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Sequential enhancer state remodelling defines human germline competence and specification

Nature Cell Biology volume 24, pages 448–460 (2022)Cite this article

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Abstract

Germline–soma segregation is a fundamental event during mammalian embryonic development. Here we establish the epigenetic principles of human primordial germ cell (hPGC) development using in vivo hPGCs and stem cell models recapitulating gastrulation. We show that morphogen-induced remodelling of mesendoderm enhancers transiently confers the competence for hPGC fate, but further activation favours mesoderm and endoderm fates. Consistently, reducing the expression of the mesendodermal transcription factor OTX2 promotes the PGC fate. In hPGCs, SOX17 and TFAP2C initiate activation of enhancers to establish a core germline programme, including the transcriptional repressor PRDM1 and pluripotency factors POU5F1 and NANOG. We demonstrate that SOX17 enhancers are the critical components in the regulatory circuitry of germline competence. Furthermore, activation of upstream cis-regulatory elements by an optimized CRISPR activation system is sufficient for hPGC specification. We reveal an enhancer-linked germline transcription factor network that provides the basis for the evolutionary divergence of mammalian germlines.

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Fig. 1: Genome-wide transcriptome and chromatin profiling revealed the trajectories of gastrulation and hPGC development.
Fig. 2: Dynamic activation of enhancers underlies cell fate transitions.
Fig. 3: SOX17 and PRDM1 drive hPGC fate interdependently.
Fig. 4: Combinatorial and individual roles of TFAP2C, SOX17 and PRDM1 in epigenetic regulation of target genes in hPGCLCs.
Fig. 5: Enhancer and promoter trigger expression of core hPGC TFs synergistically.
Fig. 6: Repression of SOX17 enhancers by CRISPRi hampers hPGC specification.
Fig. 7: Induction of hPGCLCs by CRISPRa of key cis-regulatory elements.
Fig. 8: Sequential activation of mesendoderm and germline enhancers explains germline competence.

Data availability

ChIP–seq and RNA-seq datasets are available on NCBI GEO (GSE159654). Single-cell sequencing datasets are available on ArrayExpress (E-MTAB-11135). Previously published data that were re-analysed here are: hPGC RNA-seq (GSE60138), TF KO RNA-seq (GSE99350), TFAP2C ChIP–seq (GSE140021) and OTX2 ChIP–seq (GSE61475). Genome databases used are: UCSC GRCh38/hg38, Ensembl GrCh38 v90 and Gencode Human Release 30. All other data supporting the findings of this study are available from the corresponding authors on reasonable request. Source data are provided with this paper.

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Acknowledgements

M.A.S. was supported by Wellcome Investigator Awards in Science (209475/Z/17/Z and 096738/Z/11/Z), an MRC research grant (RG85305) and a BBSRC research grant (G103986). W.W.C.T. received a Croucher Postdoctoral Research Fellowship and was supported by the Isaac Newton Trust. A.C.-V. was supported by the Wellcome 4-Year PhD Programme in Stem Cell Biology and Medicine and the Cambridge Commonwealth European and International Trust (203831/Z/16/Z). W.H.G. was supported by a BBSRC research grant (G103986). T.K. and M.A.S. were supported by Butterfield Awards of Great Britain Sasakawa Foundation. T.K. was supported by the Astellas Foundation for Research on Metabolic Disorders. D.S. was supported by a Wellcome Trust PhD studentship (109146/Z/15/Z) and the Department of Pathology, University of Cambridge. N.I. was supported by an MRC research grant (RG85305). We thank R. Barker and X. He for providing human embryonic tissues, and C. Bradshaw for bioinformatic support. We also thank The Weizmann Institute of Science for the WIS2 hESC line and the Genomics Core Facility of CRUK Cambridge Institute for sequencing services, and R. Alberio and members of the Surani lab for insightful comments and critical reading of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Walfred W. C. Tang, Aracely Castillo-Venzor, Wolfram H. Gruhn.

