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Heterochromatin rewiring and domain disruption-mediated chromatin compaction during erythropoiesis

Nature Structural & Molecular Biology volume 30, pages 463–474 (2023)Cite this article

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Abstract

Mammalian erythropoiesis involves progressive chromatin compaction and subsequent enucleation in terminal differentiation, but the mechanisms underlying the three-dimensional chromatin reorganization remain obscure. Here, we systematically analyze the higher-order chromatin in purified populations of primary human erythroblasts. Our results reveal that heterochromatin regions undergo substantial compression, with H3K9me3 markers relocalizing to the nuclear periphery and forming a significant number of long-range interactions, and that ~58% of the topologically associating domain (TAD) boundaries are disrupted, while certain TADs enriched for markers of the active transcription state and erythroid master regulators, GATA1 and KLF1, are selectively maintained during terminal erythropoiesis. Finally, we demonstrate that GATA1 is involved in safeguarding selected essential chromatin domains during terminal erythropoiesis. Our study therefore delineates the molecular characteristics of a development-driven chromatin compaction process, which reveals transcription competence as a key indicator of the selected domain maintenance to ensure appropriate gene expression during the extreme compaction of chromatin.

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Fig. 1: Dynamic chromatin architecture during progressive nuclear condensation of terminal erythropoiesis.
Fig. 2: Chromatin condensation in terminal erythropoiesis is coupled with relocalization and establishment of long-range interactions of heterochromatin.
Fig. 3: A vast number of chromatin domain boundaries are disrupted in terminal erythropoiesis.
Fig. 4: Domain maintenance in the Ortho-E stage is determined by chromatin activity and erythroid gene expression.
Fig. 5: Erythroid master regulators GATA1 and KLF1 are strongly associated with maintained chromatin domains.
Fig. 6: GATA1 is essential in safeguarding selected chromatin domains in terminal erythropoiesis.

Data availability

The data in this manuscript have been deposited in the Genome Sequence Archive of the National Genomics Data Center. The assigned accession numbers of the submission are HRA001026 and HRA002092. H3K27ac HiChIP data were downloaded from the Gene Expression Omnibus (accession code GSE131055). The public database of the hg19 genome and annotation files (v27lift37) are available from the GENCODE portal (https://www.gencodegenes.org). Source data are provided with this paper. The large source data files are available from https://github.com/LeeSlab/Code-for-Li-et-al.

Code availability

The code is available from https://github.com/LeeSlab/Code-for-Li-et-al.

References

  1. Hattangadi, S. M., Wong, P., Zhang, L., Flygare, J. & Lodish, H. F. From stem cell to red cell: regulation of erythropoiesis at multiple levels by multiple proteins, RNAs, and chromatin modifications. Blood 118, 6258–6268 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Lodish, H., Flygare, J. & Chou, S. From stem cell to erythroblast: regulation of red cell production at multiple levels by multiple hormones. IUBMB Life 62, 492–496 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Ji, P., Jayapal, S. R. & Lodish, H. F. Enucleation of cultured mouse fetal erythroblasts requires Rac GTPases and mDia2. Nat. Cell Biol. 10, 314–321 (2008).

