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DNA methylation restricts coordinated germline and neural fates in embryonic stem cell differentiation

Nature Structural & Molecular Biology volume 31, pages 102–114 (2024)Cite this article

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Abstract

As embryonic stem cells (ESCs) transition from naive to primed pluripotency during early mammalian development, they acquire high DNA methylation levels. During this transition, the germline is specified and undergoes genome-wide DNA demethylation, while emergence of the three somatic germ layers is preceded by acquisition of somatic DNA methylation levels in the primed epiblast. DNA methylation is essential for embryogenesis, but the point at which it becomes critical during differentiation and whether all lineages equally depend on it is unclear. Here, using culture modeling of cellular transitions, we found that DNA methylation-free mouse ESCs with triple DNA methyltransferase knockout (TKO) progressed through the continuum of pluripotency states but demonstrated skewed differentiation abilities toward neural versus other somatic lineages. More saliently, TKO ESCs were fully competent for establishing primordial germ cell-like cells, even showing temporally extended and self-sustained capacity for the germline fate. By mapping chromatin states, we found that neural and germline lineages are linked by a similar enhancer dynamic upon exit from the naive state, defined by common sets of transcription factors, including methyl-sensitive ones, that fail to be decommissioned in the absence of DNA methylation. We propose that DNA methylation controls the temporality of a coordinated neural–germline axis of the preferred differentiation route during early development.

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Fig. 1: TKO ESCs are competent for early pluripotency progression.
Fig. 2: TKO ESCs can induce a neural fate but fail to consolidate long-term repression of pluripotency.
Fig. 3: TKO EpiLCs demonstrate longer opportunities for germline competence in vitro.
Fig. 4: Early primed TKO EpiLCs display globally relaxed chromatin.
Fig. 5: TKO EpiLCs retain active chromatin marks specifically at NPC and PGCLC putative enhancers.
Fig. 6: Differential mobilization of DNA methylation-sensitive TFs at NPC and PGCLC enhancers.
Fig. 7: Graphical summary for the role of DNA methylation in tempering neural and germline fates as default differentiation routes.

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Data availability

The data supporting the findings of this study are available in the main text and the Supplementary Information. All sequencing data have been deposited in the Gene Expression Omnibus repository under accession number GSE214496. Other publicly available datasets can be found at the Gene Expression Omnibus: GSE99494 (RNA-seq of EpiSCs)28, GSE117473 (RNA-seq of PGCLCs)36 and GSE71593 and GSE121405 (J1 WT WGBS of day 0 and day 7 EpiLCs, respectively)40,76. Source data are provided with this paper.

References

  1. Li, E., Bestor, T. H. & Jaenisch, R. Targeted mutation of the DNA methyltransferase gene results in embryonic lethality. Cell 69, 915–926 (1992).

    Article  CAS  PubMed  Google Scholar 

  2. Okano, M., Bell, D. W., Haber, D. A. & Li, E. DNA methyltransferases Dnmt3a and Dnmt3b are essential for de novo methylation and mammalian development. Cell 99, 247–257 (1999).

    Article  CAS  PubMed  Google Scholar 

  3. Zhang, Y. et al. Dynamic epigenomic landscapes during early lineage specification in mouse embryos. Nat. Genet. 50, 96–105 (2018).

    Article  CAS  PubMed  Google Scholar 

  4. Leitch, H. G. et al. Naive pluripotency is associated with global DNA hypomethylation. Nat. Struct. Mol. Biol. 20, 311–316 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Nichols, J. & Smith, A. Naive and primed pluripotent states. Cell Stem Cell 4, 487–492 (2009).

    CAS  Google Scholar 

  6. Morgani, S., Nichols, J. & Hadjantonakis, A.-K. The many faces of pluripotency: in vitro adaptations of a continuum of in vivo states. BMC Dev. Biol. 17, 7 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Smith, A. Formative pluripotency: the executive phase in a developmental continuum. Development 144, 365–373 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Yamaji, M. et al. Critical function of Prdm14 for the establishment of the germ cell lineage in mice. Nat. Genet. 40, 1016–1022 (2008).

