Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Effector differentiation downstream of lineage commitment in ILC1s is driven by Hobit across tissues

Abstract

Innate lymphoid cells (ILCs) participate in tissue homeostasis, inflammation, and early immunity against infection. It is unclear how ILCs acquire effector function and whether these mechanisms differ between organs. Through multiplexed single-cell mRNA sequencing, we identified cKit+CD127hiTCF-1hi early differentiation stages of T-bet+ ILC1s. These cells were present across different organs and had the potential to mature toward CD127intTCF-1int and CD127TCF-1 ILC1s. Paralleling a gradual loss of TCF-1, differentiating ILC1s forfeited their expansion potential while increasing expression of effector molecules, reminiscent of T cell differentiation in secondary lymphoid organs. The transcription factor Hobit was induced in TCF-1hi ILC1s and was required for their effector differentiation. These findings reveal sequential mechanisms of ILC1 lineage commitment and effector differentiation that are conserved across tissues. Our analyses suggest that ILC1s emerge as TCF-1hi cells in the periphery and acquire a spectrum of organ-specific effector phenotypes through a uniform Hobit-dependent differentiation pathway driven by local cues.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Differential expression of CD127 identifies helper-like and cytotoxic-like hepatic ILC1s.
Fig. 2: CD127+ ILC1s are precursors of CD127 cytotoxic-like ILC1s.
Fig. 3: TCF-1hi early ILC1s give rise to downstream effector ILC1s.
Fig. 4: Hobit marks lineage-committed ILC1s.
Fig. 5: Hobit drives the effector differentiation of lineage-committed ILC1s.
Fig. 6: Hobit is continuously required to regulate the differentiation of TCF-1hi Gzmlo versus TCF-1lo Gzmhi effector ILC1s.
Fig. 7: Effector differentiation of ILC1s is regulated by Hobit across tissues.
Fig. 8: The developmental potential of ILC1s is conserved across tissues.

Similar content being viewed by others

Data availability

The sequencing data that were generated for this report have been deposited in Gene Expression Omnibus under the accession number GSE163452. An interactive online resource of single cell sequencing analyses is available on https://go.uniwue.de/hobit. Sequencing data for ILCP analysis was recently published by Harly et al.29.

Source data are provided with this paper in the Supplementary File. Source data are provided with this paper.

References

  1. Colonna, M. Innate lymphoid cells: diversity, plasticity, and unique functions in immunity. Immunity 48, 1104–1117 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Vivier, E. et al. Innate lymphoid cells: 10 years on. Cell 174, 1054–1066 (2018).

    Article  CAS  PubMed  Google Scholar 

  3. Riggan, L., Freud, A. G. & O’Sullivan, T. E. True detective: unraveling group 1 innate lymphocyte heterogeneity. Trends Immunol. 40, 909–921 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Zeis, P. et al. In situ maturation and tissue adaptation of type 2 innate lymphoid cell progenitors. Immunity 53, 775–792.e779 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Ghaedi, M. et al. Single-cell analysis of RORα tracer mouse lung reveals ILC progenitors and effector ILC2 subsets. J. Exp. Med. 217, e20182293 (2020).

    Article  PubMed  CAS  Google Scholar 

  6. Lim, A. I. et al. Systemic human ILC precursors provide a substrate for tissue ILC differentiation. Cell 168, 1086–1100 (2017).

    Article  CAS  PubMed  Google Scholar 

  7. Lim, A. I. & Di Santo, J. P. ILC-poiesis: ensuring tissue ILC differentiation at the right place and time. Eur. J. Immunol. 49, 11–18 (2019).

    Article  CAS  PubMed  Google Scholar 

  8. Stokic-Trtica, V., Diefenbach, A. & Klose, C. S. N. NK cell development in times of innate lymphoid cell diversity. Front. Immunol. 11, 813 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Krabbendam, L., Bernink, J. H. & Spits, H. Innate lymphoid cells: from helper to killer. Curr. Opin. Immunol. 68, 28–33 (2020).