    Article  CAS  PubMed  Google Scholar 

  4. Ji, P., Murata-Hori, M. & Lodish, H. F. Formation of mammalian erythrocytes: chromatin condensation and enucleation. Trends Cell Biol. 21, 409–415 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Keerthivasan, G., Small, S., Liu, H., Wickrema, A. & Crispino, J. D. Vesicle trafficking plays a novel role in erythroblast enucleation. Blood 116, 3331–3340 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Keerthivasan, G., Wickrema, A. & Crispino, J. D. Erythroblast enucleation. Stem Cells Int. 2011, 139851 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Keerthivasan, G. et al. A novel role for survivin in erythroblast enucleation. Haematologica 97, 1471–1479 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Zhao, B. et al. Disruption of erythroid nuclear opening and histone release in myelodysplastic syndromes. Cancer Med. 8, 1169–1174 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ji, P., Yeh, V., Ramirez, T., Murata-Hori, M. & Lodish, H. F. Histone deacetylase 2 is required for chromatin condensation and subsequent enucleation of cultured mouse fetal erythroblasts. Haematologica 95, 2013–2021 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Popova, E. Y. et al. Chromatin condensation in terminally differentiating mouse erythroblasts does not involve special architectural proteins but depends on histone deacetylation. Chromosome Res. 17, 47–64 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Jayapal, S. R. et al. Down-regulation of Myc is essential for terminal erythroid maturation. J. Biol. Chem. 285, 40252–40265 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Shearstone, J. R. et al. Global DNA demethylation during mouse erythropoiesis in vivo. Science 334, 799–802 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Bartholdy, B. et al. Mechanisms of establishment and functional significance of DNA demethylation during erythroid differentiation. Blood Adv. 2, 1833–1852 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Yu, M. & Ren, B. The three-dimensional organization of mammalian genomes. Annu. Rev. Cell Dev. Biol. 33, 265–289 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Dekker, J. & Mirny, L. The 3D genome as moderator of chromosomal communication. Cell 164, 1110–1121 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Dixon, J. R., Gorkin, D. U. & Ren, B. Chromatin domains: the unit of chromosome organization. Mol. Cell 62, 668–680 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Rao, S. S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Dixon, J. R. et al. Chromatin architecture reorganization during stem cell differentiation. Nature 518, 331–336 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ji, X. et al. 3D chromosome regulatory landscape of human pluripotent cells. Cell Stem Cell 18, 262–275 (2016).

    Article  CAS  PubMed  Google Scholar 

  21. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Allshire, R. C. & Madhani, H. D. Ten principles of heterochromatin formation and function. Nat. Rev. Mol. Cell Biol. 19, 229–244 (2018).

    Article  CAS  PubMed  Google Scholar 

  23. Buchwalter, A., Kaneshiro, J. M. & Hetzer, M. W. Coaching from the sidelines: the nuclear periphery in genome regulation. Nat. Rev. Genet. 20, 39–50 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Wong, P. et al. Gene induction and repression during terminal erythropoiesis are mediated by distinct epigenetic changes. Blood 118, e128–e138 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. An, X. et al. Global transcriptome analyses of human and murine terminal erythroid differentiation. Blood 123, 3466–3477 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ludwig, L. S. et al. Transcriptional states and chromatin accessibility underlying human erythropoiesis. Cell Rep. 27, 3228–3240.e7 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Gao, X. et al. Thyroid hormone receptor beta and NCOA4 regulate terminal erythrocyte differentiation. Proc. Natl Acad. Sci. USA 114, 10107–10112 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Westers, T. M. et al. Immunophenotypic analysis of erythroid dysplasia in myelodysplastic syndromes. A report from the IMDSFlow working group. Haematologica 102, 308–319 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Pop, R. et al. A key commitment step in erythropoiesis is synchronized with the cell cycle clock through mutual inhibition between PU.1 and S-phase progression. PLoS Biol. 8, e1000484 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Hu, J. et al. Isolation and functional characterization of human erythroblasts at distinct stages: implications for understanding of normal and disordered erythropoiesis in vivo. Blood 121, 3246–3253 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhang, H. et al. Chromatin structure dynamics during the mitosis-to-G1 phase transition. Nature 576, 158–162 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Heinz, S. et al. Transcription elongation can affect genome 3D structure. Cell 174, 1522–1536.e22 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Murphy, Z. C. et al. Regulation of RNA polymerase II activity is essential for terminal erythroid maturation. Blood 138, 1740–1756 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Mumbach, M. R. et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat. Methods 13, 919–922 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Crane, E. et al. Condensin-driven remodelling of X chromosome topology during dosage compensation. Nature 523, 240–244 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Rao, S. S. P. et al. Cohesin loss eliminates all loop domains. Cell 171, 305–320.e24 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Rubio, E. D. et al. CTCF physically links cohesin to chromatin. Proc. Natl Acad. Sci. USA 105, 8309–8314 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Gillespie, M. A. et al. Absolute quantification of transcription factors reveals principles of gene regulation in erythropoiesis. Mol. Cell 78, 960–974.e11 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Nora, E. P. et al. Targeted degradation of CTCF decouples local insulation of chromosome domains from genomic compartmentalization. Cell 169, 930–944.e22 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Morris, S. A. et al. Overlapping chromatin-remodeling systems collaborate genome wide at dynamic chromatin transitions. Nat. Struct. Mol. Biol. 21, 73–81 (2014).

