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Epigenetic programming defines haematopoietic stem cell fate restriction

Nature Cell Biology volume 25, pages 812–822 (2023)Cite this article

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Abstract

Haematopoietic stem cells (HSCs) are multipotent, but individual HSCs can show restricted lineage output in vivo. Currently, the molecular mechanisms and physiological role of HSC fate restriction remain unknown. Here we show that lymphoid fate is epigenetically but not transcriptionally primed in HSCs. In multi-lineage HSCs that produce lymphocytes, lymphoid-specific upstream regulatory elements (LymUREs) but not promoters are preferentially accessible compared with platelet-biased HSCs that do not produce lymphoid cell types, providing transcriptionally silent lymphoid lineage priming. Runx3 is preferentially expressed in multi-lineage HSCs, and reinstating Runx3 expression increases LymURE accessibility and lymphoid-primed multipotent progenitor 4 (MPP4) output in old, platelet-biased HSCs. In contrast, platelet-biased HSCs show elevated levels of epigenetic platelet-lineage priming and give rise to MPP2 progenitors with molecular platelet bias. These MPP2 progenitors generate platelets with faster kinetics and through a more direct cellular pathway compared with MPP2s derived from multi-lineage HSCs. Epigenetic programming therefore predicts both fate restriction and differentiation kinetics in HSCs.

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Fig. 1: Fate-restricted HSC clones are internally coherent and molecularly distinct.
Fig. 2: Identification of transcriptional and epigenetic programs of fate-restricted HSCs.
Fig. 3: HSC fate is predicted by epigenetic priming.
Fig. 4: Lymphoid gene expression during HSC commitment is driven by primed enhancers.
Fig. 5: Epigenetic priming identifies an increased abundance of PLT HSCs in aged mouse bone marrow.
Fig. 6: Ectopic expression of Runx3 rescues epigenetic lymphoid priming and corrects Meg bias in aged HSCs.
Fig. 7: Distinct MPP2 pathways arise from PLT and MUL HSCs.
Fig. 8: Accelerated platelet production by PLT HSC-derived MPP2s.

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Data availability

All of the raw and processed high-throughput sequencing data generated in this study have been deposited in the Gene Expression Omnibus database under accession number GSE188119. Public datasets used in this study include the JASPAR 2020 motif database52, RNA-seq of HSCs/MPPs21, available in the ArrayExpress database (http://www.ebi.ac.uk/arrayexpress) under accession number E-MTAB-2262, and haematopoietic progenitors24, available in the Haemospere database (https://www.haemosphere.org/). All other data generated in this study are available upon reasonable request. Requests for materials and manuscript correspondence should be directed to the corresponding author. Source data are provided with this paper.

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Acknowledgements

We thank the Weatherall Institute of Molecular Medicine (WIMM) FACS facility for assistance with cell sorting and the WIMM Computational Biology Research Group for computational support. This research was funded in whole or in part by Biotechnology and Biological Sciences Research Council project grant BB/V002198/1 and Medical Research Council (MRC) Unit grants MC_UU_12009/7 and MC_UU_00029/9 (to C.N.) and MRC Unit grant MC_UU_12009/5 and Swedish Research Council grant 538-2013-8995 (to S.E.J.). For the purpose of open access, the authors have applied a CC BY public copyright licence to any author accepted manuscript version arising from this submission. The WIMM FACS Core Facility is supported by the MRC Human Immunology Unit, MRC Molecular Haematology Unit (MC_UU_12009), National Institute for Health Research Oxford Biomedical Research Centre and John Fell Fund (131/030 and 101/517), EPA Cephalosporin fund (CF182 and CF170) and WIMM Strategic Alliance awards (G0902418 and MC_UU_12025).

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Authors and Affiliations

  1. MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK

    Yiran Meng, Joana Carrelha, Roy Drissen, Xiying Ren, Bowen Zhang, Adriana Gambardella, Simona Valletta, Sten Eirik Jacobsen & Claus Nerlov

  2. Haematopoietic Stem Cell Laboratory, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK

    Joana Carrelha & Sten Eirik Jacobsen

  3. MRC WIMM Centre for Computational Biology, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK

    Supat Thongjuea

  4. Department of Cell and Molecular Biology, Wallenberg Institute for Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden

    Sten Eirik Jacobsen

  5. Department of Medicine Huddinge, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden

    Sten Eirik Jacobsen

  6. Karolinska University Hospital, Stockholm, Sweden

    Sten Eirik Jacobsen

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Contributions

C.N. and S.E.J. conceived of the study and supervised the experiments. Y.M., J.C., R.D., X.R., B.Z., A.G. and S.V. performed the experiments. Y.M., J.C. and C.N. analysed the data. Y.M., B.Z., S.T. and C.N. performed the bioinformatics analysis. C.N. and Y.M. wrote the manuscript.