    Article  CAS  PubMed  Google Scholar 

  9. Ohinata, Y. et al. Blimp1 is a critical determinant of the germ cell lineage in mice. Nature 436, 207–213 (2005).

    Article  CAS  PubMed  Google Scholar 

  10. Seisenberger, S. et al. The dynamics of genome-wide DNA methylation reprogramming in mouse primordial germ cells. Mol. Cell 48, 849–862 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Tsumura, A. et al. Maintenance of self-renewal ability of mouse embryonic stem cells in the absence of DNA methyltransferases Dnmt1, Dnmt3a and Dnmt3b. Genes Cells 11, 805–814 (2006).

    Article  CAS  PubMed  Google Scholar 

  12. Sakaue, M. et al. DNA methylation is dispensable for the growth and survival of the extraembryonic lineages. Curr. Biol. 20, 1452–1457 (2010).

    Article  CAS  PubMed  Google Scholar 

  13. Schmidt, C. S. et al. Global DNA hypomethylation prevents consolidation of differentiation programs and allows reversion to the embryonic stem cell state. PLoS ONE 7, e52629 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Bell, E. et al. Dynamic CpG methylation delineates subregions within super-enhancers selectively decommissioned at the exit from naive pluripotency. Nat. Commun. 11, 1112 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Kalkan, T. et al. Tracking the embryonic stem cell transition from ground state pluripotency. Development 144, 1221–1234 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Rulands, S. et al. Genome-scale oscillations in DNA methylation during exit from pluripotency. Cell Syst. 7, 63–76 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. King, A. D. et al. Reversible regulation of promoter and enhancer histone landscape by DNA methylation in mouse embryonic stem cells. Cell Rep. 17, 289–302 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zhu, H., Wang, G. & Qian, J. Transcription factors as readers and effectors of DNA methylation. Nat. Rev. Genet. 17, 551–565 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kreibich, E., Kleinendorst, R., Barzaghi, G., Kaspar, S. & Krebs, A. R. Single-molecule footprinting identifies context-dependent regulation of enhancers by DNA methylation. Mol. Cell 83, 787–802 (2023).

    Article  CAS  PubMed  Google Scholar 

  20. Damelin, M. & Bestor, T. H. Biological functions of DNA methyltransferase 1 require its methyltransferase activity. Mol. Cell. Biol. 27, 3891–3899 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Nowialis, P. et al. Catalytically inactive Dnmt3b rescues mouse embryonic development by accessory and repressive functions. Nat. Commun. 10, 4374 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Hayashi, K., Ohta, H., Kurimoto, K., Aramaki, S. & Saitou, M. Reconstitution of the mouse germ cell specification pathway in culture by pluripotent stem cells. Cell 146, 519–532 (2011).

    Article  CAS  PubMed  Google Scholar 

  23. Buecker, C. et al. Reorganization of enhancer patterns in transition from naive to primed pluripotency. Cell Stem Cell 14, 838–853 (2014).

    CAS  Google Scholar 

  24. Blaschke, K. et al. Vitamin C induces Tet-dependent DNA demethylation and a blastocyst-like state in ES cells. Nature 500, 222–226 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Choi, H. W. et al. Distinct enhancer activity of Oct4 in naive and primed mouse pluripotency. Stem Cell Reports 7, 911–926 (2016).

    CAS  Google Scholar 

  26. Dubois, A. et al. H3K9 tri-methylation at Nanog times differentiation commitment and enables the acquisition of primitive endoderm fate. Development 149, dev201074 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kinoshita, M. et al. Disabling de novo DNA methylation in embryonic stem cells allows an illegitimate fate trajectory. Proc. Natl Acad. Sci. USA 118, e2109475118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Bao, S. et al. Derivation of hypermethylated pluripotent embryonic stem cells with high potency. Cell Res. 28, 22–34 (2018).

    Article  CAS  PubMed  Google Scholar 

  29. Borgel, J. et al. Targets and dynamics of promoter DNA methylation during early mouse development. Nat. Genet. 42, 1093–1100 (2010).

    Article  CAS  PubMed  Google Scholar 

  30. Hargan-Calvopina, J. et al. Stage-specific demethylation in primordial germ cells safeguards against precocious differentiation. Dev. Cell 39, 75–86 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Ying, Q. L., Stavridis, M., Griffiths, D., Li, M. & Smith, A. Conversion of embryonic stem cells into neuroectodermal precursors in adherent monoculture. Nat. Biotechnol. 21, 183–186 (2003).

