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Subsets of ILC3−ILC1-like cells generate a diversity spectrum of innate lymphoid cells in human mucosal tissues

Nature Immunology volume 20, pages 980–991 (2019)Cite this article

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A Publisher Correction to this article was published on 06 August 2019

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Abstract

Innate lymphoid cells (ILCs) are tissue-resident lymphocytes categorized on the basis of their core regulatory programs and the expression of signature cytokines. Human ILC3s that produce the cytokine interleukin-22 convert into ILC1-like cells that produce interferon-γ in vitro, but whether this conversion occurs in vivo remains unclear. In the present study we found that ILC3s and ILC1s in human tonsils represented the ends of a spectrum that included additional discrete subsets. RNA velocity analysis identified an intermediate ILC3–ILC1 cluster, which had strong directionality toward ILC1s. In humanized mice, the acquisition of ILC1 features by ILC3s showed tissue dependency. Chromatin studies indicated that the transcription factors Aiolos and T-bet cooperated to repress regulatory elements active in ILC3s. A transitional ILC3–ILC1 population was also detected in the human intestine. We conclude that ILC3s undergo conversion into ILC1-like cells in human tissues in vivo, and that tissue factors and Aiolos were required for this process.

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Fig. 1: Identification of ILC3a, ILC3b, ILC1b and ILC1a by flow cytometry.
Fig. 2: Transcriptome analysis places ILC3b and ILC1b between ILC3a and ILC1a.
Fig. 3: Molecules with progressively decreased or increased expression in the ILC3–ILC1 spectrum.
Fig. 4: Mass spectrometry analysis separates the ILC3–ILC1 spectrum and NK cells.
Fig. 5: The scRNA-seq of the ILC3–ILC1 spectrum identifies an intermediate cluster transitioning to ILC1s.
Fig. 6: Cytokine production in clones derived from ILC3a, ILC3b, ILC1b and ILC1a.
Fig. 7: Aiolos represses ILC3 lineage genes in cooperation with T-bet.
Fig. 8: Identification of ILC clusters in small intestinal lamina propria of controls and CD patients.

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Data availability

Microarray data, bulk RNA sequencing and chromatin profiling data have been deposited in the GEO repository under accession code GSE130775. All the other data will be available upon reasonable request.

Change history

  • 06 August 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Acknowledgements

We thank the Genome Technology Access Center in the Department of Genetics at Washington University School of Medicine for help with genomic analysis. The Center is partially supported by NCI Cancer Center Support grant no. P30 CA91842 to the Siteman Cancer Center and by ICTS/CTSA grant no. UL1 TR000448 from the National Center for Research Resources. We thank E. Lantelme and D. Brinja, and the Pathology and Immunology Flow Cytometry Core for cell sorting. We thank O. Malkova, R. Lin and S. Oh, and the Center for Human Immunology at Washington University for help in processing the samples for mass spectrometry and for advice on analysis. We thank the Washington University Digestive Disease Research Core (NIDDK P30 DK052574) for support. We thank Regeneron for providing MISTRG-6 and IL-15 humanized mice. We thank S. Henikoff (Fred Hutchison Cancer Research Center, Seattle, USA) for providing protein-A-Mnase. This work was in part supported by grant nos UO1 AI095542 and RO1 DE025884 (to M. Colonna) and RO1 AI134035 (to M. Colonna and E.M.O.). R.A.F. is supported by the Howard Hughes Institute.

Author information

Author notes

  1. Patrick L. Collins & Eugene M. Oltz

    Present address: Department of Microbial Infection and Immunity, Ohio State University, Wexner School of Medicine, Columbus, OH, USA

  2. Michelle L. Robinette

    Present address: Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

  3. Konstantin Zaitsev

    Present address: Computer Technologies Department, ITMO University, St Petersburg, Russia

  4. William Gordon

    Present address: Cogen Therapeutics, Cambridge, MA, USA

  5. Victor Cortez

    Present address: Department of Microbiology and Immunology, University of California, San Francisco, CA, USA

  6. Lih-Ling Lin & Michael Bowman

    Present address: Immunology and Inflammation, Sanofi, Cambridge, MA, USA

  7. These authors contributed equally: Marina Cella, Ramya Gamini.

Authors and Affiliations

  1. Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA

    Marina Cella, Cristiane Sécca, Patrick L. Collins, Vincent Peng, Michelle L. Robinette, Konstantin Zaitsev, Jennifer K. Bando, Kentaro Yomogida, Victor Cortez, Susan Gilfillan, Maxim N. Artyomov, Eugene M. Oltz & Marco Colonna

  2. Pfizer Worldwide Research and Development, Cambridge, MA, USA

    Ramya Gamini, Shanrong Zhao, Jorge Schettini, William Gordon, Lih-Ling Lin, Michael Bowman & Scott A. Jelinsky