    Article  PubMed  CAS  Google Scholar 

  10. McFarland, A. P. et al. Multi-tissue single-cell analysis deconstructs the complex programs of mouse natural killer and type 1 innate lymphoid cells in tissues and circulation. Immunity 54, 1320–1337.e1324 (2021).

    Article  CAS  PubMed  Google Scholar 

  11. Daussy, C. et al. T-bet and Eomes instruct the development of two distinct natural killer cell lineages in the liver and in the bone marrow. J. Exp. Med. 211, 563–577 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Weizman, O. E. et al. ILC1 confer early host protection at initial sites of viral infection. Cell 171, 795–808 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Sojka, D. K. et al. Tissue-resident natural killer (NK) cells are cell lineages distinct from thymic and conventional splenic NK cells. eLife 3, e01659 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Takeda, K. et al. TRAIL identifies immature natural killer cells in newborn mice and adult mouse liver. Blood 105, 2082–2089 (2005).

    Article  CAS  PubMed  Google Scholar 

  15. Peng, H. et al. Liver-resident NK cells confer adaptive immunity in skin-contact inflammation. J. Clin. Invest. 123, 1444–1456 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Mackay, L. K. et al. Hobit and Blimp1 instruct a universal transcriptional program of tissue residency in lymphocytes. Science 352, 459–463 (2016).

    Article  CAS  PubMed  Google Scholar 

  17. Behr, F. M. et al. Tissue-resident memory CD8+ T cells shape local and systemic secondary T cell responses. Nat. Immunol. 21, 1070–1081 (2020).

    Article  CAS  PubMed  Google Scholar 

  18. Robinette, M. L. et al. Transcriptional programs define molecular characteristics of innate lymphoid cell classes and subsets. Nat. Immunol. 16, 306–317 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Ducimetiere, L. et al. Conventional NK cells and tissue-resident ILC1s join forces to control liver metastasis. Proc. Natl Acad. Sci. USA 118, e2026271118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Dadi, S. et al. Cancer immunosurveillance by tissue-resident innate lymphoid cells and innate-like T Cells. Cell 164, 365–377 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Krabbendam, L., Heesters, B. A., Kradolfer, C. M. A., Spits, H. & Bernink, J. H. Identification of human cytotoxic ILC3s. Eur. J. Immunol. 51, 811–823 (2020).

    Article  CAS  Google Scholar 

  22. Fuchs, A. et al. Intraepithelial type 1 innate lymphoid cells are a unique subset of IL-12- and IL-15-responsive IFN-γ-producing cells. Immunity 38, 769–781 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Bernink, J. H. et al. Human type 1 innate lymphoid cells accumulate in inflamed mucosal tissues. Nat. Immunol. 14, 221–229 (2013).

    Article  CAS  PubMed  Google Scholar 

  24. Vonarbourg, C. et al. Regulated expression of nuclear receptor RORγt confers distinct functional fates to NK cell receptor-expressing RORγt+ innate lymphocytes. Immunity 33, 736–751 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Walker, J. A. et al. Polychromic reporter mice reveal unappreciated innate lymphoid cell progenitor heterogeneity and elusive ILC3 progenitors in bone marrow. Immunity 51, 104–118.e107 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Klose, C. S. N. et al. Differentiation of type 1 ILCs from a common progenitor to all helper-like innate lymphoid cell lineages. Cell 157, 340–356 (2014).

    Article  CAS  PubMed  Google Scholar 

  27. Grün, D. et al. De novo prediction of stem cell identity using single-cell transcriptome data. Cell Stem Cell 19, 266–277 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Xu, W. et al. An Id2(RFP)-reporter mouse redefines innate lymphoid cell precursor potentials. Immunity 50, 1054–1068.e1053 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Harly, C. et al. The transcription factor TCF-1 enforces commitment to the innate lymphoid cell lineage. Nat. Immunol. 20, 1150–1160 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Bai, L. et al. Liver type 1 innate lymphoid cells develop locally via an interferon-γ-dependent loop. Science 371, eaba4177 (2021).