    Article  CAS  PubMed  Google Scholar 

  41. Barisic, D., Stadler, M. B., Iurlaro, M. & Schubeler, D. Mammalian ISWI and SWI/SNF selectively mediate binding of distinct transcription factors. Nature 569, 136–140 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Stadhouders, R. et al. Transcription factors orchestrate dynamic interplay between genome topology and gene regulation during cell reprogramming. Nat. Genet. 50, 238–249 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Beagan, J. A. et al. YY1 and CTCF orchestrate a 3D chromatin looping switch during early neural lineage commitment. Genome Res. 27, 1139–1152 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Phillips-Cremins, J. E. et al. Architectural protein subclasses shape 3D organization of genomes during lineage commitment. Cell 153, 1281–1295 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Fulco, C. P. et al. Activity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbations. Nat. Genet. 51, 1664–1669 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Qi, Q. et al. Dynamic CTCF binding directly mediates interactions among cis-regulatory elements essential for hematopoiesis. Blood 137/10, 1327–1339 (2020).

    Google Scholar 

  47. Kang, Y. A. et al. Autophagy driven by a master regulator of hematopoiesis. Mol. Cell. Biol. 32, 226–239 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Jiang, Y. et al. Genome-wide analyses of chromatin interactions after the loss of Pol I, Pol II, and Pol III. Genome Biol. 21, 158 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Tsai, S. F. et al. Cloning of cDNA for the major DNA-binding protein of the erythroid lineage through expression in mammalian cells. Nature 339, 446–451 (1989).

    Article  CAS  PubMed  Google Scholar 

  50. Fujiwara, T. et al. Discovering hematopoietic mechanisms through genome-wide analysis of GATA factor chromatin occupancy. Mol. Cell 36, 667–681 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Yu, M. et al. Insights into GATA-1-mediated gene activation versus repression via genome-wide chromatin occupancy analysis. Mol. Cell 36, 682–695 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Wagenblast, E. et al. Functional profiling of single CRISPR/Cas9-edited human long-term hematopoietic stem cells. Nat. Commun. 10, 4730 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Soler, E. et al. The genome-wide dynamics of the binding of Ldb1 complexes during erythroid differentiation. Genes Dev. 24, 277–289 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Deng, W. et al. Reactivation of developmentally silenced globin genes by forced chromatin looping. Cell 158, 849–860 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Grigoryev, S. A. Higher-order folding of heterochromatin: protein bridges span the nucleosome arrays. Biochem. Cell Biol. 79, 227–241 (2001).

    Article  CAS  PubMed  Google Scholar 

  56. Shannon, M. F., Wigley, P. L. & Wells, J. R. Histone H5 and H1 cross-reacting material is restricted to erythroid cells in chicken. FEBS Lett. 186, 180–186 (1985).

    Article  CAS  PubMed  Google Scholar 

  57. Fishman, V. et al. 3D organization of chicken genome demonstrates evolutionary conservation of topologically associated domains and highlights unique architecture of erythrocytes’ chromatin. Nucleic Acids Res. 47, 648–665 (2019).

    Article  CAS  PubMed  Google Scholar 

  58. Ryzhkova, A., Taskina, A., Khabarova, A., Fishman, V. & Battulin, N. Erythrocytes 3D genome organization in vertebrates. Sci. Rep. 11, 4414 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Kieffer-Kwon, K. R. et al. Myc regulates chromatin decompaction and nuclear architecture during B cell activation. Mol. Cell 67, 566–578.e10 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Weintraub, A. S. et al. YY1 is a structural regulator of enhancer–promoter loops. Cell 171, 1573–1588.e28 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Deng, W. et al. Controlling long-range genomic interactions at a native locus by targeted tethering of a looping factor. Cell 149, 1233–1244 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Lee, H. Y. et al. PPAR-α and glucocorticoid receptor synergize to promote erythroid progenitor self-renewal. Nature 522, 474–477 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Lamsfus-Calle, A. et al. Comparative targeting analysis of KLF1, BCL11A, and HBG1/2 in CD34+ HSPCs by CRISPR/Cas9 for the induction of fetal hemoglobin. Sci. Rep. 10, 10133 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. 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  Google Scholar 