  3. The McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA

    Catrina Fronick & Robert Fulton

  4. Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA

    Richard A. Flavell

  5. Howard Hughes Medical Institute, Yale University School of Medicine, New Haven, CT, USA

    Richard A. Flavell

  6. Department of Medicine, Division of Infectious Diseases, Washington University School of Medicine, St Louis, MO, USA

    Liang Shan

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Contributions

C.S, P.L.C. and S.Z. contributed equally to this work. M. Cella designed, performed and interpreted experiments. R.G. and S.Z. analyzed the scRNA-seq data and wrote the methods for the scRNA-seq analysis. C.S. generated Aiolos- and T-bet-transduced MNK3 cells. M.L.R. and V.P. analyzed the microarray data and RNA-seq data. K.Z. and M.N.A. provided bioinformatic support. J.K.B., K.Y. and V.C. helped in flow cytometry data presentation and analysis. C.F. and R.F. generated libraries for scRNA-seq. J.S. provided critical advice for Cytof analysis. W.G., L.-L.L. and M.B. provided critical insights to the study. S.G., R.A.F. and L.S. provided key reagents. P.L.C. performed the cut-and-run experiment and interpreted data under the supervision of E.M.O. S.A.J. and M. Colonna supervised the study. M. Cella, S.A.J. and M. Colonna wrote the manuscript and all the authors contributed to editing and suggestions.

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Correspondence to Scott A. Jelinsky or Marco Colonna.

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Competing interests

R.G., S.Z., W.G., J.S., L.-L.L., M.B. and S.A.J. are Pfizer employees. M. Colonna received funding from Pfizer to study ILC biology in inflammatory bowel disease. The other authors declare no conflicts of interests.

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Supplementary Figure 1 Representative gating strategy for sorting ILC subsets.

a-e, Dot plots illustrating gatings. Gates were applied on lymphocytes (a), singlets (b), CD56+CD3–CD19 (c), NKp44+CD103– (d) to identify CD300LF+CCR6+ ILC3a (e), or NKp44+CD103+ (d) cells to identify ILC3b, ILC1b and ILC1a (f). One representative experiment of more than 50 is shown.

Supplementary Figure 2 Bulk RNA-seq of ILC1a, ILC1b, ILC3b, and ILC3a subsets.

a, Volcano plot of differentially expressed (DE) genes between ILC1a (blue) and ILC3a (red) subsets (p < 0.05, Base Mean > 50). b, Selected transcription factors, effector molecules, and cell surface proteins that are differentially expressed between ILC3a and ILC1a (p < 0.05, Wald test). c, Heatmap of normalized expression, z-scored by row, of all differentially expressed genes (p< 0.05) between ILC1a/ILC1b and ILC3a/ILC3b. Green boxes indicate clusters of genes which form a gradient of expression between ILC1a and ILC3a. d, Venn diagram of ILC subset-signature genes. ILC1a and ILC3a signature genes were determined by selecting genes enriched in ILC1a (FC > 0, p <0.05) and ILC3a (FC < 0, p <0.05) when comparing ILC1a and ILC3a. ILC1b signature genes were determined by selecting genes enriched in ILC1b (FC < 0, p <0.05) when comparing DE genes between ILC1a and ILC1b. ILC3b signature genes were determined by selecting genes enriched in ILC3b (FC < 0, p <0.05) when comparing DE genes between ILC3a and ILC3b. Data are obtained from 3 independent biological replicates each subset.

Supplementary Figure 3 ScRNAseq of the ILC3-ILC1 spectrum identifies an intermediate cluster transitioning to ILC1s.

a, Unsupervised tSNE analysis of ILC3-ILC1 subsets to define clusters. b, Heat map showing the top 10 differentially expressed genes in each cluster. Top genes expressed in each cluster are indicated on the right of the heat map. c, tSNE of representative ILC3-related or ILC1-related genes. d, RNA velocity analysis to predict cell dynamics. One donor representative of two is shown.

Supplementary Figure 4 Cytokine production in clones derived from ILC3a, ILC3b, ILC1b and ILC3a.

a, IFN-γ and IL-22 production by 2 of the most represented clones (panels on the left and in the middle) and by one of the least represented clones (panels on the right) derived from ILC3a, ILC3b, ILC1b and ILC1a. i, Percentages of cells producing IL-22 only, IFN-γ plus IL-22, or IFN-γ only in each clone tested. Data are mean±SD. A total of 98 clones were tested (ILC3a=33; ILC3b=11; ILC1b=29; ILC1a=25). One donor representative of 2 is shown. Significance was calculated by ordinary one-way ANOVA multiple comparison test using the Prism7 software. *p≤0.05, **p≤0.001, ***p≤0.005, ****p≤0.0001.