Authors and Affiliations

  1. Wellcome Trust/Cancer Research UK Gurdon Institute, Henry Wellcome Building of Cancer and Developmental Biology, Cambridge, UK

    Walfred W. C. Tang, Aracely Castillo-Venzor, Wolfram H. Gruhn, Christopher A. Penfold, Dawei Sun, Naoko Irie & M. Azim Surani

  2. Physiology, Development and Neuroscience Department, University of Cambridge, Cambridge, UK

    Walfred W. C. Tang, Aracely Castillo-Venzor, Wolfram H. Gruhn, Christopher A. Penfold, Dawei Sun, Naoko Irie & M. Azim Surani

  3. Wellcome–MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, Cambridge, UK

    Aracely Castillo-Venzor, Christopher A. Penfold & M. Azim Surani

  4. Division of Mammalian Embryology, Center for Stem Cell Biology and Regenerative Medicine, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Toshihiro Kobayashi

  5. Center for Genetic Analysis of Behavior, National Institute for Physiological Sciences, Okazaki, Japan

    Toshihiro Kobayashi

  6. Centre for Trophoblast Research, University of Cambridge, Cambridge, UK

    Christopher A. Penfold

  7. Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK

    Michael D. Morgan

  8. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK

    Michael D. Morgan

Authors
  1. Walfred W. C. Tang
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  9. M. Azim Surani
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Contributions

M.A.S. and W.W.C.T. conceived the study. W.W.C.T. designed experiments, collected human embryonic tissues and performed bioinformatic analysis. A.C.-V. designed experiments, performed cell culture, cloning and luciferase assay and collected in vitro samples. W.W.C.T. and W.H.G. optimized and generated ATAC–seq and ULI-NChIP–seq libraries. T.K. and N.I. generated TF ChIP–seq libraries. T.K. and A.C.-V. generated RNA-seq libraries. A.C.-V. generated the scRNA-seq libraries. A.C.-V., M.D.M. and C.A.P. analysed the scRNA-seq data. W.W.C.T., A.C.-V., T.K. and D.S. designed and performed CRISPRa and interference assay. W.W.C.T., M.A.S., A.C.-V. and W.H.G. wrote the manuscript with inputs from all authors.

Corresponding authors

Correspondence to Walfred W. C. Tang or M. Azim Surani.

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W.W.C.T. is currently employed by Adrestia Therapeutics Ltd. The other authors declare no competing interests.

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Nature Cell Biology thanks Hiromitsu Nakauchi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Sample collection and overview of transcriptomic and epigenomic data.

a, Fluorescence-activated cell sorting (FACS) pseudocolor plots showing the cell populations collected for transcriptomic and epigenomic analysis (red gates). b, Table showing the hESC-derived cell types and the number of cells used to generate RNA-seq, ATAC-seq, and ChIP-seq libraries. c, Table showing details of the human embryos used for hPGC isolation and the number of hPGCs used to generate RNA-seq, ATAC-seq, and ChIP-seq libraries. Asterisks indicate RNA-seq samples published in a previous study16. d, Heatmaps showing Spearman’s correlation coefficient of gene expression, ATAC-seq, H3K4me1, H3K4me3, H3K27ac, and H3K27me3 ChIP-seq signals in biological replicates. For RNA expression, correlation was based on the log2(normalized counts) of the top 25% most variable protein coding genes and lincRNA. For ATAC-seq, signals (log2(normalized counts)) at combined peaks across the 6 cell types were used. For H3K4me1, H3K4me3, H3K27ac, and H3K27me3 ChIP-seq, signals (log2(normalized counts)) at 1 kb bins of combined peaks were used. The samples were clustered using (1 - Spearman’s correlation coefficient) as the distance measure (Ward’s method with optimal tree ordering). See Methods. e, Distance distribution between the summit of ATAC peaks or the centre of histone modification peaks and the closest TSS. Shown are all peaks and dynamic peaks with differential chromatin signals in the sample pairs shown in Extended Data Fig. 2d (log2(signal fold change) >1 and adjusted p-value <0.05). Note that most of the dynamic ATAC, H3K4me1, H3K4me3, H3K27ac and H3K27me3 peaks were >2 kb away (dotted line) from the TSS.