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Correspondence to Claus Nerlov.

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Extended data

Extended Data Fig. 1 Sorting of single HSCs for transplantation and molecular profiling.

a) Flow cytometry plots showing the gating strategy used for sorting of VT+ HSCs for single cell transplantation and single cell RNAseq. b) Flow cytometry plots showing the gating strategy used for sorting HSCs from single-cell transplanted mice for scRNAseq.

Source data

Extended Data Fig. 2 Expression of random forest genes in fate-restricted single HSCs.

UMAP plots showing the expression of the top 10 genes identified by random forest importance score in single HSCs from Fig. 1d.

Source data

Extended Data Fig. 3 Lineage-specific chromatin signatures.

a) Heatmap of promoter accessibility signatures specific for MkPs (Meg signature), CFU-Es (Ery), NMPs (Myl) and proB/DN3 cells (Lym). Each signature contains the top 1500 features identified as selectively accessible in each of the 4 cell populations/combined cell populations using DESeq2 (Padj<0.05 and |log2(fold change)| >1). N = 3 biological replicates for all populations. Scale shows row z-score of transformed gene expression levels. b) Heatmap of URE accessibility signatures specific for MkPs (Meg signature), CFU-Es (Ery), NMPs (Myl) and proB/DN3 cells (Lym). Each signature contains the top 1500 features identified as selectively accessible in each of the 4 cell populations/combined cell populations using DESeq2 (Padj<0.05 and |log2(fold change)| >1). N = 3 biological replicates for all populations. Scale shows row z-score of transformed chromatin accessibility levels. c) Enrichment of TF motifs differentially accessible in HSC subtypes (from Fig. 3a) in lymphoid-specific promoter peaks calculated using PWMEnrich-based sequence analysis. These values are those plotted along the Fig. 4a x-axis. d) Enrichment analysis of HSC-subtype TF motifs (from Fig. 2a) in Lym URE peaks calculated using PWMEnrich, corresponding to Fig. 4a y-axis.

Source data

Extended Data Fig. 4 Expression of lymphoid TFs in HSC subtypes and HSC signatures in old HSCs.

a) The expression of genes encoding B-cell specific (Ebf1 and Pax5) and T-cell specific (Lef1 and Tcf7) TFs in PLT-, PEM- and MUL-HSC. RPKM values from bulk RNAseq data are shown. Statistical analysis was performed using two-tailed Welch’s t-test. Biological replicates: PLT-HSC, N = 3; PEM-HSC, N = 6; MUL-HSC, N = 4. Mean values with SD are shown. NA indicates that all values in the comparison are zero. b) GSEA analysis comparing young and old HSCs using bulk RNAseq. Plots show the enrichment of transcriptional PLT-HSC and MUL-HSC signatures (from Supplementary Table 2) in old HSCs. P-values and normalised enrichment scores (NES) are shown. c) GSEA analysis comparing young and old HSCs using bulk ATAC. Plots show the enrichment of epigenetic PLT-HSC and MUL-HSC signatures (from Supplementary Table 3) in old HSCs. P-values and normalised enrichment scores (NES) are shown.

Source data

Extended Data Fig. 5 The role of RUNX factors in epigenetic lymphoid priming.

a) The expression of Runx1 and Runx2 in young and old HSCs. RPKM values from bulk RNAseq data are shown. Statistical analysis was performed using two-tailed Welch’s t-test (biological replciates: young, N = 4; old, N = 3). Mean values with SD are shown. b) The expression of Runx1 and Runx2 in PLT-, PEM- and MUL-HSC. RPKM values from bulk RNAseq data are shown. Statistical analysis was performed using two-tailed Welch’s t-test. Biological replicates: PLT-HSC, N = 3; PEM-HSC, N = 6; MUL-HSC, N = 4. Mean values with SD are shown. c) Schematic diagram of the Runx3-expressing lentiviral vector used for overexpressing RUNX3 in old HSCs and transplantation. The human CD2 (hCD2) surface marker was used to identify successfully transduced cells. d) Experimental workflow of transplantation of old HSCs transduced with mock or RUNX3-expressing lentivirus. e) Flow cytometry plots showing the gating strategy for identifying HSC, MPP2, MPP3 and MPP4 derived by donor HSCs transduced with mock or RUNX3-expressing lentivirus for cell sorting and analysis.