    Article  CAS  PubMed  Google Scholar 

  31. Yao, C. et al. BACH2 enforces the transcriptional and epigenetic programs of stem-like CD8+ T cells. Nat. Immunol. 22, 370–380 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ataide, M. A. et al. BATF3 programs CD8+ T cell memory. Nat. Immunol. 21, 1397–1407 (2020).

    Article  CAS  PubMed  Google Scholar 

  33. Gasteiger, G., Fan, X., Dikiy, S., Lee, S. Y. & Rudensky, A. Y. Tissue residency of innate lymphoid cells in lymphoid and nonlymphoid organs. Science 350, 981–985 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Huang, Y. et al. S1P-dependent interorgan trafficking of group 2 innate lymphoid cells supports host defense. Science 359, 114–119 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Cortez, V. S. et al. Transforming growth factor-β signaling guides the differentiation of innate lymphoid cells in salivary glands. Immunity 44, 1127–1139 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Tessmer, M. S., Reilly, E. C. & Brossay, L. Salivary gland NK cells are phenotypically and functionally unique. PLoS Pathog. 7, e1001254 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Chiossone, L. et al. Maturation of mouse NK cells is a 4-stage developmental program. Blood 113, 5488–5496 (2009).

    Article  CAS  PubMed  Google Scholar 

  38. Jeevan-Raj, B. et al. The transcription factor Tcf1 contributes to normal NK cell development and function by limiting the expression of granzymes. Cell Rep. 20, 613–626 (2017).

    Article  CAS  PubMed  Google Scholar 

  39. Escobar, G., Mangani, D. & Anderson, A. C. T cell factor 1: a master regulator of the T cell response in disease. Sci. Immunol. 5, eabb9726 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Oherle, K. et al. Insulin-like growth factor 1 supports a pulmonary niche that promotes type 3 innate lymphoid cell development in newborn lungs. Immunity 52, 275–294.e279 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Collins, P. L. et al. Gene regulatory programs conferring phenotypic identities to human NK cells. Cell 176, 348–360.e312 (2019).

    Article  CAS  PubMed  Google Scholar 

  42. Vieira Braga, F. A. et al. Blimp-1 homolog Hobit identifies effector-type lymphocytes in humans. Eur. J. Immunol. 45, 2945–2958 (2015).

    Article  CAS  PubMed  Google Scholar 

  43. Behr, F. M. et al. Blimp-1 rather than hobit drives the formation of tissue-resident memory CD8+ T cells in the lungs. Front. Immunol. 10, 400 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Jansen, C. S. et al. An intra-tumoral niche maintains and differentiates stem-like CD8 T cells. Nature 576, 465–470 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Krishna, S. et al. Stem-like CD8 T cells mediate response of adoptive cell immunotherapy against human cancer. Science 370, 1328–1334 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Siddiqui, I. et al. Intratumoral Tcf1+PD-1+CD8+ T cells with stem-like properties promote tumor control in response to vaccination and checkpoint blockade immunotherapy. Immunity 50, 195–211 (2019).

    Article  CAS  PubMed  Google Scholar 

  47. Narni-Mancinelli, E. et al. Fate mapping analysis of lymphoid cells expressing the NKp46 cell surface receptor. Proc. Natl Acad. Sci. USA 108, 18324–18329 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Zhu, Y. et al. T-bet and eomesodermin are required for T cell-mediated antitumor immune responses. J. Immunol. 185, 3174–3183 (2010).

    Article  CAS  PubMed  Google Scholar 

  49. Kim, J. M., Rasmussen, J. P. & Rudensky, A. Y. Regulatory T cells prevent catastrophic autoimmunity throughout the lifespan of mice. Nat. Immunol. 8, 191–197 (2007).