    Article  CAS  PubMed  Google Scholar 

  32. Argelaguet, R. et al. Multi-omics profiling of mouse gastrulation at single-cell resolution. Nature 576, 487–491 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Bleckwehl, T. & Rada-Iglesias, A. Transcriptional and epigenetic control of germline competence and specification. Curr. Opin. Cell Biol. 61, 1–8 (2019).

    Article  CAS  PubMed  Google Scholar 

  34. Gruhn, W. H. & Günesdogan, U. in Epigenetic Reprogramming During Mouse Embryogenesis (eds Ancelin, K. & Borensztein, M.) 75–89 (Springer, 2021).

  35. Mulas, C., Kalkan, T. & Smith, A. NODAL secures pluripotency upon embryonic stem cell progression from the ground state. Stem Cell Reports 9, 77–91 (2017).

    Google Scholar 

  36. Hackett, J. A. et al. Tracing the transitions from pluripotency to germ cell fate with CRISPR screening. Nat. Commun. 9, 4292 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Kim, I. S. et al. Parallel single-cell RNA-seq and genetic recording reveals lineage decisions in developing embryoid bodies. Cell Rep. 33, 108222 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Aramaki, S. et al. Residual pluripotency is required for inductive germ cell segregation. EMBO Rep. 22, e52553 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Shirane, K. et al. Global landscape and regulatory principles of DNA methylation reprogramming for germ cell specification by mouse pluripotent stem cells. Dev. Cell 39, 87–103 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Greenberg, M., Teissandier, A., Walter, M., Noordermeer, D. & Bourc’his, D. Dynamic enhancer partitioning instructs activation of a growth-related gene during exit from naïve pluripotency. eLife 8, e44057 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Creyghton, M. P. et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc. Natl Acad. Sci. USA 107, 21931–21936 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Cruz-Molina, S. et al. PRC2 facilitates the regulatory topology required for poised enhancer function during pluripotent stem cell differentiation. Cell Stem Cell 20, 689–705 (2017).

    CAS  Google Scholar 

  43. Kalkan, T. et al. Complementary activity of ETV5, RBPJ, and TCF3 drives formative transition from naive pluripotency. Cell Stem Cell 24, 785–801 (2019).

    CAS  Google Scholar 

  44. Yang, S.-H. et al. ZIC3 controls the transition from naïve to primed pluripotency. Cell Rep. 27, 3215–3227 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Okashita, N. et al. PRDM14 drives OCT3/4 recruitment via active demethylation in the transition from primed to naive pluripotency. Stem Cell Reports 7, 1072–1086 (2016).

    CAS  Google Scholar 

  46. Eckersley-Maslin, M. A. et al. Epigenetic priming by Dppa2 and 4 in pluripotency facilitates multi-lineage commitment. Nat. Struct. Mol. Biol. 27, 696–705 (2020).

  47. Gretarsson, K. H. & Hackett, J. A. Dppa2 and Dppa4 counteract de novo methylation to establish a permissive epigenome for development. Nat. Struct. Mol. Biol. 27, 706–716 (2020).

    Article  CAS  PubMed  Google Scholar 

  48. Schwarz, B. A. et al. Prospective isolation of poised iPSC intermediates reveals principles of cellular reprogramming. Cell Stem Cell 23, 289–305 (2018).

    CAS  Google Scholar 

  49. Hon, G. C. et al. Epigenetic memory at embryonic enhancers identified in DNA methylation maps from adult mouse tissues. Nat. Genet. 45, 1198–1206 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Domcke, S. et al. Competition between DNA methylation and transcription factors determines binding of NRF1. Nature 528, 575–579 (2015).

    Article  CAS  PubMed  Google Scholar 

  52. Luo, X. et al. Coordination of germ layer lineage choice by TET1 during primed pluripotency. Genes Dev. 34, 598–618 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Dura, M. et al. DNMT3A-dependent DNA methylation is required for spermatogonial stem cells to commit to spermatogenesis. Nat. Genet. 54, 469–480 (2022).