Source data

Extended Data Fig. 6 Platelet kinetics in Gata1-CreERT2 transgenic mice.

a) Schematic diagram of the Gata1-CreERT2 (GCET2) transgene used for lineage tracing of GE+ progenitors. b) Representative flow cytometry plots showing EGFP and tdTomato labelling of BM LK and LSK populations in control (No Tamoxifen) GCET2/Ai9 mice (top panels) and in GCET2/Ai9 mice 2 days after tamoxifen administration (bottom panels). c) Representative flow cytometry plots showing the gating strategy for the identification of peripheral blood platelets. d) Representative flow cytometry plots showing the extent of tdTomato labelling of platelets in control GE/GCET2/Ai9 mice (No Tamoxifen), and GE/GCET2/Ai9 mice 7 and 20 days after tamoxifen administration. e) Representative flow cytometry plot showing the identification of donor-derived Gata1-EGFP+/Rosa26-tdTomato+ (GE+RT+) and Gata1-EGFP+/Rosa26-tdTomato– (GE+RT–), as well as recipient-and competitor-derived Gata1-EGFP– (GE–) platelets in peripheral blood of a recipient mouse transplanted with a single GE/GCET2/Ai9 MUL-HSC at day 16 after Tamoxifen administration. The fraction of lineage traced platelets was calculated as GE+RT+/((GE+RT+)+(GE+RT–)).

Source data

Supplementary information

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Supplementary Tables

Supplementary Table 1. Cluster distribution of clonal single-HSC transcriptomes. For each clone, the number of cells associated with each cluster is shown, as in the total cell number, the number of cells in the most populated cluster and the probability of the distribution being random, using a binomial distribution test. Supplementary Table 2. Genes differentially regulated between HSC subtypes. Differential genes in bulk RNA-seq (Fig. 2a) were identified using the DEseq2 package (P < 0.05; log2[fold change] > 0.5). Regulons are defined by pairwise comparing of HSC subtypes and identification of genes selectively upregulating in a single subtype (for example, the PLT regulon is defined by PLT > PEM and PLT > MUL) or upregulated in two subtypes compared with the third subtype (for example, the PLT + PEM regulon is defined by PLT > MUL and PEM > MUL). Supplementary Table 3. Chromatin peaks differentially accessible between HSC subtypes. Differentially accessible peaks (P < 0.05; log2[fold change] > 0.5 using DEseq2) for each HSC subtype identified by bulk ATAC-seq (Fig. 2b). For each peak, the genomic coordinates and cell type are shown. Supplementary Table 4. Transcription factor motifs differentially enriched in HSC subtypes. Motifs significantly upregulated (P < 0.05 using chromVAR) in each of the indicated regulons from Fig. 3a are shown. Supplementary Table 5. Chromatin peaks differentially accessible in committed progenitors. Each signature contains the top 2,000 features identified as selectively accessible in each of the four individual or combined cell populations. For each peak, the genomic coordinates and cell type are shown. Supplementary Table 6. Promoter and URE peaks differentially accessible in committed progenitors. Each signature contains the top 1,500 promoter- and URE-associated features identified as selectively accessible in each of the four individual or combined cell populations. For each peak, the genomic coordinates, cell type and element type are shown. Supplementary Table 7. Lymphoid leading-edge genes. Nearest-neighbour genes for the LymURE leading-edge peaks from the comparison of PLT and MUL HSC ATAC-seq. Supplementary Table 8. List of antibodies used for flow cytometry analysis and cell sorting. For each antibody, the antigen, conjugated fluorophore, clone, supplier, catalogue number and application are shown. Supplementary Table 9. Metadata of single-cell-engrafted mice used for ATAC- and RNA-seq. For clones used for single-cell and bulk sequencing, the reconstitution pattern, engraftment level in the LSK CD150+CD48− HSC compartment, time point of HSC re-isolation after transplantation, cellular phenotype re-isolated and number of cells used for each type of analysis are shown. Supplementary Table 10. LymURE leading-edge genomic regions. Leading-edge genomic regions from the GSEA comparing the accessibility of LymURE peaks in PLT versus MUL HSCs (from Fig. 4c), young versus old HSCs (from Fig. 5d) and Runx3 versus control HSCs (from Fig. 6c). These were used for respective leading-edge analyses. For each peak, the genomic coordinates and comparison are shown.

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Meng, Y., Carrelha, J., Drissen, R. et al. Epigenetic programming defines haematopoietic stem cell fate restriction. Nat Cell Biol 25, 812–822 (2023). https://doi.org/10.1038/s41556-023-01137-5

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