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Multi-omics profiling of mouse gastrulation at single-cell resolution

Nature volume 576, pages 487–491 (2019)Cite this article

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Abstract

Formation of the three primary germ layers during gastrulation is an essential step in the establishment of the vertebrate body plan and is associated with major transcriptional changes1,2,3,4,5. Global epigenetic reprogramming accompanies these changes6,7,8, but the role of the epigenome in regulating early cell-fate choice remains unresolved, and the coordination between different molecular layers is unclear. Here we describe a single-cell multi-omics map of chromatin accessibility, DNA methylation and RNA expression during the onset of gastrulation in mouse embryos. The initial exit from pluripotency coincides with the establishment of a global repressive epigenetic landscape, followed by the emergence of lineage-specific epigenetic patterns during gastrulation. Notably, cells committed to mesoderm and endoderm undergo widespread coordinated epigenetic rearrangements at enhancer marks, driven by ten-eleven translocation (TET)-mediated demethylation and a concomitant increase of accessibility. By contrast, the methylation and accessibility landscape of ectodermal cells is already established in the early epiblast. Hence, regulatory elements associated with each germ layer are either epigenetically primed or remodelled before cell-fate decisions, providing the molecular framework for a hierarchical emergence of the primary germ layers.

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Fig. 1: Single-cell multi-omics profiling of mouse gastrulation.
Fig. 2: Multi-omics factor analysis reveals coordinated epigenetic and transcriptomic variation at enhancer elements during germ-layer commitment.
Fig. 3: DNA methylation and chromatin accessibility dynamics at lineage-defining enhancers across development.
Fig. 4: TET enzymes are required for efficient demethylation of mesoderm-defining enhancers and subsequent blood differentiation in embryoid bodies.

Data availability

Raw sequencing data together with processed files (RNA counts, CpG methylation reports, GpC accessibility reports) are available in the Gene Expression Omnibus under accession number GSE121708. Processed data can be downloaded from ftp://ftp.ebi.ac.uk/pub/databases/scnmt_gastrulation.

Code availability

All code used for analysis is available at https://github.com/rargelaguet/scnmt_gastrulation.

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Acknowledgements

R.A. is a member of Robinson College at the University of Cambridge. We thank K. Tabbada, C. Murnane and N. Forrester of the Babraham Next Generation Sequencing Facility for assistance with Illumina sequencing; members of the Babraham Flow Cytometry Core Facility for cell sorting and the Babraham Biological Support Unit for animal work; Y. Zhang for help in processing the ChIP–seq data. L.C.S. was supported by an EMBO postdoctoral fellowship (ALTF 417-2018) and is currently a Marie Sklodowska-Curie fellow funded by the European Commission under the H2020 Programme. J.C.M. is supported by core funding from EMBL and CRUK. R.A. is supported by the EMBL International Predoc Programme. X.I.-S. is supported by Wellcome Trust Grant 108438/E/15/Z. F.B. is supported by the UK Medical Research Council (Career Development Award MR/M01536X/1). B.G. and J.N. are supported by core funding by the MRC and Wellcome Trust to the Wellcome–MRC Cambridge Stem Cell Institute. W.R. is supported by Wellcome (105031/Z/14/Z; 210754/Z/18/Z) and BBSRC (BBS/E/B/000C0422). O.S. is supported by core funding from EMBL and DKFZ and the EU (ERC project DECODE 810296).

Author information

Author notes

  1. These authors contributed equally: Ricard Argelaguet, Stephen J. Clark, Hisham Mohammed, L. Carine Stapel

Authors and Affiliations

  1. European Bioinformatics Institute (EMBL-EBI), Cambridge, UK

    Ricard Argelaguet, Oliver Stegle & John C. Marioni

  2. Epigenetics Programme, Babraham Institute, Cambridge, UK

    Stephen J. Clark, Hisham Mohammed, L. Carine Stapel, Christel Krueger, Tim Lohoff, Courtney W. Hanna, Sebastien Smallwood, Peter J. Rugg-Gunn, Gavin Kelsey & Wolf Reik

  3. School of Informatics, University of Edinburgh, Edinburgh, UK

    Chantriolnt-Andreas Kapourani & Guido Sanguinetti

  4. MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK

    Chantriolnt-Andreas Kapourani

  5. Wellcome–MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK

    Ivan Imaz-Rosshandler, Tim Lohoff, Berthold Göttgens, Peter J. Rugg-Gunn & Jennifer Nichols

  6. Department of Haematology, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK

    Ivan Imaz-Rosshandler, Berthold Göttgens & Peter J. Rugg-Gunn

  7. Center for Stem Cell Biology and Regenerative Medicine, MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China

    Yunlong Xiang & Wei Xie

  8. THU-PKU Center for Life Sciences, Tsinghua University, Beijing, China

    Yunlong Xiang & Wei Xie

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

    Courtney W. Hanna, Peter J. Rugg-Gunn, Gavin Kelsey & Wolf Reik

  10. Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK

    Ximena Ibarra-Soria & John C. Marioni

  11. Helmholtz Zentrum München–German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany

    Florian Buettner

  12. Bioinformatics Group, Babraham Institute, Cambridge, UK

    Felix Krueger

  13. Department of Biochemistry and Molecular Biology, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada

    Wendy Dean

  14. European Molecular Biology Laboratory (EMBL), Heidelberg, Germany

    Oliver Stegle

  15. Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Oliver Stegle

  16. Wellcome Sanger Institute, Cambridge, UK

    John C. Marioni & Wolf Reik

Authors
  1. Ricard Argelaguet
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  2. Stephen J. Clark
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  3. Hisham Mohammed
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  4. L. Carine Stapel
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Contributions

H.M., W.D. and W.R. conceived the project. S.S. and H.M. designed the study and generated pilot data. W.D., J.N. and L.C.S. performed embryo dissections and single-cell isolation. L.C.S and T.L. performed in vitro differentiation experiments. S.J.C. and H.M. performed scNMT-seq library preparation. F.K. processed and managed sequencing data. C.K. analysed ChIP–seq datasets with assistance from Y.X. and C.W.H. R.A. and S.J.C. performed pre-processing and quality control of scNMT-seq data. R.A. and I.I.-R. mapped cells to the scRNA-seq atlas. R.A., S.J.C., F.B., L.C.S., X.I.-S., C.-A.K. and C.K. performed computational analysis. R.A. generated figures. R.A. S.J.C., L.C.S., O.S., J.C.M. and W.R. interpreted results and drafted the manuscript. G.S., P.J.R-G., W.X., G.K., O.S., B.G., J.C.M. and W.R. supervised the project. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Stephen J. Clark, Oliver Stegle, John C. Marioni or Wolf Reik.

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Competing interests

W.R. is a consultant and shareholder of Cambridge Epigenetix. The remaining authors declare no competing interests.

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

Peer review information Nature thanks Andrew Adey and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 scNMT-seq quality controls.

a, b, Number of observed cytosines in CpG (red; a) or GpC (blue; b) contexts respectively. Each bar corresponds to one cell. Cells are sorted by total number of CpG or GpC sites. Cells below the dashed line were discarded on the basis of poor coverage (n = 1,105). c, RNA-library size per cell. Top, total number of reads. Bottom, number of expressed genes (read counts >0). Cells below the dashed line were discarded on the basis of poor coverage (n = 2,524). d, Venn diagram displaying the number of cells that pass quality control for RNA expression (green), DNA methylation (red) and chromatin accessibility (blue). e, Number of cells that pass quality control for each molecular layer, grouped by stage. For 1,419 out of 2,524 total cells, only the RNA expression was sequenced.

Extended Data Fig. 2 Cell-type assignments based on RNA expression.

a, b, Lineage assignment of E4.5 cells (a; n = 175) and E5.5 cells (b; n = 173). Top left, SC3 consensus plots representing the similarity between cells on the basis of averaging of clustering results from multiple combinations of clustering parameters. Top right, heat map showing the RNA expression (log normalized counts) of the ten most informative gene markers for each cluster. Bottom left, t-distributed stochastic neighbour embedding (t-SNE) representation of the RNA-expression data coloured by the expression of Fgf4 and Pou5f1, known E4.5 and E5.5 epiblast markers50,51, respectively. Bottom right, t-SNE representation of the RNA-expression data coloured by the expression of Gata6 and Amn, known E4.5 primitive endoderm and E5.5 visceral endoderm markers52. c, d, Lineage assignment of E6.5 cells (c; n = 977) and E7.5 cells (d; n = 1,155). Left, UMAP projection of the atlas dataset (stages E6.5 to E7.0 to assign E6.5 cells and E7.0 to E8.0 to assign E7.5 cells). In the top-left panel, cells are coloured by lineage assignment. In the bottom-left panel, the cells coloured in red are the nearest neighbours that were used to transfer labels to the scNMT-seq dataset. In right panels, cells are coloured by the relative RNA expression of lineage-marker genes. e, Top, number of cells per lineage, using the maximally resolved cell types reported in ref. 4. Bottom, number of cells per lineage after aggregation of cell types belonging to the same germ layer or extra-embryonic tissue type, as used in this study.

Extended Data Fig. 3 Global methylation and chromatin accessibility dynamics.

a, b, Distribution of DNA methylation (a) and chromatin accessibility levels (b) per stage and genomic context. When aggregating over genomic features, CpG methylation and GpC accessibility levels (%) are computed assuming a binomial model, with the number of trials being the total number of observed CpG (or GpC) sites and the number of successes being the number of methylated CpG (or GpC) sites (Methods). Notably, this implies that DNA methylation and chromatin accessibility are quantified as a percentage and are not binarized into low or high states. As this figure shows, the distribution of DNA methylation and chromatin accessibility across loci (after aggregating measurements across all cells per stage) is largely continuous and does not show bimodality. Hence, a binary approach similar to that sometimes used for differentiated cell types would not provide a good representation of the data. c, d, Box plots showing the distribution of genome-wide CpG methylation levels (c) or GpC accessibility levels (d) per stage and lineage. Each dot represents a single cell. Box plots show median levels and the first and third quartile, whiskers show 1.5× the interquartile range. At a significance threshold of 0.01 (t-test, two-sided), the global DNA methylation levels differ between embryonic and extra-embryonic lineages, but the global chromatin accessibility levels do not. e, f, Dimensionality reduction of DNA methylation (e) and chromatin accessibility (f) data. To perform dimensionality reduction while handling the large amount of missing values, we used a Bayesian factor analysis model (Methods). Scatter plots of the first two latent factors (sorted by variance explained) for models trained with cells from the indicated stages are shown. From E4.5 to E6.5, cells are coloured by embryonic and extra-embryonic origin. At E7.5, cells are coloured by the primary germ layer. All lineage assignments were made using the cells’ corresponding RNA-expression levels (Extended Data Fig. 2). The fraction of variance explained by each factor is displayed in parentheses. The input data were M-values quantified over DNase I hypersensitive sites profiled in ES cells (n = 175,231, subset to the top 5,000 most variable sites to fit the model).

Extended Data Fig. 4 DNA methylation and chromatin accessibility changes in promoters are associated with repression of early pluripotency and germ cell markers.

a, Volcano plots display differential RNA-expression levels between E4.5 and E7.5 cells (in log2 counts, x axis) versus adjusted correlation P values (FDR <10% in red, Benjamini–Hochberg correction, n = 5,000 genes). Left, DNA methylation versus RNA-expression correlations; right, chromatin accessibility versus RNA expression. Negative values for differential RNA expression indicate higher expression in E4.5, whereas positive values indicate higher expression in E7.5. b, Illustrative examples of epigenetic repression of early pluripotency and germ cell markers. Box and violin plots show the distribution of RNA expression (log2 counts, green), DNA methylation (red) and chromatin accessibility (blue) levels per stage. Box plots show median coverage and the first and third quartile, whiskers show 1.5× the interquartile range. Each dot corresponds to one cell. For each gene a genomic track is shown on top, and the promoter region that is used to quantify DNA methylation and chromatin accessibility levels is highlighted in yellow.

Extended Data Fig. 5 Characterization of lineage-specific H3K27ac and H3K4me3 ChIP–seq data.

a, Percentage of peaks overlapping promoters (±500 bp of TSS of annotated mRNAs (Ensembl v.87); lighter colour) and not overlapping promoters (distal peaks, darker colour). H3K27ac peaks tend to be distal from the promoters, marking putative enhancer elements53. H3K4me3 peaks tend to overlap promoter regions, marking TSS54. b, Venn diagrams showing overlap of peaks for each lineage, for distal H3K27ac (left) and H3K4me3 (right). This shows that H3K27ac peaks tend to be lineage-specific, whereas H3K4me3 peaks tend to be shared between lineages. c, Illustrative example of the ChIP–seq profile for the ectoderm marker Cxcl12. The top tracks show wiggle plots of ChIP–seq read density (normalized by total read count) for lineage-specific H3K27ac and H3K4me3. The coding sequence is shown in black. The bottom tracks show the lineage-specific peak calls (Methods). H3K27ac peaks are split into distal (putative enhancers) and proximal to the promoter. d, Left, bar plot of the fraction of E7.5 lineage-specific enhancers (n = 691 for ectoderm, 618 for endoderm and 340 for mesoderm) that are uniquely marked by H3K27ac in either E10.5 midbrain, E12.5 gut or E10.5 heart. Right, heat map displaying H3K27ac levels at individual lineage-specific enhancers (n = 2,039 for ectoderm, 1,124 for endoderm and 631 for mesoderm) in more differentiated tissues. E7.5 enhancers are predominantly marked in their differentiated-tissue counterparts (midbrain for ectoderm, gut for endoderm and heart for mesoderm).

Extended Data Fig. 6 Differential DNA methylation and chromatin accessibility analysis at E7.5 for different genomic contexts.

a, Bar plots showing the fraction (left) or the total number (right) of differentially methylated (red) or accessible (blue) loci (FDR <10%, y axis) per genomic context (x axis). Each subplot corresponds to the comparison of one cell type (group A) against cells comprising the other cell types present at E7.5 (group B). In the graphs on the right, positive values indicate an increase in DNA methylation or chromatin accessibility in group A, whereas negative values indicate a decrease in DNA methylation or chromatin accessibility. Differential analysis of DNA methylation and chromatin accessibility was performed independently for each genomic element using a two-sided Fisher’s exact test of equal proportions (Methods). b, Scatter plots showing differential DNA methylation (x axis) versus chromatin accessibility (y axis) analysis at promoters. Ectoderm versus non-ectoderm cells (left), endoderm versus non-endoderm cells (middle) and mesoderm versus non-mesoderm cells (right) are shown. Each dot corresponds to a gene (n = 2,038). Labelled black dots highlight genes with lineage-specific RNA expression that show significant differential methylation or accessibility in their promoters (FDR <10%).

Extended Data Fig. 7 Illustrative examples of putative epigenetic regulation in enhancer elements during germ-layer commitment.

a–c, Box and violin plots showing the distribution of RNA expression (log2 counts, green), enhancer DNA methylation (red) and chromatin accessibility (blue) levels for key germ-layer markers per stage and cell type. Marker genes for ectoderm (a), mesoderm (b) and endoderm (c) are shown. Box plots show median levels and the first and third quartile, whiskers show 1.5× the interquartile range. Each dot corresponds to a single cell. For each gene, a genomic track is shown on the top. The enhancer region that is used to quantify DNA methylation and chromatin accessibility levels is represented with a star and highlighted in yellow. Genes were linked to putative enhancers by overlapping genomic coordinates with a maximum distance of 50 kb.

Extended Data Fig. 8 Characterization of MOFA factors.

a, Factor 1 as mesoderm commitment factor. Left, RNA-expression loadings for factor 1. Genes with large positive loadings increase expression in the positive factor values (mesoderm cells). Middle, scatter plot of factor 1 (x axis) and factor 2 (y axis) values. Each dot corresponds to a single cell, coloured by the average methylation levels of the top 100 enhancers with highest loading. Right, as the middle panel, except cells are coloured by the average accessibility levels. b, Factor 2 as the endoderm commitment factor. Left, RNA-expression loadings for factor 2. Genes with large positive loadings increase expression in the positive factor values (endoderm cells). Middle, scatter plot of factor 1 (x axis) and factor 2 (y axis) values. Each dot corresponds to a single cell, coloured by the average methylation levels (%) of the top 100 enhancers with highest loading. Right, as the middle panel, but cells are coloured by the average accessibility levels. c, Characterization of MOFA factor 3 as anteroposterior axial patterning and mesoderm maturation. Left, bee swarm plot of factor 3 values, grouped and coloured by cell type. The mesoderm cells are subclassified into nascent and mature mesoderm (Extended Data Fig. 2). Right, gene set enrichment analysis of the gene loadings of factor 3. The top most significant pathways from MSigDB C255 (Methods) are shown. d, Characterization of MOFA Factor 6 as cell cycle. Left, bee swarm plot of factor 6 values, grouped by cell type and coloured by inferred cell-cycle state using cyclone56 (G1/2, cyan; G2/M, yellow). Right, gene set enrichment analysis of the gene loadings of factor 6. The top most significant pathways from MSigDB C255 are shown. e, Characterization of MOFA factor 4 as notochord formation. Left, bee swarm plot of factor 4 values, grouped and coloured by cell type. The endoderm cells are subclassified into notochord (dark green) and not notochord (green) (Extended Data Fig. 2). Middle, RNA-expression loadings for factor 4. Genes with large negative loadings increase expression in the negative factor values (notochord cells). Right, same bee swarm plots as in left but coloured by the relative RNA expression of Calca (gene with the highest loading).

Extended Data Fig. 9 DNA methylation and chromatin accessibility dynamics of E7.5 lineage-specific enhancers and transcription factor motifs across development.

a, Box plots showing the distribution of DNA methylation (top) or chromatin accessibility (bottom) levels of E7.5 lineage-defining enhancers, across stages and cell types. Box plots show median levels and the first and third quartile, whiskers show 1.5× the interquartile range. The dashed lines represent the global background levels of DNA methylation at E7.5 (Extended Data Fig. 3). b, Box plots showing the distribution of chromatin accessibility levels (scaled to the genome-wide background) for 200-bp windows around transcription factor motifs associated with commitment to ectoderm (top), endoderm (middle) and mesoderm (bottom). Box plots show median levels and the first and third quartile, whiskers show 1.5× the interquartile range.

Extended Data Fig. 10 E7.5 ectoderm enhancers contain a mixture of pluripotency and neural signatures with different epigenetic dynamics.

a, Scatter plot showing H3K27ac levels for individual ectoderm enhancers (n = 2,039) quantified in serum-grown ES cells (pluripotency enhancers, x axis) versus E10.5 midbrain (neuroectoderm enhancers, y axis). H3K27ac levels in the two lineages are negatively correlated (Pearson’s R = −0.44), indicating that most enhancers are either marked in ES cells or in the brain. The top 250 enhancers that show the strongest differential H3K27ac levels between midbrain and ES cells (blue for midbrain-specific enhancers and grey for ES cell-specific enhancers) are highlighted. b, Density plots of H3K27ac levels in ES cells versus E10.5 midbrain. H3K27ac levels are negatively correlated at E7.5 ectoderm enhancers, but not in E7.5 endoderm (n = 1,124) or mesoderm enhancers (n = 631). c, Profiles of DNA methylation (red) and chromatin accessibility (blue) along the epiblast–ectoderm trajectory. Panels show different genomic contexts: E7.5 ectoderm enhancers that are specifically marked by H3K27ac in the midbrain (middle) or ES cells (bottom) (highlighted populations in a). Running averages of 50-bp windows around the centre of the ChIP–seq peaks (2 kb upstream and downstream) are shown. Solid lines display the mean across cells (within a given lineage) and shading displays the s.d. Dashed horizontal lines represent genome-wide background levels for DNA methylation (red) and chromatin accessibility (blue). For comparison, we have also incorporated E7.5 endoderm enhancers (top), which follow the genome-wide repressive dynamics. d, Box plots of the distribution of DNA methylation (top) and chromatin accessibility (bottom) levels along the epiblast–ectoderm trajectory. Panels show different genomic contexts: E7.5 ectoderm enhancers that are specifically marked by H3K27ac in the midbrain (middle) or ES cells (right) (highlighted populations in a). Box plots show median levels and the first and third quartile, whiskers show 1.5× the interquartile range. Dashed lines denote background DNA methylation and chromatin accessibility levels at the corresponding stage and lineage. For comparison, we have also incorporated E7.5 endoderm enhancers (left), which follow the genome-wide repressive dynamics.

Extended Data Fig. 11 Silencing of ectoderm enhancers precedes activation of mesoderm and endoderm enhancers.

a, Reconstructed mesoderm (top) and endoderm (bottom) commitment trajectories using a diffusion pseudotime method applied to the RNA-expression data (Methods). Scatter plots of the first two diffusion components are shown, with cells coloured according to their lineage assignment (n = 1,154 for endoderm and n = 1,511 for mesoderm). For both cases, ranks along the first diffusion component are selected to order cells according to their differentiation state. b, DNA methylation (red) and chromatin accessibility (blue) dynamics of lineage-defining enhancers along the mesoderm (top) and endoderm (bottom) trajectories. Each dot denotes a single cell (n = 387 for endoderm and n = 474 for mesoderm) and black curves represent non-parametric locally estimated scatterplot smoothing regression estimates. In addition, for each scenario we fit a piecewise linear regression model for epiblast, primitive streak and mesoderm or endoderm cells (vertical lines indicate the discretized lineage transitions). For each model fit, the slope (r) and its significance level are displayed in the top (− for nonsignificant, 0.01<*P < 0.1 and **P < 0.01). c, Density plots showing differential DNA methylation (x axis) and chromatin accessibility (y axis) at lineage-defining enhancers calculated for each of the lineage transitions.

Extended Data Fig. 12 Embryoid bodies recapitulate the transcriptional, methylation and accessibility dynamics of the embryo.

a, Embryoid bodies show high transcriptional similarity to gastrulation-stage embryos. Top left, UMAP projection of RNA expression for the embryoid body dataset (n = 775). Cells are coloured by lineage assignment and shaped by genotype (WT or Tet TKO). Bottom left, UMAP projection of stages E6.5 to E8.5 of the atlas dataset (no extra-embryonic cells) with the nearest neighbours that were used to assign cell type labels to the scNMT-seq embryoid body dataset coloured in red (WT) or blue (Tet TKO). Middle, UMAP projection of embryoid body cells coloured by the relative RNA expression of marker genes. Right, scatter plot of the differential gene expression (log2 normalized counts) between different assigned lineages for embryoid bodies (x axis) versus embryos (y axis). Each dot represents one gene. Pearson correlation coefficient with corresponding P value (two-sided) are displayed. Lines show the linear regression fit. The top-four genes with the largest differential expression are highlighted in red. b, Global DNA methylation and chromatin accessibility levels during embryoid body differentiation. Top, box plots showing the distribution of genome-wide CpG methylation (left) or GpC accessibility levels (right) per time point and lineage (compare with Extended Data Fig. 3). Each dot represents a single cell (only wild-type cells are used). Box plots show median levels and the first and third quartile, whiskers show 1.5× the interquartile range. Bottom, heat map of DNA methylation (left) or chromatin accessibility (right) levels per time point and genomic context (compare with Fig. 1e, f). c, Ectoderm enhancers are more methylated in Tet TKO compared with wild-type epiblast cells in vivo. Bar plots show the mean (bulk) DNA methylation levels for ectoderm (left), endoderm (middle) and mesoderm (right) enhancers in E6.5 epiblast cells25. For each genotype, two replicates are shown. d, Profiles of DNA methylation (red) and chromatin accessibility (blue) at lineage-defining enhancers quantified over different lineages across embryoid body differentiation (only wild-type cells). Running averages in 50-bp windows around the centre of the ChIP–seq peaks (2 kb upstream and downstream) are shown. Solid lines display the mean across cells and shading displays the corresponding s.d. Dashed horizontal lines represent genome-wide background levels for methylation (red) and accessibility (blue).

Supplementary information

Reporting Summary

Supplementary Table 1

Methylation-transcription correlations per gene. This table displays the correlation tests between promoter DNA methylation and RNA expression per gene (across all embryonic cells). Columns contain: ensembl ID (“id”), gene name (“gene”), genomic context(“anno”), nominal p-value (“p”), Pearson correlation (“r”), t-statistic (“t”), FDR-adjusted p-value (“padj_fdr”), logical indicating whether correlation is statistically significant (“sig”).

Supplementary Table 2

Accessibility-transcription correlations per gene. This table displays the correlation tests between promoter DNA methylation and RNA expression per gene (across all embryonic cells). Columns contain: ensembl ID (“id”), gene name (“gene”), genomic context(“anno”), nominal p-value (“p”), Pearson correlation (“r”), t-statistic (“t”), FDR-adjusted p-value (“padj_fdr”), logical indicating whether correlation is statistically significant (“sig”).

Supplementary Table 3

Differential methylation analysis between germ layers. This table displays the results of differential DNA methylation analysis using a Fisher exact test for every genomic loci. Columns contain: genomic feature ID (“id”), genomic context (“anno”), rate for group A (“rateA”), rate for group B (“rateB”), differential rate between groups (“diff”). FDR-adjusted p-value (“padj_fdr”), logical indicating whether correlation is statistically significant (“sig”). Comparison as groupA_vs_groupB (“comparison”).

Supplementary Table 4

Differential accessibility analysis between germ layers. This table displays the results of differential accessibility analysis using a Fisher exact test for every genomic loci. Columns contain: genomic feature ID (“id”), genomic context (“anno”), rate for group A (“rateA”), rate for group B (“rateB”), differential rate between groups (“diff”). FDR-adjusted p-value (“padj_fdr”), logical indicating whether correlation is statistically significant (“sig”). Comparison as groupA_vs_groupB (“comparison”).

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Argelaguet, R., Clark, S.J., Mohammed, H. et al. Multi-omics profiling of mouse gastrulation at single-cell resolution. Nature 576, 487–491 (2019). https://doi.org/10.1038/s41586-019-1825-8

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