    Article  CAS  PubMed  Google Scholar 

  50. Skarnes, W. C. et al. A conditional knockout resource for the genome-wide study of mouse gene function. Nature 474, 337–342 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Kranz, A. et al. An improved Flp deleter mouse in C57Bl/6 based on Flpo recombinase. Genesis 48, 512–520 (2010).

    Article  CAS  PubMed  Google Scholar 

  52. Ventura, A. et al. Restoration of p53 function leads to tumour regression in vivo. Nature 445, 661–665 (2007).

    Article  CAS  PubMed  Google Scholar 

  53. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Stoeckius, M. et al. Cell hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol. 19, 224 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Grün, D. Revealing dynamics of gene expression variability in cell state space. Nat. Methods 17, 45–49 (2020).

    Article  PubMed  CAS  Google Scholar 

  58. Herman, J. S., Sagar & Grün, D. FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data. Nat. Methods 15, 379–386 (2018).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank C. Romagnani for critically reading the manuscript, and S. Klingler, M. Lian, S. Riedmann, L. Parga-Vidal and the IZKF FACS sorting facility Würzburg for expert technical assistance. We thank E. Vivier (Aix Marseille Univ., CNRS, INSERM, CIML, Marseille, France) and A. Rudensky (Howard Hughes Medical Institute, Immunology Program, and Ludwig Center, Memorial Sloan Kettering Cancer Center, New York, USA) for providing mice. We thank R. Naumann (MPI-CBG, Dresden) for providing Flp deleter mice and for microinjections of HobitKO ES cells. This work was supported by grants through the German Research Foundation (DFG) priority program SPP1937 – Innate lymphoid cells (GA2129/2-2 to G.Gasteiger and GR4980/1-2 to D.G.), through the European Research Council (759176-TissueLymphoContexts to G.Gasteiger, 818846-ImmuNiche to D.G. and 819329- STEP2 to W.K.). G.Gasteiger and D.G. are supported by the Max Planck Society. P.A. acknowledges financial support from Interdisciplinary Center for Clinical Research-IZKF (project Z-6). A.-E.S. is supported by the EMBO Young Investigator program. K.P.J.M.v.G. was funded by a LSBR fellowship from Landsteiner Foundation of Blood Transfusion Research (1629).

Author information

Authors and Affiliations

Authors

Contributions

G.Gasteiger and K.P.J.M.v.G. conceptualized the study, provided funding, and coordinated the project. G.Gasteiger, K.P.J.M.v.G. and C.F. wrote the manuscript with input from all authors. C.F., R.L.R.E.T., R.M. and N.A.M.K. planned and performed experiments and analyzed the data. P.A. and A.-E.S. performed scRNA-seq. R.-D.L., and G.Golda analyzed scRNA-seq experiments. M.G.d.A. and K.K. provided critical reagents. W.K. and D.G. provided intellectual input and conceptual advice.

Corresponding author

Correspondence to Georg Gasteiger.

Ethics declarations

Competing interests

K.K. is an employee of Morphosys AG and former employee of the Roche Innovation Center Munich.

Additional information

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

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

Extended data

Extended Data Fig. 1 Characterization of hepatic group 1 ILCs and gating strategy.

a Representative FACS plots display LinNK1.1+ cells of WT and HobitTom/WT mice. Bar graphs indicate absolute numbers of liver ILC1s. Data are pooled from 2 independent experiments with n=2 and 3 mice per group. b Full sorting strategy for liver ILC1 and NK cells from negatively enriched liver lymphocytes of HobitTom/WT mice. c Heat map overview of significantly differentially expressed genes of ILC1 and NK cell clusters from Fig. 1c using Seurat. d, e Gating strategy to identify Lin NK1.1+ Eomes CD49a+ liver ILC1 and histograms showing Gata3, RORγt (d) and CD49b (e) protein expression within liver ILC1. Liver and mesenteric lymph nodes are gated on LinCD127+ cells to display Gata3 and RORyt expression. Data are representative of 2 individual experiments with n=3 mice. Bar graphs indicate individual mice (symbols) and mean (bar), error bars display means ± SD. Statistical significance was calculated by unpaired two-tailed t-test.

