (p<10−10, likelihood-ratio test). Sub-clustering this population (resolution parameter r=1) revealed 6 clusters, of which two expressed the goblet score constructed using the top 25 goblet cell marker genes above mean log2(TPM+1) >1, which were merged and annotated as goblet cells. To identify the Krt4+/Krt13+ hillock-associated club cells, the remaining 17,700 club cells were re-clustered (resolution parameter r=0.2) into 5 clusters, of which one expressed much higher levels (p<10−10 in all cases) of Krt4, Krt13 and a hillock score constructed using the top 25 hillock marker genes, this cluster was annotated as ‘hillock-associated club cells’.
Estimating lineage-labeled fraction for Pulse-Seq and conventional lineage tracing. For any given sample (here, mouse) the certainty in the estimate of the proportion of labeled cells increases with the number of cells obtained; the more cells, the higher the precision of the estimate. Estimating the overall fraction of labeled cells (from conventional lineage tracing; FIG. 39F, FIG. 46 and FIG. 48, or Pulse-seq lineage tracing FIG. 39 and FIG. 48) based on the individual estimates from each mouse is analogous to performing a meta-analysis of several studies, each of which measures a population proportion; studies with greater power (higher n) carry more information, and should influence the overall estimate more, while low n studies provide less information and should not have as much influence. Generalized linear mixed models (GLMM) provide a framework to obtain an overall estimate in this manner99. Accordingly, Applicants implemented a fixed effects logistic regression model to compute the overall estimate and 95% confidence interval using the function ‘metaprop’ from the R package ‘meta’100.
Testing for difference in labeled fraction for Pulse-Seq and conventional lineage tracing. To assess the significance of changes in the labeled fraction of cells in different conditions, Applicants used a negative binomial regression model of the counts of cells at each time-point, controlling for variability amongst biological (mouse) replicates. For each cell-type, Applicants model the number of lineage-labeled cells detected in each analyzed mouse as a random count variable using a negative binomial distribution. The frequency of detection is modeled by using the natural log of the total number of cells of that type profiled in a given mouse as an offset. The time-point of each mouse (0, 30 or 60 days post tamoxifen) is provided as a covariate. The negative binomial model was fit using the R command ‘glm.nb’ from the ‘MASS’ package. The p-value for the significance of the change in labeled fraction size between time-points was assessed using a likelihood-ratio test, computing using the R function ‘anova’.
Estimating turnover rate using quantile regression. Given the relatively few samples (n=9 mice) with which to model the rate of new lineage-labeled cells, Applicants used the more robust quantile regression101, which models the conditional median (rather than the conditional mean, as captured by least-squares linear regression, which can be sensitive to outliers). The fraction of labeled cells in each mouse was modeled as a function of days post tamoxifen (FIG. 48C) using the function ‘rq’ from the R package ‘quantReg’. Significance of association between increasing labeled fraction and time were computing using Wald tests implemented with the ‘summary.rq’ function, while tests comparing the slopes of fits were conducted using ‘anova.rq’.
Statistical analysis of qRT-PCR data. ΔΔCT values were generated by normalization to the average of loading controls Hprt and Ubc, followed by comparison to wild type samples. Statistical analysis was performed at the ΔCT stage. For single comparisons, all datasets passed the Shapiro-Wilk normality test, which was followed by apost-hoc two-tailed t-test. For multiple comparisons, all datasets passed the Shapiro-Wilk normality test for equal variance. Data was then tested by two-way ANOVA, with sex as the second level of variance. In a few certain cases, sex trended towards significance, however, not enough to justify separate analysis. Post hoc multiple comparisons to the control group were performed using Dunn's Method. In the single case of Foxi1 KO (FIG. 41E), two heterozygous samples were identified as outliers and removed using a standard implementation of DBscan clustering using the full dataset of all genes assayed using qRT-PCR. These two samples exhibited gene expression closer to full Foxi1 knockouts and were removed from consideration. In all cases, error bars represent the calculated 95% CI, and *p<0.05, **p<0.01, ***p<0.001.
Data Availability. All data is deposited in GEO (GSE103354) and in the Single Cell Portal (portals.broadinstitute.org/single_cell/study/trachea-epithelium).
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The invention is further described by the following numbered paragraphs:


    
    
        1. A method for identifying tuft cells in a sample, comprising detecting expression of any one or more of Cd24a, Tas1r3, Ffar3, Sucnr1, Gabbr1 or Drd3 protein or mRNA, wherein said expression indicates tuft cells.
        2. The method of paragraph 1, further comprising detecting expression of any one or more of Ptprc or Tslp protein or mRNA, wherein said expression indicates a subset of tuft cells.
        3. The method of paragraph 1, further comprising detecting expression of any one or more of Nrep, Nradd, Ninj1, and Plekhg5 protein or mRNA, wherein said expression indicates a subset of tuft cells.
    
    


Various modifications and variations of the described methods, pharmaceutical compositions, and kits of the invention will be apparent to those skilled in the art without departing from the scope and spirit of the invention. Although the invention has been described in connection with specific embodiments, it will be understood that it is capable of further modifications and that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the described modes for carrying out the invention that are obvious to those skilled in the art are intended to be within the scope of the invention. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure come within known customary practice within the art to which the invention pertains and may be applied to the essential features herein before set forth.