Allelic reprogramming of 3D chromatin architecture during early mammalian development (original) (raw)

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Acknowledgements

We thank B. Ren, C. Li, Y. Chen, F. Yue and L. An for discussions, and members of the Xie laboratory for comments. This work was supported by the National Key R&D Program of China (2016YFC0900301 to W.X.), the National Basic Research Program of China (2015CB856201 to W.X.; 2012CB316503 to M.Q.Z.), the National Natural Science Foundation of China (31422031 to W.X.; 61472205 to J.Z.; 31371341 to X.W.; 31361163004 and 31671383 to J.G.; 91519326, 31671384, 31361163004 and 91329000 to M.Q.Z.), the THU-PKU Center for Life Sciences (W.X.), the Youth Thousand Scholar Program of China (W.X., J.Z.), State Key Research Development Program of China (2016YFC1200303 to J.G.), TNLIST Cross-discipline Foundation (M.Q.Z.), the NIH (MH102616 to M.Q.Z.) and Beijing Advanced Innovation Center for Structural Biology (W.X. and J.Z.).

Author information

Authors and Affiliations

  1. Center for Stem Cell Biology and Regenerative Medicine, MOE Key Laboratory of Bioinformatics, THU-PKU Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, 100084, China
    Zhenhai Du, Hui Zheng, Jingyi Wu, Jing He, Yunlong Xiang, Qiujun Wang, Yuanyuan Li, Jing Ma, Ke Zhang & Wei Xie
  2. PKU-THU Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
    Bo Huang
  3. Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China
    Rui Ma & Jianyang Zeng
  4. Joint Graduate Program of Peking-Tsinghua-NIBS, School of Life Sciences, Tsinghua University, Beijing, 100084, China
    Jingyi Wu & Wei Xie
  5. MOE Key Laboratory of Bioinformatics,
    Xianglin Zhang, Xu Zhang, Yang Wang, Michael Q. Zhang, Juntao Gao & Xiaowo Wang
  6. Bioinformatics Division, TNLIST, Beijing, 100084, China
    Xianglin Zhang, Xu Zhang, Yang Wang, Michael Q. Zhang, Juntao Gao & Xiaowo Wang
  7. Department of Biological Sciences, Center for Systems Biology, The University of Texas, Dallas 800 West Campbell Road, RL11 Richardson, Texas, 75080-3021, USA
    Michael Q. Zhang
  8. The Salk Institute for Biological Studies, La Jolla, 92037, California, USA
    Jesse R. Dixon

Authors

  1. Zhenhai Du
  2. Hui Zheng
  3. Bo Huang
  4. Rui Ma
  5. Jingyi Wu
  6. Xianglin Zhang
  7. Jing He
  8. Yunlong Xiang
  9. Qiujun Wang
  10. Yuanyuan Li
  11. Jing Ma
  12. Xu Zhang
  13. Ke Zhang
  14. Yang Wang
  15. Michael Q. Zhang
  16. Juntao Gao
  17. Jesse R. Dixon
  18. Xiaowo Wang
  19. Jianyang Zeng
  20. Wei Xie

Contributions

Z.D. and W.X. conceived the project. Z.D. developed and conducted the sisHi-C experiments. B.H., Z.D., Y.X., and J.M. performed the mouse embryo experiments. Z.D., H.Z., R.M. and J.W. performed bioinformatics analysis. Xi.Z., J.H. and Y.W. helped with the Hi-C analysis pipeline setup. Y.L. and Q.W. performed NGS sequencing. Xu.Z. and K.Z. helped with various experiments. M.Q.Z., J.G., J.R.D., X.W., J.Z. and W.X. supervised experiments or data analyses. Z.D., H.Z. and W.X. wrote the manuscript.

Corresponding author

Correspondence toWei Xie.

Ethics declarations

Competing interests

Z.D. and W.X. are co-inventors on a filed patent application for sisHi-C.

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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 Figure 1 Comparison of sisHi-C and in situ Hi-C3.

sisHi-C procedures are shown by the red boxes and arrows on the left, and in situ Hi-C procedures are represented by blue boxes and arrows on the right. The black boxes in the middle show procedures shared by sisHi-C and in situ Hi-C.

Extended Data Figure 2 Validation of sisHi-C data.

a, Hi-C interaction frequency heatmaps (100-kb bin) of chromosome 19 in mouse ES cells as determined by conventional Hi-C4 or sisHi-C using 500 cells (2 replicates). Zoomed-in views (40-kb bin) are also shown. b, Scatter plots showing the comparison of chromatin interaction frequencies between sisHi-C and conventional Hi-C data in mouse ES cells4, or between the two replicates of 500 mouse ES cell sisHi-C samples. Pearson correlation coefficients are also shown. c, Chromatin compartments represented by the first principal component (PC1) values from PCA analyses are shown for the sisHi-C (2 replicates) and conventional Hi-C data in mouse ES cells4. Positive PC1 values represent compartment A regions (yellow), and negative values represent compartment B regions (blue). The gene-dense regions are also shown. d, Bar charts showing the Pearson correlation between the replicates (n = 2–4) of Hi-C data for mouse gametes and early embryos using chromatin interaction frequency. The error bars denote the s.d. of correlation values among different pairs of replicates. e, Heatmaps showing normalized Hi-C interaction frequency (100-kb bin, chromosome 19) in mouse sperm from this study or a previous study5. Zoomed-in views (40-kb bin) are also shown. f, Scatter plots showing the comparison of chromatin interaction frequencies in mouse sperm from this study and a previous study5, with Pearson correlation coefficient shown.

Extended Data Figure 3 Higher-order chromatin structure of MII oocytes.

a, Percentages of inter-chromosomal read pairs among all valid read pairs for gametes and early embryos (replicates pooled, n = 2–4). The error bars denote the s.e.m. among different replicates. b, Hi-C interaction frequency heatmaps comparing the chromatin interaction frequency between human mitotic chromatin (HeLaS3 cells)9 (top right) and mouse MII oocytes (middle right) at 100-kb resolution. The interaction heatmaps for asynchronized human ES cells4 (top left) and mouse ES cells (middle left) are shown as controls. The chromatin contact probabilities relative to genomic distance (P(s) curves) are also shown for each cell type (bottom). P(s) ~ _s_−0.5 and ~ _s_−1 represent the predicted mitotic and fractal globule states, respectively. The estimated turning points of chromatin interactions on the P(s) curves for MII oocytes (solid blue) and mitotic HeLaS3 cells (dashed blue) are shown by arrowheads.

Extended Data Figure 4 Establishment of TADs during mouse early development.

a, Hi-C interaction heatmaps (40-kb bin) showing dynamics of local interactions and TADs in mouse preimplantation development (pooled data from 2–4 biological replicates). b, Differential interaction heatmaps showing the changes in interaction frequency between consecutive developmental stages. Regions with increased and decreased interactions from early to late stages (pooled data from 2–4 biological replicates) are shown in yellow and blue, respectively. Hi-C interaction heatmap and identified TADs in ICMs are also shown to indicate the positions of TADs. A pair of subdomains within a TAD that show increased interactions from the 8-cell to ICM stage are indicated by black arrows. c, Overlap of TAD boundaries identified in mouse ES cells (this study) and ICMs (pooled data from three biological replicates). d, Schematic shows the method to compute consolidation score for a TAD. Consolidation score is the ratio between the average intra-TAD interaction frequencies (red) and the average local non-TAD background interaction frequencies in the nearby regions (green). Short-distance interactions (<400 kb) (orange) are excluded. iTF, intra-TAD interaction frequencies; nTF, non-TAD background interaction frequencies. e, Boxplots showing consolidation scores at different developmental stages (pooled data from 2–4 biological replicates). The significance of differences for consolidation scores between two consecutive stages was evaluated by Wilcoxon rank-sum test (two-tailed, with Benjamini–Hochberg multiple testing correction) as shown.

Extended Data Figure 5 TAD boundaries emerge as early as the zygote stage.

a, Hi-C interaction heatmaps showing the chromatin interaction frequencies for TADs in PN5 zygotes (pooled data, n = 4), ICMs (n = 3) and mouse ES cells (n = 2). ICM TADs identified by insulation scores are also shown. Blue boxes show example ICM TAD boundaries. b, The average DI scores of early embryos and mouse ES cells at ICM TAD boundaries (± 500 kb) are shown. DI scores generated by a random valid pair data set are also shown as a control (see Methods).

Extended Data Figure 6 Allelic chromatin organization in mouse early development.

a, Boxplots showing the ratios of inter-chromosomal read pairs between maternal and paternal (MP) genomes and between the same parental genomes (MM + PP) (see Methods). The ratios were computed for each pair of different chromosomes (chromosomes 1–2, 1–3 and so on) separately. Boxplots show the variations across all such pairs of chromosomes. b, Percentages of distal intra-chromosomal read pairs (>2 Mb) assigned to the paternal or maternal genome. The significance of allelic imbalance was assessed by comparison with that of the cortex (control) using a two-tailed _t_-test; **P < 0.001; *P < 0.05; ns, not significant. c, Heatmaps showing the allelic chromatin interaction frequencies (300-kb bin, chromosome 15) of sperm (replicates pooled, n = 3), MII oocyte (n = 2), PN3 (n = 2) and PN5 (n = 2) zygotes. d, Heatmaps showing the normalized average interaction frequencies for all ICM TADs as well as their nearby regions for sperm, MII oocyte and each allele of PN3 zygotes, PN5 zygotes and ICMs.