Extended Data Fig. 2 CD127 cytotoxic-like cells are bona-fide ILC1s.

a Dot plot representation of selected ILC1 and NK cell marker gene expression associated with the clusters identified in Fig. 1c. Color indicates z-score of mean expression across clusters and dot size represents fraction of cells in the cluster expressing the respective gene. b, c, f Frequency of liver ILC1s expressing indicated proteins in NKp46Cre versus NKp46Cre Eomesfl/fl mice (b), WT versus Rag1-/- mice (c) and SPF versus germ free mice (f). Data are representative of 2 independent experiments with n=2 and 3 mice per group (b, c, f). d, e Representative FACS analysis of liver ILC1 of RORc-eYFP fate mapper (FM) mice. (d) FACS analysis and bar graph shows frequency of RORc fate map-positive cells within CD127+ and CD127 liver ILC1 and NK cells. Data are representative of 2 independent experiments with n=3 mice per group. (e) FACS plots show marker protein expression of RORc fate mapper-positive (top) and negative (bottom) liver ILC1s. Bar graphs show frequency of Gzmb-positive cells within FM-positive and FM-negative ILC1s. Data are pooled from 2 independent experiments with n=2 mice per group. Bar graphs indicate individual mice (symbols) and mean (bar), error bars display means ± s.d. Statistical significance was calculated by unpaired two-tailed t-test; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Source data

Extended Data Fig. 3 CD127+ ILC1s give rise to CD127 cytotoxic-like ILC1s in vitro and in vivo.

a, b Sorting strategy for CD127+ and CD127 liver ILC1 and NK cells from WT mice, gated on live CD45+ cells. b Sorting purity. c-f, h, i In vitro cultures of indicated liver ILC1 subsets with IL-2 (c, d, f, h, i) or IL-15 (e) and OP9-DL1 cells (c, e, h, i) or the indicated stromal cell line (d) for 7 days. Representative FACS analysis showing phenotype of NK1.1+ cells. Red quadrants on FACS plots highlight phenotypic differences of CD127+ ILC1 cultured in the presence of IL-2 versus IL-15. d Frequency of Gzma+Gzmb+ cells on day 7. e Absolute numbers on d7. Dashed line indicates number of cells on d0 (input=400 cells). f In vitro proliferation of ILC1 subsets assessed by cell tracer violet dilution analyzed on the indicated days. Data are representative of 2 independent experiments with n=4 and 5 (d), n=2 (e), n=4 (f) replicates per experiment. g In vivo co-transfer of congenically marked CD127+ and CD127 liver ILC1s into sublethally irradiated Rag2-/-γc-/- mice. Representative FACS analysis of liver ILC1s derived from indicated transferred ILC1 subsets on d15. i Isotype control stainings of ILC1s cultured in vitro as in h. Bar graphs indicate replicates (symbols) and mean (bar), error bars display means ± s.d.

Source data

Extended Data Fig. 4 scRNA-seq analysis of hepatic ILC1s from WT and HobitKO mice.

