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Epigenetic imprinting in innate lymphoid cell precursors directs the lineage segregation of innate lymphoid cells

Nature Immunology volume 26, pages 1686–1698 (2025)Cite this article

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Abstract

Innate lymphoid cells (ILCs) are essential for mucosal homeostasis, but the epigenetic regulation of their lineage segregation remains elusive. Here we simultaneously profiled the single-cell DNA methylome, chromatin accessibility and transcriptome of ILC subsets and ILC precursors (ILCPs) and found that ILCPs could be divided into two subgroups (ILCP1 and ILCP2). ILCP2s had highly heterogeneous DNA methylation profiles and could be divided into three groups according to their DNA methylation characteristics, which matched those of ILC subsets. We identified the signature methylation regions (SMRs) of each ILC subset and traced the DNA methylation imprinting during ILCP differentiation. ILCP2s with hypomethylated SMRs characteristic of ILC subsets differentiated into those subsets. DNA methylation editing of SMRs suppressed ILC lineage segregation, while deletion of Dnmt1 in ILCPs abrogated the heterogeneous distribution of SMRs and resulted in ILC differentiation defects. These findings provide evidence that epigenetic imprinting determines lineage segregation during immune cell development.

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Fig. 1: Single-cell multi-omics sequencing defines two stages of ILCPs.
Fig. 2: Features of DNA methylation and DMRs in ILCP1s and ILCP2s.
Fig. 3: Correlations among DNA methylation, chromatin accessibility and gene expression in ILCP subsets.
Fig. 4: DNA methylation heterogeneity and identification of SMRs in ILCP2s.
Fig. 5: DNA methylation imprints from ILCP2s to ILC subsets.
Fig. 6: DNA methylation contributes to maintaining the homeostasis of ILCPs.

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Data availability

All data, including raw sequencing files, have been deposited in the Genome Sequence Archive in the BIG Data Center, Chinese Academy of Sciences, under BioProject accession number PRJCA032162, and accession code CRA020302 at https://ngdc.cncb.ac.cn/gsa/. Source data are provided with this paper.

Code availability

The code used in analyzing the dataset is available on GitHub at https://github.com/lz269141050/single-cell-multiomics-sequencing-data-analyses.

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Acknowledgements

We thank T. Zhao (Institute of Microbiology, Chinese Academy of Sciences) and K. Zhang (Department of Laboratory Animal Science, Health Science Center, Peking University) for their technical support. We thank Z. Zhang (Institute of Biophysics, Chinese Academy of Sciences) for valuable advice. S.W. received support from the National Key R&D Program of China (2022YFC2302900 and 2021YFA1300202), the CAS Project for Young Scientists in Basic Research (YSBR-010)，Key Research Program of Frontier Sciences of Chinese Academy of Sciences (ZDBS-LY-SM025) and the Beijing Natural Science Foundation (JQ24043).

Author information

Author notes

  1. These authors contributed equally: Zhen Liu, Fei Shao, Qiang Zhang.

Authors and Affiliations

  1. Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China

    Zhen Liu, Fei Shao, Min Zhao, Xusheng Zhang, Dou Yu & Shuo Wang

  2. University of Chinese Academy of Sciences, Beijing, China

    Zhen Liu, Fei Shao, Xusheng Zhang, Dou Yu & Shuo Wang

  3. Institute of Cardiovascular Diseases, Xiamen Cardiovascular Hospital, School of Medicine, Xiamen University, Xiamen, China

    Qiang Zhang

  4. State Key Laboratory of Animal Biotech Breeding, Frontiers Science Center for Molecular Design Breeding (MOE), College of Animal Science and Technology, China Agricultural University, Beijing, China

    Qiang Zhang, Shuai Gao & Jingyao Zhang

  5. Department of Immunology, School of Basic Medical Sciences, NHC Key Laboratory of Medical Immunology, Peking University, Beijing, China

    Pengyan Xia

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Contributions

Z.L., F.S. and Q.Z. performed the experiments and analyzed data. M.Z., S.G., X.Z., D.Y. and J.Z. carried out the experiments. P.X. performed data analysis. S.W. initiated the study, organized, designed and wrote the paper.

