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The landscape of accessible chromatin in mammalian preimplantation embryos

Abstract

In mammals, extensive chromatin reorganization is essential for reprogramming terminally committed gametes to a totipotent state during preimplantation development. However, the global chromatin landscape and its dynamics in this period remain unexplored. Here we report a genome-wide map of accessible chromatin in mouse preimplantation embryos using an improved assay for transposase-accessible chromatin with high throughput sequencing (ATAC-seq) approach with CRISPR/Cas9-assisted mitochondrial DNA depletion. We show that despite extensive parental asymmetry in DNA methylomes, the chromatin accessibility between the parental genomes is globally comparable after major zygotic genome activation (ZGA). Accessible chromatin in early embryos is widely shaped by transposable elements and overlaps extensively with putative cis-regulatory sequences. Unexpectedly, accessible chromatin is also found near the transcription end sites of active genes. By integrating the maps of cis-regulatory elements and single-cell transcriptomes, we construct the regulatory network of early development, which helps to identify the key modulators for lineage specification. Finally, we find that the activities of cis-regulatory elements and their associated open chromatin diminished before major ZGA. Surprisingly, we observed many loci showing non-canonical, large open chromatin domains over the entire transcribed units in minor ZGA, supporting the presence of an unusually permissive chromatin state. Together, these data reveal a unique spatiotemporal chromatin configuration that accompanies early mammalian development.

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Figure 1: Accessible chromatin landscape in mouse preimplantation embryos.
Figure 2: Allele-specific accessible chromatin landscape in mouse early embryos.
Figure 3: Unique characteristics of accessible chromatin in mouse early embryos.
Figure 4: Identification of candidate regulators for early development.
Figure 5: Transcription regulation and chromatin state in minor ZGA.

Accession codes

Primary accessions

Gene Expression Omnibus

Data deposits

All data have been deposited to GEO with the accession number GSE66390.

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Acknowledgements

We appreciate B. Ren, D. Leung, H. Yang and the members of the Xie laboratory for comments during preparation of the manuscript. This work is supported by the funding provided by the National Basic Research Program of China (973 program) 2015CB856201 (W.Xie), the National Natural Science Foundation of China 31422031 (W.Xie), 31171381 (J.N.), 81472855 (X.Y.), the National Basic Research Program of China 2012CB966701 (J.N.), the Beijing Natural Science Foundation grant 5152014 (J.N.), Tsinghua University Initiative Scientific Research Program (20131089278, 2014z21046) (X.Y.), the funding from the THU-PKU Center for Life Sciences (W.Xie, X.Y.), and the Youth Thousand Scholar Program of China (W.Xie, X.Y.).

Author information

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Authors

Contributions

J.W., B.H. and W.Xie conceived and designed the experiments. J.W. conducted the ATAC-seq experiments. J.W., B.L., W.Xia and Q.W. developed CARM. B.H. performed the mouse embryo experiments with help from Y.X., J.M., W.L. and J.Z. H.C. prepared the RNA-seq libraries. Q.Y. and W.Z. helped with various experiments. J.W., H.C., H.Z., Y.L., X.Y. and W.Xie performed the bioinformatics analysis of the data. B.Z. conducted the ChIP-seq experiment. X.P., F.X., G.T. advised the development or application of ATAC-seq and CARM. Y.L. and Q.W. performed NGS sequencing. Z.C. and J.N. supervised the mouse work. J.W., H.C., B.H. and W.Xie wrote the manuscript.

Corresponding author

Correspondence to Wei Xie.

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The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks T. Magnuson and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 Development of CARM, a method to deplete mitochondrial DNA from ATAC-seq library.

a, A snapshot of the UCSC browser view shows enrichment of ATAC-seq (with replicates) in early embryos and mESCs, as well as the enrichment of DNase-seq in mESCs. b, Scatter plots comparing the ATAC-seq enrichment (RPKM, 5-kb-window for the entire genome) between samples using various numbers of mESCs. The Pearson correlation coefficients are shown. c, Bar charts showing the average percentages of monoclonal nuclear DNA reads in ATAC-seq sequencing libraries before and after mitochondrial DNA (mtDNA) depletion for early mouse embryos at each developmental stage. d, Schematic of mtDNA depletion for the ATAC-seq library. mtDNA in ATAC-seq library was digested by Cas9 with sgRNAs targeting mtDNA. These digested mtDNA cannot form sequencing clusters during sequencing due to the lack of adaptors on both ends. e, Bar chart shows the mtDNA depletion efficiency for samples of different stages. The depletion efficiency refers to the percentages of total mtDNA reads that were removed by CARM. f, The DNA gel showing the Cas9 digestion efficiency for mtDNA. A total of sixteen 1 kb mtDNA amplicons covering the entire mitochondrial genome were pooled and subjected to Cas9 digestion together with mtDNA sgRNAs. g, Scatter plots showing the correlation of ATAC-seq enrichment for the 2-cell embryos between pre-depletion and post-depletion. A similar analysis was performed between sequencing and re-sequencing results for the same library as a control.