a Schematic representation of the Hobit locus of Hobit KO mice. b Absolute numbers of liver NK cells of WT and HobitKO mice. c Representative FACS analysis displaying LinNK1.1+ hepatic ILC1s and NK cells in HobitTom/WT and HobitTom/KO mice. Bar graphs indicate absolute numbers of liver ILC1s. Data in (b) and (c) are pooled from 2 independent experiments with n=3 (b) and n=2 and 3 (c) mice per group. d Representative FACS analysis of liver ILC1s of HobitKO RORc-eYFP fate mapper (FM) mice. Bar graph shows frequency of RORc fate map-positive cells within CD127+ and CD127 liver ILC1s. Data are representative of 2 independent experiments with n=2-3 mice per group. e-g scRNA-seq of hash-tagged liver ILC1 sorted from HobitTom/WT and HobitTom/KO mice. e General QC, low quality cells (feature counts > 1000 and percentage of genes mapped to mitochondrial genome < 8) and cell doublets filtering and demultiplexing of Hashtag (HT) antibodies allows to separate HobitKO/Tom (KO) and HobitTom/WT (WT). Single cell transcriptome visualization using a UMAP color-coded by clusters and the sample WT or KO as indicated by the binary detection of the HT antibodies. f Volcano plot displaying significantly differentially expressed genes between WT and HobitKO ILC1, using Wilcoxon test. Thresholds are displayed at Log2 FC equal 0.5 and Adjusted P value of 0.05. Genes on the right are overexpressed in KO, genes on the left are overexpressed in WT. g Heat map of marker gene expression differentially expressed across liver ILC1s. Cluster number refers to panel (e). h, i Pseudo-temporal ordering of ILC1 single cell transcriptomes with Slingshot. Expression of candidate genes across pseudotime within hash-tagged liver ILC1s sorted from HobitTom/WT and HobitTom/KO mice. Color barcode in (h) indicates cluster identity (as identified in e). Heatmap of expression of selected genes in ILC1 populations along pseudotime (i). Bar graphs indicate individual mice (symbols) and mean (bar), error bars display means ± s.d. Statistical significance was calculated by unpaired two-tailed t-test; **P < 0.01.

Source data

Extended Data Fig. 5 Hobit drives the development and effector maturation of hepatic ILC1s and is expressed in committed ILC1s.

a-c Representative FACS analysis of liver ILC1s in mixed bone marrow chimeric mice containing a congenically marked WT and HobitKO compartment (a). Frequency of liver ILC1 expressing indicated marker (b) and gMFI of CD127, TCF-1 and IL-18R1 expression within the respective marker-positive cells (c). Data are representative of 3 individual experiments with n=4 mice per group (a-c). d-g Analysis of an unbiased ILCP core signature derived from a scRNA-seq dataset of M progenitors generated by Harly et al29. Violin plots display ILCP score calculation and heat maps display individual gene expression across identified clusters (d, f) and in the dataset generated by Harly et al29(e, g). ALP - all-lymphoid progenitors, sEILP - specific early innate lymphoid progenitors, cEILP - committed early innate lymphoid progenitors, ILCP - innate lymphoid progenitors. Bar graphs indicate individual mice (symbols) and mean (bar), error bars display means ± s.d. Statistical significance was calculated by unpaired two-tailed t-test; **P < 0.01, ***P < 0.001, ****P < 0.0001.

Source data

Extended Data Fig. 6 Effector differentiation of ILC1s is regulated by Hobit across tissues.

a Gating strategy identifying ILC1s among live CD45+ cells in the small intestine lamina propria for data depicted in Fig. 7a. To preserve the HobitTom signal, cells were gated without transcription factor staining. ILC1 (1) were enriched as Lin NKp46+ NK1.1hiCD90int CXCR6+ CD127+ cells, NK cells (2) were gated as Lin NKp46+ NK1.1hi CD90int CXCR6 CD127 cells, NKp46+ ILC3 were gated as Lin NKp46+ CD90hi cells. Purity of gating strategy is displayed by analysis of RORγt and Eomes (right). b-q Analysis of ILC1 in salivary glands (b-e), kidneys (f-i), mesenteric lymph nodes (j-m) and small intestine lamina propria (n-q). b, f, j, n Representative FACS analysis of LinNK1.1+ cells (left row of FACS plots) and EomesCD49a+ ILC1s (middle and right row) of WT and HobitKO mice. b Representative FACS plot identifying ILC1 (EomesCD49a+), Eomes+CD49a+NK1.1+ cells (Eomes+CD49a+) and cNK cells (Eomes+CD49a) within LinNK1.1+ cells (left panel). c, g, k, o Bar graphs indicate absolute numbers of ILC1. d, h, l, p Frequency of ILC1s expressing indicated marker proteins. e, i, m, q gMFI of CD127, TCF-1 and IL-18R1 expression within ILC1 that are gated as positive for the respective marker. Data are representative of 3 independent experiments with n=3 (c, e), n=4 (j, l, m, p, q) and n=5 (b, d, f-i, k, o) mice per group. Bar graphs indicate individual mice (symbols) and mean (bar), error bars display means ± s.d. Statistical significance was calculated by unpaired two-tailed t-test; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Source data

