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ILC3s select microbiota-specific regulatory T cells to establish tolerance in the gut

Nature volume 610, pages 744–751 (2022)Cite this article

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Abstract

Microbial colonization of the mammalian intestine elicits inflammatory or tolerogenic T cell responses, but the mechanisms controlling these distinct outcomes remain poorly understood, and accumulating evidence indicates that aberrant immunity to intestinal microbiota is causally associated with infectious, inflammatory and malignant diseases1,2,3,4,5,6,7,8. Here we define a critical pathway controlling the fate of inflammatory versus tolerogenic T cells that respond to the microbiota and express the transcription factor RORγt. We profiled all RORγt+ immune cells at single-cell resolution from the intestine-draining lymph nodes of mice and reveal a dominant presence of T regulatory (Treg) cells and lymphoid tissue inducer-like group 3 innate lymphoid cells (ILC3s), which co-localize at interfollicular regions. These ILC3s are distinct from extrathymic AIRE-expressing cells, abundantly express major histocompatibility complex class II, and are necessary and sufficient to promote microbiota-specific RORγt+ Treg cells and prevent their expansion as inflammatory T helper 17 cells. This occurs through ILC3-mediated antigen presentation, αV integrin and competition for interleukin-2. Finally, single-cell analyses suggest that interactions between ILC3s and RORγt+ Treg cells are impaired in inflammatory bowel disease. Our results define a paradigm whereby ILC3s select for antigen-specific RORγt+ Treg cells, and against T helper 17 cells, to establish immune tolerance to the microbiota and intestinal health.

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Fig. 1: Single-cell-resolution profiles of RORγt+ adaptive and innate lymphocytes in the mLN.
Fig. 2: LTi-like ILC3s select for microbiota-specific RORγt+ Treg cells through MHCII.
Fig. 3: MHCII+ ILC3s are sufficient for selection of microbiota-specific RORγt+ Treg cells.
Fig. 4: ITGAV on LTi-like ILC3s promotes RORγt+ Treg homeostasis.
Fig. 5: Interactions between ILC3 and RORγt+ Treg cells are altered in human IBD.

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Data availability

All data necessary to understand and evaluate the conclusions of this paper are provided in the manuscript and supplementary materials. scRNA-seq data have been deposited in the Gene Expression Omnibus database under the accession numbers GSE184175 and GSE184291. A reanalysis was performed on publicly available scRNA-seq data with the accession number GSE134809. Source data are provided with this paper.

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Acknowledgements

We thank members of the Sonnenberg Laboratory for discussions and critical reading of the manuscript. Research in the Sonnenberg Laboratory is supported by the National Institutes of Health (R01AI143842, R01AI123368, R01AI145989, U01AI095608, R21CA249274, R01AI162936 and R01CA274534), an Investigators in the Pathogenesis of Infectious Disease Award from the Burroughs Wellcome Fund, the Meyer Cancer Center Collaborative Research Initiative, the Dalton Family Foundation and Linda and Glenn Greenberg. W. Zhou, J.G., L.Z. and W. Zhang are supported by fellowships from the Crohn’s and Colitis Foundation (831404, 519428, 608975 and 901000, respectively). D.R.W. and F.G. are supported by a Senior Research Fellowship from the Wellcome Trust to D.R.W. (110199/Z/15/Z). J.G.F. is supported by P30-ES002109 and R35CA210088. G.F.S. is a CRI Lloyd J. Old STAR. We thank the Epigenomics Cores of Weill Cornell Medicine and G. Putzel for bioinformatics assistance, J. Conrad for administrative assistance, and S. Mozumder for technical assistance. The JRI IBD Live Cell Bank is supported by the JRI, the Jill Roberts Center for IBD, Cure for IBD, the Rosanne H. Silbermann Foundation, the Sanders Family and Weill Cornell Medicine Division of Pediatric Gastroenterology, Hepatology, and Nutrition.

Author information

Author notes

  1. Hiroaki Suzuki

    Present address: EA Pharma, Kanagawa, Japan

  2. These authors contributed equally: Mengze Lyu, Hiroaki Suzuki

Authors and Affiliations

  1. Joan and Sanford I. Weill Department of Medicine, Division of Gastroenterology and Hepatology, Weill Cornell Medicine, Cornell University, New York, NY, USA

    Mengze Lyu, Hiroaki Suzuki, Lan Kang, Wenqing Zhou, Jeremy Goc, Lei Zhou, Jordan Zhou, Wen Zhang, Gregory F. Sonnenberg, Wenqing Zhou & Gregory F. Sonnenberg

  2. Department of Microbiology and Immunology, Weill Cornell Medicine, Cornell University, New York, NY, USA

    Mengze Lyu, Hiroaki Suzuki, Lan Kang, Wenqing Zhou, Jeremy Goc, Lei Zhou, Jordan Zhou, Wen Zhang, Gregory F. Sonnenberg, Wenqing Zhou & Gregory F. Sonnenberg

  3. Jill Roberts Institute for Research in Inflammatory Bowel Disease, Weill Cornell Medicine, Cornell University, New York, NY, USA

    Mengze Lyu, Hiroaki Suzuki, Lan Kang, Wenqing Zhou, Jeremy Goc, Lei Zhou, Jordan Zhou, Wen Zhang, David Artis, Randy Longman, Gregory F. Sonnenberg, Ellen Scherl, Dana Lukin, Robert Battat, Thomas Ciecierega, Aliza Solomon, Elaine Barfield, Kimberley Chien, Johanna Ferriera, Jasmin Williams, Shaira Khan, Peik Sean Chong, Samah Mozumder, Lance Chou, Wenqing Zhou, Anees Ahmed, Connie Zhong, Ann Joseph, Sanchita Kashyap, Joseph Gladstone, Samantha Jensen & Gregory F. Sonnenberg

  4. Institute of Immunology and Immunotherapy, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK

    Fabrina Gaspal & David R. Withers

  5. Division of Comparative Medicine, Massachusetts Institute of Technology, Cambridge, MA, USA

    Zeli Shen & James G. Fox

  6. Department of Pediatrics, Division of Gastroenterology, Hepatology, and Nutrition, Weill Cornell Medicine, Cornell University, New York, NY, USA

    Robbyn E. Sockolow

  7. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Robbyn E. Sockolow & Terri M. Laufer

  8. Philadelphia Veterans Affairs Medical Center, Philadelphia, PA, USA

    Terri M. Laufer

  9. Institute of Cellular Therapeutics, Allegheny Health Network, Pittsburgh, PA, USA

    Yong Fan

  10. Microenvironment and Immunity Unit, Institut Pasteur, Paris, France

    Gerard Eberl

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  1. Mengze Lyu
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  2. Hiroaki Suzuki
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Consortia

JRI Live Cell Bank

  • David Artis
  • , Randy Longman
  • , Gregory F. Sonnenberg
  • , Ellen Scherl
  • , Robbyn E. Sockolow
  • , Dana Lukin
  • , Robert Battat
  • , Thomas Ciecierega
  • , Aliza Solomon
  • , Elaine Barfield
  • , Kimberley Chien
  • , Johanna Ferriera
  • , Jasmin Williams
  • , Shaira Khan
  • , Peik Sean Chong
  • , Samah Mozumder
  • , Lance Chou
  • , Wenqing Zhou
  • , Anees Ahmed
  • , Connie Zhong
  • , Ann Joseph
  • , Sanchita Kashyap
  • , Joseph Gladstone
  •  & Samantha Jensen

Contributions

M.L., H.S. and G.F.S. conceived the project. M.L. performed most experiments and analysed the data. H.S., L.K., F.G., J.G., W. Zhou, L.Z., W. Zhang and J.Z. helped with experiments and data analyses. J.G.F., Z.S., Y.F., T.M.L., G.E. and D.R.W. provided essential tools, scientific advice and expertise. R.E.S. and JRI Live Cell Bank contributed to clinical sample acquisition and processing. M.L. and G.F.S. wrote the manuscript, with input from all authors.

Corresponding author

Correspondence to Gregory F. Sonnenberg.

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Competing interests

H.S. is currently employed by EA Pharma. The other authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 scRNA-seq profiling of RORγt+ cells from mouse mLN.

a, Gating strategy to sort GFP+TCRβ+ and GFP+TCRβ− cells (1:1, n = 3) for scRNA-seq. b, Doublet test showing cluster 8 as doublets. c, d, Representative flow cytometry plot of the frequency of RORγt+ cells in CD127+ ILC fractions (CD45+CD3ε−CD5−NK1.1−Ly6G−TCRγ/δ−B220−CD11b−CD11c−KLRG1−CD127+), CD19+ B cell fractions (CD45+CD19+) and CD172a+ cDC2 fractions (CD45+ CD3ε−CD5−NK1.1−Ly6G−TCRγ/δ−B220−CD64−CD11c+MHCII+XCR1−CD172a+) from RORγt-eGFP reporter mice (n = 3) (c) and RorccreRosa26lsl−YFP fate mapped mice (n = 3) (d). e–k, Violin plot showing the expression of Rorc (e), Cd3e (f), Foxp3 (g), Xcr1 (h), Clec9a (i), Clec10a (j), Clec12a (k) among all the identified clusters.

Extended Data Fig. 2 Characterization of RORγt+ eTACs and LTi-like ILC3s in mouse mLN.

a, Violin plot showing the expression of Siglecg and Dpp4 among all the identified clusters of non-T lymphocytes. b, Gating strategy to identify ILC3s and RORγt+ eTACs from mLN of RORγt-eGFP reporter mice (n = 4) for data shown in Fig. 1e, f. c, Quantification of indicated genes expression in LTi-like cells and RORγt+ eTACs shown in Fig. 1e (n = 4). Data in c are representative of three independent experiments. Data are shown as means ± s.e.m., statistics shown in c are obtained by unpaired Student’s t-test (two-tailed).

Source data

Extended Data Fig. 3 Gating strategies and fate mapping of RORγt+ eTACs and LTi-like ILC3s in mouse mLN.

a, Gating strategy to identify LTi-like ILC3s and RORγt+ eTACs for fate mapping analyses in Fig. 1g, h. b, Representative flow cytometry plots showing expression of CD127, CD11c, CLEC9A and AIRE among “fate-mapped” LTi-like ILC3s and RORγt+ eTACs in mLN shown in Fig. 1g, h. c, Gating strategy to identify LTi-like ILC3s, RORγt+ eTACs and RORγt+ Tregs of mLN in Fig. 2c–l, Fig. 4e, the same gating strategy applied to the LI-LP. d, Gating strategy to identify H. Hepaticus (Hh)-specific CD4+ T cells in mouse LI-LP.

Extended Data Fig. 4 Immunofluorescence and quantification of different cell types in mLN.

a, Representative flow cytometry plots of frequency (left) and percentage (right) of RORγt+FoxP3+ Tregs and RORγt+FoxP3− TH17 cells among total RORγt+CD4+ T cells in mLN of WT mice (n = 5). b, Tile-scanned (left) and magnified (right) images of mLN stained for expression of IL7Rα (red), CD3 (blue), FOXP3 (cyan), RORγt (green) and DAPI (grey). c–e, Quantification of percentage of RORγt+ Tregs among total Tregs (c), total numbers per mm2 of ILC3s (d) and RORγt+ Tregs (e) in interfollicular zone of mLN of WT-1 (n = 11 areas), WT-2 (n = 8 areas) and WT-3 (n = 12 areas) mice. f, Tile-scanned images and serial sections of mLN stained for expression of IL7Rα (red), CD11c (blue), FOXP3 (cyan), RORγt (green) and DAPI (grey). Left panel is without IL7Rα and RORγt staining, middle panel is without FOXP3 staining, and right panel is a merge with a magnified image. g, Tile-scanned (left) and magnified (right) images of mLN stained for expression of CD3 (red), FOXP3 (cyan), CD11c (green) and DAPI (grey). Scale bars: 50 μm, 20 μm (in magnified images). Data in a, b, f, g are representative of two independent experiments. Data in c–e are representative of two independent experiments containing a total of 5 mice. Data are shown as means ± s.e.m., statistics shown in a are obtained by unpaired Student’s t-test (two-tailed).

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Extended Data Fig. 5 MHCII+ ILC3s selectively regulate T cell homeostasis in the gut.

a, UMAP plots of scRNA-seq data showing expression of H2-Ab1, H2-Ab1 and Cd74 are enriched in clusters of LTi-like ILC3s and RORγt+ eTACs across all the identified clusters in mouse mLN. b–e, Cell numbers of CD4+ T cells (b), TH17 cells (c), RORγt−Tregs (d) and RORγt+ Tregs (e) in mLN (upper panel) and LI-LP (lower panel) of H2-Ab1fl/fl and MHCIIΔILC3 mice (n = 9, pooled from two independent experiments). f–k, Large intestine of H2-Ab1fl/fl (n = 6) and Cd4creH2-Ab1fl/fl (MHCIIΔT cell) mice (n = 4) (f, g), H2-Ab1fl/fl (n = 4) and Il5creH2-Ab1fl/fl (MHCIIΔILC2) mice (n = 6) (h, i), Rorcfl/fl (n = 10) and Ncr1creRorcfl/fl mice (n = 9) (j, k) were analyzed. Proportion of MHCII-expressing CD4+ T cells (f), ILC2s (h) and ILC3s (j). Frequency of each subset among CD4+ T cells and RORγt+ Tregs among total Tregs (g, i, k). TH17: Foxp3−RORγt+; Treg: Foxp3+; TH1: Foxp3−RORγ−T-bet+; TH2: Foxp3−RORγt−Gata3+. l, Quantification of RORγt+ Tregs among total CD4+ T cells in LI-LP of Airefl/fl and RorccreAirefl/fl mice (n = 5). m, n, Quantification of eTACs among total CD127−CD90− cells (m) and LTi-like ILC3s among CD45+CCR6+ cells (n) in mLN of Rorcfl/fl and AirecreRorcfl/fl mice (n = 5). o, Quantification of MHCII expression among eTACs (o, left) and LTi-like ILC3s (o, right) in mLN of H2-Ab1fl/fl and AirecreH2-Ab1fl/fl mice (n = 4). p, Quantification of MHCII expression among DCs in mLN of H2-Ab1fl/fl and Clec9acreH2-Ab1fl/fl mice (n = 4). Data are representative of two independent experiments unless otherwise indicated. Data shown as mean ± s.e.m. Statistics in f, h, j, l–p, right of g, i, k are obtained by unpaired Student’s t-test. Statistics shown in left of g, i, k are obtained by multiple unpaired t-test. Statistics are calculated by two-tailed test.

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Extended Data Fig. 6 LTi-like ILC3s support RORγt+ Tregs in a co-culture system.

a-c, Sort-purified RORγt+CD4+ T cells and LTi-like ILC3s from LI-LP and mLN (n = 6 or 5 or 3 per group as technical replicates) were co-cultured for 72 h and RORγt+ Tregs were analyzed by flow cytometry. Frequency and cell number of RORγt+ Tregs (a, RORγt+Foxp3+ among CD4+ T cells), MFI of Bim (b) and Nur77 (c) in RORγt+ Tregs. d, e, Dead cells were quantified in RORγt+ Tregs (d) and TH17 cells (e) after co-culture with or without LTi-like ILC3s for 72 h. RORγt+CD4+ T cells and LTi-like ILC3s were sort-purified from mLN and LI-LP and pooled for co-culture assay (n = 12, each dot represents samples pooled from 2 mice). Data in a–c are representative of two independent experiments. Data in d, e are pooled from two independent experiments. Data are shown as mean ± s.e.m., statistics shown in d, e were obtained by unpaired Student’s t-test (two-tailed).

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Extended Data Fig. 7 MHCII+ LTi-like ILC3s select for microbiota specific RORγt+ Tregs.

a, H. hepaticus (Hh)-specific and/or SFB-specific CD4+ T cells were transferred to H2-Ab1fl/fl and MHCIIΔILC3 mice colonized with H. hepaticus 2 weeks before experiment as shown in Fig. 3a–h, k, l. b, c, Frequency of CD44+ ratio among SFB-specific (b) or Hh-specific (c) CD4+ T cells were analyzed in Peyer’s patch for SFB (CD45.1−CD90.1+CD4+ T cells) and in LI-LP for Hh-specific (CD45.1+CD90.1−CD4+ T cells) transgenic T cells (n = 4). d, Quantification of RORγt+ Tregs and TH17 cells among total CD4+ T cells in LI-LP of Rorcfl/fl and Foxp3creRorcfl/fl mice (n = 4). e, f, Quantification of MHCII expression on LTi-like ILC3s (n = 9) (e) and eTACs (n = 4) (f) in mLN of H2-Ab1fl/fl and Il22creH2-Ab1fl/fl mice. g, Quantification of RORγt+ Tregs (among Hh-specific CD4+ T cells) and TH17 cells (among Hh-specific CD4+ T cells) were analyzed in LI-LP of H2-Ab1fl/fl and Il22creH2-Ab1fl/fl mice (n = 9). h, Representative flow cytometry plots of the frequency of MHCII expression on ILC3s, DCs in LI-LP of MHCIIneg and MHCIIILC3+ mice (n = 6) as shown in Fig. 3j. i, Frequency of CD44hiCD62Llo ratio among Hh-specific CD4+ T cells were analyzed in mLN and LI-LP for Hh-specific CD4+ T cells as shown in Fig. 3k, l (n = 6). j, k, RORγt+ Tregs in LI-LP of H2-Ab1fl/fl and MHCIIΔILC3 mice (n = 4) were analyzed by flow cytometry. Histogram and MFI of Bim (j). Proportions of Ki-67 positive cells (k). l, Quantification of RORγt+ Tregs among CD4+ T cells in LI-LP of Il2fl/fl and RorccreIl2fl/fl mice (n = 4). m, Quantification of CD25 staining or IL-2 binding in mLN of WT mice (n = 3). Naïve T cells: CD44loCD62Lhi; effector T: CD44hiCD62Llo; TH17 cells: RORγt+FoxP3−; Tregs: FoxP3+; RORγt+Tregs: RORγt+FoxP3+. Data in e, g are pooled from two independent experiments with similar results. Data in b–d, f, i–l are representative of two independent experiments. Data are shown as mean ± s.e.m., statistics shown in m are obtained by one-way ANOVA with Tukey’s multiple comparisons test, statistics shown in b–g, i–l are obtained by unpaired Student’s t-test (two-tailed).

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Extended Data Fig. 8 Itgav on LTi-like ILC3s contributes to the selection of microbiota-specific RORγt+ Tregs.

a, Representative flow cytometry plot of the frequency of Itgav expression on LTi-like ILC3s and eTACs in mLN of Itgavfl/fl and RorccreItgavfl/fl mice as shown in Fig. 4d (n = 5). b–d, Quantification of ITGAV on CD4+ T cells (b), LTi-like ILC3s (c) and eTACs (d) in mLN of Itgavfl/fl and Cd4creItgavfl/fl mice (n = 6). e, Quantification of RORγt+ Tregs in mLN of Itgavfl/fl and Cd4creItgavfl/fl mice (n = 6). f, H. Hepaticus (Hh)-specific CD4+ T cells were transferred to Itgavfl/fl and RorccreItgavfl/fl mice colonized with H. hepaticus 14 days before experiment related to Fig. 4f. Data in b–e are representative of two independent experiments. Data are shown as mean ± s.e.m., statistics shown in b–e are obtained by unpaired Student’s t-test (two-tailed).

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Extended Data Fig. 9 Gating strategy for ILC3s and T cell subsets in the human intestine.

a, Gating strategy to sort ILCs and T cells from small intestine of the IBD patient for scRNA-seq in Fig. 5. b, Gating strategy to analyse ILC3s and RORγt+ Tregs by flow cytometry as shown in Fig. 5.

Extended Data Fig. 10 ILC3s select microbiota specific Tregs to establish tolerance in the gut.

a, Violin plot of CD3E expression among clusters of scRNA-seq data as shown in Fig. 5a. b, Bar graph showing the composition of non-T lymphocytes as indicated in Fig. 5a in non-inflamed tissue (NI) versus inflamed tissue (Infla). c, Bar graph showing the composition of ILC3 lymphocytes in non-inflamed tissue (NI) versus inflamed tissue (Infla) from human IBD samples as published44. d, e, A dot plot showing the mean expression (colour) of indicated genes in ILC3 cluster (d) and Treg cluster (e) in non-inflamed versus inflamed tissue from human IBD samples as published44. f, Correlation analyses between the RORγt+ Tregs (RORγt+Helios− among CD4+ T cells) and TH17 cells (RORγt+FoxP3− among CD4+ T cells) in the cohort of CD patients as in Fig. 5d, i, j. g, h, Quantification of frequency of ILC3s among CD127+CD117+ subset (g) and RORγt+ Tregs among total Tregs (h) in a second independent cohort of individuals. Healthy donor n = 15, Crohn’s disease (CD) patients n = 15. i, Correlation analyses between the ILC3 (ILC3 among CD127+CD117+ subset) and RORγt+ Tregs (RORγt+ Helios− among FoxP3+ Tregs) in a second independent cohort of human samples as in (g, h). j, LTi-like ILC3s are necessary and sufficient in selecting for the differentiation fate of microbiota specific RORγt+ Tregs, and selecting against TH17 cells, via antigen presentation with contributions from integrin αv and gradients of competition for IL-2. This collectively enforces immunologic tolerance to microbiota and maintains intestinal homeostasis. Data in g, h are shown as means ± s.e.m., statistics shown in g, h are performed using Mann–Whitney U-test (unpaired), correlative analyses in f, i are compared by Pearson’s rank correlation coefficient (R2). Statistics are calculated by two-tailed test.

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Supplementary information

Reporting Summary

Supplementary Table 1

List of genes that are differentially expressed between RORγt+ eTACs and LTi-like ILC3s in the mouse mLN.

Supplementary Table 2

List of genes that are differentially expressed between RORγt+ Treg cells and RORγt- Treg cells in the human intestine.

Supplementary Table 3

Clinical metadata on paediatric patients with IBD and matched healthy controls.

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Lyu, M., Suzuki, H., Kang, L. et al. ILC3s select microbiota-specific regulatory T cells to establish tolerance in the gut. Nature 610, 744–751 (2022). https://doi.org/10.1038/s41586-022-05141-x

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