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T-bet and RORα control lymph node formation by regulating embryonic innate lymphoid cell differentiation

Nature Immunology volume 22, pages 1231–1244 (2021)Cite this article

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Abstract

The generation of lymphoid tissues during embryogenesis relies on group 3 innate lymphoid cells (ILC3) displaying lymphoid tissue inducer (LTi) activity and expressing the master transcription factor RORγt. Accordingly, RORγt-deficient mice lack ILC3 and lymphoid structures, including lymph nodes (LN). Whereas T-bet affects differentiation and functions of ILC3 postnatally, the role of T-bet in regulating fetal ILC3 and LN formation remains completely unknown. Using multiple mouse models and single-cell analyses of fetal ILCs and ILC progenitors (ILCP), here we identify a key role for T-bet during embryogenesis and show that its deficiency rescues LN formation in RORγt-deficient mice. Mechanistically, T-bet deletion skews the differentiation fate of fetal ILCs and promotes the accumulation of PLZFhi ILCP expressing central LTi molecules in a RORα-dependent fashion. Our data unveil an unexpected role for T-bet and RORα during embryonic ILC function and highlight that RORγt is crucial in counteracting the suppressive effects of T-bet.

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Fig. 1: scRNA-seq of fetal intestinal cells reveals a range of mature ILC subsets and progenitors.
Fig. 2: Transcriptomic profiling on single-cell level discloses heterogeneity within the embryonic ILC3 supercluster.
Fig. 3: A subset of RORγt+T-bet+CD4+ ILC3 emerges during embryonic development.
Fig. 4: T-bet deficiency rescues formation of LN in RORγt-deficient mice.
Fig. 5: Single-cell sequencing of fetal ILC populations reveals accumulation of ILCP in Rorc(gt)GFP/GFP × Tbx21−/− mice.
Fig. 6: PLZFhi ILCP from DKO mice are enriched in cells with LTi signatures.
Fig. 7: PLZFhi CXCR6+GFP+ cells persist in mLN and intestine of adult Rorc(gt)GFP/GFP × Tbx21−/− mice.
Fig. 8: LN formation and accumulation of PLZF+ ILCP is independent of DNA- and LBDs of RORγt and promoted by RORα in the absence of T-bet.

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Data availability

Raw transcriptome data reported in this paper are deposited and available from the Gene Expression Omnibus under accession codes GSE161441 (scRNA-seq) and GSE161439 (bulk RNA-seq). Sequencing data were aligned to the mm10 reference transcriptome. All other data supporting the findings of this study are available within the paper or from the corresponding author upon request. Source data are provided with this paper.

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Acknowledgements

We thank J. Kirsch, T. Kaiser (Flow Cytometry Core Facility, Deutsches Rheuma-Forschungszentrum, Berlin) and K. Lehmann (Deutsches Rheuma-Forschungszentrum) for technical help. We thank M. Babic for precise help with detection of LN phenotypes; H.-R. Rodewald (Division for Cellular Immunology, German Cancer Research Center, Heidelberg, Germany) and J. Zhu (Molecular and Cellular Immunoregulation Unit, Laboratory of Immunology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA) for providing Il7rcre and Tbx21-ZsGreen mice, respectively; A. Diefenbach and M. Hepworth for scientific discussion; G. Gasteiger, C. Klose, T. Schüler, D. Hernández, J. Zhu and A. Kruglov for critically reading the paper. Funding came from Deutsche Forschungsgemeinschaft (DFG) grants from the Heisenberg Program nos. (RO3565/1-1), RO3565/2-1, SPP 1937 (RO3565/4-1 and 4-2) to C.R., DFG-SFB TRR241 B02 to C.R and B03 to H.D.C., R.M. Schwiete Foundation to H.D.C., Leibniz-Science Campus Chronic Inflammation and Leibniz-Kooperative Exzellenz K259/2019 to C.R. and INST 335/597-1 FUGG. SPP1937 (HA5354/8-1 and 8-2) and TRR130, TP17 to A.E.H, and the state of Berlin and the ‘European Regional Development Fund’ (ERDF 2014–2020, EFRE 1.8/11, Deutsches Rheuma-Forschungszentrum) to M-F.M.)

