Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Reprogramming of the histone H3.3 landscape in the early mouse embryo

Abstract

Epigenetic reprogramming of the zygote involves dynamic incorporation of histone variant H3.3. However, the genome-wide distribution and dynamics of H3.3 during early development remain unknown. Here, we delineate the H3.3 landscapes in mouse oocytes and early embryos. We unexpectedly identify a non-canonical H3.3 pattern in mature oocytes and zygotes, in which local enrichment of H3.3 at active chromatin is suppressed and H3.3 is relatively evenly distributed across the genome. Interestingly, although the non-canonical H3.3 pattern forms gradually during oogenesis, it quickly switches to a canonical pattern at the two-cell stage in a transcription-independent and replication-dependent manner. We find that incorporation of H3.1/H3.2 mediated by chromatin assembly factor CAF-1 is a key process for the de novo establishment of the canonical pattern. Our data suggest that the presence of the non-canonical pattern and its timely transition toward a canonical pattern support the developmental program of early embryos.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: H3.3 landscapes of oocytes and early mouse embryos.
Fig. 2: Characterization of non-canonical and canonical H3.3 patterns.
Fig. 3: Establishment of a non-canonical H3.3 pattern during oogenesis.
Fig. 4: Dynamic changes in H3.3 patterns in the maternal and paternal genomes after fertilization.
Fig. 5: DNA replication is required for H3.3 reprogramming in two-cell embryos.
Fig. 6: H3.1/H3.2 deposition through CAF-1 is required for de novo establishment of the canonical H3.3 pattern and normal development.
Fig. 7: CAF-1 KD in mESCs reprograms H3.3 patterns.
Fig. 8: Model summarizing H3.3 reprogramming during mouse oogenesis and early development.

Similar content being viewed by others

Data availability

The sequencing data from this study are available at the Gene Expression Omnibus under accession code GSE139527. Source data are provided with this paper.

References

  1. Filipescu, D., Muller, S. & Almouzni, G. Histone H3 variants and their chaperones during development and disease: contributing to epigenetic control. Annu. Rev. Cell Dev. Biol. 30, 615–646 (2014).

    Article  CAS  PubMed  Google Scholar 

  2. Goldberg, A. D. et al. Distinct factors control histone variant H3.3 localization at specific genomic regions. Cell 140, 678–691 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Pchelintsev, N. A. et al. Placing the HIRA histone chaperone complex in the chromatin landscape. Cell Rep. 3, 1012–1019 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Elsasser, S. J., Noh, K. M., Diaz, N., Allis, C. D. & Banaszynski, L. A. Histone H3.3 is required for endogenous retroviral element silencing in embryonic stem cells. Nature 522, 240–244 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Smith, S. & Stillman, B. Purification and characterization of CAF-I, a human cell factor required for chromatin assembly during DNA replication in vitro. Cell 58, 15–25 (1989).

    Article  CAS  PubMed  Google Scholar 

  6. Shibahara, K. & Stillman, B. Replication-dependent marking of DNA by PCNA facilitates CAF-1-coupled inheritance of chromatin. Cell 96, 575–585 (1999).

    Article  CAS  PubMed  Google Scholar 

  7. Murzina, N., Verreault, A., Laue, E. & Stillman, B. Heterochromatin dynamics in mouse cells: interaction between chromatin assembly factor 1 and HP1 proteins. Mol. Cell 4, 529–540 (1999).

    Article  CAS  PubMed  Google Scholar 

  8. Quivy, J. P. et al. A CAF-1 dependent pool of HP1 during heterochromatin duplication. EMBO J. 23, 3516–3526 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Loyola, A. et al. The HP1α-CAF1-SetDB1-containing complex provides H3K9me1 for Suv39-mediated K9me3 in pericentric heterochromatin. EMBO Rep. 10, 769–775 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Houlard, M. et al. CAF-1 is essential for heterochromatin organization in pluripotent embryonic cells. PLoS Genet. 2, e181 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Huang, H. et al. Drosophila CAF-1 regulates HP1-mediated epigenetic silencing and pericentric heterochromatin stability. J. Cell Sci. 123, 2853–2861 (2010).

    Article  CAS  PubMed  Google Scholar 

  12. Clement, C. et al. High-resolution visualization of H3 variants during replication reveals their controlled recycling. Nat. Commun. 9, 3181 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Torres-Padilla, M. E., Bannister, A. J., Hurd, P. J., Kouzarides, T. & Zernicka-Goetz, M. Dynamic distribution of the replacement histone variant H3.3 in the mouse oocyte and preimplantation embryos. Int. J. Dev. Biol. 50, 455–461 (2006).

