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

Nature Medicine volume 26, pages 1480–1490 (2020)Cite this article

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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.

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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.

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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.

Author information

Author notes

  1. These authors contributed equally: Daniele Corridoni, Agne Antanaviciute, Tarun Gupta.

Authors and Affiliations

  1. Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK

    Daniele Corridoni, Agne Antanaviciute, Tarun Gupta, David Fawkner-Corbett, Anna Aulicino, Marta Jagielowicz, Kaushal Parikh, Giorgio Napolitani, Hashem Koohy & Alison Simmons

  2. Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, Oxford, UK

    Daniele Corridoni, Tarun Gupta, David Fawkner-Corbett, Anna Aulicino, Marta Jagielowicz, Kaushal Parikh, Oliver Brain & Alison Simmons

  3. MRC WIMM Centre For Computational Biology, MRC WIMM, John Radcliffe Hospital, University of Oxford, Oxford, UK

    Agne Antanaviciute & Hashem Koohy

  4. Computational Biology Research Group, University of Oxford, John Radcliffe Hospital, Oxford, UK

    Emmanouela Repapi & Steve Taylor

  5. Department of Gastroenterology, Juntendo University School of Medicine, Tokyo, Japan

    Dai Ishikawa

  6. Department of Therapy Development and Innovation for Immune Disorders and Cancers, Juntendo University, Tokyo, Japan

    Ryo Hatano & Chikao Morimoto

  7. Department of Pathology, Saitama Medical University, Saitama, Japan

    Taketo Yamada

  8. Department of Pathology, Case Western Reserve University, Cleveland, OH, USA

    Wei Xin

  9. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK

    Hubert Slawinski & Rory Bowden

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  1. Daniele Corridoni
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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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Competing interests

The authors declare no competing interests.

Additional information

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.

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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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