Author information

Author notes

  1. These authors contributed equally: Jakob Zimmermann, Chiara Romagnani.

Authors and Affiliations

  1. Innate Immunity, German Rheumatism Research Centre—a Leibniz Institute, Berlin, Germany

    Christina Stehle, Timo Rückert & Chiara Romagnani

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

    Rémi Fiancette, Dominika W. Gajdasik, Claire Willis & David R. Withers

  3. Immune Dynamics, German Rheumatism Research Centre—a Leibniz Institute, Berlin, Germany

    Carolin Ulbricht & Anja Erika Hauser

  4. Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Rheumatology and Clinical Immunology, Berlin, Germany

    Carolin Ulbricht & Anja Erika Hauser

  5. Cell Biology, German Rheumatism Research Centre—a Leibniz Institute, Berlin, Germany

    Pawel Durek

  6. Therapeutic Gene Regulation, German Rheumatism Research Centre—a Leibniz Institute, Berlin, Germany

    Mir-Farzin Mashreghi

  7. Berlin Institute of Health (BIH) at Charité—Universitätsmedizin Berlin, BIH Center for Regenerative Therapies (BCRT), Berlin, Germany

    Mir-Farzin Mashreghi

  8. Department of Biomedicine and University Children’s Hospital of Basel, University of Basel, Basel, Switzerland

    Daniela Finke

  9. Schwiete Laboratory for Microbiota and Inflammation, German Rheumatism Research Centre—a Leibniz Institute, Berlin, Germany

    Hyun-Dong Chang

  10. Department of Cytometry, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany

    Hyun-Dong Chang

  11. Maurice Müller Laboratories, Universitätsklinik für Viszerale Chirurgie und Medizin Inselspital, University of Bern, Bern, Switzerland

    Jakob Zimmermann

  12. Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Gastroenterology, Infectious Diseases, Rheumatology, Berlin, Germany

    Chiara Romagnani

  13. Leibniz-Science Campus Chronic Inflammation, Berlin, Germany

    Chiara Romagnani

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  1. Christina Stehle
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Contributions

C.S. designed, performed and analyzed experiments and wrote the paper. T.R. designed, performed and analyzed single-cell experiment together with C.S. R.F., D.W.G. and C.W. performed experiments. P.D. and M-F.M. performed and analyzed the bulk sequencing experiment together with C.S. and T.R. C.U. and A.E.H performed immunofluorescence experiments. D.F. and D.R.W. provided mouse mutants. H-D.C. designed experiments. J.Z. generated DKO mice, discovered the initial phenotype, designed experiments and edited the manuscript. C.R. supervised the study and wrote the paper together with C.S., with input from all coauthors.

Corresponding author

Correspondence to Chiara Romagnani.

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The authors declare no competing interests.

Additional information

Peer review information Nature Immunology thanks the anonymous reviewers for their contribution to the peer review of this work. Laurie 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.

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

Extended data

Extended Data Fig. 1 Single-cell sequencing of E18.5 intestinal cells identifies ILC progenitors and mature subsets.

a, Flow cytometric representative plots showing expression of transcription factors on CD127+ and/or CD122+ on LinLD−CD45+ cells of the E18.5 small intestine (SI). (b) Violin plots of selected dendritic cell (DC)-associated markers. (c) Heatmap displaying the top 50 differentially expressed genes (DEGs) within each cluster. Cluster gene examples are given. (d) UMAP-projected expression of Eomes transcripts. (e) Trajectory analysis using Partition-based Graph Abstraction (PAGA) lineage interference method.

Extended Data Fig. 2 Gene expression of specified ILC3 subsets defined by scRNA-seq.

a, Heatmap displaying the top 30 differentially expressed genes (DEGs) and (b) expression levels of selected genes and corresponding proteins as assessed by CITE-seq within ILC3 subclusters.

Extended Data Fig. 3 Gene expression of specified ILC3 subsets defined by scRNA-seq.

a, Heatmap displaying the top 30 differentially expressed genes (DEGs) and (b) expression levels of selected genes and corresponding proteins as assessed by CITE-seq within ILC3 subclusters.

Source data

Extended Data Fig. 4 T-bet-deficient mice have normal LN development.

a, In vivo photos of mesenteric or inguinal lymph nodes (mLN, iLN) of adult Tbx21−/− x Rorc(gt)GFP/GFP mice or after isolation (b). (c) Quantification (n = 12) of frequencies of mice with lymphoid structures compared to control mouse strain in adult animals. PP’s, Peyer’s patches; nd, not detected. Data are representative of 2 independent experiments. (d) Photos of isolated mLN in indicated mouse strains. Data are representative of 2 independent experiments.

Extended Data Fig. 5 Comparative gene expression of ILCP from reporter, RKO and DKO mice.

a, Violin plots for enrichment score of gene modules of top 100 differentially expressed genes from reporter ILC2 within ILC2 cluster in indicated mouse strains. Statistical significance was calculated using two-sided Wilcoxon test with Bonferroni correction, all ns. (b) Eomes expression in DKO mice projected on UMAP. (c) Expression of selected ILCP-associated genes in cells from ILCP cluster in indicated mouse strains. (d) Expression of selected proteins on UMAP projection analysed by CITE-Seq. (e) Violin plot for Mki67 transcripts among ILCP in all mouse strains, legend see in (c). (f) Representative flow cytometry of Ki67 expression in LinLD−CD45+ CD127+ RORγt−PD-1hiPLZFhi cells from E15.5 SI (reporter n = 6, RKO n = 4, DKO n = 6 from 2 independent experiments). Quantification as mean ± SEM with Kruskal–Wallis significance and Dunn’s correction. Detailed statistics are available in source data.

Source data

Extended Data Fig. 6 Gene and protein expression patterns in ILCP of reporter, RKO and DKO mouse strains.

a, Expression levels of Tbx21 within ILCP of all strains. (b) Gene expression profiles of the top 100 genes differentially expressed in ILC3 of reporter mice within cells from ILCP cluster in the different mouse strains. (c) Violin plots depicting expression of selected transcripts in ILCP of the three mouse strains, legend see in (A). (d) Violin plot depicting Rorc expression within ILCP of indicated mouse strains. (e) Geometric mean fluorescence intensity of CXCR5 and RANKL within LinLD−CD45+ CD127+ and/or CD122+ cells isolated from E18.5 SI (n = 7 from 2 independent experiments) depicted as mean ± SEM and Kruskal-Wallis significance with Dunn’s correction. Data from two independent experiments. (f) Representative flow cytometry from E18.5 SI of LinLD−CD45+ CD127+ and/or CD122+ cells and quantification as mean ± SEM of GFP+ cells (n = 7 from 2 independent experiments) with Kruskal-Wallis significance and Dunn’s correction. (g) Frequencies of CXCR5+ RANKL+ cells among LinLD−CD45+ CD127+ and/or CD122+ cells in fetal liver (FL) of E18.5 embryos (n = 4 from 2 independent experiments) with Kruskal-Wallis significance and Dunn’s correction. (h) In vitro differentiation of E14.5 fetal liver-derived ILC progenitors on OP9 stromal cells for 5–8 days in the presence of SCF and IL-7 and analysis by flow cytometry. Quantification of LinLD−CD45+ ICOShiGATA3hi ILC2 and LinLD−CD45+ ICOS− NK1.1+ group 1 ILCs cells in the different mouse strains shown as mean ± SEM. Data are representative of 2-3 independent experiments (reporter n = 5 from 2 independent experiments, RKO n = 9 and DKO n = 11 from 3 independent experiments). Kruskal-Wallis significance and Dunn’s correction. Detailed statistics are available in source data.

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Extended Data Fig. 7 GFP+ ILCs persist in double knockout mouse models and develop independently from RAG proteins.

a, Flow cytometry representative plots of mLN from 4-week-old mice of indicated mouse strains. Quantification of frequencies from two independent experiments depicted as mean ± SEM (reporter n = 14, DKO n = 8 from 4 independent experiments). Kruskal-Wallis testing with Dunn’s multiple comparison correction. (b) Representative flow cytometry of cells from SI isolated from 4-week-old mice. Quantification of frequencies from 2 independent experiments as mean ± SEM, Kruskal-Wallis testing with Dunn’s multiple correction, n = 4. P values are provided in source data. Detailed statistics are available in source data.

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Extended Data Fig. 8 Thymic CD4+ CD8+ compartments are not restored in RORγt/T-bet double-deficient mice.

a, Mapping of reads detected by bulk RNA-seq to the mm10 mouse Rorc locus in indicated mouse strains. Red box designates localization of transcriptional start site (TSS) ATG of exon 1γt. (b) Expression of Rorc exon1-3 and Rorc exon5-6 junctions in small intestinal LinLD−CD45+ CD3− GFP+ ILCs of 4-week-old mice determined by quantitative PCR. Values are normalized to housekeeping gene Gapdh. Each symbol represents an individual mouse. Data show mean ± SEM, reporter and DKO n = 6, RKO n = 2 examined over 2 independent experiments. (c,d,e) Representative flow cytometry plots of indicated populations and compartments isolated from 4-week-old mice. (f) Violin plot of Rora expression within ILCP of designated mouse strains from E18.5 scRNA-seq dataset. Detailed statistics are available in source data.

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Stehle, C., Rückert, T., Fiancette, R. et al. T-bet and RORα control lymph node formation by regulating embryonic innate lymphoid cell differentiation. Nat Immunol 22, 1231–1244 (2021). https://doi.org/10.1038/s41590-021-01029-6

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