Article

A single-cell survey of the small intestinal epithelium

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Abstract

Intestinal epithelial cells absorb nutrients, respond to microbes, function as a barrier and help to coordinate immune responses. Here we report profiling of 53,193 individual epithelial cells from the small intestine and organoids of mice, which enabled the identification and characterization of previously unknown subtypes of intestinal epithelial cell and their gene signatures. We found unexpected diversity in hormone-secreting enteroendocrine cells and constructed the taxonomy of newly identified subtypes, and distinguished between two subtypes of tuft cell, one of which expresses the epithelial cytokine Tslp and the pan-immune marker CD45, which was not previously associated with non-haematopoietic cells. We also characterized the ways in which cell-intrinsic states and the proportions of different cell types respond to bacterial and helminth infections: Salmonella infection caused an increase in the abundance of Paneth cells and enterocytes, and broad activation of an antimicrobial program; Heligmosomoides polygyrus caused an increase in the abundance of goblet and tuft cells. Our survey highlights previously unidentified markers and programs, associates sensory molecules with cell types, and uncovers principles of gut homeostasis and response to pathogens.

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References

  1. 1.

    et al. Identification of stem cells in small intestine and colon by marker gene Lgr5. Nature 449, 1003–1007 (2007)

  2. 2.

    , , & Tuft-cell-derived IL-25 regulates an intestinal ILC2-epithelial response circuit. Nature 529, 221–225 (2016)

  3. 3.

    et al. Mex3a marks a slowly dividing subpopulation of Lgr5+ intestinal stem cells. Cell Stem Cell 20, 801–816.e7 (2017)

  4. 4.

    et al. Induced quiescence of Lgr5+ stem cells in intestinal organoids enables differentiation of hormone-producing enteroendocrine cells. Cell Stem Cell 20, 177–190.e4 (2017)

  5. 5.

    et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525, 251–255 (2015)

  6. 6.

    et al. Non-equivalence of Wnt and R-spondin ligands during Lgr5+ intestinal stem-cell self-renewal. Nature 545, 238–242 (2017)

  7. 7.

    et al. Intestinal enteroendocrine lineage cells possess homeostatic and injury-inducible stem cell activity. Cell Stem Cell 21, 78–90.e6 (2017)

  8. 8.

    et al. Haplotyping germline and cancer genomes with high-throughput linked-read sequencing. Nat. Biotechnol. 34, 303–311 (2016)

  9. 9.

    & Maps of random walks on complex networks reveal community structure. Proc. Natl Acad. Sci. USA 105, 1118–1123 (2008)

  10. 10.

    et al. Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell 166, 1308–1323.e30 (2016)

  11. 11.

    et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545–552 (2013)

  12. 12.

    et al. Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells. Genome Res. 25, 1860–1872 (2015)

  13. 13.

    , , & Examining the role of Paneth cells in the small intestine by lineage ablation in transgenic mice. J. Biol. Chem. 272, 23729–23740 (1997)

  14. 14.

    & Enteroendocrine cells: chemosensors in the intestinal epithelium. Annu. Rev. Physiol. 78, 277–299 (2016)

  15. 15.

    et al. Tuft cells, taste-chemosensory cells, orchestrate parasite type 2 immunity in the gut. Science 351, 1329–1333 (2016)

  16. 16.

    et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protocols 9, 171–181 (2014)

  17. 17.

    et al. Dietary modulation and structure prediction of rat mucosal pentraxin (Mptx) protein and loss of function in humans. Genes Nutr. 2, 275–285 (2007)

  18. 18.

    Pentraxins: structure, function, and role in inflammation. ISRN Inflamm. 2013, 379040 (2013)

  19. 19.

    et al. The zinc-finger transcription factor Klf4 is required for terminal differentiation of goblet cells in the colon. Development 129, 2619–2628 (2002)

  20. 20.

    , & The bile acid TGR5 membrane receptor: from basic research to clinical application. Dig. Liver Dis. 46, 302–312 (2014)

  21. 21.