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Correspondence to Shuo Wang.

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Nature Immunology thanks Yi Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ioana Staicu, in collaboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 Single-cell multi-omics analyses reveal two developmental stages of ILCPs.

a, Schematic representation showing the isolation of Lin(CD3/CD19/CD11b/Gr-1/TER-119/CD45R)−CD45+CD127+NK1.1+NKp46+ ILC1s and Lin(CD3/CD19/CD11b/Gr-1/TER-119/CD45R)−CD45+CD127+KLRG1+ ILC2s from the lamina propria of small intestine of wild-type (WT) C57BL/6 mice and Lin(CD3/CD19/CD11b/Gr-1/TER-119/CD45R/NK1.1/KLRG1)−CD45+CD127+RORγt-GFP+ ILC3s from RORγt-GFP+ mice, Lin(CD3/CD19/CD45R/CD11b/TER-119/Gr-1)−c-Kit+CD127+α4β7+PLZF-GFP+ ILCPs from the bone marrow of Zbtb16 (encodes PLZF)-GFP mice, followed by single-cell multi-omics sequencing and analyses of the transcriptome, chromatin accessibility and DNA methylome. b, Flow cytometry gating strategy showing the isolation of Lin(CD3/CD19/CD45R/CD11b/TER-119/Gr-1)−CD127+c-Kit+α4β7+PLZF-GFP+ ILCPs from bone marrow of Zbtb16-GFP mice. c, Heatmap showing expression levels of the marker genes in ILCP1s and ILCP2s. Statistical analyses were performed using Wilcoxon Rank Sum test. d, UMAP plots showing the distribution of CytoTRACE scores in ILCPs. e, RNA velocity analysis showing potential developmental trajectories of ILCPs. f, Dot plot showing positive regulation of lymphoid differentiation scores along pseudotime in ILCPs, calculated using the AddModuleScore function. g, Two-dimensional plots showing dynamic expression of Dnmt1 and Uhrf1 genes in ILCPs along the pseudotime. b-g, Data are representative of at least two independent experiments. Schematic in a was created using BioRender.com.

Extended Data Fig. 2 Chromatin accessibility and DNA methylation properties of ILCP1s and ILCP2s.

a, GCH methylation analysis showing chromatin accessibility 1 kb upstream and 1 kb downstream of the transcription start sites (TSSs) in ILCP1s and ILCP2s. b, GCH methylation analysis showing chromatin accessibility around 2 kb upstream and 2 kb downstream of the gene body in ILCP1s and ILCP2s. c, Bar plot showing relative enrichment scores of NDRs in genomic elements in ILCP subsets. Enrichment scores were calculated as the ratio of NDRs in each genomic element compared to genomic background. SINE (short interspersed nuclear element), LINE (long interspersed nuclear element), LTR (long terminal repeat). d, Heatmap showing chromatin accessibility of common NDRs (C1, n = 16,898), ILCP1-specific NDRs (C2, n = 23,021), and ILCP2-specific NDRs (C3, n = 56,986) in ILCP1s and ILCP2s. e, Pie charts showing distribution of common NDRs (C1), ILCP1-specific NDRs (C2), and ILCP2-specific NDRs (C3) across genomic elements in ILCP1s and ILCP2s. f, Alluvial plots showing dynamic changes of promoter states (homogeneously open, divergent, closed) from ILCP1s to ILCP2s. g, Bar graph showing the distribution of DNA methylation levels in all WCG sites in ILCP1s and ILCP2s. All DNA methylation values were retained to one decimal place. h, Boxplot showing the expression levels of Dnmt1 and Uhrf1 genes in ILCP1s (n = 106 cells) and ILCP2s (n = 206 cells). Each box represented the median and the 25% and 75% quartiles, and the whiskers indicate 1.5 times the interquartile range. Statistical analyses were performed using two-tailed unpaired Student’s t test. i, Two-dimensional plot showing the dynamic expression of scores for DNA methylation maintenance genes along the pseudotime. Data are representative of at least two independent experiments.

Extended Data Fig. 3 Correlation among DNA methylation, chromatin accessibility and gene expression in ILCPs.

a, Volcano plot showing DMRs and genes related to DMRs in their enhancers in ILCP2s compared to ILCP1s. The genes with hypomethylated DMRs in ILCP2s were shown in blue (methylation difference of DMR < -0.1, P < 0.05). The genes with hypermethylated DMRs of ILCP2s were shown in red (methylation difference of DMR > 0.1, P < 0.05). Statistical analyses were performed using Fisher’s exact test. b, Bar graph representing enrichment scores of genes with differentially hypomethylated WCG in enhancers (Diff-hypomethyl-WCG), genes with differentially hypermethylated GCH in enhancers (Diff-hypermethyl-GCH) and upregulated genes in TH1 + TH2 or TH17 cell differentiation KEGG pathways in ILCP2s compared to ILCP1s. Statistical analyses were performed using hypergeometric test. Data are representative of at least two independent experiments.

Extended Data Fig. 4 DNA methylation heterogeneity in ILCP2s.

a-c, Representative flow cytometry plots showing expression of UHRF1-mCherry in Lin(CD3/CD19/CD11b/Gr-1/TER-119/CD45R)−CD45+CD127+NK1.1+NKp46+ ILC1s (a), Lin(CD3/CD19/CD11b/Gr-1/TER-119/CD45R)−CD45+CD127+KLRG1+ ILC2s (b) and Lin(CD3/CD19/CD11b/Gr-1/TER-119/CD45R/NK1.1/KLRG1)−CD45loCD127+CD90hi ILC3s (c) from the small intestine of UHRF1-mCherry reporter mice and WT mice. d, tSNE plot showing gene expression analysis of ILCP1s, ILCP2s, mCherry+ ILCPs, mCherry− ILCPs, and mCherry+ ILCPs cultured in vitro for two days (Day 2 in vitro). e, Heatmap showing the expression of marker genes of ILCP1s and ILCP2s in ILCP1s, ILCP2s, mCherry+ ILCPs, mCherry− ILCPs, and mCherry+ ILCPs cultured in vitro for two days. f, Heatmap showing WCG methylation levels of selected SMRs in ILCP1s (n = 106 cells), ILCP2_ILC1s (n = 67 cells), ILCP2_ILC2s (n = 69 cells), and ILCP2_ILC3s (n = 70 cells). g, Dot plot showing RNA expression levels of SMR-related genes, including ILCP signature genes (Zbtb16 and Tox), ILC1 signature genes (Tbx21 and Ifng), ILC2 signature genes (Bcl11b and Il4), and ILC3 signature genes (Rorc and Il17a) in ILCP2_ILC1, ILCP2_ILC2, ILCP2_ILC3, ILC1, ILC2 and ILC3 subsets. h, Dot plot displaying GCH methylation levels at SMRs in ILCP and ILC signature genes in ILCP2_ILC1, ILCP2_ILC2, ILCP2_ILC3, ILC1, ILC2 and ILC3 subsets. i, UMAP analysis showing cell type clustering based on transcriptome data of ILCP2_ILC1s (n = 67 cells), ILCP2_ILC2s (n = 69 cells), ILCP2_ILC3s (n = 70 cells), ILC1Ps (n = 46 cells), ILC2Ps (n = 43 cells), and ILC3Ps (n = 35 cells). (ILC1Ps, ILC2Ps, and ILC3Ps from dataset GSE131038 and GSE193835). j, Heatmap showing expression levels of representative ILC marker genes in ILCP2 subsets and precursor cells of ILCs. Lineage-determining transcription factors are highlighted in red. Statistical analyses were performed using two-sided Wilcoxon rank sum test. Data represented at least two (d-j) or three (a-c) independent experiments.

Extended Data Fig. 5 DNA methylation tracing and editing of SMRs in ILCPs.

a, Representative flow cytometry plots showing GFP expression in Lin−CD127+c-Kit+α4β7+PD-1+ ILCPs transduced with lentivirus carrying Dazl-CGI-Snrpn-GFP methylation reporter systems. The GFP expression in mCherry+ ILCPs was measured two days after transduction. b, Barplots showing proportions of GFP+ ILCPs among mCherry+ ILCPs expressing Tbx21-SMR-Snrpn-GFP (Tbx21), Bcl11b-SMR-Snrpn-GFP (Bcl11b), and Rorc-SMR-Snrpn-GFP (Rorc) reporter systems two days after lentiviral transduction. Data are shown as mean ± s.d. (n = 4 cultures per group). c, Boxplot showing DNA methylation levels of SMRs after DNA methylation editing in ILCPs. Lin−CD127+c-Kit+α4β7+PD-1+ ILCPs were transduced with lentiviruses encoding either active dCas9-DNMT1 (dC-DNMT1) or a catalytically inactive mutant (dC-dDNMT1), along with SMR-targeting gRNAs. The DNA methylation levels of SMRs were measured by bisulfite sequencing and shown. Each box represents the median and the 25% and 75% quartiles, and the whiskers indicate 1.5 times the interquartile range. Statistical analyses were performed using two-tailed unpaired Student’s t test. (n = 10 per group). Data are representative of at least three independent experiments.

Source data

Extended Data Fig. 6 Hypomethylation of SMRs guides SWI/SNF complex binding.

a, Volcano plot showing DEGs between ILCs and ILCP2s. Statistical analyses were performed using two-tailed unpaired Student’s t test. The blue dots represent genes which were downregulated in ILCs compared with ILCP2s (log2(fold change) < -0.4, P < 0.05). The red dots represent genes which were upregulated in ILCs compared with ILCP2s (log2(fold change) > 0.4, P < 0.05). b, Boxplots showing Smarcc1 (coding BAF155) expression levels in ILCP1s, ILCP2s and ILCP2s in differentiation stages. UHRF1-mCherry+ ILCP (ILCP1s) and UHRF1-mCherry− ILCPs (ILCP2s) were isolated and ILCP2s were cultured with OP9-DL1 cells in media with 25 ng/ml IL-7 and 25 ng/ml SCF for the indicated days in vitro. The expression levels of Smarcc1 gene were analyzed by qPCR and shown. Each box represents the median and the 25% and 75% quartiles, and the whiskers indicate 1.5 times the interquartile range. Statistical analyses were performed using one-way ANOVA. (n = 3 cultures per group). c, Bar graph showing BAF155 occupancy at SMRs in ILCPs assessed by CUT&Tag-qPCR. ILCPs were transduced with dC-DNMT1 or catalytically inactive dC-dDNMT1, and enrichment of BAF155 was quantified. Data are presented as mean ± s.d. Statistical analyses were performed using two-tailed unpaired Student’s t-test. (n = 3 cultures per group). d, ATAC-seq analysis showing chromatin accessibility of SMRs in ILCPs transduced with dC-DNMT1 or dC-dDNMT1 and SMR-targeting gRNAs after 7 days in vitro. e, Boxplots showing the expression levels of Tbx21, Bcl11b, and Rorc genes in ILCPs transduced with dC-DNMT1 or dC-dDNMT1 and SMR-targeting gRNAs at day 7 of culture of ILCPs. Each box represents the median and the 25% and 75% quartiles, and the whiskers indicate 1.5 times the interquartile range. Statistical analyses were performed using two-tailed unpaired Student’s t test. (n = 3 cultures per group). Data are representative of at least two (a, d) or three (b-c, e) independent experiments.