    Article  CAS  PubMed  Google Scholar 

  54. Park, J. et al. Targeted erasure of DNA methylation by TET3 drives adipogenic reprogramming and differentiation. Nat. Metab. 4, 918–931 (2022).

    Article  CAS  PubMed  Google Scholar 

  55. Akagi, T. et al. ETS-related transcription factors ETV4 and ETV5 are involved in proliferation and induction of differentiation-associated genes in embryonic stem (ES) cells. J. Biol. Chem. 290, 22460–22473 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Mayer, D. et al. Zfp281 orchestrates interconversion of pluripotent states by engaging Ehmt1 and Zic2. EMBO J. 39, e102591 (2020).

    Article  CAS  PubMed  Google Scholar 

  57. Bentsen, M. et al. ATAC-seq footprinting unravels kinetics of transcription factor binding during zygotic genome activation. Nat. Commun. 11, 4267 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Tischler, J. et al. Metabolic regulation of pluripotency and germ cell fate through α‐ketoglutarate. EMBO J. 38, e99518 (2019).

    Article  PubMed  Google Scholar 

  59. Betto, R. M. et al. Metabolic control of DNA methylation in naive pluripotent cells. Nat. Genet. 53, 215–229 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Kaluscha, S. et al. Evidence that direct inhibition of transcription factor binding is the prevailing mode of gene and repeat repression by DNA methylation. Nat. Genet. 54, 1895–1906 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Banerjee, K. K. et al. Enhancer, transcriptional, and cell fate plasticity precedes intestinal determination during endoderm development. Genes Dev. 32, 1430–1442 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Rauch, A. et al. Osteogenesis depends on commissioning of a network of stem cell transcription factors that act as repressors of adipogenesis. Nat. Genet. 51, 716–727 (2019).

    Article  CAS  PubMed  Google Scholar 

  63. Charlton, J. et al. TETs compete with DNMT3 activity in pluripotent cells at thousands of methylated somatic enhancers. Nat. Genet. 52, 819–827 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Ginno, P. A. et al. A genome-scale map of DNA methylation turnover identifies site-specific dependencies of DNMT and TET activity. Nat. Commun. 11, 2680 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Clark, S. J. et al. Single-cell multi-omics profiling links dynamic DNA methylation to cell fate decisions during mouse early organogenesis. Genome Biol. 23, 202 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Matos, B., Publicover, S. J., Castro, L. F. C., Esteves, P. J. & Fardilha, M. Brain and testis: more alike than previously thought? Open Biol. 11, 200322 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Wilda, M. et al. Do the constraints of human speciation cause expression of the same set of genes in brain, testis, and placenta? Cytogenet. Cell Genet. 91, 300–302 (2000).

    Article  CAS  PubMed  Google Scholar 

  68. Karlsson, M. et al. A single-cell type transcriptomics map of human tissues. Sci. Adv. 7, eabh2169 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Gibson, D. G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009).

    Article  CAS  PubMed  Google Scholar 

  70. Karimi, M. et al. LUMA (luminometric methylation assay)—a high throughput method to the analysis of genomic DNA methylation. Exp. Cell. Res. 312, 1989–1995 (2006).

    Article  CAS  PubMed  Google Scholar 

  71. Schomacher, L. et al. Neil DNA glycosylases promote substrate turnover by Tdg during DNA demethylation. Nat. Struct. Mol. Biol. 23, 116–124 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    Article  CAS  PubMed  Google Scholar 

  73. Skene, P. J. & Henikoff, S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. eLife 6, e21856 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 2015, 21.29.1–21.29.9 (2015).

