Skip to main content

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

Epigenetic regulator function through mouse gastrulation

Abstract

During ontogeny, proliferating cells become restricted in their fate through the combined action of cell-type-specific transcription factors and ubiquitous epigenetic machinery, which recognizes universally available histone residues or nucleotides in a context-dependent manner1,2. The molecular functions of these regulators are generally well understood, but assigning direct developmental roles to them is hampered by complex mutant phenotypes that often emerge after gastrulation3,4. Single-cell RNA sequencing and analytical approaches have explored this highly conserved, dynamic period across numerous model organisms5,6,7,8, including mouse9,10,11,12,13,14,15,16,17,18. Here we advance these strategies using a combined zygotic perturbation and single-cell RNA-sequencing platform in which many mutant mouse embryos can be assayed simultaneously, recovering robust  morphological and transcriptional information across a panel of ten essential regulators. Deeper analysis of central Polycomb repressive complex (PRC) 1 and 2 components indicates substantial cooperativity, but distinguishes a dominant role for PRC2 in restricting the germline. Moreover, PRC mutant phenotypes emerge after gross epigenetic and transcriptional changes within the initial conceptus prior to gastrulation. Our experimental framework may eventually lead to a fully quantitative view of how cellular diversity emerges using an identical genetic template and from a single totipotent cell.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Single-cell profiling of early post-implantation development.
Fig. 2: Morphological and molecular consequences of epigenetic regulator mutation.
Fig. 3: Phenotypic and molecular abnormalities of PRC regulator mutants.
Fig. 4: The EED mutant phenotype extends from disrupted pluripotency exit.

Data availability

All datasets have been deposited in the Gene Expression Omnibus (GEO) and are accessible under GSE137337 (or published previously under GSE122187). Source data for Figs. 1a, b, 2, 3a, b, d–f, h, i, 4a, c–f, Extended Data Figs. 1b, c, e–i, 2b–f, 3, 4b–f, 5a–c, e, 6, 7, 8a, 9b, 10b–g, 11c, d are available at https://oc-molgen.gnz.mpg.de/owncloud/s/F8g3y5F79JZRyof. Previously published data used in this study include H3K27me ChIP–seq data (GSE98149), WGBS data for sperm and oocyte (GSE112320), preimplantation samples, including 8 cell stage embryos and the inner cell mass and trophectoderm of the E3.5 blastocyst (GSE84236), and late stage samples including an average of somatic tissues and the E14.5 placenta (GSE42836).

Code availability

Code is available at https://github.com/HeleneKretzmer/EpigeneticRegulators_MouseGastrulation.

References

  1. 1.

    Hemberger, M., Dean, W. & Reik, W. Epigenetic dynamics of stem cells and cell lineage commitment: digging Waddington’s canal. Nat. Rev. Cell Biol. 10, 526–537 (2009).

    CAS  Google Scholar 

  2. 2.

    Meissner, A. Epigenetic modifications in pluripotent and differentiated cells. Nat. Biotechnol. 28, 1079–1088 (2010).

    CAS  PubMed  Google Scholar 

  3. 3.

    Surani, M. A., Hayashi, K. & Hajkova, P. Genetic and epigenetic regulators of pluripotency. Cell 128, 747–762 (2007).

    CAS  PubMed  Google Scholar 

  4. 4.

    Rivera-Pérez, J. A. & Hadjantonakis, A.-K. The dynamics of morphogenesis in the early mouse embryo. Cold Spring Harb. Perspect. Biol. 7, a015867 (2014).

    PubMed  Google Scholar 

  5. 5.

    Plass, M. et al. Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics. Science 360, eaaq1723 (2018).

    PubMed  Google Scholar 

  6. 6.

    Briggs, J. A. et al. The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution. Science 360, eaar5780 (2018).

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Wagner, D. E. et al. Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo. Science 360, 981–987 (2018).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Farrell, J. A. et al. Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis. Science 360, eaar3131 (2018).

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Scialdone, A. et al. Resolving early mesoderm diversification through single-cell expression profiling. Nature 535, 289–293 (2016).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Peng, G. et al. Spatial transcriptome for the molecular annotation of lineage fates and cell identity in mid-gastrula mouse embryo. Dev. Cell 36, 681–697 (2016).

    CAS  PubMed  Google Scholar 

  11. 11.

    Mohammed, H. et al. Single-cell landscape of transcriptional heterogeneity and cell fate decisions during mouse early gastrulation. Cell Rep. 20, 1215–1228 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Ibarra-Soria, X. et al. Defining murine organogenesis at single-cell resolution reveals a role for the leukotriene pathway in regulating blood progenitor formation. Nat. Cell Biol. 20, 127–134 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Peng, G. et al. Molecular architecture of lineage allocation and tissue organization in early mouse embryo. Nature 572, 528–532 (2019).

    ADS  CAS  PubMed  Google Scholar 

  14. 14.

    Pijuan-Sala, B. et al. A single-cell molecular map of mouse gastrulation and early organogenesis. Nature 566, 490–495 (2019).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Nowotschin, S. et al. The emergent landscape of the mouse gut endoderm at single-cell resolution. Nature 569, 361–367 (2019).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Chan, M. M. et al. Molecular recording of mammalian embryogenesis. Nature 570, 77–82 (2019).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Nguyen, S., Meletis, K., Fu, D., Jhaveri, S. & Jaenisch, R. Ablation of de novo DNA methyltransferase Dnmt3a in the nervous system leads to neuromuscular defects and shortened lifespan. Dev. Dyn. 236, 1663–1676 (2007).

    CAS  PubMed  Google Scholar 

  20. 20.

    Laugesen, A. & Helin, K. Chromatin repressive complexes in stem cells, development, and cancer. Cell Stem Cell 14, 735–751 (2014).

    CAS  PubMed  Google Scholar 

  21. 21.

    Piunti, A. & Shilatifard, A. Epigenetic balance of gene expression by Polycomb and COMPASS families. Science 352, aad9780 (2016).

    PubMed  Google Scholar 

  22. 22.

    Glaser, S. et al. Multiple epigenetic maintenance factors implicated by the loss of Mll2 in mouse development. Development 133, 1423–1432 (2006).

    CAS  PubMed  Google Scholar 

  23. 23.

    Rossant, J., Chazaud, C. & Yamanaka, Y. Lineage allocation and asymmetries in the early mouse embryo. Philos. Trans. R. Soc. Lond. B Biol. Sci. 358, 1341–1349 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Smith, Z. D. et al. Epigenetic restriction of extraembryonic lineages mirrors the somatic transition to cancer. Nature 549, 5434–547 (2017).

    Google Scholar 

  25. 25.

    Gil, J. & Peters, G. Regulation of the INK4b-ARF-INK4a tumour suppressor locus: all for one or one for all. Nat. Rev. Mol. Cell Biol. 7, 667–677 (2006).

    CAS  PubMed  Google Scholar 

  26. 26.

    Boulard, M., Edwards, J. R. & Bestor, T. H. FBXL10 protects Polycomb-bound genes from hypermethylation. Nat. Genet. 47, 479–485 (2015).

    CAS  Google Scholar 

  27. 27.

    Walsh, C. P., Chaillet, J. R. & Bestor, T. H. Transcription of IAP endogenous retroviruses is constrained by cytosine methylation. Nat. Genet. 20, 116–117 (1998).

    CAS  PubMed  Google Scholar 

  28. 28.

    Qin, J. et al. The polycomb group protein L3mbtl2 assembles an atypical PRC1-family complex that is essential in pluripotent stem cells and early development. Cell Stem Cell 11, 319–332 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Faust, C., Schumacher, A., Holdener, B. & Magnuson, T. The eed mutation disrupts anterior mesoderm production in mice. Development 121, 273–285 (1995).

    CAS  PubMed  Google Scholar 

  30. 30.

    Voncken, J. W. et al. Rnf2 (Ring1b) deficiency causes gastrulation arrest and cell cycle inhibition. Proc. Natl Acad. Sci. USA 100, 2468–2473 (2003).

    ADS  CAS  PubMed  Google Scholar 

  31. 31.

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

    CAS  PubMed  Google Scholar 

  32. 32.

    Żylicz, J. J. et al. The Implication of early chromatin changes in X chromosome inactivation. Cell 176, 182–197.e23 (2019).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Wang, J. et al. Imprinted X inactivation maintained by a mouse Polycomb group gene. Nat. Genet. 28, 371–375 (2001).

    CAS  PubMed  Google Scholar 

  34. 34.

    Li, Y. et al. Genome-wide analyses reveal a role of Polycomb in promoting hypomethylation of DNA methylation valleys. Genome Biol. 19, 18–16 (2018).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Leitch, H. G. & Smith, A. The mammalian germline as a pluripotency cycle. Development 140, 2495–2501 (2013).

    CAS  PubMed  Google Scholar 

  36. 36.

    Forlani, S., Lawson, K. A. & Deschamps, J. Acquisition of Hox codes during gastrulation and axial elongation in the mouse embryo. Development 130, 3807–3819 (2003).

    CAS  PubMed  Google Scholar 

  37. 37.

    Saitou, M. Specification of the germ cell lineage in mice. Front. Biosci. 14, 1068–1087 (2009).

    CAS  Google Scholar 

  38. 38.

    Nicetto, D. et al. H3K9me3-heterochromatin loss at protein-coding genes enables developmental lineage specification. Science 363, 294–297 (2019).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Tzouanacou, E., Wegener, A., Wymeersch, F. J., Wilson, V. & Nicolas, J.-F. Redefining the progression of lineage segregations during mammalian embryogenesis by clonal analysis. Dev. Cell 17, 365–376 (2009).

    CAS  PubMed  Google Scholar 

  40. 40.

    Wang, H. et al. One-step generation of mice carrying mutations in multiple genes by CRISPR/Cas-mediated genome engineering. Cell 153, 910–918 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Platt, R. J. et al. CRISPR-Cas9 knockin mice for genome editing and cancer modeling. Cell 159, 440–455 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Montague, T. G., Cruz, J. M., Gagnon, J. A., Church, G. M. & Valen, E. CHOPCHOP: a CRISPR/Cas9 and TALEN web tool for genome editing. Nucleic Acids Res. 42, W401–W407 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Nakagata, N. Cryopreservation of mouse spermatozoa and in vitro fertilization. Methods Mol. Biol. 693, 57–73 (2011).

    CAS  PubMed  Google Scholar 

  44. 44.

    Ohinata, Y. et al. A comprehensive, non-invasive visualization of primordial germ cell development in mice by the Prdm1-mVenus and Dppa3-ECFP double transgenic reporter. Reproduction 136, 504–514 (2008).

    Google Scholar 

  45. 45.

    Ying, Q.-L. & Smith, A. G. Defined conditions for neural commitment and differentiation. Methods Enzymol. 365, 327–341 (2003).

    CAS  PubMed  Google Scholar 

  46. 46.

    Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).

    CAS  Google Scholar 

  47. 47.

    Hahne, F. & Ivanek, R. Visualizing genomic data using Gviz and Bioconductor. Methods Mol. Biol. 1418, 335–351 (2016).

    PubMed  Google Scholar 

  48. 48.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  Google Scholar 

  49. 49.

    Krueger, F. & Andrews, S. R. SNPsplit: Allele-specific splitting of alignments between genomes with known SNP genotypes. F1000Res. 5, 1479 (2016).

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Andergassen, D. et al. Mapping the mouse Allelome reveals tissue-specific regulation of allelic expression. eLife 6, e146 (2017).

    Google Scholar 

  52. 52.

    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Wang, C. et al. Reprogramming of H3K9me3-dependent heterochromatin during mammalian embryo development. Nat. Cell Biol. 20, 620–631 (2018).

    CAS  PubMed  Google Scholar 

  54. 54.

    McInnes, L., Healy, J. & Melville, J. UMAP: Uniform Manifold Approximation and Projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).

  55. 55.

    La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15–5 (2018).

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Sun, D. et al. MOABS: model based analysis of bisulfite sequencing data. Genome Biol. 15, R38–R12 (2014).

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    Keane, T. M. et al. Mouse genomic variation and its effect on phenotypes and gene regulation. Nature 477, 289–294 (2011).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Lei, H. et al. De novo DNA cytosine methyltransferase activities in mouse embryonic stem cells. Development 122, 3195–3205 (1996).

    CAS  PubMed  Google Scholar 

  60. 60.

    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).

    CAS  PubMed  Google Scholar 

  61. 61.

    Tachibana, M. et al. G9a histone methyltransferase plays a dominant role in euchromatic histone H3 lysine 9 methylation and is essential for early embryogenesis. Genes Dev. 16, 1779–1791 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Yu, B. D., Hess, J. L., Horning, S. E., Brown, G. A. & Korsmeyer, S. J. Altered Hox expression and segmental identity in Mll-mutant mice. Nature 378, 505–508 (1995).

    ADS  CAS  PubMed  Google Scholar 

  63. 63.

    Hammoud, S. S. et al. Chromatin and transcription transitions of mammalian adult germline stem cells and spermatogenesis. Cell Stem Cell 15, 239–253 (2014).

    CAS  PubMed  Google Scholar 

  64. 64.

    Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Nashun, B. et al. Continuous histone replacement by Hira is essential for normal transcriptional regulation and de novo DNA methylation during mouse oogenesis. Mol. Cell 60, 611–625 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Auclair, G., Guibert, S., Bender, A. & Weber, M. Ontogeny of CpG island methylation and specificity of DNMT3 methyltransferases during embryonic development in the mouse. Genome Biol. 15, 545 (2014).

    PubMed  PubMed Central  Google Scholar 

  68. 68.

    Wymeersch, F. J. et al. Position-dependent plasticity of distinct progenitor types in the primitive streak. eLife 5, e10042 (2016).

    PubMed  PubMed Central  Google Scholar 

  69. 69.

    Niswander, L., Yee, D., Rinchik, E. M., Russell, L. B. & Magnuson, T. The albino deletion complex and early postimplantation survival in the mouse. Development 102, 45–53 (1988).

    CAS  PubMed  Google Scholar 

  70. 70.

    Faust, C., Lawson, K. A., Schork, N. J., Thiel, B. & Magnuson, T. The Polycomb-group gene eed is required for normal morphogenetic movements during gastrulation in the mouse embryo. Development 125, 4495–4506 (1998).

    CAS  PubMed  Google Scholar 

  71. 71.

    Kalantry, S. & Magnuson, T. The Polycomb group protein EED is dispensable for the initiation of random X-chromosome inactivation. pLoS Genet. 2, e66 (2006).

    PubMed  PubMed Central  Google Scholar 

  72. 72.

    Niswander, L., Yee, D., Rinchik, E. M., Russell, L. B. & Magnuson, T. The albino-deletion complex in the mouse defines genes necessary for development of embryonic and extraembryonic ectoderm. Development 105, 175–182 (1989).

    CAS  PubMed  Google Scholar 

  73. 73.

    Han, J. et al. Tbx3 improves the germ-line competency of induced pluripotent stem cells. Nature 463, 1096–1100 (2010).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Magnúsdóttir, E. & Surani, M. A. How to make a primordial germ cell. Development 141, 245–252 (2014).

    PubMed  Google Scholar 

  75. 75.

    Arnold, S. J. & Robertson, E. J. Making a commitment: cell lineage allocation and axis patterning in the early mouse embryo. Nat. Rev. Mol. Cell Biol. 10, 91–103 (2009).

    CAS  PubMed  Google Scholar 

  76. 76.

    Semrau, S. et al. Dynamics of lineage commitment revealed by single-cell transcriptomics of differentiating embryonic stem cells. Nat. Commun. 8, 1096 (2017).

    ADS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank A. Bolondi, R. Weigert and other members of the Meissner laboratory, M. Chan and D. Hnisz for discussions and advice, S. Otto for experimental support characterizing the EED-knockout mES cell line, M. Walter for support with embryo isolations, T. Ahsendorf for help with initial efforts to optimize our genotyping pipeline, and D. Andergassen for discussions on SNP typing. We are also grateful to F. Koch and the transgenic facility, including M. Peetz, and C. Giesecke-Thiel of the Flow Cytometry Facility for their feedback and support. We thank M. Saitou for the mVenus Prdm14 promoter sperm that were provided by the RIKEN BRC through the National Bio-Resource Project of the MEXT/AMED, Japan (Acc. No. CDB0461T). This work was funded by the National Institutes of Health (NIH; 1P50HG006193, P01GM099117, 1R01HD078679 and 1DP3K111898) and the Max Planck Society.

Author information

Affiliations

Authors

Contributions

S.G., Z.D.S. and A.M. designed and conceived the study. S.G., H.K., Z.D.S. and A.M. prepared the manuscript with the assistance of the other authors. Z.D.S. performed the mouse experiments for scRNA-seq and WGBS. S.G. and Z.D.S. performed all scRNA-seq experiments. L.W. and A.S.K. performed in vitro fertilization and electroporations for reporter experiments. A.S.K. conducted the staining and imaging of the embryos supervised by S.G. S.G. and Z.D.S. made the scRNA-seq libraries with raw data processing by S.K., B.T. and H.K. Downstream data processing was performed by H.K. Analysis of repetitive elements from scRNA-seq data was performed by S.H. Data analysis was performed by S.G., H.K. and Z.D.S. and assisted by S.M. S.G. conducted in vitro experiments and analysed Nanostring data. A.M. supervised the work.

Corresponding author

Correspondence to Alexander Meissner.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Haruhiko Koseki, Terry Magnuson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data figures and tables

Extended Data Fig. 1 SNP-based genotyping and assignment of single cells into 42 discrete cell states.

a, SNP-based cell-to-embryo assignment strategy. Embryos were generated by intracytoplasmic sperm injection using sperm from hybrid males (C57BL/6J × CAST/EiJ) to confer a randomly inherited CAST/EiJ haplotype. Siblings (individually coloured embryos) are pooled before scRNA-seq and computationally deconvoluted based on their embryo-specific SNP profiles. In brief, the ratios of CAST-specific SNPs (orange) are scored per chromosome to cluster cells into distinct embryos. We use B6D2F1 (C57BL/6J × DBA) oocytes, whose genotypes differ by only approximately 4.5 million SNPs compared with 17.7 million for CAST/EiJ58. b, SNP-based deconvolution of seven pooled E7.5 wild-type embryos. Left, principal component analysis (PCA) projection of autosomal CAST SNP ratios for all sequenced cells with ≥1,000 covered SNPs. Cells are coloured by cluster assignment, indicating individual genotypes (embryos). Centre, iterative sampling of 20% covered SNPs per cell flags cells with unstable embryo assignments. Flagged cells with lower than median SNP counts represent low quality cells, whereas those with higher counts collect between clusters and probably reflect doublets. Cells with unstable genotype assignments were excluded from further analysis. Right, PCA projection of all cells that were stably assigned to an embryo. c, Per embryo fraction of cells with Xist (grey) and three Y-chromosome linked gene transcripts (Erdr1, Ddx3y or Eif2s3y; blue) used for sex-typing. For cell numbers, see Supplementary Tables 1 and 2. d, Summary statistics of profiled wild-type embryos from E6.5–E8.5 (n = 50 total). e, Left, fraction of variable genes that are uniquely assigned to a single state when taking the top N-most differentially expressed genes per cluster. We selected the top 30 most unique genes per cluster (n = 712 genes) because it maximizes the information per cluster under the constraint that the number of marker genes be as similar across states as possible. Right, ranked order distribution for the fraction of all variable or of the top 30 marker genes expressed in each of our 42 states. Our top 30 marker criterion reduces the range of variable genes that are used to assign single cells to each state. f, Single-cell Euclidean distances to their closest (green) or second closest (grey) state. The distribution of differences between first and second closest cluster are all significant (P < 2 × 10−16, Wilcoxon test, two tailed, paired test). g, Per embryo bar plots show the percentage of cells (y axis) assigned to each cell state (n = 42 states, 50 embryos total). For absolute cell counts, see Supplementary Tables 1 and 5. h, Left, heat map of the prevalence of cell states across profiled embryonic stages. The median state proportions are calculated across embryos for each time point, and then row-normalized across time points to show their dynamics. Right, expression heat map of our 712 marker genes, with key markers for each state highlighted (see Supplementary Information). Mean state expression for each marker gene is normalized over the column and arranged by maximal expression value across states. i, Left, UMAP of wild-type cells (n = 88,779) coloured by time point from dark to light grey. Right, wild-type UMAP overlaid with RNA velocity55 information as an indicator of transcriptome dynamics between different cell states.

Extended Data Fig. 2 Efficient genetic perturbation of epigenetic regulators and cell-state characteristics across embryo replicates.

a, Top, epigenetic regulators investigated here with information about their target residues, function and grouped into three key pathways: regulation by DNA methylation, Polycomb or Trithorax. Most lethal phenotypes occur soon after our last experimental collection time point (E8.5)22,26,28,29,30,59,60,61,62. L3MBTL2 is a methyl-histone binding protein that participates in the regulation of PRC1 as part of non-canonical PRC1.6. L3MBTL2 and EED do not possess denoted enzymatic activities (asterisks) but are involved in the functionality of a multicomponent complex. DNMT3A mutants die postnatally (w, weeks), with signs of defective neural development that may initiate in utero. Bottom, summary statistics of scRNA-seq data generated for E8.5-isolated embryos with mutations in one of ten target epigenetic regulators (n = 103 embryos total). b, Fraction of cells positive for selected epigenetic regulator genes in wild-type embryos ordered by developmental stage (E6.5–E8.5). The de novo DNA methyltransferases Dnmt3b and to some degree Dnmt3a become less expressed as the embryo develops, congruent with their early role in remethylating the genome shortly after implantation. The n values reflect the number of embryos collected at each time point. c, Fraction of cells positive for selected epigenetic regulator genes in wild-type embryos for eight major developmental lineages. The n values reflect the number of embryos from which each lineage was recovered. d, Reads spanning the sgRNA protospacer sequences confirm highly efficient disruption of epigenetic regulator loci. Reads are grouped into the following categories: mismatched, at least one base is a mismatch, deletion or insertion; spliced/deleted, split read spans discontinuously over the protospacer sequence; insufficient, reads do not span the entire cut site; complete, reads map without any mismatches to the cut site. Mapping distribution of scRNA-seq reads from wild-type E8.5 embryos is shown in comparison for each target site. A more comprehensive analysis of zygotic disruption is presented for EED-mutant embryos in Extended Data Fig. 9. e, Mutant embryo cells can be described using wild-type-defined states. Box plots show the single cell Euclidean distances to their closest (green) and second closest (grey) states per experiment. The differences between first and second closest cluster are all significant (P < 2 × 10−16, Wilcoxon test, two tailed, paired test). We observe similar differences between first and second state assignment between mutant cells as we do for the wild-type cells from which our state kernels were derived. n = 88,779; 20,890; 18,320; 25,408; 20,389; 22,896; 15,589; 18,943; 7,548; 15,776; and 15,603 (left to right). f, Bar plots showing the percentage of cells per embryo (x axis) that were assigned to each of our 42 cell states (colours, y axis) with E8.5 wild-type embryos provided for comparison. Notably, mutant embryos frequently match earlier developmental stages (Fig. 2a, b, Extended Data Fig. 1g, for comparison). Aberrant cell-state proportions indicate morphological abnormalities beyond developmental delay. For example, L3MBTL2 mutants underproduce early embryonic states, whereas EED and RNF2 mutants initially progress through gastrulation but substantially overproduce posterior products, such as allantois and amnion (states 5, 15 and 41, respectively). For absolute cell counts, see Supplementary Table 5.

Extended Data Fig. 3 Quantifying developmental delay of mutant embryos by cell-state composition.

a, Cell-state composition of epigenetic regulator mutants. Cells were assigned to one of 42 wild-type cell states and projected onto our wild-type-defined gastrulation UMAP. That mutant cells fall within wild-type states cannot confirm equivalent functionality or potential, but does suggest that cell states are largely constrained even without key epigenetic regulators. Instead, many mutant embryos differ from wild-type embryos by cell-state composition. Adjacent bar plots reflect the median embryo composition. A reference key for our wild-type time series is provided. n denotes number of cells. b, Distribution across principal component 1 (PC1) for wild-type embryos (dots n = 10, 9, 11, 10 and 10) per time point using two data resolutions: a thresholded, binarized score of state presence (left), or the exact proportion (right). In PC1 space, embryos from early developmental stages (that is, E6.5–E7.5) are better resolved according to the presence or absence of key states associated with the primitive streak, whereas later time points (that is, E8.0–E8.5) share many of the same states, but at different proportions. Tissues prone to technical recovery biases during embryo isolation (Xecto and Xendo) were excluded from this analysis (Supplementary Table 5). c, PC1 values for median wild-type embryos (n = 5 time points, asterisks). PCAs were based on the binary presence or absence of cell states (x axis) and on cell-state proportions (y axis). d, Developmental staging of single mutant embryo replicates (squares) by projecting them onto the wild-type-defined PCA space described in c. Mutants of the DNA methyltransferases and the histone methyltransferases KMT2A and G9A show no or mild developmental delays. The Polycomp components EED, RNF2, KDM2B and L3MBTL2 exhibit stronger setbacks in developmental progression, with greater variability. For staging information, see Supplementary Table 1. n = 12, 10, 8, 11, 10, 11, 10, 10, 11 and 10 embryos. e, Clustering of epigenetic regulator mutants based on genes that are recurrently differentially expressed across cell states. Expression changes were determined from scRNA-seq data by comparing each mutant to wild-type cell state, split by embryonic (top) or extraembryonic (bottom) origin (Supplementary Table 6). Differentially up- or downregulated genes found in at least two states are shown in red and blue, with colour intensity reflecting the fraction of cell states that change in a given direction (calculated as an average of +1 and −1 states). Within the embryonic lineage, the KDM2B mutant clusters with canonical PRC subunits, even though it progresses further in development. Other regulators show expression differences in fewer cells states and many correspond to within-lineage transitions (Supplementary Tables 6, 7). In these contexts, we cannot distinguish whether lineage-specific regulation has been impeded or whether these differences are merely a consequence of subtly offset development. Additional Gene Ontology (GO) term analysis: upregulated genes in mutants of the three DNMT enzymes significantly overlap with imprinted genes (Q = 1.3 × 10−12 for DNMT1 mutant embryonic, upregulated) and our G9A mutant is statistically enriched for genes upregulated in a transgenic G9A-knockout model (Q = 0.002 for G9A-mutant embryonic, upregulated).

Extended Data Fig. 4 Aberrant DNA methylation in epigenetic regulator mutants at the onset of gastrulation.

a, Overview of WGBS data for epiblast and Xecto of E6.5 wild-type and mutant embryos. Correlations with wild-type methylation profiles are lowest for mutants of DNA methyltransferases, as expected. Additional data generated using both Dnmt3a and Dnmt3b sgRNAs confirms the redundancy of the enzymes and results in a gross reduction in DNA methylation to levels seen for the DNMT1 mutant embryos. b, Pearson correlation heat maps of global DNA methylation at single CpG resolution between all epigenetic regulator mutants as well as wild-type embryos, clustered independently for the epiblast and Xecto . c, Violin plots of single CpG methylation status in wild-type and epigenetic regulator mutants. Although most mutants do not show obvious differences from wild-type embryos, large drops in methylation were observed for mutations targeted to the maintenance methyltransferase DNMT1 and for DNMT3A and DNMT3B combined. The effect for DNMT3B mutations alone is substantially weaker albeit more pronounced in Xecto, where it represents the primary de novo methyltransferase. Number of CpGs per sample is reported in a. Epiblast n = 21,232,347; 20,746,311; 19,640,675; 19,972,783; 20,121,708; 16,248,772; 20,976,243; 16,664,297; 20,129,240; 20,731,153; 19,503,271; and 20,680,467; Xecto: n = 20,310,650; 20,431,529; 18,644,801; 19,348,253; 17,908,853; 18,481,468; 20,190,473; 20,773,191; 20,483,148; 20,127,465; 19,532,818; and 20,532,593 CpGs. d, Scatterplots of CGI methylation in epigenetic regulator mutants (y axis) versus wild-type embryos (x axis) for the E6.5 epiblast or Xecto. Red and blue indicate methylation increases or decreases compared to wild-type embryos (≥0.1, light; ≥ 0.25, dark). DNMT1-mutant embryos show the greatest loss in both epiblast and Xecto, followed by DNMT3B-mutant embryos. KDM2B mutants show substantial gain specifically within the epiblast, which is also apparent in RNF2 and L3MBTL2 mutants to lesser degrees. By contrast, EED mutants lose CGI methylation within Xecto. KMT2B mutants have the greatest increase in CGI methylation within both the epiblast and Xecto. n = 12,410 CGIs displayed across all plots. e, CGI methylation in KDM2B or KMT2B mutants is largely associated with genes that are lowly or not expressed. Left, Venn diagram of hypermethylated CGI promoters between KMT2B and KDM2B mutants shows a large overlap. Furthermore, hypermethylated CGI promoters have an approximately 2.5-fold enrichment for H3K27me3-based regulation compared to background53. Right, box plots showing the expression of genes with CGI-containing promoters, calculated as the fraction of positive cells for each embryonic cell state. Data are shown for all CGI promoter-containing genes or for those that are hypermethylated in either the KMT2B- or KDM2B-mutant epiblast (bold circles in left, n = 1,026). Overall, genes that gain promoter methylation are lowly expressed across lineages independent of methylation state. The KMT2A mutant is shown for comparison because it does not gain promoter methylation at E6.5. f, Distance to the nearest CGI centre for all CpGs in the genome as well as for hypermethylated (≥0.1) CpGs in EED-, RNF2-, KDM2B- and KMT2B-mutant epiblast. KMT2B-hypermethylated CpGs are strongly shifted towards the centre, whereas PRC mutations tend to methylate CpGs in close proximity to, but not within, CGIs.

Extended Data Fig. 5 DNA methylation-dependent changes in gene and retrotransposon expression.

a, Average E6.5 DNA methylation (top) and E8.5 expression (bottom) for retrotransposon families. Expression was calculated as the normalized fraction of reads recovered from scRNA-seq data for each subfamily. DNMT1 mutants show the strongest reduction in methylation across retrotransposons in the epiblast and Xecto. The ERVK family of LTRs shows the strongest corresponding increase in expression, which is higher in the embryonic lineage than in Xecto. b, IAP expression as detected by scRNA-seq depends on DNA methylation. Top, DNA methylation levels as profiled by WGBS. The largest drop in global and IAP-specific methylation is observed for DNMT1 mutants. Bottom, mean expression within the embryonic and Xecto lineages of E8.5 mutant embryos, shown as the fraction of total reads. For the DNA methylation data, Epiblast IAPEz-int: n = 5,585; 5,579; 5,510; 5,440; and 5,210, Xecto IAPEz-int: n = 5,576; 5,577; 5,498; 5,421; 5,367; 5,575; 5,529; 5,518; 5,500; 5,411; and 5,543. c, Scatterplot of E6.5 promoter DNA methylation and E8.5 expression differences in the Xecto lineage of L3MBTL2 mutant compared to wild-type embryos, as shown for the embryonic lineage in Fig. 2g. Differentially hypomethylated (≥ 0.1) and derepressed genes (≥ 0.2 fraction positive cells) in L3MBTL2-mutants (green) were strongly enriched in GO terms related to gametogenesis (green asterisks, P < 0.05), in line with previous reports on ncPRC1.6 targets. These genes contain key members of the Piwi-interacting RNA biogenesis pathway, including the dead-box helicase Ddx4 (VASA homologue) and Mael as well as other genes with known functions or expression during gametogenesis. Extraembryonic lineages naturally express certain gametogenesis-associated regulators, which may explain their ability to proliferate in L3MBTL2-mutant embryos, whereas embryonic lineages arrest shortly after gastrulation onset. d, Genome browser tracks of WGBS methylation data for three aberrantly regulated loci in L3MBTL2-mutant embryos. The bidirectional genes Lypd4 and Dmrtc2 initiate from the same CGI, whereas Tex101 does not have a CGI, but does have a higher than genomic average CpG density (see density track). These promoters are specifically hypomethylated in gametes and throughout preimplantation, followed by de novo methylation by E6.5 that increases over subsequent development. De novo methylation does not occur in L3MBTL2 mutants and corresponds with sharp increases in gene expression. Wild-type data from gametes, preimplantation embryos and late stage samples such as somatic tissues and the E14.5 placenta are from refs. 63,64,65,66. e, Promoter DNA methylation (top) and E8.5 expression (bottom, shown as fraction of positive cells per embryo replicate) box plots of L3MBTL2-sensitive genes (n = 12 genes taken from Fig. 2g, green). Many gametogenesis genes are regulated by ‘weak’ CGI-containing promoters that become methylated during development67. In line with this, the promoters of L3MBTL2 sensitive genes are hypomethylated in gametes and over preimplantation, but become de novo methylated shortly afterwards. Derepression is specific to L3MBTL2-mutant embryos, and does not occur in DNMT1 or DNMT3B mutants, in which methylation levels drop globally. Expression changes are also not substantial for RNF2 or G9A mutants although these regulators are also expected to participate in ncPRC1.6 complex-directed repression. A single outlier gene, Ttr, is expressed in all mutant and wild-type embryos, but is still upregulated in L3MBTL2 mutants. Additional data taken from previous studies63,64,65,68.

Extended Data Fig. 6 Effect of derepressed Polycomb group regulator targets.

a, Euclidean distances of PGC-assigned cells from our wild-type, EED-, RNF2- and KDM2B-mutant embryos to the mean marker gene expression of our PGC (state 27, magenta), their second closest (light grey) or the epiblast (state 17, dark grey) cell states. PGC-assigned mutant cells are transcriptionally distinct from the next closest or epiblast state, supporting our observation that this state is specifically overproduced in EED mutants. We include the epiblast state as it shares some master regulators with PGCs and because some cells of this state are still present in EED-mutant embryos. The differences between first and second closest or the epiblast state are all significant (P < 0.05 for all tests, Wilcoxon test, two tailed). For each box plot, centre line denotes the median; edges denote the IQR; whiskers denote 1.5× the IQR; outliers are individually plotted. Number of recovered PGC-state assigned cells is n = 290, 1,564, 250 and 44 for wild-type, EED, RNF2 and KDM2B, respectively. P = 2.644257 × 10−49, 3.733801 × 10−257, 9.3103 × 10−43 and 1.136868 × 10−13, respectively, for PGC versus second closest state. b, Per cell ratio of chromosome X to autosome transcripts for PRC regulator mutant and wild-type cells, separated by sex. In our breeding system, X chromosomes are exclusively the C57BL/6J genotype, which impedes us from evaluating mono- versus biallelic transcription. However, these internally normalized measurements reveal increased transcription of chromosome X-linked genes within certain lineages of female embryos. The EED- mutant Xecto is most extreme and is strongly diminished at E8.5 (see Fig. 3d). Within EED-mutants, female-specific chromosome X deregulation is more subtly observed for the Xendo and embryonic lineages. This may indicate either higher redundancy between PRC1 and PRC2 after the allocation of the trophectoderm or a lineage-specific failure to renormalize the transcriptional output of chromosome X within Xecto. In RNF2 mutants, the effects generally follow a similar trend but are more muted. Embryonic cells: n = 39,411; 37,887; 5,391; 9,233; 4,448; 5,248; 7,459; and 11,264, Xecto cells: n = 1,769; 3,685; 755; 1,372; 1,465; 1,220; 19; and 1,594, Xendo cells: n = 2,509; 3,518; 773; 1,419; 1,745; 1,463; 1,013; and 1,547. c, Reads spanning the sgRNA protospacer sequences confirms high efficiency disruption of Eed and Cdkn2a loci in single (CDKN2A) and double (EED+CDKN2A) sgRNA-injected embryos. Figure as in Extended Data Fig. 2d. d, Single cells from CDKN2A single and EED+CDKN2A double mutant embryos were assigned to one of our 42 wild-type cell states, projected onto our wild-type gastrulation UMAP and compared to E8.5 EED-mutant and wild-type embryos. Bar plot shows the median embryo composition. In general, our double mutant resembles EED, demonstrating that the derepression of the Cdkn2a locus in EED mutants is not responsible for the overall phenotype. The CDKN2A mutant is highly similar to wild-type embryos. n denotes number of cells. e, Correlation heat map of average cell-state composition for our CDKN2A single and EED+CDKN2A double mutant embryos compared to wild-type stages and other core PRC component mutants, including a 24 h resolution EED-mutant time series described below (Fig. 4, Extended Data Fig. 10). CDKN2A-mutant embryos cluster with wild-type E8.0 and E8.5 embryos, whereas the Eed+CDKN2A double mutant clusters with wild-type E7.5 as well as our EED- and RNF2-mutant embryos. n = 42 cell states, Pearson correlation.

Extended Data Fig. 7 Molecular abnormalities of Polycomb group regulator mutants.

a, Left, large, multi-kilobase DMVs associated with developmental genes gain DNA methylation in PRC-mutant embryos. We clustered 8,972 DMVs that exist within the wild-type E6.5 epiblast according to their methylation in our PRC regulator mutants. A discrete set of 248 is specifically methylated within our EED, RNF2 and KDM2B mutants (cluster 1). Compared to the non-dynamic set (no change), these differentially methylated DMVs are enriched for marker genes as identified by this study, the modification H3K27me3, and for CGI hypermethylation within the Xecto lineage. They are also approximately 4.3 times larger than constitutively hypomethylated DMVs (mean span = 12.2 kb for dynamically methylated, 2.8 kb for 'no change'). Enrichment is calculated as an odds ratio (OR) or fold change (FC) compared to 'no change'. DMV methylation status across these regulator mutants is available as Supplementary Table 8. n = 248 DMVs in cluster 1 versus n = 6,888 for 'no change'. Right, DNA methylation violin plots of the 248 DMVs that gain CpG methylation within the E6.5 epiblast of our PRC mutants. ‘DMV’ measures methylation of all non-CGI CpGs within DMV boundaries for ‘cluster 1’, whereas ‘CGI’ measures those for all CGIs positioned within DMV boundaries (n = 529). ‘CGI (Xecto hyper)’ measures the methylation for the subset of DMV-associated CGI that are specifically de novo methylated in wild-type Xecto (n = 191). In epiblast, DMV methylation is highest for RNF2 mutants and lower for the same regions in EED- mutants. By contrast, KDM2B mutation shows substantial heterogeneity, with >55% of DMVs showing lower methylation compared to EED. The DMVs that gain DNA methylation in the epiblast of PRC regulator mutants are generally naturally de novo methylated in the Xecto (including methylation of CGIs). Here, the CGIs in the EED mutant Xecto pose an exception as they show a specific loss of methylation. b, Heat maps showing the wild-type expression status of 303 genes contained within differentially methylated DMVs. In PRC regulator mutants, the loss of epigenetic repression may prime genes for induction. However, there is no clear correlation between the genes located within differentially methylated DMVs and the lineages that are ultimately overproduced. Although the exact relationship remains unclear, our DNA methylation analysis indicates that aspects of the PRC mutant phenotype begin to manifest within the pre-gastrula embryo, leading to similar epigenetic changes within the promoters of master regulators associated with all three germ layers. Left, mean DMV methylation for each mutant and wild-type embryo as calculated in a (with CGI CpGs excluded). Middle, row-normalized expression of DMV-associated genes across our 42 wild-type states. Right, fraction of mutant cell states where a given gene is recurrently up- or downregulated. DMVs (rows) are clustered by methylation status and cell states (columns) by DMV-associated gene expression. Top, identity and presence of cell states in E8.5 regulator mutants (black, present; white, absent). States are designated as early, middle or late (most prevalent in wild-type embryos at E6.5 to E7.0, E7.5, or E8.0 to E8.5, respectively). The cumulative number of DMV-associated genes expressed within each state in wild-type embryos is also provided. c. The percentage of DMV-associated genes that are expressed in our 42 wild-type states collapsed into early, middle or late based upon when states emerge (E6.5–E7.0, E7.5 or E8.0–E8.5). In general, differentially methylated DMV-associated genes are normally expressed in the middle or late periods of our time series.

Extended Data Fig. 8 PRC1 and 2 converge to block non-CGI hypermethylation within DMVs.

a, Scatterplots of the difference between EED- and RNF2-mutant CGI methylation compared to wild-type embryos for E6.5 Epiblast (left) and Xecto (right), respectively. Although overall EED and RNF2 mutants share a similar DNA methylation landscape within the epiblast, we identify some regions in which the RNF2 mutant is differentially methylated and the EED mutant more closely resembles wild-type embryos. EED-mutant embryos show a more substantial loss of CGI methylation specifically within Xecto, whereas RNF2-mutant embryos show increased levels in epiblast that is primarily due to changes in flanking areas (see Fig. 3h). b, Genome browser WGBS methylation tracks for representative loci as they are regulated within the E6.5 Epiblast (upper, dark grey) and Xecto (lower, light grey) in wild-type, EED-, RNF2- or KDM2B-mutant embryos. Genes include master regulators from all three germ layers: Hand2 and Tbx1, mesoderm; Gata4, endoderm; Pax6, Otx2 and Sox1, neural ectoderm. CGI and local CpG density tracks are provided below. Promoter regions of these developmental genes are generally preserved as extended multi-kilobase hypomethylated domains. However, in EED and RNF2 mutants, non-CGI CpGs become hypermethylated whereas the CGIs remain unmethylated. This trend is also observed for KDM2B-mutant embryos but to a substantially lesser degree. Changes to promoter methylation status appear to be independent of the gene’s association with particular lineages or expression status at E6.5: mesodermal, endodermal and ectodermal regulators are affected. These regions are also extensively de novo methylated within the Xecto lineage during normal development, including at the CGIs themselves. Within the Xecto, EED mutation specifically causes loss of CGI methylation. Notably, EED mutation-specific methylation changes within the Xecto are also found at loci that do not acquire methylation changes in Epiblast, such as for Otx2. c, Genome browser WGBS tracks for the Prdm14 locus in the epiblast (left) and Xecto (right) of wild-type, EED-, RNF2-, KDM2B- and L3MBTL2-mutant embryos. Although this region is retained as a hypomethylated DMV in the wild-type and EED-mutant epiblast, it is specifically methylated in PRC1 subunit mutations. In Xecto, the Prdm14 promoter is naturally methylated but specifically unmethylated in EED-mutant embryos.

Extended Data Fig. 9 Efficient Cas9-mediated zygotic disruption of the Eed locus across an expanded time series.

a, Description of our zygotic perturbation strategy for the Eed locus. Three sgRNAs were designed to balance high efficiency cutting, off target potential, and coverage across the first half of the coding sequence (Methods). Then, selected sgRNAs were injected as a pool to provide a high likelihood of functionally disruptive mutations. b, Comprehensive analysis of scRNA-seq reads aligned to the Eed transcript from E6.5, E7.5 and E8.5 wild-type and EED-mutant embryo data. Top, composite plot showing the fraction of reads that map continuously (light blue) or discontinuously due to spliced or deleted sequences (dark grey) to the Eed mRNA annotation. Substantially more reads map discontinuously in mutant compared to wild-type embryos, reflecting alterations in the transcripts as a result of Cas9-mediated genetic disruption. Middle, read-level analysis of our E6.5, E7.5 and E8.5 wild-type embryo data. Positions of the three sgRNA target sequences (red) within the Eed mRNA are shown. The sgRNA target regions are magnified with aligned scRNA-seq reads from each embryo shown below (colour bar to the left of each read stack). Each row of the read stack represents the mapped sequence of an scRNA-seq read. Reads are colour coded as exactly matched (light blue) or spanning the deleted/spliced out target site (dark grey). Light grey indicates no data for this read at a given position (read ends). Even though the scRNA-seq strategy preferentially profiles the 3′ end of transcripts, many reads can be found that span sgRNA target regions in data from wild-type embryos, with a subset covering the entire target site without mismatches, insertions or deletions. Bottom, read-level analysis for our EED-mutant embryos from each time point. Compared to wild-type embryos, a much lower number of reads from the EED-mutant data match the target sites, probably a result of nonsense mediated decay or improper transcript processing. Moreover, aligned reads are imperfect, either spanning a deleted/spliced out target site (dark grey) or mapping with mismatches (dark blue), local deletions (green) or insertions (orange indicates the nucleotide to the right of an insertion). c, Representative immunofluorescence staining of H3K27me3 in wild-type and EED-mutant embryos. Single z-stack displaying an anterior region of size-matched wild-type (E7.5) and EED-mutant (E8.5) embryos (H3K27me3, red; nuclei stained by DAPI, blue). The nuclear signal for H3K27me3 is readily detectable in wild-type but absent in the EED mutant. Two independent experiments were conducted with similar results.

Extended Data Fig. 10 Developmental roles of PRC2 during gastrulation.

a, Our scRNA-seq profiled EED-mutant series isolated at E6.5, E7.5 and E8.5. See Supplementary Tables 1 and 5 for information on sex-typing and cell-state composition of individual embryos. b, Representative wild-type and EED-mutant embryos at gestational days E6.5, E7.5 and E8.5, with size information (image area occluded by an embryo in μm2, n denotes embryos imaged, all experiments had been replicated at least once, with similar results). EED-mutant embryos initially appear similar to wild-type embryos in size and morphology, but become substantially smaller and more variable in morphology, consistent with previous reports using transgenic models29,69,70. The initial lack of obvious abnormalities at E6.5 may indicate a later biological requirement or mitigating effects of maternally loaded PRC2, which is detectable until E3.5 (ref. 71). Complete Eed disruption is supported by the consistency of the resulting phenotype, as Eed+/– mice are viable and appear phenotypically normal during this period72. Wild-type embryos shown here are from natural matings isolated at the same gestational age. c, Connected bar plots of median cell-state composition across developmental stages for wild-type and EED-mutant embryos, respectively. Wild-type embryos rapidly increase in complexity, whereas EED mutants advance more slowly and become substantially biased towards PGCs and extraembryonic mesoderm. The lack of more advanced neural ectoderm (dark greens) and embryonic mesoderm (purples) may be due to developmental delay or the abnormality of precursor states. Outermost extraembryonic tissues (Xendo, Xecto) can be technically variable during isolation and their proportions should be taken with caution. d, Absolute PGC numbers estimated for individual embryos (dots) show that EED mutants overproduce PGCs beyond what is observed for wild-type embryos over gastrulation. EED-mutant embryos are presented after accounting for their developmental delay (that is, PGC numbers of E8.5-isolated mutant embryos that match developmental stage E7.5 are displayed for E7.5). Wilcoxon test, two-sided, P values: 0.322 (E6.5), 0.008 (E7.5), 0.0003 (E8.5); *P < 0.05, **P < 0.01, ***P < 0.001; n = 10, 15, 9, 10, 11, 9, 10 and 10 embryos (left to right). e, Fraction of cells positive for the Cdkn2a transcript in recovered cell states (dots), shown per lineage across our wild-type and EED-mutant time series. Cdkn2a is broadly derepressed across lineages in EED-mutant embryos from E6.5 onward. n = 10; 19; 1; 3; 1; 4 and 3 cell states. f, Ratio of X chromosomal to autosomal transcripts for all male and female cells isolated across our wild-type and EED mutant time series, separated according to preimplantation lineage (Embryonic, Xendo and Xecto). Derepression of chromosome X-linked genes happens as early as E6.5 in EED-mutant females. Xecto also becomes substantially underproduced in these same embryos over time (n = 428, 295 and 19 female EED-mutant cells from E6.5–E8.5). Xendo and embryonic lineages show increased chromosome X transcription, but not to the degree that is observed in Xecto. Embryonic: n = 581; 424; 7,119; 3,714; 4,188; 18,730; 9,519; 11,551; 18,004; 3,468; 3,541; 2,410; 6,041; 2,263; 7,459; 11,264; Xecto: n = 325; 355; 830; 360; 120; 2,371; 485; 552; 9; 47; 428; 649; 295; 363; 19; 1,594; Xendo: n = 295; 300; 780; 415; 134; 1,879; 533; 704; 767; 220; 930; 947; 1,531; 548; 1,013; 1,547 cells. g, Venn diagrams of epiblast or early ectoderm 1 cells (states 17 and 8, respectively) that are positive for key transcription factors associated with germline formation73,74. These transcripts are more abundant in EED-mutant embryos and more frequently present within the same cells, suggesting a PGC-supporting subnetwork within early embryonic cell states, possibly due to insufficient or unstable silencing before gastrulation. N = total cells per state.

Extended Data Fig. 11 Differentiation of EED-knockout mES cells recapitulates many features of the in vivo mutant phenotype.

a, Generation of a homozygous EED-knockout mES cell line. The Eed gene was deleted in V6.5 mES cells by simultaneous Cas9-targeting of flanking sequences (red) to create a >20-kb deletion. Sanger sequencing confirmed complete deletion by non-homologous end joining for both alleles (sequences aligned to chromosomal sequence above with dashes for missing nucleotides). b, Western blot of histone extracts for H3K27me3 confirms depletion of H3K27 trimethylation and homozygous Eed deletion. Histone 4 served as loading control. c, Transcript counts of 44 genes associated with pluripotency, early germ layers, and the germline75 over directed differentiation experiments. Wild-type and EED-knockout mES cells were maintained in conditions supporting a naive, inner cell mass-like state (2i), then subjected to low concentrations of bFGF for 24 h followed by culture in neural ectodermal or mesendodermal inducers for an additional 48 h. Top, the combination of signalling molecules and/or inhibitors used. Concentration ranges are indicated by circle diameter and small molecule inhibitors by crosses. Inhibitors were included to promote neural ectodermal gene induction by counteracting competing pathways. 12 ng ml−1 bFGF; 0.25 μM RA; 5 and 500 ng ml−1 BMP4; 10, 100, 1,000 ng ml−1 WNT3A; 10 and 1,000 ng ml−1 Activin A; 0.5 μM BMP4 pathway inhibitor LDN-193189; 3.3 μM Wnt pathway inhibitor XAV939; 10 μM TGFβ–activin–nodal pathway inhibitor SB431542. Bottom, heat map of log2-transformed molecule counts (red being highly expressed) for wild-type and EED-knockout mES cells, separately. Black tile frames indicate significant changes between wild type and knockout. During differentiation, many pluripotency factors associated with the germline remain expressed in EED-knockout mES cells, especially within mesendodermal supporting conditions. Many mesodermal genes are also induced in EED-knockouts in neural ectodermal supporting conditions. Retinoic acid treatment directs a fraction of wild-type mES cells to an extraembryonic endodermal fate76. This appears to be favoured in EED-knockout mES cells, in which many Xendo-associated genes are particularly sensitive to retinoic acid. Finally, many regulators of the endoderm, early mesendoderm and extraembryonic mesoderm, such as Gata4, Tbx20 and Bmp4, are broadly expressed in EED-knockout mES cells already in 2i. n = 3 experimental replicates profiled with the PlexSet assay on the NanoString nCounter SPRINT instrument. Significance (two-sided t-test) was tested for genes in conditions where at least one of the two cell lines produces a minimum average of 50 counts above background. d, Bar plots of normalized, absolute Nanostring molecule counts for the genes and conditions presented as fold change in Fig. 4f. Mesodermal genes exhibit some responsiveness to exogenous signals and can be induced to different degrees under supportive conditions (BMP4 and WNT3A). However, many are also more highly expressed without these stimuli, particularly genes associated with posterior, extraembryonic mesodermal fates. Raw counts were normalized based on the expression of four housekeeping genes and a conservative background subtraction to account for the potential of false negative signals. *P < 0.05, **P < 0.01, ***P < 0.001, two-sided t-test. Bars show the mean of n = 3 experimental replicates and error bars represent s.d. P values are provided in the Source Data file Nanostring_pval.tsv.

Supplementary information

Supplementary Note.

Reporting Summary

Supplementary Figure 1

Western blot for H3K27me3 and H4 in WT and EedKO mESCs. Images of the two membrane parts that were separately immunoblotted for H3K27 methylation and for histone H4 which served as a loading control.

Supplementary Table 1

| Embryos List of embryos, stage, sex, number of cells, mean number of genes and UMIs (per cell) sequenced.

Supplementary Table 2

| Single-cell barcodes and embryo IDs List assigning single-cell barcodes to embryo IDs.

Supplementary Table 3

| Marker genes List of all marker genes and their expression profiles used for cell to cell state assignment. Data is presented as processed with Cell Ranger v2 (n = 712) and v3 (n = 706) and includes which genes are designated as top 30 marker genes for each state.

Supplementary Table 4

| Percent of positive cells Expression profile per state in percent of positive cells per gene.

Supplementary Table 5

| Embryo proportions List of embryos, stage, number of cells and fraction per cell state.

Supplementary Table 6

| Differentially expressed genes Differentially expressed genes per cell state based on binary (percent positive cells) expression measurement. Percent of cells per cell state in WT and KO expressing these genes, their difference and log2 fold-change.

Supplementary Table 7

| Gene Ontology for recurrently up- and downregulated genes Associated q values for all ontologies with Q < 0.05 in at least one gene set. Columns include the Q values for recurrently deregulated gene sets, either differentially up- or downregulated in ≥2 states of any given KO within either the embryonic or extraembryonic lineage. Here, extraembryonic includes both the Xecto and Xendo states and Xmeso is part of the embryonic lineage as it is specified within the posterior-proximal most region of the epiblast.

Supplementary Table 8

| DNA methylation valleys DNA methylation valley (DMV) information, including the epiblast methylation of non-CpG island CpGs across relevant PRC KOs and WT, cluster assignment as shown in Extended Data Fig. 5g, associated gene names, and enrichment for H3K27me3.

Supplementary Table 9

| Hox gene expression List of all detected Hox genes and the percent of positive cells per cell state in WT and KO.

Supplementary Table 10

| sgRNA sequences.

Supplementary Table 11

| mESC Nanostring expression data.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Grosswendt, S., Kretzmer, H., Smith, Z.D. et al. Epigenetic regulator function through mouse gastrulation. Nature 584, 102–108 (2020). https://doi.org/10.1038/s41586-020-2552-x

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

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

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