Extended Data Figure 7 Establishment of chromatin compartments during mouse early development.

a, Pearson correlation coefficients of correlation matrices between each developmental stage (replicates pooled, n = 2–4) and ICM (n = 3) on each allele. b, Hierarchical clustering based on correlation matrix for the two parental alleles. c, UCSC genome browser view showing the enrichment of ATAC-seq and chromatin compartments (PC1 values) for two parental alleles. Gene density is also shown. d, Allelic expression levels of ZGA-only genes (FPKM <0.5 in oocyte and FPKM >1 at any preimplantation stage) that fall into either compartment A or B in early development. The significance of differences between compartments A and B for each allele of each stage was assessed using Wilcoxon rank-sum test (P < 0.02 in all cases, two-tailed, with Benjamini–Hochberg multiple testing correction). e, ATAC-seq enrichment for compartments A and B of two parental alleles in early development. The significance of differences between compartments A and B for each allele of each stage was assessed using two-tailed _t_-test (P < 5.4 × 10−45 in all cases, with Benjamini–Hochberg multiple testing correction).

Extended Data Figure 8 Chromatin organization establishment in early development can partially proceed in the absence of zygotic genome activation.

a, Gene expression levels (log2[FPKM+1]) show the effects of alpha-amanitin on genes activated during ZGA, genes expressed at all stages or genes repressed at all stages as defined previously28. P values calculated with two-tailed Wilcoxon rank-sum test are shown. Note that genes expressed at all stages presumably have both oocyte-inherited transcripts and zygotically transcribed transcripts. Their decreasing values from 20 h to 45 h in the presence of alpha-amanitin are consistent with continual maternal RNA degradation. Previous RNA-seq data sets29,30 were similarly analysed as controls. b, Heatmaps showing the chromatin interaction frequency (300-kb bin, chromosome 13) for embryos treated with or without alpha-amanitin (replicates pooled, n = 2) (see Methods). The PN3 zygotes were treated with alpha-amanitin and collected for Hi-C analyses at 20 h or 45 h. In both cases the embryos were arrested at the late 2-cell stage. The control groups developed to the late 2-cell and 8-cell stage, respectively. c, Heatmaps showing the normalized average interaction frequencies for all ICM TADs as well as their nearby regions (± 0.5 TAD length) for embryos treated with or without alpha-amanitin (replicates pooled, n = 2) (bottom). Differential interaction heatmap between embryos treated for 20 h and 45 h (top) is also shown. d, Boxplots showing the consolidation scores for embryos treated with alpha-amanitin for 20 h or 45 h for each of the two replicates. P values calculated with two-tailed Wilcoxon rank-sum test are shown.

Extended Data Figure 9 Hierarchical packaging of chromatin in early development.

a, Heatmaps showing the intra-chromosomal interaction probability relative to genomic distance between interacting regions (P(s)) across the whole genome. The P(s) ~ _s_−1 values are also shown as a reference. b, Heatmaps showing normalized P(s) distributions generated by normalizing P(s) values for each stage to the P(s) ~ _s_−1 values. c, Hi-C interaction heatmaps showing normalized Hi-C interaction frequency (100-kb bin, chromosome 5). Dashed boxes indicate various regions where chromatin compaction occurs.

Extended Data Figure 10 Hierarchical packaging of chromatin in early development.

a, The contact probability as a function of genomic distance (P(s) analysis) (averaged across all chromosomes) is shown for gametes and zygotes on each allele as previously described9. P(s) ~ _s_−0.5 and P(s) ~ _s_−1 are shown to represent the predicted mitotic and fractal globule states, respectively9. b–e, P(s) curves (averaged across all chromosomes) for every two consecutive developmental stages calculated for each allele. P(s) ~ _s_−1 is shown to represent the predicted fractal globule state9. Arrowheads indicate estimated turning points on the P(s) curves where chromatin interactions drop abruptly (interaction insulation boundary). f, P(s) analyses for individual chromosomes (1, 4, 14 and 19) at early 2-cell stage. P(s) ~ _s_−1 is shown to represent the predicted fractal globule state. The arrowhead indicates the interaction insulation boundary.

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Du, Z., Zheng, H., Huang, B. et al. Allelic reprogramming of 3D chromatin architecture during early mammalian development.Nature 547, 232–235 (2017). https://doi.org/10.1038/nature23263

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