    Google Scholar 

  75. Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Walter, M., Teissandier, A., Pérez-Palacios, R. & Bourc’his, D. An epigenetic switch ensures transposon repression upon dynamic loss of DNA methylation in embryonic stem cells. eLife 5, e11418 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Krueger, F. & Andrews, S. R. Bismark: a flexible aligner and methylation caller for bisulfite-seq applications. Bioinformatics 27, 1571–1572 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We are grateful to all Bourc’his laboratory members for their continuous support. We thank J. Hackett (EMBL, Monterontondo) for the piggyBac vector containing the H2btdTomato reporter, J. Barau (IMB, Mainz) for the LC–MS assay on TKO cells, M. Cohen-Tannoudji (Institut Pasteur, Paris) for critical reading of the paper, M. Borenzstein (IGMM, Montpellier) for advice on generating PGCLCs and I. Kucinski (MRC, Cambridge) for the Smart-seq2 RNA-seq design. We acknowledge the Core Cytometry platform and the ICGex NGS platform of the Institut Curie (supported by grants ANR-10-EQPX-03, Equipex and ANR-10-INBS-09-08, France Génomique) and the Cell and Tissue Imaging Platform (PICT-IBiSA) (member of France-BioImaging, ANR-10-INBS-04) of the UMR3215/U934 of the Institut Curie. The laboratory of D.B. is part of the LABEX DEEP (ANR-11-LABX-0044, ANR-10-IDEX-0001-02). This work was supported by the Fondation Bettencourt Schueller and the Fondation pour la Recherche Médicale (FRM, EQU201903007842). M.S. was supported by PhD fellowships from the Ministère de l’Enseignement Supérieur et de la Recherche and from the Fondation pour la Recherche Médicale (FDT202106012998). Funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. INSERM U934, CNRS UMR3215, Institut Curie, PSL Research University, Paris, France

    Mathieu Schulz, Aurélie Teissandier, Elena De La Mata Santaella, Mélanie Armand, Julian Iranzo, Fatima El Marjou & Deborah Bourc’his

  2. INSERM U900, MINES ParisTech, Institut Curie, PSL Research University, Paris, France

    Pierre Gestraud

  3. Fred Hutchinson Cancer Center, Seattle, WA, USA

    Marius Walter

  4. Wellcome–MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK

    Sarah Kinston & Berthold Göttgens

  5. Université Paris Cité, CNRS, Institut Jacques Monod, Paris, France

    Maxim V. C. Greenberg

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Contributions

M.S., M.V.C.G. and D.B. conceived the study. Most experiments were performed by M.S. A.T. performed bioinformatic analyses on ATAC-seq and CUT&RUN data, P.G. performed analyses on scRNA-seq data, and M.S. performed analyses on bulk RNA-seq data. E.D.L.M.S. performed NPC experiments. M.A. generated libraries. J.I. and F.E.M. performed chimera and whole-mount embryo IF. S.K. and B.G. performed scRNA-seq. M.W. generated WGBS data. D.B. and M.S. interpreted data and wrote the paper. All authors reviewed and approved the final paper.

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Correspondence to Deborah Bourc’his.

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The authors declare no competing interests.

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Nature Structural & Molecular Biology thanks Jamie Hackett and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Dimitris Typas, in collaboration with the Nature Structural & Molecular Biology team.

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Extended data

Extended Data Fig. 1 Characterization of DNA-methylation-free Dnmt-triple KO (TKO) ESCs.

a, Sequencing results for the E14 TKO ESC line showing allelic homozygous deletions of Dnmt genes after catalytic motif targeting by CRISPR-Cas9. b, Immunoblotting showing DNMT1, DNMT3A and DNMT3B in WT and TKO ESCs grown in serum/LIF. PCNA is used as a loading control. c, Barplot showing LC/MS quantification of methylated cytosines in serum/LIF-grown WT (blue) and TKO (orange) ESCs. Data shown are mean ± SD in technical duplicates. d, LUMA assay of genome-wide CpG methylation of serum/LIF-grown WT and TKO ESCs. Data shown are n = 4 biological replicates with mean ± SD. e, Expression of pluripotency genes measured by RT-qPCR in 2i/LIF-grown WT and TKO ESCs. Data shown are mean ± SD from biological triplicates, ∆CT values are normalized to Rplp0 and Rrm2. All unlabeled comparisons are to be considered as non-significant.