Extended Data Figure 2 Validation of ATAC-seq and RNA-seq data in early mouse embryos.

a, The Spearman correlation between the replicates of RNA-seq samples and between RNA-seq in this study and Deng et al.12 at stages when available. b, Scatter plots comparing the enrichment (5-kb window for the entire genome) between ATAC-seq replicates for mouse preimplantation embryos. The Pearson correlation of the RPKM values is shown. c, Box plots comparing the levels of gene expression and promoter ATAC-seq enrichment in the 2-cell embryos and ICM for genes with different promoter CG densities. Genes specifically expressed at the 2-cell stage were analysed. d, A snapshot of the UCSC genome browser shows the global view of ATAC-seq, H3K27ac and H3K27me3 ChIP-seq enrichment in the 2-cell embryos. e, The overlaps between ATAC-seq peaks of early embryos at each stage and H3K27ac ChIP-seq peaks in the 2-cell embryos for promoter peaks (left) or distal peaks (right). The overlaps of random promoters or peaks and H3K27ac ChIP-seq peaks in the 2-cell embryos are included as controls.

Extended Data Figure 3 Allelic accessible chromatin in early mouse embryos.

a, The percentages of regions (covered by at least 8 reads with assigned parental origins) defined as allele specific regions (blue) at various P-value cutoffs are plotted. As a control, these reads were randomly assigned to either allele and a similar percentage was calculated (red, average of 5 random permutations). A P value of 0.001 was selected as the cutoff to give the FDR (random to observed; green) less than 2% for each stage. b, Scatter plots showing the numbers of ATAC-seq reads (top) assigned to each allele in SNP-containing regions and RNA-seq reads assigned to each allele for SNP-containing genes (middle and bottom). For RNA-seq analysis, either all genes (middle) or ZGA-only genes (bottom) were used. The purple denotes allele-specific ATAC-seq regions or allele-specific genes. The red, yellow and green denote biallelic ATAC-seq regions or biallelic genes, with green to red showing high to low point densities. The numbers show the percentages of maternal- and paternal-specific ATAC-seq regions or genes. c, Snapshots show the allelic ATAC-seq signals and RNA-seq signals near the imprinted locus Snrpn and near the gene Etv6. Note, not all regions are covered by SNPs. d, Box plots showing the ratio of paternal versus maternal read numbers for all regions in the genome (1-kb window) covered by at least 8 reads with assigned parental origins. The analysis was performed for either autosomes (blue) or the X chromosome (red). e, Bar charts showing the percentages of allele specific genes (combining all stages) that contain allele specific ATAC-seq peaks at the same stages in nearby regions (within 40 kb). A P value based on the hypergeometric distribution is shown.

Extended Data Figure 4 Allelic ATAC-seq enrichment and DNA methylation levels at ATAC-seq peaks.

a, The allelic average ATAC-seq enrichment and DNA methylation levels around biallelic, maternal- and paternal-specific ATAC-seq peaks are shown for both promoter peaks (left) and distal peaks (right). M-specific, maternal specific; P-specific, paternal-specific. b, The allelic average fCAB-seq enrichment, which measures the sum of 5fC and 5mC, and TAB-seq enrichment, which measures 5hmC, are shown for both promoter peaks (left) and distal peaks (right) in the 2-cell embryos. The fCAB-seq and TAB-seq data were obtained from a previous study16. Notably, MethylC-seq measures the sum of 5mC and 5hmC. As the levels of 5hmC on both alleles are much lower compared to 5mC, the presence of 5hmC should not significantly alter the total 5mC levels. c, Box plots showing allelic DNA methylation differences (maternal − paternal) at the known germline imprinted regions47. M, maternal; P, paternal.

Extended Data Figure 5 Analyses of ATAC-seq peaks at transcription end sites.

a, The average levels of ATAC-seq enrichment for genes of different expression levels (ZGA-only) in the 2-cell embryos. b, The Gene Ontology analysis results for the top 1,000 genes ranked by TES ATAC-seq enrichment (average enrichment from TES to 2 kb downstream) in the 2-cell embryos.

Extended Data Figure 6 Widespread presence of repetitive elements in accessible chromatin in early mouse embryos.

a, Bar charts showing the percentages of distal ATAC-seq peaks in early embryos that overlap with enhancers in somatic tissues identified previously20. b, Bar charts showing the percentages of the RNA-seq reads mapped to RepBase (repeat reads) compared to all mappable reads (repeat reads + single-copy gene reads). The error bars denote the standard deviations of two biological replicates of RNA-seq for each stage. c, Bar charts showing the number of peaks with no overlap with repeats, with any regions in the peak overlapping with repeats, or with at least 20% of the peak regions overlapping with repeats. d, The snapshots of promoter (left) or distal (middle and right) ATAC-seq peaks near various types of repeats. e, Percentages of stage-specific genes (ZGA-only) of which the TSSs directly overlap with repeats are shown as bar graphs. A similar analysis result for random TSSs overlapping with repeats is included as control. f, The percentages of different classes of repeats that overlap with TSSs of stage-specific genes are shown. g, Heat maps show the enrichment of repeats in promoter and distal H3K27ac ChIP-seq peaks of early embryos and somatic tissues. h, The densities of various families of repeats near genes are shown.

