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Single-cell atlas of colonic CD8+ T cells in ulcerative colitis

Abstract

Colonic antigen-experienced lymphocytes such as tissue-resident memory CD8+ T cells can respond rapidly to repeated antigen exposure. However, their cellular phenotypes and the mechanisms by which they drive immune regulation and inflammation remain unclear. Here we compiled an unbiased atlas of human colonic CD8+ T cells in health and ulcerative colitis (UC) using single-cell transcriptomics with T-cell receptor repertoire analysis and mass cytometry. We reveal extensive heterogeneity in CD8+ T-cell composition, including expanded effector and post-effector terminally differentiated CD8+ T cells. While UC-associated CD8+ effector T cells can trigger tissue destruction and produce tumor necrosis factor (TNF)-α, post-effector cells acquire innate signatures to adopt regulatory functions that may mitigate excessive inflammation. Thus, we identify colonic CD8+ T-cell phenotypes in health and UC, define their clonal relationships and characterize terminally differentiated dysfunctional UC CD8+ T cells expressing IL-26, which attenuate acute colitis in a humanized IL-26 transgenic mouse model.

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Fig. 1: Colonic CD8+ T-cell transcriptional atlas.
Fig. 2: Colonic CD8+ T-cell remodeling in active UC.
Fig. 3: Transcriptional modules, pseudotime and clonality define UC CD8+ T-cell lineages.
Fig. 4: Combined transcriptomic and proteomic profiles of CD8+ T cells using CITE–seq and Cell Hashing.
Fig. 5: Functional CyTOF analysis of UC CD8+ T cells.
Fig. 6: IL-26 attenuates acute colitis severity.

Data availability

All raw and processed next-generation sequencing data have been deposited with GEO under accession nos. GSE148837 and GSE148505. Processed data are available as Supplementary Data. Source data are provided with this paper.

Code availability

Code used for data analysis is available at https://github.com/antanaviciute-agne/singlecellcd8ibd.

References

  1. 1.

    Mueller, S. N. & Mackay, L. K. Tissue-resident memory T cells: local specialists in immune defence. Nat. Rev. Immunol. 16, 79–89 (2016).

    CAS  PubMed  Google Scholar 

  2. 2.

    Stockenhuber, K. et al. Foxp3+ T reg cells control psoriasiform inflammation by restraining an IFN-I–driven CD8+ T cell response. J. Exp. Med. 215, 1987–1998 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Lee, J. C. et al. Gene expression profiling of CD8+ T cells predicts prognosis in patients with Crohn disease and ulcerative colitis. J. Clin. Invest. 121, 4170–4179 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    The Gene Ontology Consortium. The Gene Ontology resource: 20 years and still GOing strong. Nucleic Acids Res. 47, D330–D338 (2019).

    Google Scholar 

  5. 5.

    Cheroutre, H., Lambolez, F. & Mucida, D. The light and dark sides of intestinal intraepithelial lymphocytes. Nat. Rev. Immunol. 11, 445–456 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Silverberg, M. S. et al. Ulcerative colitis-risk loci on chromosomes 1p36 and 12q15 found by genome-wide association study. Nat. Genet. 41, 216–220 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    McGovern, D. P. B. et al. Genome-wide association identifies multiple ulcerative colitis susceptibility loci. Nat. Genet. 42, 332–337 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Saunders, P. M. et al. A bird’s eye view of NK cell receptor interactions with their MHC class I ligands. Immunol. Rev. 267, 148–166 (2015).

    CAS  PubMed  Google Scholar 

  10. 10.

    Hör, S. et al. The T-cell lymphokine interleukin-26 targets epithelial cells through the interleukin-20 receptor 1 and interleukin-10 receptor 2 chains. J. Biol. Chem. 279, 33343–33351 (2004).

    PubMed  Google Scholar 

  11. 11.

    Meller, S. et al. TH17 cells promote microbial killing and innate immune sensing of DNA via interleukin 26. Nat. Immunol. 16, 970–979 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Bahrami, S. & Drabløs, F. Gene regulation in the immediate-early response process. Adv. Biol. Regul. 62, 37–49 (2016).

    CAS  PubMed  Google Scholar 

  13. 13.

    Parikh, K. et al. Colonic epithelial cell diversity in health and inflammatory bowel disease. Nature 567, 49–55 (2019).

    CAS  PubMed  Google Scholar 

  14. 14.

    Mazzurana, L., Rao, A., Van Acker, A. & Mjösberg, J. The roles for innate lymphoid cells in the human immune system. Semin. Immunopathol. 40, 407–419 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Zeng, B. et al. ILC3 function as a double-edged sword in inflammatory bowel diseases. Cell Death Dis. 10, 315 (2019).

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    McKinney, E. F., Lee, J. C., Jayne, D. R. W., Lyons, P. A. & Smith, K. G. C. T-cell exhaustion, co-stimulation and clinical outcome in autoimmunity and infection. Nature 523, 612–616 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Maggi, L. et al. CD161 is a marker of all human IL-17-producing T-cell subsets and is induced by RORC. Eur. J. Immunol. 40, 2174–2181 (2010).

    CAS  PubMed  Google Scholar 

  18. 18.

    Constantinides, M. G., McDonald, B. D., Verhoef, P. A. & Bendelac, A. A committed precursor to innate lymphoid cells. Nature 508, 397–401 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Glanville, J. et al. Identifying specificity groups in the T cell receptor repertoire. Nature 547, 94–98 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Shugay, M. et al. VDJdb: a curated database of T-cell receptor sequences with known antigen specificity. Nucleic Acids Res. 46, D419–D427 (2018).

    CAS  PubMed  Google Scholar 

  21. 21.

    Bartolomé-Casado, R. et al. Resident memory CD8 T cells persist for years in human small intestine. J. Exp. Med. 216, 2412–2416 (2019).

  22. 22.

    Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Fujii, M. et al. Expression of Interleukin-26 is upregulated in inflammatory bowel disease. World J. Gastroenterol. 23, 5519–5529 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Schoenborn, J. R. et al. Comprehensive epigenetic profiling identifies multiple distal regulatory elements directing Ifng transcription. Nat. Immunol. 8, 732–742 (2007).

    PubMed Central  Google Scholar 

  25. 25.

    Collins, P. L., Henderson, M. A. & Aune, T. M. Lineage-specific adjacent IFNG and IL26 genes share a common distal enhancer element. Genes Immun. 13, 481–488 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Lopetuso, L. R. et al. IL-33 promotes recovery from acute colitis by inducing miR-320 to stimulate epithelial restitution and repair. Proc. Natl Acad. Sci. USA 115, E9362–E9370 (2018).

    CAS  PubMed  Google Scholar 

  27. 27.

    Bamias, G., Corridoni, D., Pizarro, T. T. & Cominelli, F. New insights into the dichotomous role of innate cytokines in gut homeostasis and inflammation. Cytokine 59, 451–459 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Grenningloh, R., Kang, B. Y. & Ho, I.-C. Ets-1, a functional cofactor of T-bet, is essential for Th1 inflammatory responses. J. Exp. Med. 201, 615–626 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Vivekanand, P. & Rebay, I. The SAM domain of human TEL2 can abrogate transcriptional output from TEL1 (ETV-6) and ETS1/ETS2. PLoS ONE 7, e37151 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Correia, M. P. et al. Distinct human circulating NKp30+ FcεRIγ+ CD8+ T cell population exhibiting high natural killer-like antitumor potential. Proc. Natl Acad. Sci. USA 115, E5980–E5989 (2018).

    CAS  PubMed  Google Scholar 

  31. 31.

    Bamias, G., Arseneau, K. O. & Cominelli, F. Mouse models of inflammatory bowel disease for investigating mucosal immunity in the intestine. Curr. Opin. Gastroenterol. 33, 411–416 (2017).

    CAS  PubMed  Google Scholar 

  32. 32.

    Ueno, A. et al. Th17 plasticity and its relevance to inflammatory bowel disease. J. Autoimmun. 87, 38–49 (2018).

    CAS  PubMed  Google Scholar 

  33. 33.

    Abraham, C. & Cho, J. Interleukin-23/Th17 pathways and inflammatory bowel disease. Inflamm. Bowel Dis. 15, 1090–1100 (2009).

    PubMed  Google Scholar 

  34. 34.

    Travis, S. P. L. et al. Developing an instrument to assess the endoscopic severity of ulcerative colitis: the Ulcerative Colitis Endoscopic Index of Severity (UCEIS). Gut 61, 535–542 (2012).

    PubMed  Google Scholar 

  35. 35.