Extended Data Fig. 2 Characterisation of dynamically active enhancers.

a, Chromatin profile heatmaps of ATAC, H3K4me3, H3K4me1, H3K27ac, H3K27me3 and input signals in hPGCLCs at ATAC-seq summit ± 3 kb. Segregation of ATAC-seq summits by K-means clustering using normalised chromatin mark signals. b, Distribution of chromatin mark signals at active, mixed, primed, poised, repressed, and neutral enhancers in hPGCLCs (see Fig. 2a). Enhancers per violin/box plot: 23255 active, 1288 mixed, 36999 primed, 5648 poised, 3984 repressed, 79290 neutral. Box plots depict the median, lower and upper hinges correspond to the 25th and 75th percentiles. c, Enhancer state transitions of DE-active enhancers. Distal OCRs not overlapping any histone modification peak in the analysed cell types were referred to as ‘neutral’ enhancers. d, Putative enhancers with differential H3K27ac levels (absolute(log2(fold change)) >1 and adjusted p-value < 0.05) in the indicated sample pairs. e, High confidence enhancer-gene associations. Putative enhancers were assigned to the nearest TSS. The relevance of the enhancer-gene pair was assessed by the Kendall’s rank correlation analysis between the enhancer H3K27ac signals and the expression levels of the associated genes across the 6 cell types and 2 replicates (see Methods). In the simplified model shown, the Enh1-geneA and Enh3-geneB pairs (green text and arrows) were identified as high confidence associations based on positive correlation between H3K27ac and gene expression levels. f, Expression levels of genes associated with active enhancers in different cell types. Compared to simply associating genes to the nearest active enhancer, high confidence active enhancer associated genes (Kendall rank correlation coefficient >0.3; empirical p-value < 0.05) exhibited significantly higher expression in all cell types. Two-sided Wilcoxon rank sum test with FDR correction. Gene number per violin/box plot (all nearest, high confidence): hESC (8686, 1279), PreME (9782, 1413), ME (11494, 1853), DE (12279, 2224), hPGCLC (9954, 1726), hPGC (6612, 1024). Box plot organisation as in Extended Data Fig. 2b. g, Distribution of ATAC, H3K27ac and H3K27me3 signals in dynamically active enhancers and high confidence target gene expression. Enhancers were segregated into nine clusters (Fig. 2c). Enhancer per clusters as in Fig. 2c. Box plot organisation as in Extended Data Fig. 2b.

Extended Data Fig. 3 Characterisation of dynamically active and repressed promoters during hPGC development.

a, Promoter classification in hPGCLCs based on the intersection of histone modification peaks at promoter regions (TSS ± 1 kb). b, Receiver operating characteristic (ROC) curves of ATAC, H3K4me3, H3K4me1, H3K27ac, and H3K27me3 signals at promoter (TSS ± 1 kb) as predictors of gene activity in hPGCLCs. The top 1000 (top panel) or the bottom 1000 (bottom panel) expressed genes were used as positives. Promoters not overlapping any chromatin peak were excluded. Note that H3K27ac (area under the curved (AUC) of top genes = 0.770) and H3K27me3 (AUC of bottom genes = 0.681) were the best predictors of expressed and repressed genes, respectively. c, Distribution of chromatin mark signals at active, mixed, poised, repressed, and neutral promoters and the expression of their associated genes in hPGCLCs. Promoter number: 30261 active, 2833 mixed, 7089 poised, 1579 repressed, 19832 neutral. Associated gene number: 12629 active, 1526 mixed, 3662 poised, 1144 repressed, 13038 neutral. Box plot organisation as in Extended Data Fig. 2b d, K-means clustering of dynamically repressed promoters into 7 clusters (C) by H3K27me3 signals. Dynamically repressed promoters were promoters that exhibited ‘mixed’, ‘poised’ or ‘repressed’ state (see Methods) in any cell type with differential H3K27me3 signals. e, Box plots showing expression levels of genes associated with the C6 dynamically repressed promoters in c. **** p-value < 0.00001 (Wilcoxon rank sum test adjusted by the Holm method) in marked against each unmarked cell type. Box plot organisation as in Extended Data Fig. 2b. f, Heatmaps showing the expression levels of representative genes associated with C6 in c. The right panel shows the representative enriched gene ontology terms. g, Dot plots showing the enrichment of transcription regulator (TR) binding site in the dynamic repressed promoters in C6 in c. The top 20 enriched TRs (out of 1,135 in the ReMap2020 database42) are shown. TRs were annotated against 5 gene ontology terms associated with repressive functions. The dot size represents promoter fraction overlapping with the TR binding sites. The dot colour indicates the expression levels of the enriched transcription regulators in hPGCs.