  65. Durand, N. C. et al. Juicebox provides a visualization system for Hi-C contact maps with unlimited zoom. Cell Syst. 3, 99–101 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. 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 

  67. 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 

  68. Flyamer, I. M., Illingworth, R. S. & Bickmore, W. A. Coolpup.py: versatile pile-up analysis of Hi-C data. Bioinformatics 36, 2980–2985 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. 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 

  70. Jou, J. et al. The ENCODE portal as an epigenomics resource. Curr. Protoc. Bioinformatics 68, e89 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Skene, P. J., Henikoff, J. G. & Henikoff, S. Targeted in situ genome-wide profiling with high efficiency for low cell numbers. Nat. Protoc. 13, 1006–1019 (2018).

    Article  CAS  PubMed  Google Scholar 

  73. 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 

  74. Schmid, M., Durussel, T. & Laemmli, U. K. ChIC and ChEC; genomic mapping of chromatin proteins. Mol. Cell 16, 147–157 (2004).

    CAS  PubMed  Google Scholar 

  75. Liu, N. et al. Direct promoter repression by BCL11A controls the fetal to adult hemoglobin switch. Cell 173, 430–442.e17 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. 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 

  77. Meers, M. P., Tenenbaum, D. & Henikoff, S. Peak calling by Sparse Enrichment Analysis for CUT&RUN chromatin profiling. Epigenetics Chromatin 12, 42 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  79. 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 

  80. Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  PubMed  Google Scholar 

  81. Wagner, G. P., Kin, K. & Lynch, V. J. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci. 131, 281–285 (2012).

    Article  CAS  PubMed  Google Scholar 

  82. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  86. Yu, G., Wang, L. G. & He, Q. Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).

    Article  CAS  PubMed  Google Scholar 

  87. Vainshtein, Y., Rippe, K. & Teif, V. B. NucTools: analysis of chromatin feature occupancy profiles from high-throughput sequencing data. BMC Genomics 18, 158 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Hu, S. et al. CAM: a quality control pipeline for MNase-seq data. PLoS ONE 12, e0182771 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Lareau, C. A. & Aryee, M. J. hichipper: a preprocessing pipeline for calling DNA loops from HiChIP data. Nat. Methods 15, 155–156 (2018).

    Article  CAS  PubMed  Google Scholar 

  90. Niu, J. et al. A novel cytogenetic method to image chromatin interactions at subkilobase resolution: Tn5 transposase-based fluorescence in situ hybridization. J. Genet. Genomics 47, 727–735 (2020).

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank the Flow Cytometry Core and Imaging Core of the National Center for Protein Sciences at Peking University. We thank the High-Performance Computing Platform of the Center for Life Sciences (Peking University) for supporting data analysis. We are grateful to H. Lyu, F. Wang, Y. Guo and C. Shan for technical support. We thank Z. Wang at the Chinese Academy of Medical Sciences and Peking Union Medical College for helping with the immunofluorescence assay and proofreading the manuscript. We thank X. Ji, Y. Jiang and R. Wang at Peking University for helping with the Hi-C method. We thank C. Li, W. Tao and C. Zhang at Peking University for advice on data analysis. This project was supported by the National Key Research & Development Program of China (2022YFA1103300), the National Natural Science Foundation of China (81970110) and the Peking-Tsinghua Center for Life Sciences and School of Life Sciences at Peking University (to H.-Y.L.). This work was supported by the National Key Research and Development Program (2017YFA0104500) and the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (81621001) (to X.-J.H.).

Author information

Author notes

  1. These authors contributed equally: Dong Li, Fan Wu.

Authors and Affiliations

  1. Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing, China

    Dong Li, Fan Wu, Shuo Zhou & Hsiang-Ying Lee

  2. Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

    Dong Li, Fan Wu, Shuo Zhou, Xiao-Jun Huang & Hsiang-Ying Lee

  3. Joint Graduate Program of Peking-Tsinghua-NIBS, School of Life Sciences, Tsinghua University, Beijing, China

    Dong Li

  4. Peking University People’s Hospital, Peking University Institute of Hematology, National Clinical Research Center for Hematologic Disease, Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Collaborative Innovation Center of Hematology, Peking University, Beijing, China

    Xiao-Jun Huang & Hsiang-Ying Lee

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Contributions

D.L., F.W., X.-J.H. and H.-Y.L. conceptualized this project and designed the experiments. D.L. and S.Z. performed the experiments. F.W. and D.L. conducted the bioinformatics analyses. D.L., F.W., X.-J.H. and H.-Y.L. wrote the manuscript. All authors discussed the results and commented on the manuscript.

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Correspondence to Hsiang-Ying Lee.

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Nature Structural & Molecular Biology thanks Daria Onichtchouk and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Sara Osman and Dimitris Typas, in collaboration with the Nature Structural & Molecular Biology team.

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Extended data

Extended Data Fig. 1 Purification and validation of human erythroblasts at distinct terminal stages.

a, Fluorescence-activated cell sorting (FACS) scheme for isolating primary human terminal erythroblasts of distinct stages from the ex vivo CD34+ erythroid differentiation system. To avoid the effects of the cell cycle, G0/G1 cells were gated based on the Hoechst 33342 signal. CD235a+ CD71+ CD45low CD36+ CD117high CD105high cells are pro-erythroblasts (Pro-Es), CD235a+ CD71+ CD45low CD36+ CD117dim CD105dim cells are basophilic erythroblasts (Baso-Es), CD235a+ CD71+ CD45low CD36+ CD117− CD105-FSChigh cells are polychromatic erythroblasts (Poly-Es), and CD235a+ CD71+ CD45low CD36+ CD117− CD105− FSClow cells are orthochromatic erythroblasts (Ortho-Es) (n = 3). b, Representative images of purified primary Pro-Es, Baso-Es, Poly-Es and Ortho-Es by benzidine-Giemsa staining (3 independent experiments). c, Pellets of 1 million cells at the indicated stages demonstrating the accumulation of hemoglobin along with erythroid differentiation. d, Nuclear diameter and area of sorted Pro-Es, Baso-Es, Poly-Es and Ortho-Es (n = 20, the median is shown at the center line, and the whisker indicate the min and max value). e, Cell cycle profiles of Pro-Es, Baso-Es, Poly-Es and Ortho-Es without gating for specific cell cycle stages. f, Proliferation assay of FACS-purified Pro-Es, Baso-Es, Poly-Es and Ortho-Es (starting from 1 million cells/sample, mean ± SD, n = 5). g, Expression of CD105 and CD117 (c-Kit) on the cell surface of uncultured (Day 0) and four-day cultured (Day 4) Pro-Es (n = 3). h, Cell size (indicated by forward scatter, FSC) of uncultured (Day 0) and four-day cultured (Day 4) Pro-Es (n = 3). i, Hoechst 33342 staining of uncultured (Day 0) and four-day cultured (Day 4) Pro-Es. The arrow indicates enucleated reticulocytes (3 independent experiments). j, Representative image of four-day cultured (Day 4) Pro-Es by Giemsa staining. (arrows indicate enucleated reticulocytes) (3 independent experiments).

Source data

Extended Data Fig. 2 Overview of chromatin architecture during terminal erythropoiesis.