Supplementary Figure 5 In vivo transfer of human ILC3a into humanized mice.

a, Schematic design of in vivo transfer of ILC3a into MISTRG-6-15 mice. b, Identification of human cells in spleen, liver, intra-epithelia lymphocytes (IEL) and lamina propria (LP) of MISTRG-6-15 mice 4 weeks after transfer by staining for human CD45 and human MHCI. c, Percentages of human ILC3s in spleen and liver (n=5). Data are mean±SD. Significance was calculated by unpaired t test using the Prism7 software. ***p≤0.005.

Supplementary Figure 6 Functional analysis of in vivo transferred ILC3a into humanized mice.

a, Expression of RORγt, Aiolos and T-bet in ILC3a at the time of transfer. b, Production of IL-22 and IFN-γ by ILC3a at the time of transfer in the absence or presence of IL-23 stimulation. c, Percentages of human cells present in mouse spleen and liver at day 19 or day 39 post-transfer. d, Expression of RORγt, Aiolos and T-bet in ILCs from spleen and liver at day 19 and day 39 post-transfer in one representative mouse. e-f, Quantification of the percentages of Aiolos+ (e), Tbet+ (f) at the indicated time points in spleen and liver (n=4 mice, each time point). g, Production of IFN-γ and IL-22 by ILCs from spleen and liver at day 19 and day 39 post transfer in one representative mouse. Cells were stimulated with PMA/I. h-i, Quantification of the percentages of IFN-γ producing (h) and IL-22-secreting (i) cells at the indicated time points (n=4 mice, each time point) in spleen and liver. One experiment representative of two is shown. e, f, h, i, Data are mean±SD. Significance was calculated by ordinary one-way ANOVA multiple comparison test using the Prism7 software. *p≤0.05, **p≤0.001, ****p≤0.0001. Spl, spleen; Lv, liver.

Supplementary Figure 7 Expression of T-bet and Aiolos in transduced MNK3 cells.

a, Expression of the reporter proteins GFP and mCherry indicative of T-bet and Aiolos in untransduced MNK3 cells (control) and MNK3 transduced with T-bet, or both T-bet and Aiolos. b, Expression of the transduced transcription factors by intracellular staining. One representative of 3 experiment is shown. c, Intracellular staining for IL-22 in control MNK3 cells or MNK3 transduced with Aiolos and stimulated with IL-23 and IL-1β. d, IL-22 in supernatants of stimulated cells assessed by ELISA (n=5). Data represent mean±SD. One experiment representative of 4 (c) or 2 (d) is shown.

Supplementary Figure 8 Combined expression of Aiolos and T-bet represses type 3 signature genes.

a, UCSC snapshots of the Rorc locus. Tracks represent cut and run for H3K27ac (green, 0-300 RPKM), IKZF3 (blue, 0 to 100 RPKM) or ATAC-seq (red, 0 to 100 RPKM). Fold change plots below H3K27ac tracks represent differences between H3K27ac in MNK3 expressing T-bet or both Aiolos and T-bet. Conserved IKZF3 motifs between mouse loci (top snapshots) and human loci (bottom snapshots) are indicated in dashed lines. Relative kb scale and gene locations are indicated on top. b, RORγt expression by intracellular staining in control MNK3, MNK3 expressing T-bet and MNK3 expressing Aiolos and T-bet. c, RORγt MFI (n=3). Significance was calculated by ordinary one-way ANOVA multiple comparison test using the Prism7 software. *p≤0.05. One experiment representative of 3 is shown. d, UCSC snapshots of representative ILC1/NK genes (Ifng, Eomes), housekeeping genes (B2m, Suv39h2) and ILC3 genes (Il17a/f, S100a4). Tracks represent cut and run for H3K27ac (green, 0-300 RPKM) or IKZF3 (blue, 0 to 100 RPKM). Relative kb scale and gene locations are indicated on top. e, Specificity of Aiolos binding peaks at the II22 locus. Peaks are not observed on control or Tbet transduced MNK3. f, Mean H3K27ac cut and run values across all H3K27ac peaks that overlap (left) or do not overlap (right) IKZF3 peaks. g, De-novo motif prediction of genome-wide IKZF3 peaks. Left: de novo peak. Middle: closest match in the HOMER database. Right: p-value of motif enrichment. Rep, replicate.

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Cella, M., Gamini, R., Sécca, C. et al. Subsets of ILC3−ILC1-like cells generate a diversity spectrum of innate lymphoid cells in human mucosal tissues. Nat Immunol 20, 980–991 (2019). https://doi.org/10.1038/s41590-019-0425-y

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