Repair of tissue damaged during inflammatory processes is key to the return of local homeostasis and restoration of epithelial integrity. Here we describe CD161+ regulatory T (Treg) cells as a distinct, highly suppressive population of Treg cells that mediate wound healing. These Treg cells were enriched in intestinal lamina propria, particularly in Crohn’s disease. CD161+ Treg cells had an all-trans retinoic acid (ATRA)-regulated gene signature, and CD161 expression on Treg cells was induced by ATRA, which directly regulated the CD161 gene. CD161 was co-stimulatory, and ligation with the T cell antigen receptor induced cytokines that accelerated the wound healing of intestinal epithelial cells. We identified a transcription-factor network, including BACH2, RORγt, FOSL2, AP-1 and RUNX1, that controlled expression of the wound-healing program, and found a CD161+ Treg cell signature in Crohn’s disease mucosa associated with reduced inflammation. These findings identify CD161+ Treg cells as a population involved in controlling the balance between inflammation and epithelial barrier healing in the gut.
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The data generated for this study have been deposited at the Gene Expression Omnibus (GEO) under accession code GSE119375.
Edozie, F. C. et al. Regulatory T-cell therapy in the induction of transplant tolerance: the issue of subpopulations. Transplantation 98, 370–379 (2014).
Sakaguchi, S., Wing, K., Onishi, Y., Prieto-Martin, P. & Yamaguchi, T. Regulatory T cells: How do they suppress immune responses? Int. Immunol. 21, 1105–1111 (2009).
Roychoudhuri, R. et al. BACH2 represses effector programs to stabilize Treg-mediated immune homeostasis. Nature 498, 506–510 (2013).
Yadav, M. et al. Neuropilin-1 distinguishes natural and inducible regulatory T cells among regulatory T cell subsets in vivo. J. Exp. Med. 209, 1713–1722 (2012).
Thornton, A. M. et al. Expression of Helios, an Ikaros transcription factor family member, differentiates thymic-derived from peripherally induced Foxp3+ T regulatory cells. J. Immunol. 184, 3433–3441 (2010).
Floess, S. et al. Epigenetic control of the foxp3 locus in regulatory T cells. PLoS Biol. 5, e38 (2007).
Ohkura, N. et al. T cell receptor stimulation-induced epigenetic changes and Foxp3 expression are independent and complementary events required for Treg cell development. Immunity 37, 785–799 (2012).
Polansky, J. K. et al. DNA methylation controls Foxp3 gene expression. Eur. J. Immunol. 38, 1654–1663 (2008).
Shih, H.-Y. et al. Transcriptional and epigenetic networks of helper T and innate lymphoid cells. Immunol. Rev. 261, 23–49 (2014).
Oldenhove, G. et al. Decrease of Foxp3+ Treg cell number and acquisition of effector cell phenotype during lethal infection. Immunity 31, 772–786 (2009).
Chaudhry, A. et al. CD4+ regulatory T cells control TH17 responses in a Stat3-dependent manner. Science 326, 986–991 (2009).
Zheng, Y. et al. Regulatory T-cell suppressor program co-opts transcription factor IRF4 to control TH2 responses. Nature 458, 351–356 (2009).
Koch, M. A. et al. The transcription factor T-bet controls regulatory T cell homeostasis and function during type 1 inflammation. Nat. Immunol. 10, 595–602 (2009).
Wohlfert, E. A. et al. GATA3 controls Foxp3+ regulatory T cell fate during inflammation in mice. J. Clin. Invest. 121, 4503–4515 (2011).
Yu, F., Sharma, S., Edwards, J., Feigenbaum, L. & Zhu, J. Dynamic expression of transcription factors T-bet and GATA-3 by regulatory T cells maintains immunotolerance. Nat. Immunol. 16, 197–206 (2015).
Kordasti, S. et al. Deep phenotyping of Tregs identifies an immune signature for idiopathic aplastic anemia and predicts response to treatment. Blood 128, 1193–1205 (2016).
Miyara, M. et al. Functional delineation and differentiation dynamics of human CD4+ T cells expressing the FoxP3 transcription factor. Immunity 30, 899–911 (2009).
Booth, N. J. et al. Different proliferative potential and migratory characteristics of human CD4+ regulatory T cells that express either CD45RA or CD45RO. J. Immunol. 184, 4317–4326 (2010).
Maloy, K. J. & Powrie, F. Intestinal homeostasis and its breakdown in inflammatory bowel disease. Nature 474, 298–306 (2011).
Povoleri, G. A. M. et al. Thymic versus induced regulatory T cells—Who regulates the regulators. Front. Immunol. 4, 169 (2013).
Maloy, K. J. et al. CD4+CD25+ T(R) cells suppress innate immune pathology through cytokine-dependent mechanisms. J. Exp. Med. 197, 111–119 (2003).
Maul, J. et al. Peripheral and intestinal regulatory CD4+CD25(high) T cells in inflammatory bowel disease. Gastroenterology 128, 1868–1878 (2005).
Afzali, B. et al. CD161 expression characterizes a subpopulation of human regulatory T cells that produces IL-17 in a STAT3-dependent manner. Eur. J. Immunol. 43, 2043–2054 (2013).
Lanier, L. L., Chang, C. & Phillips, J. H. Human NKR-P1A. A disulfide-linked homodimer of the C-type lectin superfamily expressed by a subset of NK and T lymphocytes. J. Immunol. 153, 2417–2428 (1994).
Fergusson, J. R. et al. CD161 defines a transcriptional and functional phenotype across distinct human T cell lineages. Cell Rep. 9, 1075–1088 (2014).
Cosmi, L. et al. Human interleukin 17-producing cells originate from a CD161+CD4+ T cell precursor. J. Exp. Med. 205, 1903–1916 (2008).
Germain, C. et al. Induction of lectin-like transcript 1 (LLT1) protein cell surface expression by pathogens and interferon-γ contributes to modulate immune responses. J. Biol. Chem. 286, 37964–37975 (2011).
Wolfkamp, S. C. S. et al. Single nucleotide polymorphisms in C-type lectin genes, clustered in the IBD2 and IBD6 susceptibility loci, may play a role in the pathogenesis of inflammatory bowel diseases. Eur. J. Gastroenterol. Hepatol. 24, 965–970 (2012).
Diggins, K. E., Ferrell, P. B. & Irish, J. M. Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data. Methods 82, 55–63 (2015).
Qiu, P. et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotechnol. 29, 886–891 (2011).
Thomas, S. Y. et al. CD1d-restricted NKT cells express a chemokine receptor profile indicative of Th1-type inflammatory homing cells. J. Immunol. 171, 2571–2580 (2003).
Venturi, V. et al. Method for assessing the similarity between subsets of the T cell receptor repertoire. J. Immunol. Methods 329, 67–80 (2008).
Ferraro, A. et al. Interindividual variation in human T regulatory cells. Proc. Natl Acad. Sci. USA 111, E1111–E1120 (2014).
Kim, Y. C. et al. Oligodeoxynucleotides stabilize Helios-expressing Foxp3+ human T regulatory cells during in vitro expansion. Blood 119, 2810–2818 (2012).
Scotta, C. et al. Differential effects of rapamycin and retinoic acid on expansion, stability and suppressive qualities of human CD4+CD25+FOXP3+ T regulatory cell subpopulations. Haematologica 98, 1291–1299 (2013).
Afzali, B. et al. Comparison of regulatory T cells in hemodialysis patients and healthy controls: implications for cell therapy in transplantation. Clin. J. Am. Soc. Nephrol. 8, 1396–1405 (2013).
Thornton, A. M. & Shevach, E. M. CD4+CD25+ immunoregulatory T cells suppress polyclonal T cell activation in vitro by inhibiting interleukin 2 production. J. Exp. Med. 188, 287–296 (1998).
Cao, X. et al. Granzyme B and perforin are important for regulatory T cell-mediated suppression of tumor clearance. Immunity 27, 635–646 (2007).
Rosen, D. B. et al. Functional consequences of interactions between human NKR-P1A and its ligand LLT1 expressed on activated dendritic cells and B cells. J. Immunol. 180, 6508–6517 (2008).