Source data

Extended Data Fig. 2 TKO EpiLCs acquire functional early primed features in vitro and in vivo.

a, Apoptosis rate of WT and TKO cells, measured by live staining of Caspase 3/7 on Incucyte. Data shown are from biological triplicates with mean ± SD. b, Representative images of WT (blue) and TKO (orange) cells during EpiLC differentiation (performed in 3 independent replicates). Scale: 100 µm. c, Barplot representing Luciferase activity from the distal (dark grey) and proximal (light grey) Oct4 enhancers in WT and TKO clone #2 in D0 ESCs and D4 EpiLCs. Data are mean ± SD from biological triplicates (two-tailed unpaired student t-test). d, Barplot displaying numbers of reverted ESC colonies upon 2i+VitC medium replacement at D2, D4 and D7 of EpiLC differentiation in WT and TKO clone #2. Data are from biological duplicates with mean ± SD. e, Flow cytometry analysis of H2B::tdTOMATO reporter expression in WT clone #A5 (blue) and TKO clone #C5 (orange), compared to untransfected negative control cells. f, Karyotyping and chromosome counting in WT #A5 and TKO #C5 reporter cell lines, that were selected for chimera aggregation. g, Representative microscopy pictures after 24 h of culture of aggregates of host-morula/ H2B::tdTOMATO ESCs. The merge reveals contribution of both WT and TKO ESCs to the blastocyst ICM. Scale: 400 µm. h, Statistics for aggregation experiments. Top: six aggregation experiments (n = 6) were performed, totalizing 202 aggregates of WT #A5 and 231 aggregates of TKO #C5 reporter ESCs with host morulae. Integration efficiency, measured by the number of blastocysts with correctly integrated reporter ESCs after 24 h of culture. Bottom: five chimera assays (n = 5) were recovered after re-implantation of the aggregates in pseudo-pregnant females. Despite low yield and some non-chimeric embryos (94.1% and 82% of chimeras from WT and TKO aggregates, respectively), all recovered chimerae showed contribution of reporter cell lines in the embryonic epiblast only. i, Confocal images of E7.5 chimeric embryos obtained by morula aggregation with WT or TKO H2B::tdTOMATO-transgenic ESCs. Embryos were stained for DAPI (gray) and AP2γ (red, extra-embryonic marker). Both WT and TKO H2B::tdTOMATO cells showed contribution to the epiblast, but not extra-embryonic tissues. TKO chimerae appeared delayed compared to WT chimera. Scale: 100 µm. j, Expression of the trophectoderm marker Ascl2 during EpiLC differentiation. Expression is shown as normalized Log2 CPM counts (average between biological duplicates). All unlabeled comparisons are to be considered as non-significant.

Source data

Extended Data Fig. 3 TKO EpiLCs upregulate late gametogenesis genes and some classes of transposable elements.

a, b, Volcano plots showing differential expression in TKO over WT EpiLCs at D4 (a) and D7 (b) in Log2FC versus -log10(adj. pvalues). Red: up-regulated genes; Grey: down-regulated genes; Blue: germline genes. Thresholds were set up at FDR < 5% and Log2(FC) > 1 (two-sided t-test). Data obtained from biological duplicates. c, Density plot showing differences in DNA methylation between D0, D3 and D7 of WT EpiLC differentiation at gene promoters (<10 kb from TSS) as measured by WGBS34. Threshold for regions defined as differentially methylated (DMRs) was arbitrarily set-up at 50% between D0-D3 and 75% between D0-D7. d, Scatter plots showing the correlation between DNA methylation gain at gene promoters during WT EpiLC differentiation and expression in Log2(FC) in TKO versus WT EpiLCs. Left: DNA methylation level changes at D3 over D0 ESCs and expression changes at D4. Right: DNA methylation level changes at D7, expression changes at D7. Red: up-regulated genes with a DMR promoter; Blue: germline genes. e, Heatmap of Oxidative Phosphorylation metabolism-associated gene expression showing normalized fold-change in TKO over WT cells during EpiLC differentiation. Black stars (*) represent significance (FDR < 1%, Log2FC > 1). Expression was obtained for each condition from the average between biological duplicates. f, Heatmap of transposable element expression showing normalized fold-change in TKO over WT cells during EpiLC differentiation. Black stars (*) represent significance (FDR < 1%, Log2FC > 1). Expression was obtained for each condition from the average between biological duplicates.