    Stoeckius, M. et al. Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol. 19, 224 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Corridoni, D. et al. Genetic deletion of the bacterial sensor NOD2 improves murine Crohn’s disease-like ileitis independent of functional dysbiosis. Mucosal Immunol. 10, 971–982 (2017).

    CAS  PubMed  Google Scholar 

  37. 37.

    Itoh, T. et al. Biological effects of IL-26 on T cell-mediated skin inflammation, including psoriasis. J. Invest. Dermatol. 139, 878–889 (2019).

    CAS  PubMed  Google Scholar 

  38. 38.

    Hatano, R. et al. Characterization of novel anti-IL-26 neutralizing monoclonal antibodies for the treatment of inflammatory diseases including psoriasis. MAbs 11, 1428–1442 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Corridoni, D. et al. Dysregulated NOD2 predisposes SAMP1/YitFc mice to chronic intestinal inflammation. Proc. Natl Acad. Sci. USA 110, 16999–17004 (2013).

    CAS  PubMed  Google Scholar 

  40. 40.

    Andrews, S. FastQC: a quality control tool for high throughput sequence data. Babraham Bioinformatics http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).

  41. 41.

    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10 (2011).

    Google Scholar 

  42. 42.

    Kuhn, R. M., Haussler, D. & Kent, W. J. The UCSC genome browser and associated tools. Brief. Bioinformatics 14, 144–161 (2013).

    CAS  PubMed  Google Scholar 

  43. 43.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  Google Scholar 

  44. 44.

    Wysoker, A., Tibbetts, K. & Fennell, T. Picard tools v.1.90 http://broadinstitute.github.io/picard/ (Broad Institute, 2013).

  45. 45.

    Li, H. et al. The sequence alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Liao, Y., Smyth, G. K. & Shi, W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 41, e108 (2013).

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Karolchik, D. et al. The UCSC table browser data retrieval tool. Nucleic Acids Res. 32, 493D–496D (2004).

    Google Scholar 

  49. 49.

    Griffiths, J. A., Richard, A. C., Bach, K., Lun, A. T. L. & Marioni, J. C. Detection and removal of barcode swapping in single-cell RNA-seq data. Nat. Commun. 9, 2667 (2018).

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Lun, A. T. L. et al. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 20, 63 (2019).

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Scialdone, A. et al. Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods 85, 54–61 (2015).

    CAS  PubMed  Google Scholar 

  53. 53.

    Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression. Nat. Biotechnol. 33, 495–502 (2015).

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).

  55. 55.

    Smillie, C. S. et al. Intra- and inter-cellular rewiring of the human colon during ulcerative colitis. Cell 178, 714–730 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Finak, G. 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).

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Wickham, H. ggplot2 https://doi.org/10.1007/978-3-319-24277-4 (Springer, 2016).

  59. 59.

    Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15, 1484–1506 (2020).

  60. 60.

    Wang, Y. et al. iTALK: an R package to characterize and illustrate intercellular communication. Preprint at https://www.biorxiv.org/content/10.1101/507871v1 (2019).

  61. 61.

    Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).

    PubMed  PubMed Central  Google Scholar 

  62. 62.

    Huynh-Thu, V. A., Irrthum, A., Wehenkel, L. & Geurts, P. Inferring regulatory networks from expression data using tree-based methods. PLoS ONE 5, e12776 (2010).

    PubMed  PubMed Central  Google Scholar 

  63. 63.

    Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Shugay, M. et al. VDJtools: unifying post-analysis of T cell receptor repertoires. PLoS Comput. Biol. 11, e1004503 (2015).

    PubMed  PubMed Central  Google Scholar 

  65. 65.

    Lareau, C. A., Ma, S., Duarte, F. M. & Buenrostro, J. D. Inference and effects of barcode multiplets in droplet-based single-cell assays. Nat. Commun. 11, 866 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Lex, A., Gehlenborg, N., Strobelt, H., Vuillemot, R. & Pfister, H. UpSet: visualization of intersecting sets. IEEE Trans. Vis. Comput. Graph. 20, 1983–1992 (2014).

    PubMed  PubMed Central  Google Scholar 

  67. 67.

    Slowikowski, K., Hu, X. & Raychaudhuri, S. SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci. Bioinformatics 30, 2496–2497 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Kinchen, J. et al. Structural remodeling of the human colonic mesenchyme in inflammatory bowel disease. Cell 175, 372–386 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019).