Kitoh, A. et al. Indispensable role of the Runx1-Cbfβ transcription complex for in vivo-suppressive function of FoxP3+ regulatory T cells. Immunity 31, 609–620 (2009).
Ciofani, M. et al. A validated regulatory network for Th17 cell specification. Cell 151, 289–303 (2012).
Afzali, B. et al. BACH2 immunodeficiency illustrates an association between super-enhancers and haploinsufficiency. Nat. Immunol. 18, 813–823 (2017).
Hong, S. N. et al. RNA-seq reveals transcriptomic differences in inflamed and noninflamed intestinal mucosa of Crohn’s disease patients compared with normal mucosa of healthy controls. Inflamm. Bowel Dis. 23, 1098–1108 (2017).
Sefik, E. et al. Individual intestinal symbionts induce a distinct population of RORγ+ regulatory T cells. Science 349, 993–997 (2015).
Kim, B.-S. et al. Generation of RORγt+ antigen-specific T regulatory 17 cells from Foxp3+ precursors in autoimmunity. Cell Rep. 21, 195–207 (2017).
Hovhannisyan, Z., Treatman, J., Littman, D. R. & Mayer, L. Characterization of interleukin-17-producing regulatory T cells in inflamed intestinal mucosa from patients with inflammatory bowel diseases. Gastroenterology 140, 957–965 (2011).
Blatner, N. R. et al. Expression of RORγt marks a pathogenic regulatory T cell subset in human colon cancer. Sci. Trans. Med. 4, 164ra159 (2012).
Komatsu, N. et al. Pathogenic conversion of Foxp3+ T cells into TH17 cells in autoimmune arthritis. Nat. Med. 20, 62–68 (2014).
Yang, B.-H. et al. Foxp3+ T cells expressing RORγt represent a stable regulatory T-cell effector lineage with enhanced suppressive capacity during intestinal inflammation. Mucosal Immunol 9, 444–457 (2016).
Nosbaum, A. et al. Cutting edge: regulatory T cells facilitate cutaneous wound healing. J. Immunol. 196, 2010–2014 (2016).
O’Connor, W. et al. A protective function for interleukin 17A in T cell-mediated intestinal inflammation. Nat. Immunol. 10, 603–609 (2009).
Hueber, W. et al. Secukinumab, a human anti-IL-17A monoclonal antibody, for moderate to severe Crohn’s disease: unexpected results of a randomised, double-blind placebo-controlled trial. Gut 61, 1693–1700 (2012).
Lindemans, C. A. et al. Interleukin-22 promotes intestinal-stem-cell-mediated epithelial regeneration. Nature 528, 560–564 (2015).
Zenewicz, L. A. et al. Innate and adaptive interleukin-22 protects mice from inflammatory bowel disease. Immunity 29, 947–957 (2008).
Amir, E.-A. D. et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545–552 (2013).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Barrientos, S., Stojadinovic, O., Golinko, M. S., Brem, H. & Tomic-Canic, M. Growth factors and cytokines in wound healing. Wound Repair Regen. 16, 585–601 (2008).
Deonarine, K. et al. Gene expression profiling of cutaneous wound healing. J. Transl. Med. 5, 11 (2007).
Peake, M. A. et al. Identification of a transcriptional signature for the wound healing continuum. Wound Repair Regen. 22, 399–405 (2014).
Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
Gong, W., Kwak, I.-Y., Pota, P., Koyano-Nakagawa, N. & Garry, D. J. DrImpute: imputing dropout events in single cell RNA sequencing data. BMC Bioinformatics 19, 220 (2018).
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
Zheng, Y. et al. Role of conserved non-coding DNA elements in the Foxp3 gene in regulatory T-cell fate. Nature 463, 808–812 (2010).
Robins, H. S. et al. Comprehensive assessment of T-cell receptor beta-chain diversity in alphabeta T cells. Blood 114, 4099–4107 (2009).
Carlson, C. S. et al. Using synthetic templates to design an unbiased multiplex PCR assay. Nat. Commun. 4, 2680 (2013).
Mathelier, A. et al. JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 44, D110–D115 (2016).