Extended Data Fig. 4 TKO cells can differentiate under directed, but not undirected conditions.

a, Representative brightfield images of embryoid bodies (EBs) generated from WT (blue) and TKO (orange) ESCs cultured in serum for 8 days (performed in 2 independent replicates). Scale: 500 µm. b, Boxplot representation of EB diameter across differentiation. Boxes represent 5-95 confidence interval, n = 80 EBs counted/genotype/timepoint. Data shown are the median with upper and lower hinges corresponding to 75 and 25% quantile (two-tailed unpaired student t-test). c, EB diameter size curve showing the effect of increasing initial cell density during a five-day long differentiation. Data shown are biological replicates (n = 10) with mean ± SD. d, Expression of pluripotency (Oct4), neural (Sox1, Nestin, Ascl1) and meso-endoderm (Gata4, Foxa2, Eomes, Mesp1) markers measured by RT-qPCR in WT and TKO during EB differentiation. Data shown are mean ± SD from biological triplicates. ∆CT values were normalized to Rplp0 and Rrm2 (two-tailed unpaired student t-test). The two panels represent two independent TKO and WT clone pairs. Higher variability at D6 may reflect increasing divergence in lineage distribution with EB differentiation time and/or secondary effects of prolonged TKO growth arrest. e, Heatmap of germline markers showing normalized Log2 CPM counts during NPC differentiation. Expression is obtained for each condition from the average between biological duplicates. f, Heatmap of transposable elements showing Log2(FC) expression in TKO over WT cells during NPC differentiation. Black stars (*) represent significance (FDR < 1%, Log2FC > 1). g, Growth curve of WT and TKO ESCs subjected to meso-endoderm differentiation. Data are mean ± SD in biological triplicates. h, Expression of pluripotency (Oct4), neural (Sox1, Nestin, Ascl1) and meso-endoderm (Gata4, Foxa2, Eomes, Mesp1) markers measured by RT-qPCR in WT and TKO during meso-endoderm differentiation. Data are mean ± SD in biological triplicates. ∆CT values were normalized to Rplp0 and Rrm2 (two-tailed unpaired student t-test). The two panels represent two independent TKO and WT clone pairs. All unlabeled comparisons are to be considered as non-significant.

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Extended Data Fig. 5 TKO EpiLCs can undergo PGCLC differentiation in vitro.

a, Representative brightfield images of cultured PGCLC aggregates at D4 generated from WT and TKO cells cultured in presence or absence of PGCLC-inducing cytokines (n = 4 at ESCs and 24 h EpiLCs, n = 7 at 40 h and 48 h and 95 h EpiLCs, n = 3 at 168 h EpiLCs). Scale: 250 µm. b, FACS strategy: after dissociation from aggregates grown under PGCLC-inducing conditions, single cells were selected based on SSC/FSC. Then, single cells were identified by comparison between SSC-area/SSC-height. Next, live cells were selected based on DAPI integration. Finally, PGCLCs were identified for positive staining with SSEA1 and CD61 surface markers. Thresholds were set by fluorescence minus one (FMO) conditions and negative control, that is cells cultured without PGCLC-inducing cytokines. c, Expression of PGCLC markers measured by RT-qPCR in WT (blue) and TKO (orange) PGCLCs. Data shown are mean ± SD from ∆CT values from biological triplicates, normalized to Rplp0 and Rrm2 (two-tailed unpaired student t-test). d, Representative FACS plot of SSEA1-Pos and CD61-Pos WT and TKO PGCLCs generated from ESCs and EpiLCs at different time points. Cell percentages are indicated in each quarter. e, Barplot showing the percentage of specified WT and TKO PGCLCs generated from EpiLCs at different times, in TKO clone #2. Data shown are mean ± SD from biological replicates (n = 3 at 40 h EpiLCs or n = 4 at 48 h and 96 h EpiLCs), two-tailed unpaired student t-test). f, FACS plot of Blimp1-GFP transgenic WT ESCs comparing levels of transgene activation in 2i and 2i+Vitamin C medium. Thresholds of activation was set according to negative control WT cells in 2i medium. g, Heatmap of transposable element expression showing normalized fold-change in TKO over WT cells during PGCLC differentiation. Black stars (*) represent significance (FDR < 1%, Log2FC > 1). Expression was obtained for each condition from the average between biological duplicates. All unlabeled comparisons are to be considered as non-significant.

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Extended Data Fig. 6 Increased germline competence of TKO EpiLCs does not originate from a ‘germline-competent’ subpopulation.

a, b, c, UMAP dimensionality reduction of scRNA-seq data from WT and TKO EpiLCs highlighting (a) timing of differentiation, (b) condition (WT in blue, TKO in orange) and (c) unbiased cell clustering for a total n = 672 cells, from two biological duplicates. d, Dotplot of expression levels of key pluripotency and priming markers in each cluster detected for WT and TKO cells. Diameter of the dots represents the percentage of cell expressing the marker, colors represent average expression levels. e, Ridgeplot displaying gametogenesis gene expression (in log scale) in each identified cluster for WT and TKO cells. f, Violin plot showing expression of PGC fate regulators in WT and TKO EpiLCs in each of the identified clusters. g, PCA dimensionality reduction for pseudotime trajectory of WT and TKO EpiLCs highlighted for identified clusters during EpiLC differentiation.

Extended Data Fig. 7 Uncoupling between chromatin and expression changes in TKO EpiLCs.

a, b, Scatterplots showing Differential Enrichment in log2 Fold-change in (a) ATAC and (b) H3K27ac vs Differential Expression in log2 Fold-change in RNA-seq in D4 EpiLCs. Regions were annotated to genes at close proximity (<5 kb from TSS). Colored dots depict the status of expression and/or enrichment. Numbers between brackets represent the number of regions marked by (a) ATAC in green and (b) H3K27ac in blue in D4 EpiLCs. For differentially expressed (DE) genes with differentially enriched (DE) regions in close proximity (red dots), the top 10 DE genes are displayed. c, d, Violin plots showing the dynamics of methylated CpGs during WT EpiLC differentiation (D0, D3, D7) around promoters (<1 kB) of DE genes associated with differential enrichment in (c) ATAC and (d) H3K27ac in D4 TKO EpiLCs, using previously published WGBS datasets40. Data shown are the median with upper and lower hinges corresponding to 75 and 25% quantile.

Extended Data Fig. 8 DNA methylation changes at NPC, PGCLC and DE enhancers.

a, Boxplot showing enrichment in log(RPKM + 1) of ATAC and H3K27ac at defined ectoderm enhancers (n = 4142)32, for WT (blue) and TKO (orange) differentiating EpiLCs. Data shown are the median with upper and lower hinges corresponding to 75 and 25% quantile (two-tailed unpaired student t-test). b, Genomic annotation of DE enhancers in D4 TKO EpiLCs. c, Expression of genes in the vicinity of all identified DE putative enhancers (within 50 kb) in WT and TKO EpiLCs, NPCs and PGCLCs (40 h). Data shown are the median with upper and lower hinges corresponding to 75 and 25% quantile. d, GSEA genes linked to DE enhancers (50 kb, pvalue cutoff = 0.05). e, Scatterplot showing DNA methylation gain between D0/D3 (Top) or D0/D7 (Bottom) in WT EpiLCs at NPC, PGCLC and DE enhancers, according to GC content. Thresholds were defined (red dotted lines) for subsets of enhancers gaining high DNA methylation levels during EpiLC differentiation. Orange curves correspond to generalized additive mode smoothing method mean value with standard error (yellow shading). All unlabeled comparisons are to be considered as non-significant.

Extended Data Fig. 9 Motif analysis at meso-endoderm enhancers and binding activity at NPC and PGCLC enhancers.

a, Heatmaps showing TOBIAS motif binding score (left panel) and expression of associated factors (right panel) during EpiLC differentiation in WT and TKO cells at NPC, PGCLC and DE enhancers. Values shown are Z- score transformed across rows. b, ZIC3 corrected ATAC-seq footprints. Line curves show the aggregated footprinting plot matrix for all ZIC3 transcription factor binding sites in WT (blue) and TKO (orange) during EpiLC differentiation at DE enhancers. Plots are centered around binding motifs (n = 344 identified binding sites).

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Schulz, M., Teissandier, A., De La Mata Santaella, E. et al. DNA methylation restricts coordinated germline and neural fates in embryonic stem cell differentiation. Nat Struct Mol Biol 31, 102–114 (2024). https://doi.org/10.1038/s41594-023-01162-w

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