Extended Data Figure 7 Analysis of stage-specific distal ATAC-seq peaks.

a, Histogram shows the distribution of the Shannon entropy of enrichment levels for ATAC-seq promoter peaks and distal peaks across early embryos and mESCs. b, A heat map showing the ATAC-seq enrichment at stage specific distal ATAC-seq peaks (left). The functional enrichment of genes nearby (by the GREAT analysis25) is also shown (right). c, Box plots showing the expression levels of all chromatin regulator genes (as identified by the GREAT tool) near stage-specific distal ATAC-seq peaks in early development. d, Scatter plots comparing the gene expression changes between ICMs and mESCs (log ratio of FPKM) for each gene in this study and in Tang et al.46. Genes that are lowly expressed in both ICMs and mESCs (FPKM <1) were removed. e, A heat map showing the expression levels of the ICM- and mESC-specific genes (identified in both this study and Tang et al.46) (left). The Gene Ontology terms are also shown (right).

Extended Data Figure 8 Analysis of single-cell RNA-seq and identification of putative promoter–enhancer pairs in early embryos.

a, A heat map shows the differential expression levels between ICM and TE for all genes ranked by their ICM:TE expression ratio (left). Genes that with low expression in both ICM and TE (FPKM <1) were removed. Selected ICM and TE regulators/markers are shown with their differential expression rank indicated in parentheses (middle). Positive and negative ranks indicate the ranking in ICM-specific and TE-specific genes, respectively. Examples of ICM-specific genes are shown on the right, including those exclusively expressed in ICM (right top) or known ICM regulators (right bottom). b, Schematic shows the method to identify target genes of a TF. The correlation of ATAC-seq enrichment of distal peaks and their nearby (within 100 kb) promoter peaks was calculated across all developmental stages. Distal-promoter peak pairs showing high correlation are considered as putative enhancer-promoter pairs (see Supplementary Information). Candidate regulatory TFs were identified by searching motifs in distal peaks, and distal-peak linked promoters were defined as the potential targets of TFs. c, Heat maps showing the ATAC-seq enrichment at stage-specific distal ATAC-seq peaks, their predicted target promoters, and the expression of corresponding genes. Only genes that are not expressed in oocytes (ZGA-only) were included in this analysis. d, Box plots showing the correlation between enrichment levels of distal ATAC-seq peaks and expression of their predicted target genes, compared to random distal peak-promoter peak pairs. A P value based on the t-test is shown.

Extended Data Figure 9 NR5A2 regulates expression of key TFs for ICM and TE at the 8-cell stage.

a, Bar charts showing the expression levels of Nr5a2 in preimplantation embryos using data from this study or Deng et al.12. The error bars denote the standard deviation of FPKM values across two RNA-seq replicates. b, Heat maps show the correlation between the expression of Nr5a2 and all genes across individual single cells from the 8-cell embryos (left) or blastocysts (right) using data from Deng et al.12. Scatter plots comparing expression of Nr5a2 with Pou5f1 or Cdx2 across individual cells from the 8-cell embryos or blastocysts. c, Schematic of the Nr5a2 knockdown experiments. A Venn diagram shows the overlap between Nr5a2-knockdown downregulated genes and 8-cell-specific genes. A P value based on the hypergeometric distribution is shown.

Extended Data Figure 10 Transcription and chromatin states in minor ZGA.

a, A snapshot of the UCSC genome browser shows an example of early 2-cell gene family (Zscan4) that reside in clusters in the genome. b, Box plots show the expression levels of ZGA-only genes that are activated by the 2-cell stage (activated either at zygote, early 2-cell or 2-cell stages). c, Pie chart shows the percentages of early 2-cell genes that reside in clusters or are solitary. d, Bar chart shows the numbers of ATAC-seq peaks identified at each stage. Two programs were used (MACS and HOMER26) to verify the ATAC-seq peak analyses in early 2-cell stages. e, Bar chart shows the genome coverages by ATAC-seq peaks at different stages called either by MACS or HOMER. f, The enrichment (log ratio of observed to random) of repeat subfamily in ATAC-seq promoter and distal peaks in the early 2-cell embryos. g, Bar chart shows the expression levels of MERVL in oocyte and preimplantation embryos. h, The average ATAC-seq enrichment in the early 2-cell embryos is shown for regions near MERVLs with high, median and low levels of expression. The MERVL region is not shown due to the low mappability. i, Bar chart shows the numbers of early 2-cell genes that fall into the broad domains of open chromatin. A similar analysis for a set of random domains (of equal length as each corresponding broad domain) was performed as a control. The P value was calculated based on the hypergeometric distribution. j, Bar chart shows the expression levels of Zfp352 in oocyte and preimplantation embryos.

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Wu, J., Huang, B., Chen, H. et al. The landscape of accessible chromatin in mammalian preimplantation embryos. Nature 534, 652–657 (2016). https://doi.org/10.1038/nature18606

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