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Stepwise chromatin and transcriptional acquisition of an intraepithelial lymphocyte program

Nature Immunology volume 22, pages 449–459 (2021)Cite this article

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Abstract

Mesenteric lymph node (mLN) T cells undergo tissue adaptation upon migrating to intestinal lamina propria and epithelium, ensuring appropriate balance between tolerance and resistance. By combining mouse genetics with single-cell and chromatin analyses, we uncovered the molecular imprinting of gut epithelium on T cells. Transcriptionally, conventional and regulatory (Treg) CD4+ T cells from mLN, lamina propria and intestinal epithelium segregate based on the gut layer they occupy; trajectory analysis suggests a stepwise loss of CD4 programming and acquisition of an intraepithelial profile. Treg cell fate mapping coupled with RNA sequencing and assay for transposase-accessible chromatin followed by sequencing revealed that the Treg cell program shuts down before an intraepithelial program becomes fully accessible at the epithelium. Ablation of CD4-lineage-defining transcription factor ThPOK results in premature acquisition of an intraepithelial lymphocyte profile by mLN Treg cells, partially recapitulating epithelium imprinting. Thus, coordinated replacement of the circulating lymphocyte program with site-specific transcriptional and chromatin changes is necessary for tissue imprinting.

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Fig. 1: IE imprints a cytotoxic program on migrating CD4+ T cells.
Fig. 2: Tissue segregation precedes acquisition of an IEL profile.
Fig. 3: Treg program shutdown precedes IEL programming through a pre-IEL stage.
Fig. 4: Runx BMs are increasingly accessible during the iTreg-to-IEL progression.
Fig. 5: Abrogation of ThPOK in Treg cells anticipates progression to IELs at the transcriptional level.
Fig. 6: Abrogation of ThPOK in Treg cells enhances progression to IELs at the chromatin level.

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Data availability

All generated raw and processed sequencing data for this study (all main figures) can be accessed at GEO: GSE146292 and BioProject: PRJNA610060. Publicly available data on ThPOK motif were obtained24 and can be accessed at GEO: GSE116506 and BioProject: PRJNA478869. All other data that support the findings of this study are available from the corresponding author upon request.

References

  1. Mucida, D. et al. Oral tolerance in the absence of naturally occurring Tregs. J. Clin. Invest. 115, 1923–1933 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Bilate, A. M. & Lafaille, J. J. Induced CD4+Foxp3+ regulatory T cells in immune tolerance. Annu. Rev. Immunol. 30, 733–758 (2012).

    Article  CAS  PubMed  Google Scholar 

  3. Furusawa, Y. et al. Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells. Nature 504, 446–450 (2013).

    Article  CAS  PubMed  Google Scholar 

  4. Hadis, U. et al. Intestinal tolerance requires gut homing and expansion of FoxP3+ regulatory T cells in the lamina propria. Immunity 34, 237–246 (2011).

    Article  CAS  PubMed  Google Scholar 

  5. Sujino, T. et al. Tissue adaptation of regulatory and intraepithelial CD4+ T cells controls gut inflammation. Science 352, 1581–1586 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Atarashi, K. et al. Induction of colonic regulatory T cells by indigenous Clostridium species. Science 331, 337–341 (2011).

    Article  CAS  PubMed  Google Scholar 

  7. Atarashi, K. et al. Treg induction by a rationally selected mixture of Clostridia strains from the human microbiota. Nature 500, 232–236 (2013).

    Article  CAS  PubMed  Google Scholar 

  8. Olivares-Villagómez, D. & Van Kaer, L. Intestinal intraepithelial lymphocytes: sentinels of the mucosal barrier. Trends Immunol. 39, 264–275 (2018).

    Article  PubMed  Google Scholar 

  9. McDonald, B. D., Jabri, B. & Bendelac, A. Diverse developmental pathways of intestinal intraepithelial lymphocytes. Nat. Rev. Immunol. 18, 514–525 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Vantourout, P. & Hayday, A. Six-of-the-best: unique contributions of γδ T cells to immunology. Nat. Rev. Immunol. 13, 88–100 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Reis, B. S., Rogoz, A., Costa-Pinto, F. A., Taniuchi, I. & Mucida, D. Mutual expression of the transcription factors Runx3 and ThPOK regulates intestinal CD4+ T cell immunity. Nat. Immunol. 14, 271–280 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Reis, B. S., Hoytema van Konijnenburg, D. P., Grivennikov, S. I. & Mucida, D. Transcription factor T-bet regulates intraepithelial lymphocyte functional maturation. Immunity 41, 244–256 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Konkel, J. E. et al. Control of the development of CD8αα+ intestinal intraepithelial lymphocytes by TGF-β.Nat. Immunol. 12, 312–319 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Bilate, A. M. et al. Tissue-specific emergence of regulatory and intraepithelial T cells from a clonal T cell precursor. Sci. Immunol. 1, eaaf7471 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Mucida, D. et al. Transcriptional reprogramming of mature CD4+ helper T cells generates distinct MHC class II–restricted cytotoxic T lymphocytes. Nat. Immunol. 14, 281–289 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Rubtsov, Y. P. et al. Stability of the regulatory T cell lineage in vivo. Science 329, 1667–1671 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Setoguchi, R. et al. Repression of the transcription factor Th-POK by Runx complexes in cytotoxic T cell development. Science 319, 822–825 (2008).

    Article  CAS  PubMed  Google Scholar 

  18. Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2019).

    Article  CAS  Google Scholar 

  19. Zemmour, D. et al. Single-cell gene expression reveals a landscape of regulatory T cell phenotypes shaped by the TCR. Nat. Immunol. 19, 291–301 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Miragaia, R. J. et al. Single-cell transcriptomics of regulatory T cells reveals trajectories of tissue adaptation. Immunity 50, 493–504.e7 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Bilate, A. M. et al. T cell receptor is required for differentiation, but not maintenance, of intestinal CD4+ intraepithelial lymphocytes. Immunity 53, 1001–1014 e1020 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Badis, G. et al. Diversity and complexity in DNA recognition by transcription factors. Science 324, 1720–1723 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Ciucci, T. et al. The emergence and functional fitness of memory CD4+ T cells require the transcription factor Thpok. Immunity 50, 91–105.e4 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Machanick, P. & Bailey, T. L. MEME-ChIP: motif analysis of large DNA datasets. Bioinformatics 27, 1696–1697 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Egawa, T. & Littman, D. R. ThPOK acts late in specification of the helper T cell lineage and suppresses Runx-mediated commitment to the cytotoxic T cell lineage. Nat. Immunol. 9, 1131–1139 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Naoe, Y. et al. Repression of interleukin-4 in T helper type 1 cells by Runx/Cbfβ binding to the Il4 silencer. J. Exp. Med. 204, 1749–1755 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Powrie, F., Leach, M. W., Mauze, S., Caddle, L. B. & Coffman, R. L. Phenotypically distinct subsets of CD4+ T cells induce or protect from chronic intestinal inflammation in C. B-17 scid mice. Int. Immunol. 5, 1461–1471 (1993).

    Article  CAS  PubMed  Google Scholar 

  29. DiSpirito, J. R. et al. Molecular diversification of regulatory T cells in nonlymphoid tissues. Sci. Immunol. 3, eaat5861 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Delacher, M. et al. Precursors for nonlymphoid-tissue Treg cells reside in secondary lymphoid organs and are programmed by the transcription factor BATF. Immunity 52, 295–312 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Samstein, R. M. et al. Foxp3 exploits a pre-existent enhancer landscape for regulatory T cell lineage specification. Cell 151, 153–166 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Bruno, L. et al. Runx proteins regulate Foxp3 expression. J. Exp. Med. 206, 2329–2337 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. 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).

    Article  CAS  PubMed  Google Scholar 

  34. Klunker, S. et al. Transcription factors RUNX1 and RUNX3 in the induction and suppressive function of Foxp3+ inducible regulatory T cells. J. Exp. Med. 206, 2701–2715 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Carpenter, A. C. et al. Control of regulatory T cell differentiation by the transcription factors Thpok and LRF. J. Immunol. 199, 1716–1728 (2017).

    Article  CAS  PubMed  Google Scholar 

  36. Kanamori, M., Nakatsukasa, H., Okada, M., Lu, Q. & Yoshimura, A. Induced regulatory T cells: their development, stability, and applications. Trends Immunol. 37, 803–811 (2016).

    Article  CAS  PubMed  Google Scholar 

  37. Masopust, D. & Soerens, A. G. Tissue-resident T cells and other resident leukocytes. Annu. Rev. Immunol. 37, 521–546 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Fonseca, R. et al. Developmental plasticity allows outside-in immune responses by resident memory T cells. Nat. Immunol. 21, 412–421 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Gebhardt, T. et al. Different patterns of peripheral migration by memory CD4+ and CD8+ T cells. Nature 477, 216–219 (2011).

    Article  CAS  PubMed  Google Scholar 

  40. Ricardo-Gonzalez, R. R. et al. Tissue signals imprint ILC2 identity with anticipatory function. Nat. Immunol. 19, 1093–1099 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Cohen, M. et al. Lung single-cell signaling interaction map reveals basophil role in macrophage imprinting. Cell 175, 1031–1044.e18 (2018).

    Article  CAS  PubMed  Google Scholar 

  42. Chassaing, B. et al. Fecal lipocalin 2, a sensitive and broadly dynamic non-invasive biomarker for intestinal inflammation. PloS ONE 7, e44328 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Boyman, O., Kovar, M., Rubinstein, M. P., Surh, C. D. & Sprent, J. Selective stimulation of T cell subsets with antibody-cytokine immune complexes. Science 311, 1924–1927 (2006).

    Article  CAS  PubMed  Google Scholar 

  44. Webster, K. E. et al. In vivo expansion of T reg cells with IL-2-mAb complexes: induction of resistance to EAE and long-term acceptance of islet allografts without immunosuppression. J. Exp. Med. 206, 751–760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Trombetta, J. J. et al. Preparation of single-cell RNA-seq libraries for next generation sequencing. Curr. Protoc. Mol. Biol. 107, 4.22.1–4.22.17 (2014).

    Article  Google Scholar 

  46. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).

    Article  CAS  PubMed  Google Scholar 

  48. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    Article  CAS  PubMed  Google Scholar 

  51. Frankish, A. et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 47, D766–D773 (2019).

    Article  CAS  PubMed  Google Scholar 

  52. Pimentel, H., Bray, N. L., Puente, S., Melsted, P. & Pachter, L. Differential analysis of RNA-seq incorporating quantification uncertainty. Nat. Methods 14, 687–690 (2017).

    Article  CAS  PubMed  Google Scholar 

  53. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).

  54. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Sergushichev, S. A. An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation. Preprint at bioRxiv https://doi.org/10.1101/060012 (2016).

  56. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Fornes, O. et al. JASPAR 2020: update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 48, D87–D92 (2020).

    CAS  PubMed  Google Scholar 

  58. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Bailey, T. L. & Machanick, P. Inferring direct DNA binding from ChIP–seq. Nucleic Acids Res. 40, e128 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Jolma, A. et al. DNA-binding specificities of human transcription factors. Cell 152, 327–339 (2013).

    Article  CAS  PubMed  Google Scholar 

  61. 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).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We are grateful to A. Rogoz and S. Gonzalez for exceptional animal care, mouse colony management and genotyping and the Rockefeller University employees for continuous assistance. We thank K. Gordon and K. Chhosphel for assistance with cell sorting. We thank C. Zhao and the entire Genomics Core of Rockefeller University for library preparation for 10X Genomics and assistance with all sequencing platforms used in this paper. We are grateful to U. Schaefer (The Rockefeller University) for help with ChIP protocols and guidance and S. Larsen for help with the ATAC–seq. We thank T. Egawa (Washington University in St. Louis) for providing anti-ThPOK ChIP antibody. We thank B. Reis and G. Victora for suggestions and critical reading of the manuscript and all the members of the Mucida laboratory for fruitful discussions. This work was supported by the Leona M. and Harry B. Helmsley Charitable Trust, the Black Family Metastasis Center, the Kavli Foundation, the Burroughs Wellcome Fund PATH Award and National Institute of Health grants no. AI144827, no. DK113375 and no. DK093674 (D.M.).

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Author notes

  1. These authors contributed equally: Mariya London, Angelina M. Bilate, Tiago B. R. Castro.

Authors and Affiliations

  1. Laboratory of Mucosal Immunology, The Rockefeller University, New York, NY, USA

    Mariya London, Angelina M. Bilate, Tiago B. R. Castro & Daniel Mucida

  2. Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan

    Tomohisa Sujino

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Contributions

D.M. conceived the study. M.L., A.M.B. and D.M. designed experiments and wrote the manuscript. M.L. and A.M.B. performed experiments. T.B.R.C. performed all bioinformatics analyses. T.B.R.C., M.L. and A.M.B. analyzed experiments. T.S. helped with discussion and setting up mouse strains. M.L., A.M.B., T.B.R.C. and D.M. wrote the manuscript.

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Correspondence to Daniel Mucida.

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The authors declare no competing interests.

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Peer review information Nature Immunology thanks Laura Mackay and Harinder Singh for their contribution to the peer review of this work. Peer reviewer reports are available. L. A. Dempsey was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Related to Fig. 1.

a–k, iFoxp3TomThPOKGFP mice were treated with tamoxifen for 10 weeks, and Tomato– and Tomato+ CD4+ T cells from mesenteric lymph nodes (mLN), lamina propria (LP) and intestinal epithelium (IE) were sorted for scRNA-Seq using 10X Genomics platform. Sorted Tomato– (blue gates) and Tomato+ (red gates) cells were pooled in a 2:1 ratio per tissue, resulting in 3 separate libraries. a, b, CD4+ T cells from mLN, LP and IE before (a) and after sorting (b). c, Number of sequenced genes (left) and RNA molecules (middle) per cluster and percent of mitochondrial DNA (right) per library. d, Top expressed genes per UMAP cluster. Circle size represents proportion of cells per cluster expressing the indicated gene and color represents expression level. e, Expression levels of the motility signature among all sequenced cells. f, Expression levels of Sell (top), Klf2 (middle), and Ccr7 (bottom) of cells from indicated tissues. g, tdTomato gene expression in 6,668 sequenced cells. h,i, Expression levels of Th1 signature (h) and Cd8a (i) among all sequenced cells. j, Expression levels of indicated genes among IE clusters. k, Expression heatmap of selected genes in pre-IEL clusters.

Extended Data Fig. 2 Related to Fig. 2.

a–d, Foxp3eGFP-Cre-ERT2xRosa26lsl-tdTomatoxZbtb7bGFP(iFoxp3TomThPOKGFP) mice were treated with tamoxifen for 10 weeks, Tomato– and Tomato+ CD4+ T cells from mesenteric lymph nodes (mLN), lamina propria (LP) and intestinal epithelium (IE) were sorted for scRNA-Seq using 10X Genomics platform. Sorted Tomato– and Tomato+ cells were pooled in a 2:1 ratio per tissue, resulting in 3 separate libraries. a–c, mLN-derived cells were re-clustered into 8 new clusters (0–7). a, Re-clustered mLN cells with number indicating the new clusters, consistent throughout the figure. b, Expression levels of top differentially expressed genes among re-clustered cells. c, Proportion of original UMAP clusters per re-clustered mLN clusters (X-axis), as indicated. Top 3 original UMAP cluster contained within re-clustered mLN indicated in a box. d, Scatter plot of gene expression ranked based on pseudotime principle component 1 (PC1).

Extended Data Fig. 3 Related to Fig. 3.

a–j, iFoxp3TomThPOKGFP mice were treated with tamoxifen for 10 weeks and induced Treg cells (iTreg; CD4+Tomato+GFPHineuropilin-1–CD8α–), pre-IELs (CD4+Tomato+ GFPLoCD8α–), exTreg-IELs (CD4+Tomato+GFPLoCD8α+), and CD4-IELs (CD4+ Tomato–GFPLoCD8α+) were sorted in bulk from the IE. Assay for transposase-accessible chromatin (ATAC) or RNA libraries were prepared followed by sequencing of indicated populations. iTreg cells were also sorted from the mLN for RNA-seq. a, Gating strategy used for sorting of CD4+ T cells in sequential order. Note that this gating strategy was used in all FACS data shown in this study. b, Sorting strategy of the indicated populations. CD8α and Tomato expression among CD4+ CD8βLo T cells (left) from IE (top) and mLN (bottom). ThPOK and neuropilin-1 expression among Tomato+CD8α– (red), Tomato+CD8α+ (purple) and Tomato–CD8α+ (green) cells from IE (top) and mLN (bottom). c, Levels of ThPOK expression in each sorted population in the IE. d, Mapped accessible chromatin regions relative to promoters of all genes in ATAC-Seq data (top) and their relative read counts per genomic regions (bottom). e, ATAC-Seq peak annotations as indicated per cell type. f, Percent of total differentially accessible chromatin regions as follows: 5′UTR and promoters (Promoter; red), 3′UTR with exons and introns (Gene Body; blue), transcriptional termination site (TTS; gray) and intergenic (black). g, Euclidean distance correlation of chromatin accessibility profiles of all samples. h, Volcano representation of differentially expressed genes between IE iTreg (higher expression in blue) and mLN iTreg cells (higher expression in red), performed by Wald pairwise comparison test, padj < 0.05 values were considered significant. i, Treg cell signature from clusters 1- 3 and IEL signature from cluster 5 of the bulk RNA-seq heatmap (Fig. 3d) overlaid onto the scRNA-Seq UMAP from Fig. 1. j, Curated list of gene set enrichment analysis determined using the fgsea package with padj < 0.05 of Hallmark pathways (black), chemical genetic perturbations pathways (white), and immune pathways (Retinoic acid (RA) pathways; green, Interferon pathways; blue, CD8-program; gray, and Treg cell program; silver). Significant differentially accessible chromatin regions padj < 0.01(d–g) and significant differentially expressed genes padj < 0.05 in RNA-Seq (h-j). Each sample for ATAC-Seq was from n = 9 (1st) or n = 6 (2nd) pooled mice over 2 independent experiments. 15,000 or 19,000 iTreg cells, 30,000 or 17,000 pre-IELs, 12,000 or 11,000 exTreg-IELs and 40,000 CD4-IELs were used (d–g). Each sample for RNA-Seq (d-f) consisted of 650 or 800 cells per mouse, n = 2 or 3 mice over 2 independent experiments (h–j).

Extended Data Fig. 4 Related to Fig. 4.

a–c, iFoxp3TomThPOKGFP mice were treated with tamoxifen for 10 weeks and induced Treg cells (iTreg; CD4+Tomato+GFPHineuropilin-1–CD8α–), pre-IELs (CD4+Tomato+ GFPLoCD8α–), exTreg-IELs (CD4+Tomato+GFPLoCD8α+), and CD4-IELs (CD4+ Tomato–GFPLoCD8α+) were sorted in bulk from the IE. Assay for transposase-accessible chromatin (ATAC) or RNA libraries were prepared followed by sequencing of indicated populations. a, Pearson’s correlation (r) of log2 fold changes of RNA-Seq vs ATAC-Seq of total ATAC peaks averaged (top) or at promoters only (bottom) of indicated cell types. P value (p) for r coefficient as indicated. b, ATAC-Seq peaks of indicated populations and ThPOK chromatin immunoprecipitation followed by sequencing (ChIP-Seq) of in vivo differentiated Foxp3+ splenic Treg cells displayed on the integrative genomics viewer (IGV) in select regions as indicated. Overlap regions between ChIP- and ATAC-Seq indicated on bottom. c, Numbers of differentially expressed genes (DEGs, n = 2 or 3 per cell type) (silver for increase, gray for decrease) in between cell types in sequential progression as performed by Wald pairwise test as indicated, with differentially accessible chromatin regions (DACR, n = 2 per cell type) within those genes (red for increase, blue for decrease in accessibility). Significant DACR padj < 0.01 and significant DEG padj < 0.05 in RNA-Seq. Each sample for ATAC-Seq was from n = 9 (1st) or n = 6 (2nd) pooled mice over 2 independent experiments. 15,000 or 19,000 iTreg cells, 30,000 or 17,000 pre-IELs, 12,000 or 11,000 exTreg-IELs and 40,000 CD4-IELs were used. Each sample for RNA-Seq consisted of 650 or 800 cells per mouse, n = 2–4 mice over 2 independent experiments.

Extended Data Fig. 5 Related to Fig. 5.

a, b, Flow cytometry analysis of CD45+TCRβ+CD4+CD8βLo Tomato+ cells in the IE and mLN of Zbtb7bfl/+xRunx3fl/+xRosa26|s|tdTomatoxFoxp3CreER (iFoxp3), iFoxp3xRunx3fl/+ (iFoxp3(ΔThPOK)), iFoxp3xZbtb7bfl/+xRunx3fl/fl (iFoxp3(ΔRunx3)), iFoxp3xZbtb7bfl/flxRunx3fl/fl (iFoxp3(ΔThPOK ΔRunx3)) mice after 10 weeks of tamoxifen treatment. a, Contour plot (left) and frequency (right) of total Foxp3+ cells among Tomato+ CD4+ T cells. b, Contour plot (left) and frequency (right) of total CD8α+ cells among Tomato+ CD4+ T cells (**p = 0.0036). c, Neuropilin-1 expression among Foxp3+ Treg cells in the intestinal epithelium (IE) and mesenteric lymph nodes (mLN) of WT mice. Histogram (left) and frequency (right) (****p < 0.0001). d-g, nTreg (neuropilin-1–) or iTreg cells (neuropilin-1+) were sorted from spleens and mLNs of CD45.2 Zbtb7bfl/+xRunx3fl/+xRosa26|s|tdTomatoxFoxp3CreER (iFoxp3), iFoxp3xRunx3fl/+ (iFoxp3(ΔThPOK)), iFoxp3xZbtb7bfl/+xRunx3fl/fl (iFoxp3(ΔRunx3)), iFoxp3xZbtb7bfl/flxRunx3fl/fl (iFoxp3(ΔThPOK ΔRunx3)) mice after tamoxifen administration and co-transferred with CD45.1 naïve CD4+ T cells to Rag1–/– hosts. CD45.2+TCRβ+CD4+CD8β-Tomato+ lymphocytes from the IE and mLN were analyzed 10 weeks after transfer. d, Experimental layout. e, Frequencies of total Foxp3 cells after nTreg (left) or iTreg (right) cell transfer. f–g, Body weight of Rag1-/- recipients after nTreg (left) or iTreg (right) cell transfers (f) and levels of fecal lipocalin-2 before and after per cell type transferred (g). Dashed lines represent colitis control transfer of naïve CD45.1+CD4+ T cells only. a-b, WT n = 6, ΔThPOK n = 9, ΔRunx3 n = 7, ΔThPOKΔRunx3 n = 11. c, IE n = 7, mLN n = 8. e-g, nTreg (WT n = 6, ΔThPOK n = 7, ΔRunx3 n = 6, ΔThPOKΔRunx3 n = 5) and iTreg (WT n = 5, ΔThPOK n = 6, ΔRunx3 n = 6, ΔThPOKΔRunx3 n = 6.) Data are expressed as mean ± SEM of individual mice (3 separate experiments). *p < 0.05, **p < 0.01, ***p < 0.001 [one-way ANOVA and Bonferonni test (a,b,e–g) and unpaired parametric two-tailed student’s t test (c)].

Extended Data Fig. 6 Related to Fig. 5.

a, b, Induced Treg cells (iTreg; Tomato+CD8α–neuropilin-1–) and Treg-derived CD4-IELs (exTreg-IELs; Tomato+CD8α+) were sorted from Zbtb7bfl/flxRunx3fl/+xRosa26|s|tdTomato xFoxp3CreER (iFoxp3(ΔThPOK)) mice after 10 weeks of tamoxifen administration followed by RNA-Sequencing from IE or mLN. Numbers of differentially expressed genes (DEGs) (silver for increase, gray for decrease) with total putative ThPOK targets or ThPOK targets at promoters (blue and purple, respectively; left) or putative Runx3 targets (red, right) at indicated comparisons between wild-type (WT) cells only (a) and WT and ΔThPOK cells (b). Each sample for RNA-Seq consisted of 650 or 800 cells per mouse, n = 2–4 mice over 3 independent experiments.

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London, M., Bilate, A.M., Castro, T.B.R. et al. Stepwise chromatin and transcriptional acquisition of an intraepithelial lymphocyte program. Nat Immunol 22, 449–459 (2021). https://doi.org/10.1038/s41590-021-00883-8

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