Rovedatti, L. et al. Differential regulation of interleukin 17 and interferon gamma production in inflammatory bowel disease. Gut 58, 1629–1636 (2009).
Cooke, K. R. et al. An experimental model of idiopathic pneumonia syndrome after bone marrow transplantation: I. The roles of minor H antigens and endotoxin. Blood 88, 3230–3239 (1996).
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
Shih, H.-Y. et al. Developmental acquisition of regulomes underlies innate lymphoid cell functionality. Cell 165, 1120–1133 (2016).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Ye, T. et al. seqMINER: an integrated ChIP-seq data interpretation platform. Nucleic Acids Res. 39, e35 (2011).
Häsler, R. et al. Uncoupling of mucosal gene regulation, mRNA splicing and adherent microbiota signatures in inflammatory bowel disease. Gut 66, 2087–2097 (2016).
The authors thank patients who contributed samples toward this study. This work was supported by the Wellcome Trust (grant 097261/Z/11/Z to B.A. and WT101159 to N.P.), the Crohn’s and Colitis Foundation of America (grant CCFA no. 3765 — CCFA genetics initiative to A.L.), British Heart Foundation (grant RG/13/12/30395 to G.L.), institutional start-up fund from Purdue University and National Heart, Lung, and Blood Institute (grant 5K22HL125593-02 to M.K.). Research was also supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy’s and St Thomas’ National Health Service (NHS) Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. This research was supported (in part) by the Intramural Research Programs of the National Institute of Arthritis and Musculoskeletal and Skin Diseases, the National Institute of Diabetes and Digestive and Kidney Diseases and the National Heart, Lung, and Blood Institute of the National Institutes of Health. The authors thank J. O’Shea (National Institutes of Health) for his support and for providing access to ATAC-seq, the National Heart, Lung, and Blood Institute DNA Sequencing and Genomics Core for performing single-cell sequencing experiment and acknowledge the assistance of M. Arno (Genomics Centre, King’s College London) with gene expression microarray studies as well as S. Heck and R. Ellis (Biomedical Research Centre Flow Core Facility, King’s College London) for CyTOF data acquisition. In addition, the authors thank E. Mathé (Ohio State University) for critically reading the manuscript.
The authors declare no competing interests.
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Integrated supplementary information
(a) Heat map showing signal intensity of selected markers for each node of the populations shown in Fig. 1c. (b–e) Clustering of populations of Tconv and Treg cells by single-cell RNA-seq (scRNAseq) showing the tSNE plot of 2,636 cells from n = 3 independent donors separated into six major (clusters 0–6) and two minor (clusters 7 and 8) clusters (b), feature plots showing expression of three Treg markers (IL2RA encoding CD25, IL7R encoding CD127 and FOXP3 encoding FOXP3), KLRB1 (encoding CD161) and two naive/memory markers (SELL encoding CD62L and CCR7 encoding CCR7) (c), a heat map showing the top 20 discriminatory genes per cluster with representative genes of each cluster indicated (d) and population clustering (e) based on expression of the transcripts shown in c. Clusters in b are color-coded and labeled according to expression of the markers shown in e. (f) Clustering of Tconv and Treg subpopulations using protein expression data sourced from CyTOF in Fig. 1a incorporating the same markers as in e and also including CD45RA and CD45RO. Clustering of subpopulations in f is similar to that from scRNAseq (e). (g,h) viSNE plots (g) and 2D minimum spanning tree (h) of CD4+ T cells clustered following staining with anti-CD4, anti-CD25, anti-CD127, anti-CD45RA and anti-CD161 for flow cytometry. Shown in g are heat maps for expression of the indicated markers, with an arrow indicating CD161 expression in Treg cells. Node size in h represents cell number and color CD161 median intensity. Grouped together are naive (circled in orange), memory (circled in yellow) and CD161+ (circled in purple) Treg cells, as well as populations of naive (circled in black), memory (circled in black) and CD161+ (circled in red) Tconv cells. The data in g and h are representative of n = 4 experiments. *P < 0.01, **P < 0.001, ***P < 0.0001 by one-way ANOVA.
(a) Representative example of the FACS sorting strategy for naive (orange), memory (yellow) and CD161+ (purple) Treg cells. (b) Proportions of each Treg subpopulation in healthy human donor peripheral blood (cumulative data from n = 10 donors); bars show mean + s.e.m. (c) Representative (top) and cumulative (bottom) expression of TCR Vα24-Jα18 and Vα7.2 in total CD4+ T cells and CD161+ Treg cells. Both cell types show minimal expression of these invariant TCR chains. Data are from n = 3 independent experiments. (d) Contribution of the three highest (BV05, BV06 and BV07) and one of the lowest (BV19) TCRBV families to the overall TCR repertoire in naive, memory and CD161+ Treg cells as well as CD161+ and total Tconv cells. Data are from n = 3 independent experiments; bars show mean + s.e.m. (e) Average percentage of TCR sequences either unique or shared among naive, memory and CD161+ Treg cells, CD161+ and total Tconv cells (cumulative data from n = 3 experiments). *P < 0.01, **P < 0.001, ***P < 0.0001 by one-way ANOVA.
(a) Principal-component analysis (PCA) 2D mapping of variance in expression of transcripts in freshly isolated naive, memory and CD161+ Treg cells (n = 3). (b–d) Scatterplots showing correlation between mean gene expression of freshly isolated Treg subpopulations (n = 3). (e,f) GSEA plots for core human Treg signature genes comparing freshly isolated naive to memory Treg cells (e) and to CD161+ Tregs cells (f) (n = 3 per group). NES, normalized enrichment score; empirical P and multiple-test adjusted q values from GSEA are shown. (g) Schematic representation of FOXP3, IL2RA and CTLA4 gene loci showing the target sequence and conserved CpGs assayed for methylation analysis in Fig. 2e. (h) Mean region methylation percentage of FOXP3 TSDR, IL2RA and CTLA4 loci from three male donors. Data are from n = 3 independent experiments; bars show mean + s.e.m. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001 by one-way ANOVA.
(a) Heat map of leading edge (core enriched) genes from GSEA for ATRA-regulated genes comparing freshly isolated memory to CD161+ Treg cells (Fig. 3a). Data are from n = 3 independent experiments. (b) CCR9 surface protein expression on memory and CD161+ Treg cells; shown are representative flow cytometry plots (left) and cumulative data from n = 3 independent experiments (right). (c) CD161 expression on Treg cells co-cultured for 5 d with DCs with and without BMS493 at increasing concentrations. Shown are representative plots (left) and cumulative data (right) from n = 3 independent experiments. (d,e) Sequence logo for RARA DNA motif (d) and schematic representation of the KLRB1 and CCR9 gene loci (±5 kb) showing the predicted binding sites of RARA (e); the red arrowhead indicates the binding site for CCR9 selected for analysis in ChIP–qPCR. Bars show mean + s.e.m. throughout the figure. *P < 0.05 by paired t-test.
(a,b) Cumulative mean percentage suppressive function of freshly isolated naive, memory and CD161+ Treg cells from Fig. 4a (n = 4 experiments) (a) and representative IC50 value calculation (b; dashed arrows show the IC50 for CD161+ and memory Treg cells). (c) Cumulative mean percentage suppressive function of memory and CD161+ Treg cells after in vitro expansion for 2 weeks (n = 4 experiments). (d) Suppression of Tconv cell proliferation when in direct contact with Treg cells or when separated by a Transwell at a Treg:Tconv ratio of 1:1 (cumulative data from n = 4 independent experiments). (e) Suppressive function of Treg cells under neutral (null) conditions or in the presence of blocking antibodies directed against CD161, PDL1, TGFβRII or IL10R, all at a Treg:Tconv ratio of 1:2 (cumulative data from n = 3 independent experiments). (f) Suppressive function of CD161+ Treg cells under neutral (null) conditions or in the presence of TH1 and TH17 skewing conditions, all at a Treg:Tconv ratio of 1:2 (cumulative data from n = 3 independent experiments). (g) Expression of perforin, granzyme A and B in subpopulations of Treg cells, with NK cells as a positive control; shown are representative flow cytometry plots for each marker (left) and cumulative data from n = 3 independent experiments (right). Bar charts show mean + s.e.m. throughout the figure. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 by two-way ANOVA (a–e) and one-way ANOVA (g).
(a) Differentially expressed genes (at least twofold change at P < 0.05) following 4 h of stimulation of memory and naive Treg cells with anti-CD3/CD28. (b) Volcano plot showing significant genes differentially expressed in CD161+ Treg cells after 4 h of activation with anti-CD3/CD28. Differentially expressed genes are shown in blue; red indicates significantly upregulated cytokine genes that are differentially expressed compared to memory Treg cells (see Fig. 5c); orange indicates other cytokine genes of interest upregulated in activated CD161+ Treg cells. The data in a are b are from three independent experiments. (c,d) Cell sorting of IL-17+ and IL-17– CD161+ Treg cells by surface IL-17 capture. Shown are representative flow cytometry plots with double staining for intracellular and extracellular (captured on the surface) IL-17 with (right) and without (left) fixation and permeabilization (c) and the sorting strategy using IL-17 surface capture and post-sorting purity for the IL-17+ and IL-17– fractions of CD161+ Treg cells (d). Shown in c and d are representative examples from n = 3 and n = 4 independent experiments, respectively.
(a) Open chromatin regions (OCRs) at prototypical Treg gene loci from Fig. 6a. (b) Genomic distribution of ATAC peaks (promoter, intragenic or intergenic regions) in the three groups shown in Fig. 6a. (c) P values (cumulative hypergeometric test P values calculated by Homer) for transcription factor (TF) motifs shown in each cluster in Fig. 6c. Data in a–c are from n = 3 experiments. (d) Expression of key TFs participating in gene regulation in CD161+ Treg cells. Shown is the normalized signal intensity (mean + s.e.m.) from n = 3 microarrays for each Treg population. (e) Integrated TF network showing the contribution of each TF to DEGs and overlap between them (fold difference in expression of each TF is also indicated next to the TF). *P < 0.05, **P < 0.01, ***P < 0.001 (Partek analysis of microarrays).
(a,b) GSEA plots for wound healing–associated genes (a) and wound healing–associated soluble mediators (b) comparing activated CD161+ to memory Treg cells. Data are from n = 3 independent experiments. NES, normalized enrichment score; empirical P and multiple-test-adjusted q values from GSEA are indicated. (c) Wound closure rate of Caco-2 cells cultured in the presence of either medium alone or medium supplemented with culture supernatants (snt) from activated memory or CD161+ Treg cells. Shown are cumulative data from n = 6 independent experiments. (d) Concentration (pg/ml) of stated cytokines in supernatants of CD161+ Treg cells transduced, or not, with control lentivirus or lentivirus encoding BACH2 (cumulative data from n = 6 experiments). Bar charts show mean + s.e.m.; *P < 0.05, **P < 0.01 by one-way ANOVA.
Supplementary Figures 1–8
List of differentially expressed genes (DEGs)
Expression of killer cell lectin-like receptors (Klr) in wild-type mouse Treg cells
Open chromatin regions (OCRs) from ATAC-seq
Gene lists used for gene set enrichment analyses
Gene lists used for gene set enrichment analyses
Wound healing with medium alone. The video shows growth of Caco-2 cells cultured with medium alone. Time-lapse images were recorded from 0–120 h. Shown are representative videos from n = 3 independent experiments
Wound healing with memory Treg cell supernatants. The video shows growth of Caco-2 cells cultured with medium supplemented with culture supernatants (snt) of activated memory cells. Time-lapse images were recorded from 0–120 h. Shown are representative videos from n = 3 independent experiments
Wound healing with CD161+ Treg cell supernatants. The video shows growth of Caco-2 cells cultured with medium supplemented with culture supernatants (snt) of CD161+ Treg cells. Time-lapse images were recorded from 0–120 h. Shown are representative videos from n = 3 independent experiments
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Povoleri, G.A.M., Nova-Lamperti, E., Scottà, C. et al. Human retinoic acid–regulated CD161+ regulatory T cells support wound repair in intestinal mucosa. Nat Immunol 19, 1403–1414 (2018). https://doi.org/10.1038/s41590-018-0230-z
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