Extended Data Fig. 4 Direct targets of SOX17 in hPGCLCs and DE.

a, Cumulative distribution function plot showing the functional prediction of SOX17 and PRDM1. The ChIP peaks of SOX17/PRDM1 in hPGCLCs were assigned to genes with TSS within 100 kb of the peak summits. A regulatory potential score was calculated for each gene based on the distance between the peak summit and the TSS96. The genes were then divided into upregulated, downregulated and unchanged according to their expression patterns upon SOX17 or PRDM1 overexpression. Cumulative distribution function plot was generated for each group with genes ranked by decreasing regulatory potential. A one-tailed Kolmogorov-Smirnov test was used to determine the statistical significance between the differentially expressed groups and the unchanged group. Note that upregulated genes (but not downregulated genes) upon SOX17 induction have a significantly higher tendency to be bound by SOX17. In contrast, genes downregulated upon PRDM1 overexpression tend to be bound by PRDM1. b, GO biological process terms that were enriched in SOX17 direct up targets (red dots in Fig. 3d). c, Expression heatmap of SOX17 direct up target genes. Shown are the genes which were (1) upregulated both by SOX17 alone (log2(fold change) >1 and adjusted p-value <0.05 between Dex-treated and non-treated 12h PreME aggregates); (2) upregulated by cytokines (log2(fold change) >2 and adjusted p-value <0.05 between day 2 hPGCLCs and PreME); and (3) downregulated in SOX17 KO (log2(fold change) >1 and adjusted p-value <0.05 between SOX17 KO day 2 aggregate and WT day 2 aggregate)20. d, Top eight TFs with binding sites (ReMap2020) enriched in hPGCLC-specific and DE-specific SOX17 peaks. e, Genome browser snapshots showing the epigenetic landscape of DE-specific (CER1 and LEFTY2) and hPGCLC-specific (NANOS3 and PDPN) SOX17-bound gene targets. f, Heatmap showing expression of genes associated with the top enriched motifs (Fig. 3h) and the top enriched TF binding sites (in d) in hPGCLC-specific and DE-specific SOX17 peaks. g, UniProtKB Keywords that were enriched in PRDM1 direct down targets (blue dots in Fig. 3i).

Extended Data Fig. 5 Direct target genes of TFAP2C, SOX17 and PRDM1.