Extended Data Fig. 7 Analysis of ILC1s in mixed bone marrow chimeric mice and upon inducible deletion of Hobit.

Representative FACS analysis of ILC1s in salivary glands (a, b, g, h), kidneys (c, d, i, j) and small intestine lamina propria (e, f) of mixed bone marrow chimeric mice (a-f) or UbiCre-ERT2 Hobitfl/fl mice and littermate controls on d35 after Tamoxifen treatment (g-j). Bar graphs indicate frequency of ILC1s expressing indicated proteins. Data are representative of 2 (e, f) or 3 (a-d, g-j) individual experiments with n=3 (f), n=4 (a-d, g-j) and n=5 (d, h, j) mice per group. Bar graphs indicate individual mice (symbols) and mean (bar), error bars display means ± s.d. Statistical significance was calculated by unpaired two-tailed t-test; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Source data

Extended Data Fig. 8 Hobit regulates the effector differentiation of Eomes+CD49a+ group 1 ILCs, and phenotype of ILC1s in Treg cell-depleted mice.

a-e FACS analysis of Eomes+CD49a+ tissue-resident NK1.1+ cells in salivary glands of WT and HobitKO mice. a Representative FACS analysis of HobitTom expression within ILC1 (CD49bCD49a+), Eomes+CD49a+NK1.1+ cells (identified by gating as CD49b+CD49a+, to avoid permeabilization for the analysis of the HobitTom reporter signal) and cNK cells (CD49b+CD49a) within LinNK1.1+ cells. b Absolute numbers of Eomes+CD49a+NK1.1+ cells. c Representative FACS analysis showing indicated marker expression. d Frequency of marker-positive cells. e gMFI of TCF-1 and IL-18R1 expression within marker-positive cells. Data are representative of 3 independent experiments with n=3 mice per group. f Representative FACS plots show expression of indicated proteins of ILC1s in kidneys, mesenteric lymph nodes and small intestine lamina propria of FoxP3DTR mice on d8 of Treg cell depletion (DTx). Data are representative of 2-3 independent experiments with n=4 mice per group. Bar graphs indicate individual mice (symbols) and mean (bar), error bars display means ± s.d. Statistical significance was calculated by unpaired two-tailed t-test; **P < 0.01, ***P < 0.001.

Source data

Supplementary information

Source data

Source Data Fig. 1

Statistical Source Data

Source Data Fig. 2

Statistical Source Data

Source Data Fig. 3

Statistical Source Data

Source Data Fig. 4

Statistical Source Data

Source Data Fig. 5

Statistical Source Data

Source Data Fig. 6

Statistical Source Data

Source Data Fig. 7

Statistical Source Data

Source Data Fig. 8

Statistical Source Data

Source Data Extended Data Fig. 2

Statistical Source Data

Source Data Extended Data Fig. 3

Statistical Source Data

Source Data Extended Data Fig. 4

Statistical Source Data

Source Data Extended Data Fig. 5

Statistical Source Data

Source Data Extended Data Fig. 6

Statistical Source Data

Source Data Extended Data Fig. 7

Statistical Source Data

Source Data Extended Data Fig. 8

Statistical Source Data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Friedrich, C., Taggenbrock, R.L.R.E., Doucet-Ladevèze, R. et al. Effector differentiation downstream of lineage commitment in ILC1s is driven by Hobit across tissues. Nat Immunol 22, 1256–1267 (2021). https://doi.org/10.1038/s41590-021-01013-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-021-01013-0

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing