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DNA methyltransferases 3A and 3B target specific sequences during mouse gastrulation


In mammalian embryos, DNA methylation is initialized to maximum levels in the epiblast by the de novo DNA methyltransferases DNMT3A and DNMT3B before gastrulation diversifies it across regulatory regions. Here we show that DNMT3A and DNMT3B are differentially regulated during endoderm and mesoderm bifurcation and study the implications in vivo and in meso-endoderm embryoid bodies. Loss of both Dnmt3a and Dnmt3b impairs exit from the epiblast state. More subtly, independent loss of Dnmt3a or Dnmt3b leads to small biases in mesoderm–endoderm bifurcation and transcriptional deregulation. Epigenetically, DNMT3A and DNMT3B drive distinct methylation kinetics in the epiblast, as can be predicted from their strand-specific sequence preferences. The enzymes compensate for each other in the epiblast, but can later facilitate lineage-specific methylation kinetics as their expression diverges. Single-cell analysis shows that differential activity of DNMT3A and DNMT3B combines with replication-linked methylation turnover to increase epigenetic plasticity in gastrulation. Together, these findings outline a dynamic model for the use of DNMT3A and DNMT3B sequence specificity during gastrulation.

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Fig. 1: Dnmt3a and Dnmt3b germ layer expression asymmetry is precisely modeled in MEEBs.
Fig. 2: Epiblast arrest in methylation-deprived MEEBs.
Fig. 3: Single knockout of Dnmt3a and Dnmt3b induces endoderm–mesoderm differentiation bias.
Fig. 4: Specificity in cis of DNMT3A and DNMT3B methylation in the epiblast.
Fig. 5: Integration of strand-specific DNMT3A and DNMT3B sequence preferences and housekeeping methylation predicts epiblast methylation trends.
Fig. 6: DNMT3A and DNMT3B compensate for replication-dependent demethylation during gastrulation.
Fig. 7: DNMT3A enhancer specificity matches expression asymmetry during gastrulation.

Data availability

Sequencing data have been deposited in the Gene Expression Omnibus under accession number GSE199806.

Code availability

Analysis code and processed tables are available at GitHub (


  1. Mittnenzweig, M. et al. A single-embryo, single-cell time-resolved model for mouse gastrulation. Cell 184, 2825–2842 (2021).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  4. Gowher, H. & Jeltsch, A. Mammalian DNA methyltransferases: new discoveries and open questions. Biochem. Soc. Trans. 46, 1191–1202 (2018).

    Article  CAS  Google Scholar 

  5. Eckersley-Maslin, M. A., Alda-Catalinas, C. & Reik, W. Dynamics of the epigenetic landscape during the maternal-to-zygotic transition. Nat. Rev. Mol. Cell Biol. 19, 436–450 (2018).

    Article  CAS  Google Scholar 

  6. Smith, Z. D. et al. A unique regulatory phase of DNA methylation in the early mammalian embryo. Nature 484, 339–344 (2012).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  8. Gu, T. et al. DNMT3A and TET1 cooperate to regulate promoter epigenetic landscapes in mouse embryonic stem cells. Genome Biol. 19, 88 (2018).

    Article  Google Scholar 

  9. 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  Google Scholar 

  10. Liao, J. et al. Targeted disruption of DNMT1, DNMT3A and DNMT3B in human embryonic stem cells. Nat. Genet. 47, 469–478 (2015).

    Article  CAS  Google Scholar 

  11. 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  Google Scholar 

  12. Moris, N., Pina, C. & Arias, A. M. Transition states and cell fate decisions in epigenetic landscapes. Nat. Rev. Genet. 17, 693–703 (2016).

    Article  CAS  Google Scholar 

  13. Dai, H.-Q. et al. TET-mediated DNA demethylation controls gastrulation by regulating Lefty–Nodal signalling. Nature 538, 528–532 (2016).

    Article  Google Scholar 

  14. Du, Z. et al. Polycomb group proteins regulate chromatin architecture in mouse oocytes and early embryos. Mol. Cell 77, 825–839 (2019).

    Article  Google Scholar 

  15. Grosswendt, S. et al. Epigenetic regulator function through mouse gastrulation. Nature 584, 102–108 (2020).

    Article  CAS  Google Scholar 

  16. Handa, V. & Jeltsch, A. Profound flanking sequence preference of Dnmt3a and Dnmt3b mammalian DNA methyltransferases shape the human epigenome. J. Mol. Biol. 348, 1103–1112 (2005).

    Article  CAS  Google Scholar 

  17. Mallona, I. et al. Flanking sequence preference modulates de novo DNA methylation in the mouse genome. Nucleic Acids Res. 49, 145–157 (2021).

    Article  CAS  Google Scholar 

  18. Yagi, M. et al. Identification of distinct loci for de novo DNA methylation by DNMT3A and DNMT3B during mammalian development. Nat. Commun. 11, 3199 (2020).

    Article  CAS  Google Scholar 

  19. Baubec, T. et al. Genomic profiling of DNA methyltransferases reveals a role for DNMT3B in genic methylation. Nature 520, 243–247 (2015).

    Article  CAS  Google Scholar 

  20. 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).

    Article  Google Scholar 

  21. Dahlet, T. et al. Genome-wide analysis in the mouse embryo reveals the importance of DNA methylation for transcription integrity. Nat. Commun. 11, 3153 (2020).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  23. Xu, T., Wang, C., Shen, J., Tong, P. & O’Keefe, R. Ablation of Dnmt3b in chondrocytes suppresses cell maturation during embryonic development. J. Cell. Biochem. 119, 5852–5863 (2018).

    Article  CAS  Google Scholar 

  24. Yang, L., Rau, R. & Goodell, M. A. DNMT3A in haematological malignancies. Nat. Rev. Cancer 15, 152–165 (2015).

    Article  CAS  Google Scholar 

  25. Tsankov, A. M. et al. Loss of DNA methyltransferase activity in primed human ES cells triggers increased cell-cell variability and transcriptional repression. Development 146, dev174722 (2019).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  27. Moris, N. et al. An in vitro model of early anteroposterior organization during human development. Nature 582, 410–415 (2020).

    Article  CAS  Google Scholar 

  28. Veenvliet, J. V. et al. Mouse embryonic stem cells self-organize into trunk-like structures with neural tube and somites. Science 370, eaba4937 (2020).

    Article  CAS  Google Scholar 

  29. Xu, P.-F. et al. Construction of a mammalian embryo model from stem cells organized by a morphogen signalling centre. Nat. Commun. 12, 3277 (2021).

    Article  CAS  Google Scholar 

  30. Marks, H. et al. The transcriptional and epigenomic foundations of ground state pluripotency. Cell 149, 590–604 (2012).

    Article  CAS  Google Scholar 

  31. Behringer, R., Gertsenstein, M., Nagy, K. V. & Nagy, A. Differentiating mouse embryonic stem cells into embryoid bodies by hanging-drop cultures. Cold Spring Harb. Protoc. (2016).

  32. Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).

    Article  CAS  Google Scholar 

  33. Baran, Y. et al. MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions. Genome Biol. 20, 206 (2019).

    Article  Google Scholar 

  34. Beccari, L. et al. Multi-axial self-organization properties of mouse embryonic stem cells into gastruloids. Nature 562, 272–276 (2018).

    Article  CAS  Google Scholar 

  35. Morgani, S. M., Metzger, J. J., Nichols, J., Siggia, E. D. & Hadjantonakis, A.-K. Micropattern differentiation of mouse pluripotent stem cells recapitulates embryo regionalized cell fate patterning. eLife 7, e32839 (2018).

    Article  Google Scholar 

  36. Habibi, E. et al. Whole-genome bisulfite sequencing of two distinct interconvertible DNA methylomes of mouse embryonic stem cells. Cell Stem Cell 13, 360–369 (2013).

    Article  CAS  Google Scholar 

  37. Chaligné, R. & Heard, E. X-chromosome inactivation in development and cancer. FEBS Lett. 588, 2514–2522 (2014).

    Article  Google Scholar 

  38. Chen, G. et al. Single-cell analyses of X chromosome inactivation dynamics and pluripotency during differentiation. Genome Res. 26, 1342–1354 (2016).

    Article  CAS  Google Scholar 

  39. Mikkelsen, T. S. et al. Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 448, 553–560 (2007).

    Article  CAS  Google Scholar 

  40. 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  Google Scholar 

  41. Tanaka, S. S., Yamaguchi, Y. L., Tsoi, B., Lickert, H. & Tam, P. P. L. IFITM/Mil/fragilis family proteins IFITM1 and IFITM3 play distinct roles in mouse primordial germ cell homing and repulsion. Dev. Cell 9, 745–756 (2005).

    Article  CAS  Google Scholar 

  42. Yagi, M. et al. Derivation of ground-state female ES cells maintaining gamete-derived DNA methylation. Nature 548, 224–227 (2017).

    Article  CAS  Google Scholar 

  43. Miura, F., Enomoto, Y., Dairiki, R. & Ito, T. Amplification-free whole-genome bisulfite sequencing by post-bisulfite adaptor tagging. Nucleic Acids Res. 40, e136 (2012).

    Article  CAS  Google Scholar 

  44. Stadler, M. B. et al. DNA-binding factors shape the mouse methylome at distal regulatory regions. Nature 480, 490–495 (2011).

    Article  CAS  Google Scholar 

  45. Smith, Z. D. et al. Epigenetic restriction of extraembryonic lineages mirrors the somatic transition to cancer. Nature 549, 543–547 (2017).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  47. Clark, S. J. et al. Genome-wide base-resolution mapping of DNA methylation in single cells using single-cell bisulfite sequencing (scBS-seq). Nat. Protoc. 12, 534–547 (2017).

    Article  CAS  Google Scholar 

  48. Meir, Z., Mukamel, Z., Chomsky, E., Lifshitz, A. & Tanay, A. Single-cell analysis of clonal maintenance of transcriptional and epigenetic states in cancer cells. Nat. Genet. 52, 709–718 (2020).

    Article  CAS  Google Scholar 

  49. Charlton, J. et al. Global delay in nascent strand DNA methylation. Nat. Struct. Mol. Biol. 25, 327–332 (2018).

    Article  CAS  Google Scholar 

  50. Choi, M. et al. Epigenetic memory via concordant DNA methylation is inversely correlated to developmental potential of mammalian cells. PLoS Genet. 13, e1007060 (2017).

    Article  Google Scholar 

  51. Luo, C., Hajkova, P. & Ecker, J. R. Dynamic DNA methylation: in the right place at the right time. Science 361, 1336–1340 (2018).

    Article  CAS  Google Scholar 

  52. Shipony, Z. et al. Dynamic and static maintenance of epigenetic memory in pluripotent and somatic cells. Nature 513, 115–119 (2014).

    Article  CAS  Google Scholar 

  53. Yin, Y. et al. Impact of cytosine methylation on DNA binding specificities of human transcription factors. Science 356, eaaj2239 (2017).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  55. Dixon, J. R. et al. Chromatin architecture reorganization during stem cell differentiation. Nature 518, 331–336 (2015).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  57. Kelsey, G., Stegle, O. & Reik, W. Single-cell epigenomics: recording the past and predicting the future. Science 358, 69–75 (2017).

    Article  CAS  Google Scholar 

  58. Keren-Shaul, H. et al. MARS-seq2.0: an experimental and analytical pipeline for indexed sorting combined with single-cell RNA sequencing. Nat. Protoc. 14, 1841–1862 (2019).

    Article  CAS  Google Scholar 

  59. Xiang, Y. et al. Epigenomic analysis of gastrulation identifies a unique chromatin state for primed pluripotency. Nat. Genet. 52, 95–105 (2020).

    Article  CAS  Google Scholar 

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We thank M. Okano (Institute of Molecular Embryology and Genetics, Kumamoto University, Kumamoto, Japan) and M. Yagi (Department of Molecular Biology, Massachusetts General Hospital, Boston, USA) for generously sharing knockout cells. We thank Y. Stelzer, Y. Hanna and V. Krupalnik for many helpful discussions and the Tanay group for discussion and critical reading of the manuscript. Work in the group of A.T. was supported by the European Research Council (scAssembly, 724824), the Chen Zuckerberg Foundation Human Cell Atlas grant and the Israeli Science Foundation.

Author information

Authors and Affiliations



Z.M. and A.T. designed the experiments, Z.M. performed the experiments and A.T. designed the analysis. Z.M., A.T. and A.L. analyzed the data with help from M.M., O.B. and O.S. E.C. helped with automation of the single-cell PBAT protocol, and M.Z. performed the mouse embryo dissections. A.T. and Z.M. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Zohar Mukamel or Amos Tanay.

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The authors declare no competing interests.

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Nature Structural & Molecular Biology thanks Maxim Greenberg and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Carolina Perdigoto, in collaboration with the Nature Structural & Molecular Biology team.

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Extended data

Extended Data Fig. 1 MEEB models for epiblast and mesoendoderm bifurcation.

a, Correlation between single cell umi count in the wt dataset (downsampled profiles) is shown for genes excluded from the metacell features list. Exclusion is based on prior annotation of cell cycle and stress genes, and on quantification of the batch-to-batch differential expression of gene clusters. b, For each of 10 gene programs identified in a, the total UMI per cell per time point and replicate is shown using standard boxplots. Labeling of conditions specify MEEB day and line. The middle line indicates the median, box limits represent quartiles, and whiskers are 1.5× the interquartile range. c, 2D projection of J1 and N15 cells (see methods) from replicate experiments on different MEEB days. Shown is a partial subset of the batches with overlapping MEEB days, focusing on day 4,5,6 in which the critical stages of germ layer formation are observed. d, Distribution of annotated cell type/states over time.

Extended Data Fig. 2 Comparison of WT MEEB to in-vivo gastrulation atlases.

a, Shown are color coded expression level of key transcription factors over metacells (columns) that were derived from the Pijuan-Sala et al gastrulation atlas data set. Lower colored labels are based on the annotation in the original analysis. b, Shown are metacells (colored ovals) projection in 2D using the metacell 2D graph projection, illustrating the reference in-vivo atlas we used for initial annotation of MEEB models. c, Each cell in the MEEB data set was tested for correlation with each metacell profile in the atlas, deriving a putative cell type annotation from the best matching atlas state. Shown are the distribution of annotations in each MEEB metacell (bottom), and the entropy of this annotation, which we use to threshold the decision on MEEB metacell cell type annotation. d, For each MEEB metacell we compared the MEEB UMI distribution to a distribution derived by pooling atlas RNA according to the best matching atlas metacell for each MEEB single cell (Methods). Shown is the correlation between MEEB and pooled atlas RNAs for each metacell. e, Scatter plots compare the absolute expression (log2 Umi frequency) in MEEB metacells and the expression expected given projection on the MARS-seq temporal mouse gastrulation atlas. Good quantitative matching is observed for multiple genes, suggesting the combinatorial transcriptional state in MEEB cells is highly similar to the in-vivo state. Genes showing imperfect scaling of expression in a subset of the metacells represent in-vivo/in-vitro differences that must be considered carefully. Note increased Dnmt3b expression in gut and increased Dnmt3a expression in mesenchyme, accentuating the trends observed in vivo (Fig. 1a).

Extended Data Fig. 3 MEEB transcriptional programs.

a, 2D projections highlight gene expression distributions for key epigenetic factors and additional TFs. b, For each ESC or epiblast metacell (ovals color coded by annotation) we computed the mean MEEB day over all grouped cells (X axis), which is compared to log2 metacell enrichment (Y axis) for select genes. Focusing on representative genes that are activated or de-activated in the Dppa3+/Klf4- cell population compared to epiblasts or ESCs. c, Inferred gene programs for downstream quantitative analysis. Each bar graph shows the top correlated gene to one TF (excluding genes appearing in more than one list), defining eight programs covering the key stages of epiblast formation and differentiation in the MEEB model.

Extended Data Fig. 4 DKO MEEB expression atlas.

a, Annotation breakdown per time point. b, TF gene expression levels. c, 2D gene expression projection for key genes are depicted for the DKO MEEB atlas. d, DKO cells were grouped according to the log2 total expression of the epiblast gene program (X axis), and the mean expression (log2 RNA frequency) in each bin is shown for Lefty1,2 and Nodal (Y axis). Controls (matched J1 line) are shown in gray. e, Comparing pooled gene expression for 160 DKO single cells with the top Utf1/Epiblast score and 160 single cells with the top Dppa3 program score. Genes with top fold change are highlighted (but only when q-value (chi-square, FDR corrected) is <0.05).

Extended Data Fig. 5 TKO MEEB expression atlas.

a, TKO metacell 2D map. b, time series. c, annotation breakdown per time point. d, 2D gene expression projection for key genes are depicted for a TKO MEEB atlas. e, Each point is a metacell, color coded by annotated type and showing mean MEEB time (X axis) vs. expression of genes linked with activation of a Sox1/definitive ectoderm gene program (Y axis).

Extended Data Fig. 6 DNMT3A−/− MEEBs expression atlas.

a, TF heat map for MEEB DNMT3A−/− . b, Gene modules representative genes projected on DNMT3A −/− MEEB. c, time series for the Dnmt3a−/− MEEB dataset.

Extended Data Fig. 7 DNMT3B−/− MEEBs expression atlas.

a, TF heat map for MEEB DNMT3B−/−. b, Gene modules representative genes projected on DNMT3B −/− MEEB. c, time series for the DNMT3B−/− MEEB dataset. d, Distribution cell type/state frequencies per time, shown for the two single enzyme mutants.

Extended Data Fig. 8 Quantification of MEEB methylation.

a, Cumulative distribution of genome-wide CpG coverage on mouse embryo E8.5 bulk data that was generated to assist with selection of PBAT-capture probes. Dashed line represents the mean CpG coverage. b, Breakdown of designed methylation probes by genomic context. c, Distribution of E8.5 methylation on selected probes (Designed), compared to the genomic distribution (Other). d, Coverage distribution of selected CpGs (n=92501) per MEEB genetic background and MEEB time. The middle line indicates the median, box limits represent quartiles, and whiskers are 1.5× the interquartile range. e, f, Number of methylation calls in MEEB methylation profiles mixing capture and WGBS datasets, or including WGBS data alone. Blue – methylation calls from on-target sequences. g, Average methylation as a function of time, shown based only on probes designed to cover low CpG content loci without any known association to epigenetic activity. h, Global rate of inferred CHH methylation in the different experiments. Note that CHH methylation can be called due to sequencing errors at some basal level, but the overall trend here is consistent with increase in off-target CpG methylation and with the increase in background CpG methylation. i, Quantification of select clusters (from Fig. 4) methylation dynamics in WT (blue), Dnmt3a−/− (magenta) and Dnmt3b−/− (yellow) MEEBs. Shown is average methylation in days 1–4 for each cluster. Number of CpGs in each cluster: 1: 1179, 9: 5459, 19: 2023, 24: 1573, 30: 1295, 32: 2810, 40: 1837, 44: 1755, 53: 1606, 60: 1660. j, Nucleotide enrichment and anti-enrichment logos around CpGs in the clusters highlighted in panel (i). Logos positive values show enrichment and negative value show anti-enrichments (methods). The core CpG is drawn in fixed scale for reference. k, Comparison of MEEB methylation trends in X-linked loci. In the context of the epiblast (MEEB model, or in-vivo), X-linked trends are indistinguishable from autosomes. l, Focus on loci identified as differentially methylated by Yagi et al. 2020 Shown are methylation distributions from Dnmt3a−/−, Dnmt3b−/− and WT in MEEB (d0-d6), E6 epiblast (epi, Smith et al., 2017) and in the Yagi et al., 2020 data. n = 427 3a favoring DMRs, and 343 3b favoring DMRs. The middle line indicates the median, box limits represent quartiles, and whiskers are 1.5× the interquartile range.

Extended Data Fig. 9 Single cell methylation in-vivo and in MEEBs.

a, Number of single cells assayed from embryo E7.5 and MEEB at day5. b, Distribution of estimated unconverted C in CHH contexts over single cells. c, Distribution of the total number of methylation calls per cell. Note that our analysis aimed at a large sample of single cells with low coverage per-cell. Number of cells was 2217 for d5_3a, 2197 for d5_3b, 2984 for d5_wt and 2146 for e7.5. The middle line indicates the median, box limits represent quartiles, and whiskers are 1.5× the interquartile range. d, e, Scatter plot show distribution of CXCR4 and EPCAM levels in RNA (metacells from a gastrulation scRNA-seq manifold (left), points, color coded by cell type as in S7) and from FACS indices (on cells sorted from E7.5 embryos). Gating on EPCAM/CXCR4 is allowing (in-silico) identification of Epiblast, endoderm and mesoderm sub-populations. Blood cells were shown to have low EPCAM and low CXCR4 levels and were gated separately. Mean methylation analysis allows further separation of extraembryonic cells (right, Green points). f, Comparing total read coverage over early and late replication domains across single cells. Color coding represents the inferred cell cycle ordering, which is based on the early/late coverage ratio as well as the early/late methylation difference (Fig. 6, not shown in this panel). g, CpGs grouped according to their model (MEEB_3b/3a) sequence-based score, and according to their replication time (early or late). Box plot shows distribution of the computed differences in average methylation of CpGs with high (3b favoring) and low (3a favoring) scores, for each single cell (n=199 for ectoderm, 356 for mesoderm and 25 for endoderm). Single cells were grouped according to their germ layer (using index sorting as in D, color coded) and according to their inferred cell cycle phase (start, mid and end). The middle line indicates the median, box limits represent quartiles, and whiskers are 1.5× the interquartile range. Distributions are compared using 2-sided Kolmogorov-Smirnov statistics (**** = p < 0.0001, *** = p < 0.001, ** = p < 0.01, * = p < 0.05). h, Phased cell cycle ordering for additional batches of single cell data from WT and mutant MEEBs. i, Cell cycle trends are computed for loci with high/low sequence preferences in early and late replicating domains (as in Fig. 6), but here we are normalizing values to the maximum level across the replication cycle in each group, to allow comparison of the relative trends. Top: sequence model MEEB_3b/3a (regressing mutant difference), and methylation data is from WT MEEBs. Middle: sequence model is MEEB_3a (regression WT-Dnmt3a−/−) and methylation data is from Dnmt3b−/− MEEBs. Bottom: sequence model is MEEB_3b and methylation is from Dnmt3a−/−. We note the slower re-methylation for low-affinity sites in the middle panel, and deeper reduction in methylation for high affinity sites in the lower panel.

Extended Data Fig. 10 DNMT3A and germ layer specific methylation preferences at enhancers.

Additional hotspots around key gastrulation genes similar to Fig. 7g.

Supplementary information

Supplementary Information

Supplementary Fig. 1.

Reporting Summary

Supplementary Tables 1–4

Supplementary Table 1: scRNA-seq batches. Supplementary Table 2: methylation samples. Supplementary Table 3: DNMT3A DMRs. Supplementary Table 4: Biased DNMT3A-pro and DNMT3B-pro enhancers.

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Mukamel, Z., Lifshitz, A., Mittnenzweig, M. et al. DNA methyltransferases 3A and 3B target specific sequences during mouse gastrulation. Nat Struct Mol Biol 29, 1252–1265 (2022).

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