a, Genomic distribution of the TFAP2C peaks in hPGCLC aggregates46. b, Cumulative distribution function plot showing the functional prediction of TFAP2C. The TFAP2C peaks were assigned to genes with TSS within 100 kb of the peak summits. A regulatory potential score was calculated for each gene based on the distance between the peak summit and the TSS96. The genes were then divided into three groups (upregulated, downregulated and unchanged) according to their expression patterns in TFAP2C KO day2 hPGCLCs versus the wild-type control20. Cumulative distribution function plot was generated for each group with genes ranked by decreasing regulatory potential. A one-tailed Kolmogorov-Smirnov test was used to determine the statistical significance between the differentially expressed groups and the unchanged group. c, The enrichment of TFAP2C, SOX17 and PRDM1 peaks in genomic loci that gained ATAC, H3K4me1, H3K4me3, H3K27ac or H3K27me3 signals during the PreME to hPGCLC transition. The TF peaks were categorized into seven cooperativity classes as in Fig. 4a. The dot size represents the fraction of chromatin peaks that overlapped with the TF peaks. Dot color indicates enrichment significance. d, Alluvial plots showing the enhancer (upper panel) and promoter (lower panel) state transition from PreME to hPGCLC. The enhancers/promoters that became active/inactive in hPGCLCs were used for TF binding enrichment analysis in Fig. 4b. e, Venn diagram showing the intersection of upregulated and downregulated genes in SOX17, TFAP2C and PRDM1 KO hPGCLCs/aggregate20. Upregulated and downregulated genes were defined as (log2(fold change versus wild-type control) >1 and adjusted p-value < 0.05) and (log2(fold change versus wild-type control) <(-1) and adjusted p-value < 0.05), respectively. f, The number of direct up and down target genes of TFAP2C, SOX17 and PRDM1 based on their cooperative binding. g, Enrichment of chromatin remodelling factor binding sites in TFAP2C, SOX17 and PRDM1 peaks in hPGCLCs. The y-axis shows the chromatin remodelling factors that were amongst the top 10 enriched transcriptional regulators (ReMap2020) in any of the five peak sets (x-axis).

Extended Data Fig. 6 Inducible CRISPR activation and interference systems for activation and repression of hPGC TFs.

a, The piggyBAC plasmids encoding an optimized doxycycline-inducible dCas9-SunTag-VP64 CRISPR activation system. Upon integration of both the CRISPRa plasmid and the sgRNA plasmid into the genome and Dox treatment, the Tet-On 3G doxycycline-binding transactivator protein encoded in the sgRNA plasmid will drive the transcription of dCas9-GCN4x5-P2A-scFV-sfGFP-VP64 through the TRE3G promoter. After translation of the mRNA, the recombinant protein will be split into dCas9-GCN4x5 and scFV-sfGFP-VP64 through the P2A self-cleaving peptide. Subsequently, the dCas9-GCN4x5 will be guided to enhancer/promoter by the constitutively expressed sgRNA and recruit up to 5 copies of the scFV-sfGFP-VP64 recombinant transactivator. To improve epigenome editing efficiency, the GCN4 epitopes were separated by optimized 22-amino-acid linkers55. To increase sgRNA expression and to enhance sgRNA-dCas9 affinity, a sgRNA scaffold with an A-U flip and extended hairpin was used56. b, RT-qPCR showing CRISPR activation of SOX17 (2 days Dox treatment in hESCs) induced PRDM1 and TFAP2C mRNA expression. Activation of PRDM1 also upregulated TFAP2C. Average of 4 biological replicates, with individual replicates shown as data points. c, The epigenetic landscape of the SOX17 and PRDM1 loci in hESC, PreME, hPGCLCs and HEK293 cells. Note that the SOX17 locus in HEK293 cells does not bear H3K4me1, H3K4me3 and H3K27ac marks. For CRISPR activation (CRISPRa) assay, 3–5 sgRNAs were used to activate each putative enhancer (highlighted) and promoter. d-e, RT-qPCR of SOX17 and PRDM1 following CRISPR activation of enhancers and promoters in HEK293 cells. HEK293 cells were transiently transfected with CRISPRa (dCas9-Suntag-VP64) and sgRNA plasmids and treated with Dox for 2 days. GFP-positive cells (expressing dCas9-Suntag and scFV-sfgFP-VP64) were isolated for RT-qPCR. Average of 3 technical replicates, with individual replicates shown as data points. Assay has been performed two times independently with similar results. f, The piggyBAC plasmids encoding a re-engineered doxycycline-inducible KRAB-dCas9-DHFR CRISPR interference system (also see Fig. 6a).

Source data

Extended Data Fig. 7 Characterisation of CRISPRa-induced hPGCLCs.

a and b, FACS analysis of day 4 EBs generated from PreME of hESC lines harbouring the Dox-inducible CRISPRa transgene with the indicated sgRNA combinations. c, Immunofluorescence showing the co-expression of hPGCLC markers NANOS3-tdTomato, POU5F1 and SOX17 in hPGCLCs (yellow arrowheads) induced by CRISPRa in the absence of BMP4. White arrowheads indicate SOX17 single-positive cells (presumably DE). Representative results of 3 biological replicates. d, Induction of hPGCLCs from hESCs, PreME and ME with or without CRISPR-mediated activation of SOX17 and PRDM1 enhancers and promoters. FACS analysis of day 4 EBs shows that the co-activation of SOX17 and PRDM1 act synergistically with BMP4 to increase the efficiency of hPGCLC induction from hESCs and PreME, but not from ME. The appearance of the EBs under brightfield and tdTomato fluorescence filter are shown next to the corresponding FACS plots. Representative results of 3 independent experiments. e, Alluvial plots showing enhancer state transitions of hPGCLC-active enhancers in hESCs, PreME and ME.

Extended Data Fig. 8 Sequence conservation of the human PRDM1 regulatory element.

a, PCA analysis scRNAseq profiles of cells in the hESC, PreME and ME state b, Violin plots summarizing expression levels of the indicated genes in individual cells in the hESC, PreME and ME state analysed by scRNA-seq. c, Schematics of the inducible CRISPR interference (CRISPRi) system used to repress the two OTX2 promoters (upper panel). Western Blots depicting OTX2 and H3 levels in transgenic hESCs treated with vehicle or Doxcycline and TMP for the indicated time periods (lower panel). Molecular weights of marker proteins are depicted in kilodaltons (kDa). Representative experiment, knockdown efficiency tested two times independently at the shown timepoints. d, FACS analysis of PGCLCs expressing non-targeting or sgRNA targeting the OTX2 promoters in the presence or absence of KRAB-dCas9-ecDHFR (GFP). Representative experiment out of 3 independent technical replicates shown in Fig. 8e. e, Genome browser snapshots showing OTX2 ChIPseq signals and peaks in hESCs (GSE61475)64. Enhancer identified in this work are indicated in yellow. f, Upper panels: BLAT alignment of the core murine PRDM1 enhancer (B108)72 to the human genome. Conservation of the SOX motifs in the putative enhancer and promoter of human PRDM1 across seven mammalian species. MULTIZ whole-genome alignment showed that 4 out of 5 core SOX motifs (‘ATTGT’, underlined) in the human PRDM1 enhancer and promoter are conserved in mice. Grey dot indicates exact match. Blank space represents absence of the corresponding sequence in the indicated species. Lower panels: BLAT alignment of the human PRDM1 enhancer to the murine genome showing the conservation of the OTX2 motif in the murine PRDM1 enhancer72.

Source data

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Table 1. Sequencing and mapping summary of RNA-seq and ChIP–seq libraries. Supplementary Table 2. Gene Ontology enrichment analysis of dynamically active enhancers. Supplementary Table 3. Direct target genes of SOX17 by overexpression. Supplementary Table 4. Direct target genes of PRDM1 by overexpression. Supplementary Table 5. Direct up-target genes of TFAP2C, SOX17 and PRDM1. Supplementary Table 6. Direct down-target genes of TFAP2C, SOX17 and PRDM1. Supplementary Table 7. Antibodies, oligos and sgRNAs.

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Source Data Fig. 3

Luciferase assay source data.

Source Data Fig. 5

Fig. 5c RT–qPCR source data.

Source Data Fig. 6

Fig. 6b Relative hPGCLC induction source data.

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Fig. 7b RT–qPCR source data.

Source Data Fig. 8

Fig. 8e Relative hPGCLC induction source data.

Source Data Extended Data Fig. 6

Extended Data Fig. 6b,d,e RT–qPCR source data.

Source Data Extended Data Fig. 8

Extended Data Fig. 8c Western blot raw images.

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Tang, W.W.C., Castillo-Venzor, A., Gruhn, W.H. et al. Sequential enhancer state remodelling defines human germline competence and specification. Nat Cell Biol 24, 448–460 (2022). https://doi.org/10.1038/s41556-022-00878-z

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