a, Hi-C contact probability as a function of distance of non-cell-cycle-specific Pro-Es and Ortho-Es. P(s)~S−0.5 indicates the mitotic state, and ~S−1 indicates the fractal globule state. b, Estimation of nucleosomal-repeat length (NRL) based on MNase-seq in Pro-Es and Ortho-Es. c, Compartment analysis in Pro-Es and Ortho-Es based on the PC1 value of the Hi-C assay (AA: compartment A in Pro-Es, remaining compartment A in Ortho-Es; BB: compartment B in Pro-Es, remaining compartment B in Ortho-Es; AB: compartment A in Pro-E, switching to compartment B in Ortho-Es; BA: compartment B in Pro-Es, switching to compartment A in Ortho-Es). d-f, Normalized signal of RNA-seq (d), normalized H3K27ac signal (e) and H3K9me3 signal (f) by the CUT&RUN assay in compartment A (Pro-E: n = 11,794; Ortho-E: n = 13,512) or B (Pro-E: n = 16,632; Ortho-E: n = 14,912) of Pro-Es and Ortho-Es (res = 500kb). TPM, transcripts per kilobase million. Boxplot central line, median; upper and lower limits, 75th and 25th percentiles; whiskers, box height represents interquartile range (IQR) and whiskers are 1.5 × IQR. g, (Top) Formula for quantification of compartment strength; (bottom) compartment strength in Pro-Es and Ortho-Es. h, Schematic description of interchromosomal fraction (ICF). i-k, ICF for chromatin interactions in the whole genome (i), compartment A (j) and compartment B (k) of Pro-Es and Ortho-Es. Boxplot data points represent each 5kb bin in the whole genome (n = 565,693), compartment A (n = 210,215) and compartment B (n = 355,478). Boxplot central line, median; upper and lower limits, 75th and 25th percentiles; whiskers, box height represents interquartile range (IQR) and whiskers are 1.5 × IQR. The p values were calculated by the two-sided Wilcoxon signed-rank test (the exact p values are shown at the top of the plots).

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Extended Data Fig. 3 Characterization of heterochromatin rewiring in terminal erythroid differentiation.

a, (Left) Immunofluorescence images of the heterochromatin marker H3K9me3 (red) in Pro-Es; DNA was counterstained with DAPI (blue). (Right) Signal distribution of H3K9me3 across the corresponding line of cells indicated (n = 21 cells examined over 3 independent experiments). b, (Left) Immunofluorescence images of the heterochromatin marker H3K9me3 (red) in Ortho-Es; DNA was counterstained with DAPI (blue). (Right) Signal distribution of H3K9me3 across the corresponding line of cells indicated (n = 21 cells examined over 3 independent experiments). c, Compartment (PC1 value of Hi-C) and H3K9me3 (CUT&RUN) distribution of Pro-Es and Ortho-Es at Chr2: 0–60 Mb. Red: compartment A; blue: compartment B. d, Immunoblotting of H3K9me3 and H3K9me2 in Pro-Es, Baso-Es, Poly-Es and Ortho-Es (2 independent experiments; densitometry quantifications were performed twice, and the mean values are shown beneath the corresponding bands). e, FISH images using probes of site 2 and site 3 (genomic locations indicated in Fig. 2g.) to demonstrate intercompartment interaction between A (site 3) and B (site 2) at the single-nucleus level in Pro-Es (left) and Ortho-Es (right); Probe 2: red, Probe 3: green (3 independent experiments). f, The overlap percentage of Probe 2 and Probe 3 in Pro-Es and Ortho-Es demonstrates the frequency of A-B intercompartment interactions (n = 50 cells examined over 3 independent experiments).

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Extended Data Fig. 4 Dynamic features of TAD and domain boundary during terminal erythropoiesis.

a, TAD numbers called by different algorithms in Pro-Es and Ortho-Es. G0/G1 samples were used for analysis unless otherwise noted. b, TAD length distribution in Pro-Es and Ortho-Es. c, The average insulation score at TADs and the neighboring regions (±0.5 TAD length) of Pro-Es (cyan, purple) and Ortho-Es (red, green). The analysis is based on merged TADs of Pro-Es and Ortho-Es. d, Average insulation scores of the disrupted, maintained and de novo-formed TAD boundaries in Pro-Es and Ortho-Es. e&f, Volcano plot of differential and constant binding of CTCF (e) and SMC3 (f) between Pro-Es and Ortho-Es. The differentially peaks are called by the diffbind R package. Increased, reduced and stable peaks are plotted as orange, blue and grey dots, respectively (fold change > 2, FDR < = 0.05). g, Immunoblotting of CTCF, SMC3, RAD21, SNF2H, BRG1, CHD4, ACTB and histone H3 in the Pro-E, Baso-E, Poly-E and Ortho-E stages. (Quantification of protein level was normalized based on total protein staining for each sample using the entire lane; quantifications were performed twice, and the mean values are shown beneath the corresponding bands mean). h, Quantification of ATP levels in Pro-E, Baso-E, Poly-E and Ortho-E stages (mean ± SD, n = 4). i, Nucleosomal signal (based on MNase-seq) at the sites with CTCF loss from Pro-Es to Ortho-Es.

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Extended Data Fig. 5 Domain strength dynamics in terminal erythroid development.

a, Heatmaps showing the normalized average interaction frequencies for TADs and the neighboring ±0.5 TAD regions in 10 TAD groups (defined in Methods). The stability of TADs decreased gradually from group 1 through group 10. b, Distribution of CTCF and SMC3 signals by CUT&RUN assay in TADs and the neighboring ±0.5 Mb regions within the stable (Groups 1 & 2) vs. disrupted (Groups 9 & 10) TADs. c, (Left) Top ten GO terms of upregulated genes in TAD regions of Groups 1 & 2; (right) top ten GO terms of downregulated genes in TAD regions of Groups 9 & 10. d, Schematic of FISH probe design demonstrating a promoter-enhancer interaction in a stable TAD region. Promoter (P) probe: GATA1 promoter region (chrX:48,642,195–48,646,409), and enhancer (E) probe (chrX:48,656,933–48,660,281). The H3K27ac-HiChIP track was generated by analyzing the GSE131055 dataset. e, FISH image of the GATA1 promoter probe (green) and enhancer probe (red) demonstrates the interaction between the GATA1 promoter and its enhancer at the single-nucleus level in Pro-Es (left) and Ortho-Es (right) (3 independent experiments). f, The overlap percentage of promoter probe and enhancer probe in Pro-Es and Ortho-Es demonstrates the frequency of E-P interactions (n = 50 cells examined over 3 independent experiments).

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Extended Data Fig. 6 Analysis of chromatin architecture in expelled nuclei and that in Ortho-Es under transcription inhibition.

a, Experimental scheme of transcription inhibition (Ti; DRB + triptolide) in Ortho-Es (n = 2). b, Western blotting of Pol II and GAPDH after 4 hours of Ti treatment in Ortho-Es (n = 2). c, TAD numbers in indicated cell populations and expelled nuclei (mean, n = 2). d, Boxplot showing the TAD boundary strength in Ortho-Es (4 h control or Ti) and nuclei. Boxplot data points represent each boundary of indicated cells or nuclei (4 h control: n = 1,280; 4 h Ti, n = 1,188; nuclei, n = 835). Boxplot central line, median; upper and lower limits, 75th and 25th percentiles; whiskers, box height represents interquartile range (IQR) and whiskers are 1.5 × IQR. The p values were calculated by the two-sided Wilcoxon signed-rank test (the p values are shown in the figure). e, Representative heatmap showing the relatively stable chromatin structure at Chr3: 9–11.5 Mb in Ortho-Es (4 h control or Ti) and expelled nuclei. f, Heatmaps showing the normalized average interaction frequencies for the three types of boundaries (referring to boundaries in Fig. 3a) and their neighboring regions (±0.5 TAD length) in Ortho-Es (4 h control or Ti) and expelled nuclei. g, The ratio of trans (interchromosomal) and cis (intrachromosomal) interactions to total chromatin interactions in Pro-Es, Ortho-Es (0 h, 4 h control or Ti), and nuclei (mean, n = 2). h, Compartment analysis in Ortho-Es (4 h control or Ti) based on the PC1 value of the Hi-C assay. (AA: compartment A in control, remaining compartment A in Ti; BB: compartment B in control, remaining compartment B in Ti; AB: compartment A in control, switching to compartment B in Ti; BA: compartment B in control, switching to compartment A in Ti). i, Boxplot showing the TAD score distribution of TADs in Ortho-Es (4 h control or Ti) and expelled nuclei. TAD regions are defined in the 4 h control stage (n = 1,263). Boxplot central line, median; upper and lower limits, 75th and 25th percentiles; whiskers, box height represents interquartile range (IQR) and whiskers are 1.5 × IQR. The p values were calculated by the two-sided Wilcoxon signed-rank test (the p values are shown in the figure).

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Extended Data Fig. 7 GATA1 and KLF1 motif accessibility and chromatin binding in terminal erythroblasts.

a, Heatmap showing the motif enrichment score based on the ATAC-seq assay. Peaks of differential chromatin accessibility in Pro-Es vs. Ortho-Es were ranked by the fold change and then grouped into ~1500 peaks/bin. (Each bin is shown as a square in the plot.) The bins from left to right were arranged from the chromatin accessible sites that are most enriched in Pro-Es to those that are most enriched in Ortho-Es. A de novo motif search was conducted in the peaks of the same bin. The factors of enriched motifs are listed on the right of the plot. b, GATA1- or KLF1-associated intra-TAD interaction frequency within TAD groups 1~10. Each group has n = 298 TADs. Boxplot central line, median; upper and lower limits, 75th and 25th percentiles; whiskers, box height represents interquartile range (IQR) and whiskers are 1.5 × IQR.

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Extended Data Fig. 8 Functional assays of GATA1 in chromatin domain maintenance.

a, Schematic description of GATA1 and KLF1 knockout strategies using CRISPR/Cas9 in purified primary human Pro-E cells. b, Immunoblotting of GATA1 after knockout by control, KLF1 or GATA1 sgRNA for 48 h in Pro-E. Histone H3 is shown as the loading control (3 independent experiments). c, Genotyping of KLF1 loci after knockout with control or KLF1 sgRNA for 48 h. (The arrow indicates the rejoined DNA after complete fragment removal by paired gRNAs, 3 independent experiments) d, Images of Pro-Es after knockout by control, GATA1 or KLF1 sgRNA for 72 h by benzidine-Giemsa staining (3 independent experiments). e, Cell viability after knockout by control, GATA1 or KLF1 sgRNA for 48 h or 72 h, measured by flow cytometry (percentage of DAPI-negative cells, mean ± SD, n = 3). f&g, (Top) Hi-C contact matrices of Pro-Es after knockout by control or GATA1 sgRNA for 72 h at Chr5: 111.5–113 Mb (f) and Chr6: 134–136 Mb (g). (Bottom) Corresponding CUT&RUN signal of GATA1 in Pro-E and Ortho-E, HiChIP of GATA1 in day 12 terminal erythroblasts, and CUT&RUN tracks of GATA1 in KO-Control and KO-GATA1 samples. Juicebox parameters were balanced and normalized at 5 kb resolution. The dashed triangle shows the disrupted TAD resulting from GATA1 knockout. h & i, Hi-C contact matrices of Pro-Es after knockout by control or KLF1 sgRNA for 72 h at Chr2: 60.5–63 Mb (h) and Chr3: 182–184 Mb (i). Juicebox parameters were balanced and normalized at 5 kb resolution. j, Density maps showing corresponding SMC3 and CTCF CUT&RUN signals at the GATA1 peaks in Pro-Es and Ortho-Es.

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Extended Data Fig. 9 Model of chromatin compaction and domain disruption in terminal erythropoiesis.

During terminal erythroid differentiation, chromatin is multidimensionally rearranged to achieve nuclear compaction to 20–30% of the original volume. a, Heterochromatin reorganizes by establishing long-range interactions and aggregating at the subnuclear membrane region. (NE: nuclear envelope; A: compartment A; B: compartment B). b, A large number of TADs undergo disruption, while selected TADs enriched for transcription competence markers are maintained until the end of erythroid differentiation. c, The early erythroblasts (Pro-Es) contain sufficient energy to sustain the nuclear structure relapse between normal cell cycles; the levels of ATP, CTCF and chromatin remodeling complex (CRC) decrease as terminal differentiation progresses, and therefore, the late erythroblasts (Ortho-Es) can maintain the chromatin architecture only in selected essential regions, which are enriched for transcription activity and/or the binding of critical transcription factors such as GATA1.

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Li, D., Wu, F., Zhou, S. et al. Heterochromatin rewiring and domain disruption-mediated chromatin compaction during erythropoiesis. Nat Struct Mol Biol 30, 463–474 (2023). https://doi.org/10.1038/s41594-023-00939-3

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