    CAS  PubMed  Google Scholar 

  70. 70.

    de Lange, K. M. et al. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nat. Genet. 49, 256–261 (2017).

    PubMed  PubMed Central  Google Scholar 

  71. 71.

    Consortium, T. 1000 G. P. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Google Scholar 

  72. 72.

    Zhang, L. et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature 564, 268–272 (2018).

    CAS  PubMed  Google Scholar 

  73. 73.

    Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Zheng, C. et al. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell 169, 1342–1356 (2017).

    CAS  PubMed  Google Scholar 

  75. 75.

    Gao, X., Hu, D., Gogol, M. & Li, H. ClusterMap: compare multiple single cell RNA-Seq datasets across different experimental conditions. Bioinformatics 35, 3038–3045 (2019).

  76. 76.

    Galili, T. dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering. Bioinformatics 31, 3718–3720 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Finck, R. et al. Normalization of mass cytometry data with bead standards. Cytometry A 83A, 483–494 (2013).

    CAS  Google Scholar 

  78. 78.

    Chen, H. et al. Cytofkit: a bioconductor package for an integrated mass cytometry data analysis pipeline. PLoS Comput. Biol. 12, e1005112 (2016).

    PubMed  PubMed Central  Google Scholar 

  79. 79.

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

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank all the patients who contributed to this study, the generous support of our endoscopy teams and the clinical research nurses led by S. Fourie, who made this work possible. We acknowledge support of the MRC WIMM Flow Cytometry, Single Cell and Mass Cytometry facilities, Oxford NIHR Biomedical Research Centre, Oxford Translational Gastroenterology Unit (TGU) Investigators, Targeting Immune Pathways in IBD study investigators, NIHR CRN Thames Valley and the Oxford Single Cell Consortium. We thank O. Acuto (Dunn School, University of Oxford, UK) and T. M. Aune (Vanderbilt University, Nashville, USA) for helpful discussions and technical assistance. This work was supported by a National Institutes of Health Research (NIHR) Senior Investigator Award (to A.S.), a Wellcome Investigator Award (to A.S.), the UK Medical Research Council (to H.K. and A.S.), Crohn’s and Colitis UK (to D.C.), BMS (to A. Aulicino and A. Antanaviciute), the Oxford NIHR Biomedical Research Centre (to K.P.), The Lee Placito Medical Fund (to T.G.) and a Wellcome Trust Clinical Research Fellowship (to D.F.-C.). Data from ref. 55 were obtained from the Broad Data Use Oversight System (DUOS-000110) following institutional approval. We thank and acknowledge the original authors and funders who contributed to this study. The views expressed in this article are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

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Authors

Contributions

D.C., A. Antanaviciute and A.S. designed the project. D.C. and T.G. performed and analyzed experiments. D.F.-C., A. Aulicino, M.J., K.P., H.S. and R.B. performed wet laboratory experiments. D.C., D.I., R.H. and C.M. assisted with mouse models and in vivo experimental design. T.Y. and W.X. assisted with pathology and histology scoring. G.N. assisted with mass cytometry experiments. A. Antanaviciute and H.K. performed computational analysis. E.R. and S.T. assisted with computational analysis. D.C. and A. Antanaviciute performed mass cytometry computational analysis. Writing and editing were carried out by D.C., A. Antanaviciute, T.G., H.K. and A.S. H.K. and A.S. cosupervised. T.G was also cosupervised by O.B. A.S. obtained funding.

Corresponding authors

Correspondence to Hashem Koohy or Alison Simmons.

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The authors declare no competing interests.

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Peer review information Saheli Sadanand was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 Cluster-specific single cell gene expression.

UMAP plot overlays showing selected gene expression distribution across clusters. Cells from n = 3 donors per group.

Extended Data Fig. 2 Differential gene expression and pathway activities in UC.

a, Volcano plot highlighting significantly differentially expressed genes (< 5% FDR, in blue) in selected CD8+ sub-clusters between healthy (n = 3 donors) and UC (n = 3 donors) samples. Selected genes are labelled. Hurdle model likelihood ratio test for differential expression, Benjamini-Hochberg multiple testing correction. b, Emap plot showing clusters of Gene Ontology biological process terms enriched in differentially expressed genes between cells in UC (n = 3 donors) and health (n = 3 donors). Hypergeometric over-representation test, Benjamini-Hochberg multiple testing correction. c, Individual cell AUC score overlay for selected differential canonical pathway activities in cells in health (n = 3 donors) and UC (n = 3 donors). d, UMAP of CD8+ T cells in health (n = 3 donors) and UC (n = 3 donors), clustered by gene expression profiles, demonstrating the expression of selected genes associated with higher risks of developing UC (as identified by GWAS studies) by particular clusters of cells.

Extended Data Fig. 3 Receptor-ligand interactions between CD8+ T-Cells and Epithelial Sub-population.

a, Heatmap displaying the total number of paracrine CD8+ and epithelial cell sub-cluster receptor-ligand interactions discovered using CellphoneDB in clusters in health (left) and UC (right). b, Circos plot showing all putative alterations of cell-cell interaction events in active UC via receptor-ligand pair signalling between T-cell GZMK+ effector cells and epithelial cell sub-types. Putative gain of interaction events and loss of interaction events are shown separately for each CD8+ cluster viewpoint. Abbreviations: SCs: Stem Cells. SPs: Secretory Progenitors. TAs: transit-amplifying cells. APs: absorptive progenitors. CT Colonocytes: Crypt-top colonocytes. EECs: enteroendocrine cells. NC: No change. c, Dotplot of selected significant paracrine receptor-ligand interactions between GZMK + Effector CD8+ cells (cells from n = 3 donors per group) and epithelial cells (cells from n = 3 donors per group) discovered using CellphoneDB. CellphoneDB empirical permutation p-value.

Extended Data Fig. 4 Distribution of IL26 receptor expression in epithelial and mesenchymal compartments.

a, tSNE plots showing expression distribution of IL10RA and IL-26 receptor genes, IL10RB and IL20RA, in single cell colonic epithelium dataset in health (n = 3 donors) and UC (n = 3 donors) from (a) Parikh et al, 2019; (b) colonic mesenchymal dataset in health (n = 2 donors) and UC(n = 2 donors) from Kinchen et al, 2018. c, Violin plots (median shown) comparing expression of IL-26 receptor genes in health (cells from n = 3 donors) and UC (cells from n = 3 donors) in colonic epithelium. d, Violin plots (median shown) comparing expression of IL10RA and IL-26 receptor genes in health (cells from n = 2 donors) and UC (cells from n = 2 donors) in colonic mesenchyme. Undifferentiated cells, encompassing stem cells, transit amplifying cells and secretory and absorptive progenitor cells are denoted as ‘Undiff.’.

Extended Data Fig. 5 Cluster-specific transcription factor module activities.

a, Heatmap visualising relative transcription factor module activity (as AUC scores) for all detected transcription factor modules in cells from healthy (n = 3) and UC (n = 3) donors. b, UMAP overlay showing selected transcription factor network activity distribution at single cell level in cells from healthy (n = 3) and UC (n = 3) donors.

Extended Data Fig. 6 Exploration of innate-like CD8+ T-cells in health and UC.

a. UMAP plot visualising MAIT cell sub-populations in cells from healthy (n = 3) and UC (n = 3) donors. b. Violin plots (median shown) visualising distributions of selected marker genes for MAIT cell sub-clusters in cells from healthy (n = 3) and UC (n = 3) donors. c. UMAP plot overlay visualising single cell expression of selected genes in cells from healthy (n = 3) and UC (n = 3) donors. d. Boxplot showing expression cluster distribution of IEL-specific marker genes in CD8+ sub-populations in cells from healthy (n = 3) and UC (n = 3) donors. Boxplots show the median, first and third quartiles, 5th percentile as minima and 95th percentile as maxima e. UMAP plot overlay visualising single cell expression of selected genes in cells from healthy (n = 3) and UC (n = 3) donors.

Extended Data Fig. 7 CD8+ TILs share features of exhaustion, but not Type 17 signature with colonic IL26+ cells.

a, Hierarchical clustering of CD8+ sub-populations detected in UC (cells from n = 3 donors) together with CD8+ sub-populations detected in liver cancer tumor-infiltrating lymphocytes (TILs) (left) (cells from n = 6 donors), colorectal cancer TILs (middle)(cells from n = 11 donors) and breast cancer TILs (right) (cells from n = 1 donor). b, Boxplot showing expression of selected exhaustion (top panels) and Type-17/ILC3 (bottom panels) signature genes in colonic CD8+ populations and tumour CD8+ TILs. Boxplots show the median, first and third quartiles, 5th percentile as minima and 95th percentile as maxima.

Extended Data Fig. 8 Distribution of transcriptomic and proteomic CITE-Seq profiles in health and UC.

a, UMAP overlay of donor cell-of-origin distribution for mRNA clusters obtained from integrated scRNA-Seq (cells from n = 3 donors per group) and CITE-Seq cohorts (cells from n = 7 UC donors and n = 5 HC donors). b, Overlay of original clusters obtained from scRNA-Seq clusters onto the integrated scRNA-Seq (cells from n = 3 donors per group) and CITE-Seq (cells from n = 7 UC donors and n = 5 HC donors) cohorts, c, Boxplots showing the proportion of all CD8+ cells for each joint cluster, split by original scRNA-Seq (cells from n = 3 donors per group) and CITE-Seq cohorts (cells from n = 7 UC donors and n = 5 HC donors). Boxplots show the median, first and third quartiles, 5th percentile as minima and 95th percentile as maxima. d, Overlay of protein expression for selected proteins onto UMAP driven by mRNA expression (cells from n = 7 UC donors and n = 5 HC donors). e, Quantification of IL-26 protein levels from colonic CD8+ T cells of healthy (n = 6) and UC-inflamed donors (n = 6) following polyclonal activation with anti-CD3/anti-CD28. Mean and SEM are shown; *P = 0.0152, two-tailed Mann–Whitney test.

Source data

Extended Data Fig. 9 Heterogeneity of colonic CD8+ T-cells in health and UC by CyTOF.

a, Representative gating strategy to identify CD45+ CD3+ CD8+ cell population in Mass Cytometry experiments used for further analysis; representative of six samples (n = 6 donors) where same sorting strategy was applied. b, tSNE plot to visualise proportion of Phenograph clusters within CD45+CD3+CD8+ cells from healthy (n = 3) and UC (n = 3) donors. Selected clusters are annotated based on their phenotype. c, Bar graph showing significant sub-population changes in UC (n = 3 donors) compared to healthy (n = 3 donors). Cluster 1, t = 10.78, DF = 4, ***P = 0.0004; Cluster 3, t = 4.354, DF = 4, *P = 0.0121; Cluster 4, t = 3.072, DF = 4, *P = 0.0372; Cluster 9, t = 3.476, DF = 4, *P = 0.0254; Cluster 12, t = 3.287, DF = 4, *P = 0.0303; Cluster 14, t = 5.911, DF = 4, **P = 0.0041. Mean and SEM are shown; two-tailed unpaired t-test. d, tSNE overlay of selected protein expression markers in cells from healthy (n = 3) and UC (n = 3) donors.

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Extended Data Fig. 10 IL26 attenuates severity of acute DSS colitis and induces transcriptional changes at baseline in a humanised mouse model.

a, Representative photomicrographs of H&E-stained colonic tissues of wild-type (WT, n = 4) and humanized IL-26 transgenic (hIL-26Tg, n = 4) mice (original magnification: 20×) at steady state. b, Colonic inflammatory scores for chronic inflammation, active inflammation, transmural index and percentage of ulceration (n = 4 control mice, n = 6 DSS treatment mice, n = 5 DSS treatment + anti-IL26 mice) in hIL-26Tg and WT mice at steady state and post-DSS challenge. F = 4.230, DF = 2, *P = 0.0438 (hIL26Tg DSS vs. hIL26Tg with anti-IL26 DSS); mean and SEM shown, one-way ANOVA (Tukey’s multiple comparison test). c, Comparative expression of selected genes under DSS challenge between WT (n = 3 DSS, n = 4 control mice) and hIL-26Tg mice (n = 4 mice per group), demonstrating lower inflammatory signatures in hIL-26 expressing Tg mice. Boxplots show the median, first and third quartiles, 5th percentile as minima and 95th percentile as maxima d, Cytokine, chemokine and epithelial cell markers mRNA expression measured by qPCR in whole-colon tissue from experimental mice (n = 4 control, n = 6 DSS treatment, n = 5 DSS treatment + anti-IL26 mice). Expression was averaged for mice within each group and converted to z scores.

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Additional scRNA-seq- and CITE–seq-derived data.

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Corridoni, D., Antanaviciute, A., Gupta, T. et al. Single-cell atlas of colonic CD8+ T cells in ulcerative colitis. Nat Med 26, 1480–1490 (2020). https://doi.org/10.1038/s41591-020-1003-4

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