Source data

Extended Data Fig. 7 Dnmt1 deletion impairs ILCP differentiation.

a, Bar graph showing the proportions of UHRF1-mCherry+ and UHRF1-mCherry− ILCPs in the bone marrow of Id2CreERT2 UHRF1-mCherry mice or Id2CreERT2Dnmt1fl/fl UHRF1-mCherry mice after TMX treatment. Data were shown as mean ± s.d. (n = 3 mice per group). b, Representative flow cytometry histograms (left) showing the expression levels of UHRF1-mCherry in Id2CreERT2Dnmt1fl/fl UHRF1-mCherry ILCPs in the cell culture with OP9-DL1 cells with 25 ng/ml IL-7 and 25 ng/ml SCF at the initial time point (green line), two days later with (yellow line) or without 4-OHT treatment (red line). ILCPs from WT mice served as a control (grey line). The MFI of UHRF1-mCherry was analyzed by flow cytometry and shown as mean ± s.d (right). Statistical analyses were performed using two-tailed unpaired Student’s t test. (n = 3 cultures per group). c, Principal component analysis (PCA) showing single-cell clustering based on DNA methylation levels in promoters, colored by the indicated cell type. UHRF1-mCherry+ ILCPs (ILCP1s) from Id2CreERT2Dnmt1fl/fl UHRF1-mCherry+ mice treated with TMX (n = 26 cells) were isolated for single-cell multiomics sequencing and clustered with ILCP1s (n = 106 cells) and ILCP2s (n = 206 cells). Data are representative of at least two (c) or three (a, b) independent experiments.

Source data

Extended Data Fig. 8 DNA methylation regulates the differentiation of ILCPs.

a,b, Representative flow cytometry plots showing the GFP expression in Id2CreERT2Dnmt1fl/fl ILCPs (CD127+c-Kit+α4β7+PD-1+) transduced with Tbx21-SMR-Snrpn-GFP reporter system in the media without (a) or with (b) 4-OHT. c, d, Bisulfite sequencing analysis showing SMR DNA methylation levels in Id2CreERT2Dnmt1fl/fl ILCPs transduced with SMR reporter systems in the media without (c) or with (d) 4-OHT. e, The proportion of the indicated lymphocytes from the thymus, the small intestine (SI), and the lung of Id2CreERT2 and Id2CreERT2Dnmt1fl/fl mice treated as in Fig. 6c. The frequencies of CD45+CD4−CD8− double negative (DN) T, CD45+CD4+CD8+ double positive (DP) T, CD45+CD3+CD4+ T cells (CD4 T), CD45+CD3+CD8+ T cells (CD8 T), CD45+CD3+ T cells (T), CD45+CD19+ B cells (B) were shown as mean ± s.d. (n = 3 or 4 mice per group). f,g, Percentage of cleaved caspase3+ ILCPs among total ILCPs (f) and cell numbers of ILCPs (g) from the bone marrow of WT mice, cultured for 2 days with DNMT1 inhibitor (GSK-3484862) or not (vehicle) in vitro. Shown as mean ± s.d. (n = 4 cultures per group). h, i, Percentage of cleaved caspase3+ cells among ILC1s, ILC2s or ILC3s (h) and the number of ILC1, ILC2 and ILC3 (i) sorted from the small intestine of WT mice, and cultured for 2 days with DNMT1 inhibitor or not (vehicle) in vitro. Shown as mean ± s.d. (n = 3 cultures per group). Statistical analyses of e-i were performed using two-tailed unpaired Student’s t test. Data are representative of at least three independent experiments.

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Supplementary information

Reporting Summary

Supplementary Table 1

Pearson correlation between the gene expression modules and DEGs.

Supplementary Table 2

Methylation enzyme-related gene sets.

Supplementary Table 3

Bisulfite sequencing PCR primer list.

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Liu, Z., Shao, F., Zhang, Q. et al. Epigenetic imprinting in innate lymphoid cell precursors directs the lineage segregation of innate lymphoid cells. Nat Immunol 26, 1686–1698 (2025). https://doi.org/10.1038/s41590-025-02261-0

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