Paternal and maternal epigenomes undergo marked changes after fertilization1. Recent epigenomic studies have revealed the unusual chromatin landscapes that are present in oocytes, sperm and early preimplantation embryos, including atypical patterns of histone modifications2,3,4 and differences in chromosome organization and accessibility, both in gametes5,6,7,8 and after fertilization5,8,9,10. However, these studies have led to very different conclusions: the global absence of local topological-associated domains (TADs) in gametes and their appearance in the embryo8,9 versus the pre-existence of TADs and loops in the zygote5,11. The questions of whether parental structures can be inherited in the newly formed embryo and how these structures might relate to allele-specific gene regulation remain open. Here we map genomic interactions for each parental genome (including the X chromosome), using an optimized single-cell high-throughput chromosome conformation capture (HiC) protocol12,13, during preimplantation in the mouse. We integrate chromosome organization with allelic expression states and chromatin marks, and reveal that higher-order chromatin structure after fertilization coincides with an allele-specific enrichment of methylation of histone H3 at lysine 27. These early parental-specific domains correlate with gene repression and participate in parentally biased gene expression—including in recently described, transiently imprinted loci14. We also find TADs that arise in a non-parental-specific manner during a second wave of genome assembly. These de novo domains are associated with active chromatin. Finally, we obtain insights into the relationship between TADs and gene expression by investigating structural changes to the paternal X chromosome before and during X chromosome inactivation in preimplantation female embryos15. We find that TADs are lost as genes become silenced on the paternal X chromosome but linger in regions that escape X chromosome inactivation. These findings demonstrate the complex dynamics of three-dimensional genome organization and gene expression during early development.
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The HiC data generated and analysed are available in the GEO repository under accession number GSE129029. Previously published data were downloaded from GEO: H3K27me3 in early embryos (GSE76687); H3K27me3 in day-5 post-natal oocytes (GSE93941); single-cell RNA sequencing in early embryos (GSE80810); and HiC in gametes and early embryos (GSE82185). Source Data for Figs. 3, 4 and Extended Data Fig. 2, 6 are provided with the paper. Any other relevant data are available from the corresponding authors upon reasonable request.
The code developed for this study is available on the GitHub repository of the laboratory of E.H. (https://github.com/heard-lab).
Burton, A. & Torres-Padilla, M.-E. Chromatin dynamics in the regulation of cell fate allocation during early embryogenesis. Nat. Rev. Mol. Cell Biol. 15, 723–735 (2014).
Wu, J. et al. The landscape of accessible chromatin in mammalian preimplantation embryos. Nature 534, 652–657 (2016).
Dahl, J. A. et al. Broad histone H3K4me3 domains in mouse oocytes modulate maternal-to-zygotic transition. Nature 537, 548–552 (2016).
Liu, X. et al. Distinct features of H3K4me3 and H3K27me3 chromatin domains in pre-implantation embryos. Nature 537, 558–562 (2016).
Flyamer, I. M. et al. Single-nucleus Hi-C reveals unique chromatin reorganization at oocyte-to-zygote transition. Nature 544, 110–114 (2017).
Battulin, N. et al. Comparison of the three-dimensional organization of sperm and fibroblast genomes using the Hi-C approach. Genome Biol. 16, 77 (2015).
Jung, Y. H. et al. Chromatin states in mouse sperm correlate with embryonic and adult regulatory landscapes. Cell Rep. 18, 1366–1382 (2017).
Ke, Y. et al. 3D chromatin structures of mature gametes and structural reprogramming during mammalian embryogenesis. Cell 170, 367–381.e20 (2017).
Du, Z. et al. Allelic reprogramming of 3D chromatin architecture during early mammalian development. Nature 547, 232–235 (2017).
Wu, J. et al. Chromatin analysis in human early development reveals epigenetic transition during ZGA. Nature 557, 256–260 (2018).
Gassler, J. et al. A mechanism of cohesin-dependent loop extrusion organizes zygotic genome architecture. EMBO J. 36, 3600–3618 (2017).
Nagano, T. et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64 (2013).
Nagano, T. et al. Cell-cycle dynamics of chromosomal organization at single-cell resolution. Nature 547, 61–67 (2017).
Inoue, A., Jiang, L., Lu, F., Suzuki, T. & Zhang, Y. Maternal H3K27me3 controls DNA methylation-independent imprinting. Nature 547, 419–424 (2017).
Okamoto, I., Otte, A. P., Allis, C. D., Reinberg, D. & Heard, E. Epigenetic dynamics of imprinted X inactivation during early mouse development. Science 303, 644–649 (2004).
Naumova, N. et al. Organization of the mitotic chromosome. Science 342, 948–953 (2013).
Zheng, H. et al. Resetting epigenetic memory by reprogramming of histone modifications in mammals. Mol. Cell 63, 1066–1079 (2016).
Zhang, B. et al. Allelic reprogramming of the histone modification H3K4me3 in early mammalian development. Nature 537, 553–557 (2016).
Nora, E. P. et al. Targeted degradation of CTCF decouples local insulation of chromosome domains from genomic compartmentalization. Cell 169, 930–944.e22 (2017).
Borensztein, M. et al. Xist-dependent imprinted X inactivation and the early developmental consequences of its failure. Nat. Struct. Mol. Biol. 24, 226–233 (2017).
Inoue, A., Jiang, L., Lu, F. & Zhang, Y. Genomic imprinting of Xist by maternal H3K27me3. Genes Dev. 31, 1927–1932 (2017).
Nora, E. P. et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485, 381–385 (2012).
Okamoto, I. et al. Evidence for de novo imprinted X-chromosome inactivation independent of meiotic inactivation in mice. Nature 438, 369–373 (2005).
Tian, D., Sun, S. & Lee, J. T. The long noncoding RNA, Jpx, is a molecular switch for X chromosome inactivation. Cell 143, 390–403 (2010).
Soma, M., Fujihara, Y., Okabe, M., Ishino, F. & Kobayashi, S. Ftx is dispensable for imprinted X-chromosome inactivation in preimplantation mouse embryos. Sci. Rep. 4, 5181 (2014).
Giorgetti, L. et al. Structural organization of the inactive X chromosome in the mouse. Nature 535, 575–579 (2016).
Deng, X. et al. Bipartite structure of the inactive mouse X chromosome. Genome Biol. 16, 152 (2015).
Chaumeil, J., Le Baccon, P., Wutz, A. & Heard, E. A novel role for Xist RNA in the formation of a repressive nuclear compartment into which genes are recruited when silenced. Genes Dev. 20, 2223–2237 (2006).
Schwarzer, W. et al. Two independent modes of chromatin organization revealed by cohesin removal. Nature 551, 51–56 (2017).
Borsos, M. et al. Genome–lamina interactions are established de novo in the early mouse embryo. Nature 569, 729–733 (2019).
Laing, A. F., Lowell, S. & Brickman, J. M. Gro/TLE enables embryonic stem cell differentiation by repressing pluripotent gene expression. Dev. Biol. 397, 56–66 (2015).
Galupa, R. & Heard, E. X-chromosome inactivation: a crossroads between chromosome architecture and gene regulation. Annu. Rev. Genet. 52, 535–566 (2018).
Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 259 (2015).
Pu, J., Zheng, B., Leader, J. K. & Gur, D. An ellipse-fitting based method for efficient registration of breast masses on two mammographic views. Med. Phys. 35, 487–494 (2008).
Kerpedjiev, P. et al. HiGlass: web-based visual exploration and analysis of genome interaction maps. Genome Biol. 19, 125 (2018).
Norton, H. K. et al. Detecting hierarchical genome folding with network modularity. Nat. Methods 15, 119–122 (2018).
Ramírez, F. et al. High-resolution TADs reveal DNA sequences underlying genome organization in flies. Nat. Commun. 9, 189 (2018).
Kumar, L. & E. Futschik, M. Mfuzz: a software package for soft clustering of microarray data. Bioinformation 2, 5–7 (2007).
Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).
Servant, N. et al. HiTC: exploration of high-throughput ‘C’ experiments. Bioinformatics 28, 2843–2844 (2012).
Tan, L., Xing, D., Chang, C.-H., Li, H. & Xie, X. S. Three-dimensional genome structures of single diploid human cells. Science 361, 924–928 (2018).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Ramírez, F., Dündar, F., Diehl, S., Grüning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).
Liao, Y., Smyth, G. K. & Shi, W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 41, e108 (2013).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Chaumeil, J., Augui, S., Chow, J. C. & Heard, E. in The Nucleus (ed. Hancock, R.) 297–308 (Springer, 2008).
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).
Haeussler, M. et al. Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 17, 148 (2016).
Vandesompele, J. et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 3, RESEARCH0034 (2002).
We thank the Institut Curie Animal facility for animal welfare and husbandry; the imaging facility PICTIBiSA@BDD for technical assistance; members of the E.H. laboratory for critical input; Y. Komarnitskaya for graphic design; and F. Ramirez and G. Richard for their help with bioinformatic analysis. This work was supported by FRM (FDM20140630223 and FDM40917) to N.R., ERC Advanced Grant DEVOCHROMO to P.F., by ERC Advanced Investigator Awards ERC-ADG-2014 671027 and Labellisation la Ligue, Labex DEEP: ANR-11- LBX-0044, IDEX PSL: ANR-10-IDEX-0001-02 PSL to E.H.
The authors declare no competing interests.
Peer review information Nature thanks Takashi Sado 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 Single-cell HiC approach to studying chromosome organization in mouse preimplantation embryos.
a, Distribution of total contact versus trans ratio per single blastomere, according to developmental stage with given thresholds for exclusion. b, Fraction of maternal contacts on the X chromosome versus contacts on the Y chromosome. The colour of each dot indicates the fraction of reads that cover the maternal genome. Red rectangles highlight female diploid cells, blue rectangles highlight male cells and black rectangles highlight haploid cells (that is, oocytes or polar bodies). Cells outside these frames were excluded. c, Percentage of short-range (25 kb–2 Mb) versus long-range or mitotic contacts (2–12 Mb) per single cell, coloured by developmental stages. d, Subset of the single cells at eight-cell stage, either in G1, S or G2 phase (top) or going towards mitosis (bottom), and their corresponding pseudo-bulk HiC heat maps. e, Table for the number of single blastomeres per stage of development that passed quality control, and the selected number after cell cycle phasing that were used to produce the subsequent analysis and heat maps. f, Bar plot of domain numbers for each developmental stage.
a, d, g, HiC contact maps for different genomic locations (as indicated), from the 1-cell to 64-cell stage. b, c, e, f, h, i, Analysis of the genomic locations for boundary formation (red and green probes in bottom of a, d and g) by 3D DNA FISH in two-cell-stage to eight-cell-stage embryos and embryonic stem cells (ESC), with insets of signal for the two independent pools (b, e, h). The total number of combined signals (red plus green) is reported in the box plot in the adjacent panels (c, f, i). DNA is stained by DAPI (blue). Scale bar, 10 μm. c, Box plot (±1.5× interquartile range, 25th and 75th percentiles and median value) distribution of Pearson’s correlation coefficient for red and green signals (in pools 1 and 2) of DNA FISH analysis. a–c, Chromosome 13 (region 90 Mb–92 Mb). d–f, X chromosome (region 104 Mb–105 Mb). g–j, Chromosome 13 (region 14 Mb–15 Mb). All experiments are performed in biological replicates, n is the combined signal number, centre lines denote the median coefficient. Statistical significance (P < 0.001) was assessed using Wilcoxon’s rank sum test (two-sided).
a, Distribution of the minimal distance between cluster centroids (Dmin) for a predefined number of clusters (k) ranging from 2 to 40. Clustering was performed 100 times for each value of k. The optimal number of clusters is the highest value of k before the value Dmin becomes stagnant. b, Heat maps representing the result of clustering for different values of k. The same main categories are found for k > 8. The contact enrichment colour scale corresponds to the maternal (red) and paternal (blue) heat maps; the differential contact enrichment scale corresponds to the differential (maternal − paternal) heat maps. c, Heat maps showing domain enrichment in the bulk HiC data from GSE82185, with the same order as our clustering in Fig. 1d and showing similar dynamics. d, Single-cell projection by UMAP from the quantification of domain contacts on each allele, using all cells and all chromosomes, coloured by stage (top) or by sex (bottom). n = 669 single cells. e, As in d but excluding domains on the X chromosome. f, As in e but coloured by cell cycle phasing. g, Cell cycle phasing based on short-range versus mitotic contacts, with the same colour scale as in f. h, Single-cell projections after excluding oocytes, all cells in pre-M and M phase and domains on the X chromosome, as in Fig. 1f, coloured by sex (top) or by pseudotime overlaid with the inferred trajectory (bottom). n = 470 single cells. i, As in h, coloured by mean count per kb per million (CPKM) on each allele, for the nine clusters identified in Fig. 1d.
a, Average profile of H3K27me3 ChIP–seq signal at the domains for each parental allele at the 2-cell and 64-cell stages in clusters 1 to 9. n = 375, 238, 387, 338, 110, 327, 287, 194 and 141 for each cluster from 1 to 9). b, Distributions of H3K27me3 domain enrichment per cluster, on the maternal (red) and paternal (blue) genomes at the one-cell stage. Box plots represent ±1.5× interquartile range, 25th and 75th percentile and median value. n values are the same as in a. c, Statistical comparison, two-by-two, between each distribution in b. P values are calculated using a Wilcoxon test (two-sided, not paired). n values are the same as in a. d, e, As in b, c for H3K27me3 ChIP–seq data from epiblasts. f, Heat maps of H3K4me3 ChIP–seq signal at domains of each cluster ± 1 Mb, with parental origin. g, Heat maps of H3K27me3 ChIP–seq signal at domains of each cluster ± 1Mb in oocytes (post-natal day 5 or day 14; or ovulatory oocytes (MII)). h, Snapshots of H3K27me3 ChIP–seq signal covering 6 Mb at transiently imprinted loci (Xist, Enc1, Jade1 and Mbnl2) for different stages of oogenesis, or the maternal allele in the 2-cell and 64-cell stages. i, Compartment scores at domains of clusters 1– 9, according to parental origin. j, Dynamics of the compartment scores for each cluster. Lines represent the mean, and shading represents the 95% confidence interval of the mean. n values are the same as in a. k, Bar plot of long-range interactions per stage, corresponding to the average heat map in Fig. 2f. l, CTCF-motif enrichment around domains.
a, Distribution of gene expression (top; n = 797, 353, 612, 621, 268, 699, 562, 278 and 193 genes for clusters 1 to 9) and fraction of maternal expression (maternal/(maternal + paternal), bottom ; n = 232, 249, 256, 502, 258, 664, 497, 179 and 269 genes for which an allelic ratio could be calculated for clusters 1 to 9, respectively) for genes present within domains of the different clusters. b, Pie charts for allelic expression bias from the 2-cell to the 64-cell stage for genes within clusters 1 to 9. c, P value (hypergeometric test) of Gene Ontology term enrichment in genes within each domain cluster.
a, Snapshots of HiC matrices and H3K27me3 ChIP–seq signal, showing the parental differences between the 2-cell and 64-cell stages for maternal (red) and paternal (blue) genomes at chromosome 2 (9–13.5 Mb) containing Sfmbt2. b, As in a, for chromosome 3 (40–43 Mb) containing Jade1. c, Quantification of contacts within the region presented in Fig. 3b. d, Snapshot of HiC matrices and H3K27me3 ChIP–seq signal, showing the parental differences between the 2-cell and 64-cell stages for maternal (red) and paternal (blue) genomes at chromosome 9 (60–62.5 Mb) containing Tle3. e, Gene-expression dynamic for Tle3 for maternal (red) and paternal (blue) alleles. f, Quantification of contacts within the region shown in d. g, Snapshots of HiC matrices and H3K27me3 ChIP–seq signal, showing the parental differences between the 2-cell and 64-cell stages for maternal (red) and paternal (blue) genomes at chromosome 13 (96–100 Mb) containing Enc1. h, As in g, for chromosome 14 (115–122 Mb) containing Mbnl2. i, Relative gene expression for Xist (in red) or Jpx (in yellow) in mouse embryonic fibroblasts derived from embryos issued from crossing ∆Jpx/wild-type female mice with wild-type/Y or ∆Jpx/Y male mice. The three genotypes analysed are indicated, as well as the number of independently derived mouse embryonic fibroblast cultures from independent single embryos (n = 4, 6 and 6 for wild-type/wild-type, wild-type/∆Jpx and ∆Jpx/∆Jpx genotypes, respectively). Bar plot represents the mean of each independent expression value (for each embryo), error bars represent the s.d. and each dot represents an individual embryo value. j, Pie chart distribution of the genotypes obtained after mating ∆Jpx/wild-type female mice with ∆Jpx/Y male mice. n = 104 pups.
Extended Data Fig. 7 Analysis of X-linked gene position within the X chromosome as development progresses.
a, Clustering of X chromosome domain dynamics for contact enrichment (average Z-score). Domain number, n = 55, 75 and 26 domains for each cluster. b, Structural changes. Lines represent the mean, and shading represents the 95% confidence interval of the mean. c, Radial positions of X-linked genes, classified as early silenced, late-silenced and escapees as in a previous study20. n values are as in Fig. 4b. Box plot represents ±1.5× interquartile range, 25th and 75th percentiles and median value. Statistical difference was assessed using Wilcoxon’s rank sum test (two-sided).
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Collombet, S., Ranisavljevic, N., Nagano, T. et al. Parental-to-embryo switch of chromosome organization in early embryogenesis. Nature 580, 142–146 (2020). https://doi.org/10.1038/s41586-020-2125-z
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