    , & GPR119, a novel G protein-coupled receptor target for the treatment of type 2 diabetes and obesity. Br. J. Pharmacol. 153 (Suppl. 1), S76–S81 (2008)

  22. 22.

    et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. Proc. Natl Acad. Sci. USA 102, 7426–7431 (2005)

  23. 23.

    et al. Mapping early fate determination in Lgr5+ crypt stem cells using a novel Ki67-RFP allele. EMBO J. 33, 2057–2068 (2014)

  24. 24.

    et al. GATA factors regulate proliferation, differentiation, and gene expression in small intestine of mature mice. Gastroenterology 140, 1219–1229.e2 (2011)

  25. 25.

    , , , & The gut as a sensory organ. Nat. Rev. Gastroenterol. Hepatol. 10, 729–740 (2013)

  26. 26.

    , & Enteroendocrine cells-sensory sentinels of the intestinal environment and orchestrators of mucosal immunity. Mucosal Immunol. (2017)

  27. 27.

    , , , & Co-localisation and secretion of glucagon-like peptide 1 and peptide YY from primary cultured human L cells. Diabetologia 56, 1413–1416 (2013)

  28. 28.

    & The serotonin signaling system: from basic understanding to drug development for functional GI disorders. Gastroenterology 132, 397–414 (2007)

  29. 29.

    , & The role of leptin and ghrelin in the regulation of food intake and body weight in humans: a review. Obes. Rev. 8, 21–34 (2007)

  30. 30.

    , & The role of peptide YY in appetite regulation and obesity. J. Physiol. 587, 19–25 (2009)

  31. 31.

    & Intestinal tuft cells: epithelial sentinels linking luminal cues to the immune system. Mucosal Immunol. 9, 1353–1359 (2016)

  32. 32.

    et al. Intestinal epithelial tuft cells initiate type 2 mucosal immunity to helminth parasites. Nature 529, 226–230 (2016)

  33. 33.

    et al. Murine intestinal cells expressing Trpm5 are mostly brush cells and express markers of neuronal and inflammatory cells. J. Comp. Neurol. 509, 514–525 (2008)

  34. 34.

    et al. Epithelial microRNAs regulate gut mucosal immunity via epithelium–T cell crosstalk. Nat. Immunol. 12, 239–246 (2011)

  35. 35.

    et al. Peyer’s patch M cells derived from Lgr5+ stem cells require SpiB and are induced by RankL in cultured “miniguts”. Mol. Cell. Biol. 32, 3639–3647 (2012)

  36. 36.

    , , , & Microfold (M) cells: important immunosurveillance posts in the intestinal epithelium. Mucosal Immunol. 6, 666–677 (2013)

  37. 37.

    et al. Comprehensive gene expression profiling of Peyer’s patch M cells, villous M-like cells, and intestinal epithelial cells. J. Immunol. 180, 7840–7846 (2008)

  38. 38.

    & Intestinal epithelial cells: regulators of barrier function and immune homeostasis. Nat. Rev. Immunol. 14, 141–153 (2014)

  39. 39.

    et al. REG3γ-deficient mice have altered mucus distribution and increased mucosal inflammatory responses to the microbiota and enteric pathogens in the ileum. Mucosal Immunol. 7, 939–947 (2014)

  40. 40.

    et al. Intestinal epithelial serum amyloid A modulates bacterial growth in vitro and pro-inflammatory responses in mouse experimental colitis. BMC Gastroenterol. 10, 133 (2010)

  41. 41.

    et al. Expansion of Paneth cell population in response to enteric Salmonella enterica serovar Typhimurium infection. Infect. Immun. 80, 266–275 (2012)

  42. 42.

    et al. RELMbeta/FIZZ2 is a goblet cell-specific immune-effector molecule in the gastrointestinal tract. Proc. Natl Acad. Sci. USA 101, 13596–13600 (2004)

  43. 43.

    , & Gfi1b:green fluorescent protein knock-in mice reveal a dynamic expression pattern of Gfi1b during hematopoiesis that is largely complementary to Gfi1. Blood 109, 2356–2364 (2007)

  44. 44.

    et al. Coinfection with an intestinal helminth impairs host innate immunity against Salmonella enterica serovar Typhimurium and exacerbates intestinal inflammation in mice. Infect. Immun. 82, 3855–3866 (2014)

  45. 45.

    , & NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012)

  46. 46.

    , & Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007)

  47. 47.

    , , , & The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012)

  48. 48.

    et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093–1095 (2013)

  49. 49.

    , , & Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009)

  50. 50.

    & RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011)

  51. 51.

    & Remarks on parallel analysis. Multivariate Behav. Res. 27, 509–540 (1992)

  52. 52.

    Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15, 3221–3245 (2014)

  53. 53.

    & Visualizing Data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)

  54. 54.

    et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015)

  55. 55.

    , & Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31, 2989–2998 (2015)

  56. 56.

    ., ., & A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. 2nd Int. Conf. Knowledge, Discovery and Data Mining (KDD-96) (eds . et al.) 226–231 (AAAI, 1996)

  57. 57.

    et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015)

  58. 58.

    & Machine learning. Clustering by fast search and find of density peaks. Science 344, 1492–1496 (2014)

  59. 59.

    et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015)

  60. 60.

    & Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995)

  61. 61.

    et al. AnimalTFDB: a comprehensive animal transcription factor database. Nucleic Acids Res. 40, D144–D149 (2012)

  62. 62.

    , & Human leucine-rich repeat proteins: a genome-wide bioinformatic categorization and functional analysis in innate immunity. Proc. Natl Acad. Sci. 108, 4631–4638 (2011)

  63. 63.

    , , & Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 11, R14 (2010)

  64. 64.

    , , & Free fatty acid receptors act as nutrient sensors to regulate energy homeostasis. Prostaglandins Other Lipid Mediat. 89, 82–88 (2009)

  65. 65.

    The Bayesian bootstrap. Ann. Stat. 9, 130–134 (1981)

  66. 66.

    et al. Identification of novel genes selectively expressed in the follicle-associated epithelium from the meta-analysis of transcriptomics data from multiple mouse cell and tissue populations. DNA Res. 19, 407–422 (2012)

  67. 67.

    et al. Identification of novel genes in intestinal tissue that are regulated after infection with an intestinal nematode parasite. Infect. Immun. 73, 4025–4033 (2005)

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Acknowledgements

We thank L. Gaffney for help with figure preparation, the Broad Flow Cytometry Facility (P. Rogers, S. Saldi and C. Otis), C. Hafemeister and R. Satija for use of the ‘How Many Cells’ tool, S. Riesenfeld and A. Dixit for statistical advice, and T. Tickle for help with the Single Cell Portal. This study was supported by the Klarman Cell Observatory at the Broad Institute, NIH RC2DK114784 (A.R. and R.J.X.), HHMI (A.R.), Food Allergy Science Initiative (FASI) at the Broad Institute (A.R. and R.J.X.), and a Broadnext10 award (A.R. and R.J.X.). M.B. is supported by a postdoctoral fellowship from the Human Frontiers Science Program (HFSP). R.J.X. is supported by NIH DK43351, DK097485 and the Helmsley Charitable Trust.

Author information

Author notes

    • Adam L. Haber
    • , Moshe Biton
    •  & Noga Rogel

    These authors contributed equally to this work.

    • Moshe Biton
    • , Ramnik J. Xavier
    •  & Aviv Regev

    These authors jointly supervised this work.

Affiliations

  1. Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA

    • Adam L. Haber
    • , Moshe Biton
    • , Noga Rogel
    • , Rebecca H. Herbst
    • , Karthik Shekhar
    • , Christopher Smillie
    • , Grace Burgin
    • , Toni M. Delorey
    • , Itay Tirosh
    • , Danielle Dionne
    • , Raktima Raychowdhury
    • , Wendy S. Garrett
    • , Orit Rozenblatt-Rosen
    • , Omer Yilmaz
    • , Ramnik J. Xavier
    •  & Aviv Regev
  2. Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, Massachusetts 02114, USA

    • Moshe Biton
    •  & Ramnik J. Xavier
  3. Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02114, USA

    • Rebecca H. Herbst
    •  & Yarden Katz
  4. Department of Biology and Biotechnology, Worcester Polytechnic Institute, Worcester, Massachusetts 01609, USA

    • Toni M. Delorey
  5. Departments of Immunology and Infectious Diseases and Genetics and Complex Diseases, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, USA

    • Michael R. Howitt
    •  & Wendy S. Garrett
  6. The David H. Koch Institute for Integrative Cancer Research at Massachusetts Institute of Technology, Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

    • Semir Beyaz
    •  & Omer Yilmaz
  7. Division of Hematology/Oncology, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA

    • Semir Beyaz
  8. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Howard Hughes Medical Institute, Harvard Stem Cell Institute, Harvard Medical School, Boston, Massachusetts 02115, USA.

    • Semir Beyaz
  9. Mucosal Immunology and Biology Research Center, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts 02129, USA

    • Mei Zhang
    •  & Hai Ning Shi
  10. Departments of Pathology, Gastroenterology, and Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA

    • Omer Yilmaz
  11. Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital, Boston, Massachusetts 02114, USA

    • Ramnik J. Xavier
  12. Department of Biology, Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts 02140, USA

    • Aviv Regev

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Contributions

A.L.H., M.B. and N.R. contributed equally to this study; M.B., R.J.X. and A.R. co-conceived the study; M.B., N.R., A.L.H., R.J.X. and A.R. designed experiments and interpreted the results; N.R. and M.B. carried out all experiments; G.B., T.M.D., M.R.H., S.B., D.D., M.Z. and R.R. assisted with experiments; A.L.H. designed and performed computational analysis with assistance from R.H.H., K.S., C.S., Y.K., I.T. and A.R.; M.R.H. and W.S.G. assisted with tuft and follicle-associated epithelium experiments; M.Z. and H.N.S. assisted with pathogen infections; S.B. and O.Y. assisted with epithelial cell sorting; D.D. and O.R.-R. assisted with scRNA-seq; and A.L.H., M.B., N.R., R.J.X. and A.R. wrote the manuscript, with input from all authors.

Competing interests

A.R. is a member of the scientific advisory board of ThermoFisher, Syros Pharmaceuticals and Driver Group. R.J.X. is a consultant at Novartis, Janssen and Celgene. A.H., M.B., N.R., R.H., K.S., C.S., O.R., R.X. and A.R. are co-inventors on a provisional patent application filed by the Broad Institute relating to this manuscript.

Corresponding authors

Correspondence to Moshe Biton or Ramnik J. Xavier or Aviv Regev.

Reviewer Information Nature thanks L. Vermeulen and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Supplementary information

PDF files

  1. 1.

    Life Sciences Reporting Summary

Excel files

  1. 1.

    Supplementary Table 1 - 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.

  2. 2.

    Supplementary Table 2 - 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 (Figure 1b).

  3. 3.

    Supplementary Table 3 - 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 scRNA-seq data (Extended Data Fig. 2a).

  4. 4.

    Supplementary Table 4 - 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 scRNA-seq datasets.

  5. 5.

    Supplementary Table 5 - 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.

  6. 6.

    Supplementary Table 6 - Enteroendocrine 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 scRNA-seq data.

  7. 7.

    Supplementary Table 7 - 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 scRNA-seq data.

  8. 8.

    Supplementary Table 8 - 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 mouse follicle associated epithelia (FAE).

  9. 9.

    Supplementary Table 9 - Intestinal epithelial response to pathogenic infection.

    This table provides estimates of differential gene expression in response to infection with H. polygyrus and Salmonella enterica, for each epithelial cell type, using both full-length and 3’ droplet-based scRNA-seq data.

  10. 10.

    Supplementary 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 (Extended Data Fig. 3).