    Article  CAS  PubMed  Google Scholar 

  14. Burton, A. & Torres-Padilla, M. E. Chromatin dynamics in the regulation of cell fate allocation during early embryogenesis. Nat. Rev. Mol. Cell Biol. 15, 723–734 (2014).

    Article  CAS  PubMed  Google Scholar 

  15. Lin, C. J., Koh, F. M., Wong, P., Conti, M. & Ramalho-Santos, M. Hira-mediated H3.3 incorporation is required for DNA replication and ribosomal RNA transcription in the mouse zygote. Dev. Cell 30, 268–279 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Lin, C. J., Conti, M. & Ramalho-Santos, M. Histone variant H3.3 maintains a decondensed chromatin state essential for mouse preimplantation development. Development 140, 3624–3634 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Wen, D. et al. Histone variant H3.3 is an essential maternal factor for oocyte reprogramming. Proc. Natl Acad. Sci. USA 111, 7325–7330 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Jin, C. & Felsenfeld, G. Nucleosome stability mediated by histone variants H3.3 and H2A.Z. Genes Dev. 21, 1519–1529 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Chen, P. et al. H3.3 actively marks enhancers and primes gene transcription via opening higher-ordered chromatin. Genes Dev. 27, 2109–2124 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Brind’Amour, J. et al. An ultra-low-input native ChIP-seq protocol for genome-wide profiling of rare cell populations. Nat. Commun. 6, 6033 (2015).

    Article  PubMed  CAS  Google Scholar 

  21. Jin, C. et al. H3.3/H2A.Z double variant-containing nucleosomes mark ‘nucleosome-free regions’ of active promoters and other regulatory regions. Nat. Genet. 41, 941–945 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Mito, Y., Henikoff, J. G. & Henikoff, S. Genome-scale profiling of histone H3.3 replacement patterns. Nat. Genet. 37, 1090–1097 (2005).

    Article  CAS  PubMed  Google Scholar 

  23. Lu, F. et al. Establishing chromatin regulatory landscape during mouse preimplantation development. Cell 165, 1375–1388 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Peaston, A. E. et al. Retrotransposons regulate host genes in mouse oocytes and preimplantation embryos. Dev. Cell 7, 597–606 (2004).

    Article  CAS  PubMed  Google Scholar 

  25. Akiyama, T., Suzuki, O., Matsuda, J. & Aoki, F. Dynamic replacement of histone H3 variants reprograms epigenetic marks in early mouse embryos. PLoS Genet. 7, e1002279 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Nashun, B. et al. Continuous histone replacement by Hira is essential for normal transcriptional regulation and de novo DNA methylation during mouse oogenesis. Mol. Cell 60, 611–625 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Fulka, H., Ogura, A., Loi, P. & Fulka, J. Jr. Dissecting the role of the germinal vesicle nuclear envelope and soluble content in the process of somatic cell remodelling and reprogramming. J. Reprod. Dev. 65, 433–441 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Guo, F. et al. Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Res. 27, 967–988 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Liang, K. & Keles, S. Normalization of ChIP-seq data with control. BMC Bioinformatics 13, 199 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Maze, I. et al. Critical role of histone turnover in neuronal transcription and plasticity. Neuron 87, 77–94 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Hake, S. B. et al. Serine 31 phosphorylation of histone variant H3.3 is specific to regions bordering centromeres in metaphase chromosomes. Proc. Natl Acad. Sci. USA 102, 6344–6349 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Du, Z. et al. Allelic reprogramming of 3D chromatin architecture during early mammalian development. Nature 547, 232–235 (2017).

    Article  CAS  PubMed  Google Scholar 

  33. Liu, X. et al. Distinct features of H3K4me3 and H3K27me3 chromatin domains in pre-implantation embryos. Nature 537, 558–562 (2016).

    Article  CAS  PubMed  Google Scholar 

  34. Wang, C. et al. Reprogramming of H3K9me3-dependent heterochromatin during mammalian embryo development. Nat. Cell Biol. 20, 620–631 (2018).

    Article  CAS  PubMed  Google Scholar 

  35. Erkek, S. et al. Molecular determinants of nucleosome retention at CpG-rich sequences in mouse spermatozoa. Nat. Struct. Mol. Biol. 20, 868–875 (2013).

    Article  CAS  PubMed  Google Scholar 

  36. Inoue, A., Jiang, L., Lu, F., Suzuki, T. & Zhang, Y. Maternal H3K27me3 controls DNA methylation-independent imprinting. Nature 547, 419–424 (2017).

    Article  CAS  PubMed  Google Scholar 

  37. Santenard, A. et al. Heterochromatin formation in the mouse embryo requires critical residues of the histone variant H3.3. Nat. Cell Biol. 12, 853–862 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Davis, W. Jr., De Sousa, P. A. & Schultz, R. M. Transient expression of translation initiation factor eIF-4C during the 2-cell stage of the preimplantation mouse embryo: identification by mRNA differential display and the role of DNA replication in zygotic gene activation. Dev. Biol. 174, 190–201 (1996).

    Article  CAS  PubMed  Google Scholar 

  39. Zhang, B. et al. Allelic reprogramming of the histone modification H3K4me3 in early mammalian development. Nature 537, 553–557 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Ke, Y. et al. 3D chromatin structures of mature gametes and structural reprogramming during mammalian embryogenesis. Cell 170, 367–381 (2017).

    Article  CAS  PubMed  Google Scholar 

  41. Ray-Gallet, D. et al. Dynamics of histone H3 deposition in vivo reveal a nucleosome gap-filling mechanism for H3.3 to maintain chromatin integrity. Mol. Cell 44, 928–941 (2011).

    Article  CAS  PubMed  Google Scholar 

  42. Skene, P. J. & Henikoff, S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. Elife 6, e21856 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Ye, X. et al. Defective S phase chromatin assembly causes DNA damage, activation of the S phase checkpoint, and S phase arrest. Mol. Cell 11, 341–351 (2003).

    Article  CAS  PubMed  Google Scholar 

  44. Ishiuchi, T. et al. Early embryonic-like cells are induced by downregulating replication-dependent chromatin assembly. Nat. Struct. Mol. Biol. 22, 662–671 (2015).

    Article  CAS  PubMed  Google Scholar 

  45. Cheng, L. et al. Chromatin assembly factor 1 (CAF-1) facilitates the establishment of facultative heterochromatin during pluripotency exit. Nucleic Acids Res. 47, 11114–11131 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wu, J. et al. The landscape of accessible chromatin in mammalian preimplantation embryos. Nature 534, 652–657 (2016).

    Article  CAS  PubMed  Google Scholar 

  47. Abe, K. et al. The first murine zygotic transcription is promiscuous and uncoupled from splicing and 3′ processing. EMBO J. 34, 1523–1537 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Aoki, F., Worrad, D. M. & Schultz, R. M. Regulation of transcriptional activity during the first and second cell cycles in the preimplantation mouse embryo. Dev. Biol. 181, 296–307 (1997).

    Article  CAS  PubMed  Google Scholar 

  49. Xu, Q. & Xie, W. Epigenome in early mammalian development: inheritance, reprogramming and establishment. Trends Cell Biol. 28, 237–253 (2018).

    Article  CAS  PubMed  Google Scholar 

  50. Inoue, A., Jiang, L., Lu, F. & Zhang, Y. Genomic imprinting of Xist by maternal H3K27me3. Genes Dev. 31, 1927–1932 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Meers, M. P., Bryson, T. D., Henikoff, J. G. & Henikoff, S. Improved CUT&RUN chromatin profiling tools. Elife 8, e46314 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184–2185 (2012).

    Article  CAS  PubMed  Google Scholar 

  55. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Ramirez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Shen, L., Shao, N., Liu, X. & Nestler, E. ngs.plot: quick mining and visualization of next-generation sequencing data by integrating genomic databases. BMC Genomics 15, 284 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Takada, T. et al. The ancestor of extant Japanese fancy mice contributed to the mosaic genomes of classical inbred strains. Genome Res. 23, 1329–1338 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Boyle, A. P., Guinney, J., Crawford, G. E. & Furey, T. S. F-Seq: a feature density estimator for high-throughput sequence tags. Bioinformatics 24, 2537–2538 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Au Yeung, W. K. et al. Histone H3K9 methyltransferase G9a in oocytes is essential for preimplantation development but dispensable for CG methylation protection. Cell Rep. 27, 282–293 (2019).

    Article  CAS  PubMed  Google Scholar 

  61. Peat, J. R. et al. Genome-wide bisulfite sequencing in zygotes identifies demethylation targets and maps the contribution of TET3 oxidation. Cell Rep. 9, 1990–2000 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Wang, L. et al. Programming and inheritance of parental DNA methylomes in mammals. Cell 157, 979–991 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 259 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  67. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  68. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank H. Sugishita and A. Inoue (RIKEN) for sharing recombinant pAG-MN protein and a CUT&RUN protocol, T. Wakayama (Yamanashi University) for sharing B6;129 F1 ESCs and K. Hayashi (Kyushu University) for sharing a confocal microscope. We also thank the members of our laboratory and common research facilities of the Medical Institute of Bioregulation, Kyushu University, for technical assistance. This work was supported by grants from a MEXT Grant-in-Aid for Scientific Research on Innovative Areas (JP19H05756 and JP19H05758; T.I. and A.O.), the Kato Memorial Bioscience Foundation (T.I.), a JSPS Grant-in-Aid for Scientific Research (B) (JP16H04687; K.I.) and a JSPS Grant-in-Aid for Specially Promoted Research (JP18H05214; H.S.)

Author information

Authors and Affiliations

Authors

Contributions

T.I. conceived the project, designed all the experiments, performed the embryonic and cell culture works, prepared samples for sequencing and analyzed the data. S.A. contributed to optimization of the ULI-NChIP-seq protocol, prepared samples for sequencing and performed data analyses. K.I. performed mRNA injection and sample preparation for ChIP-seq. W.K.A.Y. performed analyses of histone modification and Hi-C data. Y.M. contributed to the immunofluorescence study. T.I., A.O. and H.S. supervised the project, interpreted the data and wrote the manuscript.

Corresponding authors

Correspondence to Takashi Ishiuchi or Hiroyuki Sasaki.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Peer reviewer reports are available. Beth Moorefield was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 Characterization and validation of H3.3 ULI-NChIP-seq data.

a, Western blotting showing the specificity of antibodies. Anti-H3.1/H3.2, anti-H3.3 and anti-pan-H3 antibodies were used to label recombinant H3.1 or H3.3. Results from two biological replicates are shown. Uncropped blot images are shown in the Source Data. b, Genome browser snapshots of the H3.3 ULI-NChIP-seq data. The H3.3 UNI-NChIP-seq data generated from 300 mESCs (this study) with prior H3.3 ChIP-seq data (GSE59189)4 shown for comparison. Enrichment is shown by log2 ratios between H3.3 ULI-NChIP and input. The midline corresponds to log2 ratio = 0. Higher and lower enrichment over input is indicated in magenta and blue, respectively. c, Hierarchical clustering and correlation analysis of H3.3 ULI-NChIP-seq data. Correlation between the data was analyzed by considering H3.3 enrichment in genome-wide 10-kb bins. Heatmaps show Spearman’s correlation coefficients between the samples. d, Plots showing average H3.3 enrichment at genic regions of the indicated samples. e, Plots showing CpG methylation level around genic regions. Genes were categorized by indicated CpG density at promoters as well as their expression levels. DNA methylation data was obtained from GSE63417 (zygote) and GSE56697 (2-cell and 4-cell). f, Plots showing average H3.3 enrichment at distal DNase I-hypersensitive sites. DNase I-hypersensitive sites distal to genic regions (top) were identified using previously published data (GSE76642)23. Bottom, H3.3 enrichment at the distal DNase I-hypersensitive sites. g, Genome browser snapshots of the H3.3 ULI-NChIP-seq data. Arrows indicate the positions of indicated repetitive elements.

Source data

Extended Data Fig. 2 Examination of possible factors affecting H3.3 distribution and H3.3 ULI-NChIP-seq data with A/B compartments or histone modifications.

a, Stacked bar charts showing the complexity and quality of the H3.3 ULI-NChIP-seq mapped reads. The proportion of PCR duplicates and MAPQ score were used to assess the complexity and quality of the mapped reads, respectively. b, Genome browser snapshots showing normalized H3.3 enrichment in adult neurons, MII oocytes, zygotes, and 2-cell embryos. H3.3 ChIP-seq data for hippocampal neurons was obtained from GSE69806 (ref. 30). c, Immunostaining for H3.3S31P in FGOs, MII oocytes, zygotes, and 2-cell embryos. Representative images from two independent experiments are shown. n = 16 FGOs, 19 MII oocytes, 12 zygotes, and 15 2-cell embryos. Scale bar = 5 μm. d, Immunostaining for H3.3 in FGOs, MII oocytes, zygotes, and 2-cell embryos. Representative images from two independent experiments are shown. n = 10 FGOs, 15 MII oocytes, 11 zygotes, and 6 2-cell embryos. Scale bar = 5 μm. e, Genome browser snapshots showing normalized H3.3 enrichment and A/B compartments. Assignment of A/B compartment was performed using published Hi-C data (GSE82185)32. f, Bar graph showing the overlap between histone modification and genomic regions that show gain or loss of H3.3 during the zygote-to-2-cell progression. The numbers below indicate the number of bins (10 kb) analyzed.

Extended Data Fig. 3 Reprogramming of H3.3 patterns during oogenesis.

a, Hierarchical clustering and correlation analysis of H3.3 ULI-NChIP-seq data. Heatmaps show Spearman’s correlation coefficients between the samples. Two biological replicates were analyzed for each developmental stage. b, Heatmaps showing genome-wide H3.3 and H3K4me3 enrichment. Published H3K4me3 dataset (GSE71434)39 was used. The analyses were performed for 10-kb bins genome-wide. The data is sorted by the H3.3 enrichment level in growing oocytes. Number of bins = 510,178. c, Heatmaps showing H3.3 (left) and H3K4me3 enrichment (right) around genic regions. The H3.3 patterns were subjected to k-means clustering. Published H3K4me3 dataset (GSE71434)39 was used.

Extended Data Fig. 4 H3.3 enrichment within parental alleles in zygotes and 2-cell embryos.

a, Scatter plots showing normalized H3.3 enrichment in the maternal or paternal allele in zygotes (left) or 2-cell embryos (right). Bin size = 500 kb. b, Scatter plots showing the correlation between H3.3 enrichment in the maternal (left) or paternal (right) allele and H3K4me3 in 2-cell embryos. c, Heatmaps showing normalized allelic H3.3, H3K4me3 and H3K27me3 enrichment data. Published histone modification data (GSE73952 and GSE97778)33,34 are used. The analyses were performed for 10-kb bins showing H3.3 enrichment either in zygotes (left) or 2-cell embryos (right). The data were sorted by H3.3 enrichment level on the maternal allele. Enrichment is shown as log2 ratios between ChIP and input.

Extended Data Fig. 5 Effect of transcription or DNA replication inhibition on H3.3 reprogramming.

a, b, The effect of aphidicolin (a) and α-amanitin (b) on DNA replication and transcription, respectively. The incorporation of EdU and EU was analyzed by microscopy. Scale bars = 50 μm. n = 12 (control for aphidicolin), 14 (aphidicolin-treated), 9 (control for α-amanitin) and 10 (α-amanitin-treated) embryos from two independent experiments. c, Heatmaps showing normalized H3.3 enrichment around genic regions. The H3.3 patterns were subjected to k-means clustering. Enrichment is shown as log2 ratios between H3.3 and input. d, Violin plots showing the size of H3.3-enriched regions in the indicated samples.

Extended Data Fig. 6 Characterization of H3.1/H3.2 incorporation in the 2-cell embryo.

a, Experimental design to compare histone levels between the zygote and the 2-cell stage. Triton pre-extraction was performed before fixation to remove free histones. Zygotes and 2-cell embryos were then pooled and processed under the same conditions. Images were also acquired with identical parameters. Scale bar, 50 μm. b, Immunostaining for pan-H3 in PN5 zygotes and 2-cell embryos (left). Scale bar =10 μm. Right, plots showing the pan-H3 signal intensity in zygotes and 2-cell embryos. Signal intensities were normalized to those of DAPI signals. P-values (Bonferroni-adjusted two-tailed Mann-Whitney U tests) are indicated. Error bars indicate mean ± s.d.. n = 12 (female PN), 12 (male PN) and 30 (nuclei in late 2-cell embryos) from two independent experiments. c, Plots showing the H3.1/H3.2 signal intensity in control (DMSO-treated) and aphidicolin-treated 2-cell embryos. Signal intensities were normalized to those of DAPI. P-values (two-tailed Mann-Whitney U tests) are indicated. Error bars indicate mean ± s.d.. n = 48 (control) and 46 (Aphidicolin) from four independent experiments. d, Plots showing the H3.3 signal intensity in control and aphidicolin-treated 2-cell embryos. The signal intensities were normalized by DAPI signal intensity. P-value (two-tailed Mann-Whitney U tests) is indicated. Error bars indicate mean ± s.d.. n = 38 (control) and 34 (Aphidicolin) from three independent experiments. Data for graphs in bd are available as source data.

Source data

Extended Data Fig. 7 Effect of a dominant-negative p150 on early embryonic transcriptional program.

a, Images showing the expression of EGFP or EGFP-p150DN after mRNA injection. Images were acquired 3 h after mRNA injection. p150DN accumulates at female and male pronuclei in zygotes. Scale bar = 50 μm. b, Plots showing the average H3.3 enrichment around TSSs in EGFP-injected and p150DN-injected 2-cell embryos. c, Stacked bar charts showing the developmental progression of non-injected embryos. Cultured embryos were observed at indicated time points (post hCG) and their developmental stages were determined. The stages are shown by indicated colors. n = 64 embryos analyzed in three independent experiments. d, Heatmaps showing levels of differentially expressed transcripts among three experimental groups. Clustering of differentially expressed transcripts between control (EGFP-injected) 8-cell, blastocyst and p150DN-injected 8-cell was performed. The differential expression is indicated by z-score.

Source data

Extended Data Fig. 8 p150 depletion reprograms H3.3 patterns.

a, Western blotting showing the efficiency of p150 knockdown. Representative images from three independent biological replicate samples are shown. β-actin, loading control. Uncropped blot images are shown as Source Data. b, Genome browser snapshots showing normalized H3.3 enrichment in control and p150-depleted mESCs. Enrichment is shown by log2 ratios between H3.3 and input. c, Genome browser snapshots of the H3.3 ULI-NChIP-seq and H3.3 CUT&RUN data in mESCs. d, Scatter plots showing H3.3 enrichment obtained from H3.3 ULI-NChIP-seq and CUT&RUN in mESCs. The number of 10-kb bins = 252,405. e, Heatmaps showing genome-wide H3.3 enrichment. H3.3 ULI-NChIP-seq and CUT&RUN data obtained by using mESCs were analyzed. The number of 10-kb bins, 252,405. f, Plots showing read enrichment around genic regions. CUT&RUN data using control (normal) IgG was generated for control and p150-depleted mESCs, and read enrichment was analyzed at genic regions. Data from two biological replicates were merged. Note that reads are enriched exactly at the TSSs in p150-depleted mESCs, possibly due to digestion of accessible chromatin by protein A/G-MNase. g, Bar graph indicating relative number of aligned reads. The number of reads from spike-in DNA was used for normalization. h, Plots showing H3.3 enrichment around genic regions. H3.3 CUT&RUN data generated from control and p150-depleted ESCs were analyzed. H3.3 enrichment after spike-in normalization (scaled) is also shown. i, Expression of the indicated transcripts during early mouse development. The published RNA-seq data46 was used. j, Quantitative real-time PCR confirmation performed in this study. Gapdh was used for normalization. Log2 fold change of each transcript between control and p150-depleted mESCs are shown. Each dot represents the data from two biological replicates. Error bars indicate mean ± s.d.. Data for graphs are available as source data.

Source data

Supplementary information

Supplementary Information

Supplementary Note 1.

Reporting Summary

Peer Review Information

Supplementary Table 1

Numbers of sequenced reads and cells for each sample.

Supplementary Table 2

Table summarizing H3.3 enrichment around TSSs in the maternal and paternal chromatin at the two-cell stage.

Source data

Source Data Fig. 1

Statistical source data for graphs

Source Data Fig. 2

Statistical source data for graphs

Source Data Fig. 6

Statistical source data for graphs

Source Data Fig. 7

Statistical source data for graphs

Source Data Extended Data Fig. 1

Unprocessed western blot images

Source Data Extended Data Fig. 6

Statistical source data for graphs

Source Data Extended Data Fig. 7

Statistical source data for graphs

Source Data Extended Data Fig. 8

Unprocessed western blot images

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ishiuchi, T., Abe, S., Inoue, K. et al. Reprogramming of the histone H3.3 landscape in the early mouse embryo. Nat Struct Mol Biol 28, 38–49 (2021). https://doi.org/10.1038/s41594-020-00521-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41594-020-00521-1

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing