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BRD2 compartmentalizes the accessible genome

Nature Genetics volume 54, pages 481–491 (2022)Cite this article

Subjects

Abstract

Mammalian chromosomes are organized into megabase-sized compartments that are further subdivided into topologically associating domains (TADs). While the formation of TADs is dependent on cohesin, the mechanism behind compartmentalization remains enigmatic. Here, we show that the bromodomain and extraterminal (BET) family scaffold protein BRD2 promotes spatial mixing and compartmentalization of active chromatin after cohesin loss. This activity is independent of transcription but requires BRD2 to recognize acetylated targets through its double bromodomain and interact with binding partners with its low-complexity domain. Notably, genome compartmentalization mediated by BRD2 is antagonized on the one hand by cohesin and on the other hand by the BET homolog protein BRD4, both of which inhibit BRD2 binding to chromatin. Polymer simulation of our data supports a BRD2–cohesin interplay model of nuclear topology, in which genome compartmentalization results from a competition between loop extrusion and chromatin-state-specific affinity interactions.

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Fig. 1: Cohesin prevents accessible chromatin from spatial mixing.
Fig. 2: BRD2 mixes and compacts active chromatin in the absence of cohesin.
Fig. 3: BRD2 interplays with cohesin to safeguard active compartments.
Fig. 4: BRD4 antagonizes the role of BRD2 in genome organization.
Fig. 5: A revised polymer model recapitulates experimental observations.

Data availability

The raw sequencing data were deposited to NCBI GEO with accession number GSE163729. Source data are provided with this paper. Relevant data, blots, gels and so on used to plot the figures in the manuscript have been submitted as source data files.

Code availability

The polymer model simulation code is available at https://github.com/ZhangGroup-MITChemistry/Chrom_cluster. The code for identifying, localizing and plotting 3D ATAC-PALM data is freely available after execution of a research license with HHMI. The codes for the auto-correlation g(r) and cluster DBSCAN analysis is available at https://github.com/ammondongp/3D_ATAC_PALM.

References

  1. Dekker, J. et al. Capturing chromosome conformation. Science 295, 1306–1311 (2002).

    Article  CAS  PubMed  Google Scholar 

  2. Wit, E. D. E. & Laat, W. D. E. A decade of 3C technologies: insights into nuclear organization. Genes Dev. 26, 11–24 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Dekker, J., Marti-Renom, M. A. & Mirny, L. A. Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat. Rev. Genet. 14, 390–403 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Rowley, M. J. & Corces, V. G. Organizational principles of 3D genome architecture. Nat. Rev. Genet. 19, 789–800 (2018).

    Article  CAS  PubMed  Google Scholar 

  6. 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 

  7. 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 

  8. Nora, E. P. et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485, 381–385 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sexton, T. et al. Three-dimensional folding and functional organization principles of the Drosophila genome. Cell 148, 458–472 (2012).

    Article  CAS  PubMed  Google Scholar 

  10. Rao, S. S. P. 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 

  11. Mirny, L. A., Imakaev, M. & Abdennur, N. Two major mechanisms of chromosome organization. Curr. Opin. Cell Biol. 58, 142–152 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Alipour, E. & Marko, J. F. Self-organization of domain structures by DNA-loop-extruding enzymes. Nucleic Acids Res. 40, 11202–11212 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Fudenberg, G. et al. Formation of chromosomal domains by loop extrusion. Cell Rep. 15, 2038–2049 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Sanborn, A. L. et al. Chromatin extrusion explains key features of loop and domain formation in wild-type and engineered genomes. Proc. Natl Acad. Sci. USA 112, E6456–E6465 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 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 

  16. Wutz, G. et al. Topologically associating domains and chromatin loops depend on cohesin and are regulated by CTCF, WAPL, and PDS5 proteins. EMBO J. 36, 3573–3599 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Schwarzer, W. et al. Two independent modes of chromatin organization revealed by cohesin removal. Nature 551, 51–56 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Kim, Y., Shi, Z., Zhang, H., Finkelstein, I. J. & Yu, H. Human cohesin compacts DNA by loop extrusion. Science 366, 1345–1349 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Davidson, I. F. et al. DNA loop extrusion by human cohesin. Science 366, 1338–1345 (2019).

    Article  CAS  PubMed  Google Scholar 

  20. Luppino, J. M. et al. Cohesin promotes stochastic domain intermingling to ensure proper regulation of boundary-proximal genes. Nat. Genet. 52, 840–848 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Szabo, Q. et al. Regulation of single-cell genome organization into TADs and chromatin nanodomains. Nat. Genet. 52, 1151–1157 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 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 

  23. Kubo, N. et al. Promoter-proximal CTCF binding promotes distal enhancer-dependent gene activation. Nat. Struct. Mol. Biol. 28, 152–161 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Xie, L. et al. 3D ATAC-PALM: super-resolution imaging of the accessible genome. Nat. Methods 17, 430–436 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Bintu, B. et al. Super-resolution chromatin tracing reveals domains and cooperative interactions in single cells. Science 362, 419-426 (2018).

  26. Miron, E. et al. Chromatin arranges in chains of mesoscale domains with nanoscale functional topography independent of cohesin. Sci. Adv. 6, eaba8811 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Nuebler, J., Fudenberg, G., Imakaev, M., Abdennur, N. & Mirny, L. A. Chromatin organization by an interplay of loop extrusion and compartmental segregation. Proc. Natl Acad. Sci. USA 115, E6697–E6706 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Qi, Y. & Zhang, B. Predicting three-dimensional genome organization with chromatin states. PLoS Comput. Biol. 15, e1007024 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Qi, Y. et al. Data-driven polymer model for mechanistic exploration of diploid genome organization. Biophys. J. 119, 1905–1916 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Xie, W. J., Qi, Y. & Zhang, B. Characterizing chromatin folding coordinate and landscape with deep learning. PLoS Comput. Biol. 16, e1008262 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Falk, M. et al. Heterochromatin drives compartmentalization of inverted and conventional nuclei. Nature 570, 395–399 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Spracklin, G. et al. Heterochromatin diversity modulates genome compartmentalization and loop extrusion barriers. Preprint at bioRxiv https://doi.org/10.1101/2021.08.05.455340 (2021).

  33. Lu, J. Y. et al. Homotypic clustering of L1 and B1/Alu repeats compartmentalizes the 3D genome. Cell Res. 31, 613–630 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Nishimura, K., Fukagawa, T., Takisawa, H., Kakimoto, T. & Kanemaki, M. An auxin-based degron system for the rapid depletion of proteins in nonplant cells. Nat. Methods 6, 917–922 (2009).

    Article  CAS  PubMed  Google Scholar 

  36. Vian, L. et al. The energetics and physiological impact of cohesin extrusion. Cell 173, 1165–1178.e20 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Liu, Z. et al. 3D imaging of Sox2 enhancer clusters in embryonic stem cells. eLife 3, e04236 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Fudenberg, G., Abdennur, N., Imakaev, M., Goloborodko, A. & Mirny, L. A. Emerging evidence of chromosome folding by loop extrusion. Cold Spring Harb. Symp. Quant. Biol. 82, 45–55 (2017).

    Article  PubMed  Google Scholar 

  39. Tark-Dame, M., Jerabek, H., Manders, E. M. M., Heermann, D. W. & van Driel, R. Depletion of the chromatin looping proteins CTCF and cohesin causes chromatin compaction: insight into chromatin folding by polymer modelling. PLoS Comput. Biol. 10, e1003877 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Rowley, M. J. et al. Evolutionarily conserved principles predict 3D chromatin organization. Mol. Cell 67, 837–852.e7 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Nguyen, V. In vivo degradation of RNA polymerase II largest subunit triggered by alpha-amanitin. Nucleic Acids Res. 24, 2924–2929 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. El Khattabi, L. et al. A pliable mediator acts as a functional rather than an architectural bridge between promoters and enhancers. Cell 178, 1145–1158.e20 (2019).

    Article  CAS  PubMed  Google Scholar 

  43. Hsieh, T.-H. S. et al. Resolving the 3D landscape of transcription-linked mammalian chromatin folding. Mol. Cell 78, 539–553.e8 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. 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 

  45. Cho, W. K. et al. Mediator and RNA polymerase II clusters associate in transcription-dependent condensates. Science 361, 412–415 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Chong, S. et al. Imaging dynamic and selective low-complexity domain interactions that control gene transcription. Science 361, eaar2555 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Sabari, B. R. et al. Coactivator condensation at super-enhancers links phase separation and gene control. Science 361, 387–392 (2018).

    Article  CAS  Google Scholar 

  48. McSwiggen, D. T., Mir, M., Darzacq, X. & Tjian, R. Evaluating phase separation in live cells: diagnosis, caveats, and functional consequences. Genes Dev. 33, 1619–1634 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Kato, M. & McKnight, S. L. A solid-state conceptualization of information transfer from gene to message to protein. Annu. Rev. Biochem. 87, 351–390 (2017).

    Article  PubMed  CAS  Google Scholar 

  50. Cochran, A. G., Conery, A. R. & Sims, R. J. Bromodomains: a new target class for drug development. Nat. Rev. Drug Discov. 18, 609–628 (2019).

    Article  CAS  PubMed  Google Scholar 

  51. Belkina, A. C. & Denis, G. V. BET domain co-regulators in obesity, inflammation and cancer. Nat. Rev. Cancer 12, 465–477 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Nabet, B. et al. The dTAG system for immediate and target-specific protein degradation. Nat. Chem. Biol. 14, 431–441 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Hsu, S. C. et al. The BET protein BRD2 cooperates with CTCF to enforce transcriptional and architectural boundaries. Mol. Cell 66, 102–116.e7 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Cheung, K. L. et al. Distinct roles of Brd2 and Brd4 in potentiating the transcriptional program for Th17 cell differentiation. Mol. Cell 65, 1068–1080.e5 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Buckle, A., Brackley, C. A., Boyle, S., Marenduzzo, D. & Gilbert, N. Polymer simulations of heteromorphic chromatin predict the 3D folding of complex genomic loci. Mol. Cell 72, 786–797 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Brackley, C. A., Johnson, J., Kelly, S., Cook, P. R. & Marenduzzo, D. Simulated binding of transcription factors to active and inactive regions folds human chromosomes into loops, rosettes and topological domains. Nucleic Acids Res. 44, 3503–3512 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Bianco, S. et al. Computational approaches from polymer physics to investigate chromatin folding. Curr. Opin. Cell Biol. 64, 10–17 (2020).

    Article  CAS  PubMed  Google Scholar 

  58. Gyuris, A. et al. The chromatin-targeting protein Brd2 is required for neural tube closure and embryogenesis. Biochim. Biophys. Acta Gene Regul. Mech. 1789, 413–421 (2009).

    Article  CAS  Google Scholar 

  59. Shang, E., Wang, X., Wen, D., Greenberg, D. A. & Wolgemuth, D. J. Double bromodomain-containing gene Brd2 is essential for embryonic development in mouse. Dev. Dyn. 238, 908–917 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Olp, M., Jackson, V. & Smith, B. Nucleosome scaffolding by Brd4 tandem bromodomains in acetylation-dependent chromatin compartmentalization. Preprint at bioRxiv https://doi.org/10.1101/699967 (2019).

  61. Rahman, S. et al. The Brd4 extraterminal domain confers transcription activation independent of pTEFb by recruiting multiple proteins, including NSD3. Mol. Cell. Biol. 31, 2641–2652 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Stigler, J., Çamdere, G. Ö., Koshland, D. E. & Greene, E. C. Single-molecule imaging reveals a collapsed conformational state for DNA-bound cohesin. Cell Rep. 15, 988–998 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Ladurner, A. G., Inouye, C., Jain, R. & Tjian, R. Bromodomains mediate an acetyl-histone encoded antisilencing function at heterochromatin boundaries. Mol. Cell 11, 365–376 (2003).

    Article  CAS  PubMed  Google Scholar 

  64. Kellner, W. A. et al. Distinct isoforms of the Drosophila Brd4 homologue are present at enhancers, promoters and insulator sites. Nucleic Acids Res. 41, 9274–9283 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Fujisawa, T. & Filippakopoulos, P. Functions of bromodomain-containing proteins and their roles in homeostasis and cancer. Nat. Rev. Mol. Cell Biol. 18, 246–262 (2017).

    Article  CAS  PubMed  Google Scholar 

  66. Kim, J. J. et al. Systematic bromodomain protein screens identify homologous recombination and R-loop suppression pathways involved in genome integrity. Genes Dev. 33, 1751–1774 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Zhao, R., Nakamura, T., Fu, Y., Lazar, Z. & Spector, D. L. Gene bookmarking accelerates the kinetics of post-mitotic transcriptional re-activation. Nat. Cell Biol. 13, 1295–1304 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Devaiah, B. N. et al. BRD4 is a histone acetyltransferase that evicts nucleosomes from chromatin. Nat. Struct. Mol. Biol. 23, 540–548 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Chen, H.-S. et al. BET-inhibitors disrupt Rad21-dependent conformational control of KSHV latency. PLoS Pathog. 13, e1006100 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Linares-Saldana, R. et al. BRD4 orchestrates genome folding to promote neural crest differentiation. Nat. Genet. 53, 1480–1492 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. 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 

  72. Stadhouders, R., Filion, G. J. & Graf, T. Transcription factors and 3D genome conformation in cell-fate decisions. Nature 569, 345–354 (2019).

    Article  CAS  PubMed  Google Scholar 

  73. Misteli, T. The self-organizing genome: principles of genome architecture and function. Cell 183, 28–45 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Shrinivas, K. et al. Enhancer features that drive formation of transcriptional condensates. Mol. Cell 75, 549–561.e7 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Cai, D. et al. Phase separation of YAP reorganizes genome topology for long-term YAP target gene expression. Nat. Cell Biol. 21, 1578–1589 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Shin, Y. et al. Liquid nuclear condensates mechanically sense and restructure the genome. Cell 175, 1481–1491 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Xiao, J. Y., Hafner, A. & Boettiger, A. N. How subtle changes in 3D structure can create large changes in transcription. eLife 10, e64320 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Bustin, M. & Misteli, T. Nongenetic functions of the genome. Science 352, aad6933–aad6933 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Krietenstein, N. et al. Ultrastructural details of mammalian chromosome architecture. Mol. Cell 78, 554–565.e7 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Chen, H. et al. Dynamic interplay between enhancer–promoter topology and gene activity. Nat. Genet. 50, 1296–1303 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Gu, B. et al. Transcription-coupled changes in nuclear mobility of mammalian cis-regulatory elements. Science 359, 1050–1055 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Zuin, J. et al. Nonlinear control of transcription through enhancer–promoter interactions. Preprint at bioRxiv https://doi.org/10.1101/2021.04.22.440891 (2021).

  83. Otterstrom, J. et al. Super-resolution microscopy reveals how histone tail acetylation affects DNA compaction within nucleosomes in vivo. Nucleic Acids Res. 47, 8470–8484 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Gibson, B. A. et al. Organization of chromatin by intrinsic and regulated phase separation. Cell 179, 470–484.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Rosencrance, C. D. et al. Chromatin hyperacetylation impacts chromosome folding by forming a nuclear subcompartment. Mol. Cell 78, 112–126.e12 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Gordon, D. E. et al. A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature 583, 459–468 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Gilan, O. et al. Selective targeting of BD1 and BD2 of the BET proteins in cancer and immunoinflammation. Science 368, 387–394 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Hansen, A. S., Pustova, I., Cattoglio, C., Tjian, R. & Darzacq, X. CTCF and cohesin regulate chromatin loop stability with distinct dynamics. eLife 6, e25776 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Wu, S.-Y. et al. Opposing functions of BRD4 isoforms in breast cancer. Mol. Cell 78, 1114–1132.e10 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Morawska, M. & Ulrich, H. D. An expanded tool kit for the auxin-inducible degron system in budding yeast. Yeast 30, 341–351 (2013).

    Article  CAS  PubMed  Google Scholar 

  91. Chen, J. et al. Single-molecule dynamics of enhanceosome assembly in embryonic stem cells. Cell 156, 1274–1285 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Chen, B. C. et al. Lattice light-sheet microscopy: Imaging molecules to embryos at high spatiotemporal resolution. Science 346, 1257998 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  93. Legant, W. R. et al. High-density three-dimensional localization microscopy across large volumes. Nat. Methods 13, 359–365 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  94. Beliveau, B. J. et al. Versatile design and synthesis platform for visualizing genomes with Oligopaint FISH probes. Proc. Natl Acad. Sci. USA 109, 21301–21306 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Boettiger, A. N. et al. Super-resolution imaging reveals distinct chromatin folding for different epigenetic states. Nature 529, 418–422 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Beliveau, B. J. et al. Single-molecule super-resolution imaging of chromosomes and in situ haplotype visualization using Oligopaint FISH probes. Nat. Commun. 6, 7147 (2015).

    Article  CAS  PubMed  Google Scholar 

  97. Rhodes, J. D. P. et al. Cohesin disrupts polycomb-dependent chromosome interactions in embryonic stem cells. Cell Rep. 30, 820–835.e10 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Huff, J. The Airyscan detector from ZEISS: confocal imaging with improved signal-to-noise ratio and super-resolution. Nat. Methods 12, i–ii (2015).

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Acknowledgements

We thank the Tjian-Darzacq laboratory members for helpful discussions, C. Cattoglio and members of the Casellas laboratory for critical reading of the manuscript and constructive suggestions. We also thank K. Schaefer from Janelia Cell and Molecular Biology Shared Resources for assistance with cell sorting and CytoFLEX flow cytometry experiments, D. Alcor from Janelia Advanced Imaging Center for assistance with imaging, S. Shrestha and A. Lemire from Janelia Quantitative Genomics core for assistance with next-generation sequencing, T. Lu and L. Xie for help with graphics and M. Radcliff for administrative support. L.X., P.D., Z.L. and R.T. are funded by the Howard Hughes Medical Institute (HHMI). L.X. also acknowledges support from the Janelia Visitor Program. Y.Q. and B.Z. are supported by the National Science Foundation Grants MCB-1715859. H.Y.C. acknowledges support by National Institutes of Health (NIH) grant P50-HG007735. X.C. is funded by Swedish Research Council International Postdoctoral Fellowship (VR-2016- 06794) and Starting grant (VR-2017-02074), Jeanssons Foundation (JS2018-0004) and Vleugl grant.

Author information

Author notes

  1. These authors contributed equally: Liangqi Xie, Peng Dong.

  2. These authors jointly supervised this work: Bin Zhang, Robert Tjian, Zhe Liu.

Authors and Affiliations

  1. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA

    Liangqi Xie, Peng Dong, Brian P. English & Zhe Liu

  2. Department of Molecular and Cell Biology, Li Ka Shing Center for Biomedical and Health Sciences, CIRM Center of Excellence, University of California, Berkeley, CA, USA

    Liangqi Xie, Tsung-Han S. Hsieh & Robert Tjian

  3. Departments of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA

    Yifeng Qi, Margherita De Marzio & Bin Zhang

  4. Howard Hughes Medical Institute, Berkeley, CA, USA

    Tsung-Han S. Hsieh & Robert Tjian

  5. Lymphocyte Nuclear Biology, NIAMS and Center of Cancer Research, NCI, NIH, Bethesda, MD, USA

    SeolKyoung Jung & Rafael Casellas

  6. Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden

    Xingqi Chen

  7. Center for Personal Dynamic Regulomes and Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA

    Howard Y. Chang

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  1. Liangqi Xie
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Contributions

L.X. and Z.L. conceived the project. L.X. constructed reagents/cell lines, performed imaging (Oligopaint, single-molecule imaging), genomics (Micro-C, ChIP-seq, ATAC-seq), biochemical experiments and related analysis. P.D. performed 3D ATAC-PALM related experiments and analysis. Y.Q. constructed the polymer model and performed related simulations. T.-H.S.H. performed the Micro-C analysis. B.P.E. assisted with single molecule tracking experiments. S.-K.J. performed in situ Hi-C analysis with supervision from R.C. M.D.M performed related polymer model simulation. X.C. and H.Y.C. provided reagents and inputs to this study. L.X., Z.L. wrote the manuscript with inputs from all authors. Z.L., R.T., B.Z. jointly supervised the study, guided experimental design and data analysis.

Corresponding authors

Correspondence to Bin Zhang, Robert Tjian or Zhe Liu.

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Nature Genetics thanks Giacomo Cavalli, Marcelo Nollmann, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Efficient AID degron-mediated RAD21 depletion.

(a) Western blot (WB) analysis of protein levels of endogenous RAD21-HaloTag-mAID at indicated time points after the auxin treatment or the wash-off recovery (independently repeated 3 times). (b) The WB intensity is represented as grey scale values for the corresponding WB bands in (a). (c) Single cell fluorescence intensity analysis of HaloTag-mAID-RAD21 and OCT4 protein levels after auxin treatment shown in Fig. 1a. FU, arbitrary fluorescent unit. The black line represents mean fluorescence value. (d) Flow cytometry analysis of RAD21 levels before (-auxin), after (+auxin) auxin treatment and during the recovery after auxin wash off. The parent Tir1 ES cells (Tir1 – auxin; Tir1 + auxin) were used as the negative control (adopted from Extended Data Fig. 4d from our previous work24). 50,000 gated live cells were recorded and analyzed for each condition. (e) DNA synthesis analysis of RAD21-HaloTag-mAID ESCs by the Click-iT EdU labeling kit at the indicated time points after auxin treatment. The same number (50,000) of cells were analyzed for all conditions. (f) Cell proliferation analysis of parental Tir1 (left) and RAD21-Halo-mAID (right) ESCs after auxin treatment. Cells were treated with auxin for indicated time points and the total cell number was measured after 48 hours. Data from two replicates were shown. (g) The inverse relationship between clustering amplitude (A) and residual RAD21 levels, in which two different auxin concentrations (5 µM and 500 µM) were used to generate a gradient of RAD21 level in single cells. (h) Pair auto-correlation function g(r) of ATAC-PALM localizations from RAD21-GFP-mAID ESCs under normal, auxin-treated and recovery conditions after auxin wash off (~24 hours). Error bar represents SEM. The two-sided Mann-Whitney U test was performed. (i) HaloTag-SOX2 fusion protein is stably expressed in the RAD21-mAID-GFP cells co-expressing the E3 ligase osTir1. Scale bar = 5µm. (j) WB analysis of RAD21, OCT4 or SOX2 upon auxin addition. SOX2 antibody detects both endogenous (lower band) and ectopic expressed HaloTag-SOX2 proteins (upper band) (independently repeated twice). (k) ATAC-seq analysis of SOX2 binding sites after acute RAD21 depletion. (l-m) ATAC-seq analysis of RAD21-HaloTag-mAID cells under conditions including without auxin, auxin treatment and washout after auxin treatment.

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Extended Data Fig. 2 Validation of 3D ATAC-PALM observations by in situ Hi-C and OligoPaint FISH.

(a) Representative in situ Hi-C contact map from the chr4: 52960000- 58120000 genomic region. The upper triangle is control and lower triangle is after 6 hours RAD21 depletion. (b) Genome-wide contact probability decaying curve by in situ Hi-C. (c) Violin plot of log2 scale fold change of total contacts from genome-wide intra segments (ATAC-rich (R) and ATAC-poor (P) based on chromatin accessibility from ATAC-seq data) from 6 hours RAD21 depletion compared to untreated control. (d) Violin plot of log2 scale fold change of total contacts from genome-wide adjacent inter segments (R-R, P-P, R-P) from RAD21 depletion vs untreated control. The contacts from neighboring R-R or P-P segments are increased after RAD21 depletion whereas those from adjacent R-P segments are decreased. For (c-d), the box and whisker plot show the minimum, lower quartile, median, upper quartile and maximum. (e) In situ Hi-C Pearson correlation matrix map of chromosome 17 for Control and RAD21 depletion for 6 hours. The compartmental scores of eigenvector PC1 value track was plotted on the bottom of each map. (f-g) Alignment of Hi-C heatmap, ATAC-seq, RAD21 ChIP-seq tracks on two chromosomal regions (chr4 and chr6). DNA-FISH probes corresponding to loci pairs in adjacent ATAC-rich segments are marked as red and green dots joined by a black line. Six ATAC-rich (R1-6; red) segments are underlined. The genomic length of each segment is listed in Supplementary Table 4. (h) 3D physical distances between intra-chromosomal loci pairs are significantly reduced upon Cohesin depletion. Specifically, cumulative distribution function (CDF) of loci pair distances was plotted before and after Cohesin depletion. (i) Representative two-color 3D Oligopaint FISH images are shown for the loci pair in Chr6 under Control (left panel) and RAD21-depleted (right panel, auxin treatment) conditions (independently repeated 3 times). Scale bar, 2 µm. (j) The CDF for the 3D distances of loci pairs between two chromosomes (red to red, green to green) upon Cohesin depletion. For (h) and (j), the two-sample Kolmogorov-Smirnov test was performed. n.s., not significant.

Extended Data Fig. 3 Polymer model simulation based on loop extrusion only.

(a) Representative iso-surfaces of an ATAC-rich segment (R5, red) in Control and RAD21 depletion conditions. Scale bar, 1 µm. (b) (Left)Violin plot of 3D volumes (the number of voxels) of six ATAC-rich segments (R1-R6) before and after RAD21 depletion. The number of alleles analyzed is indicated at the bottom. The black bar indicates the median value. (Right)The paired mean value of 3D volume for the 6 ATAC-rich regions before and after RAD21 depletion is plotted. (c) Quantification of nuclei volume as determined by DAPI signal before (n=65) and after (n=69) RAD21 depletion for 6 hours. Dashed lines indicate the first and third quartiles. (d) The schematic overview of the polymer simulation. The chromatin polymer is modeled as beads on a string. We simulated two ATAC-rich segments (pink) on both ends and one ATAC-poor segments (blue) in the middle (See more details in the Methods). (e) Contact probability map for the wild type (WT), CTCF depletion and Cohesin depletion conditions by considering loop extrusion mechanism alone. Two panels shown on the top are distinct ATAC segments and the relative positions of CTCF binding beads in the polymer model. (f-g) Distributions for the volume of the convex hull (f) or the pair auto-correlation function g(r) (g) of the ATAC-rich segments after CTCF depletion base on the ‘loop-extrusion only’ simulation model. (h-i) Simulation results are robust with respect to the setup of the model. (h) g(r) for varying the location and density of CTCF-binding sites. Dense, normal, light CTCF densities and random correspond to 36, 24, 12 and 24 CTCF sites on the chromatin, respectively. (i) Pair auto-correlation function g(r) for varying the number of Cohesin molecules on the chromatin. Dense, normal, light Cohesin densities correspond to the extrusion separation of 45kb, 90kb and 180kb, respectively. (j-k) The 3D loci pair distance from neighboring ATAC-rich segments (j) and distributions for the volume of the convex hull of ATAC-rich segments (k) were extracted in WT and Cohesin depleted conditions in which the ‘loop-extrusion only’ simulation model is considered. (l) Summary of accessible chromatin clustering, neighboring distance, and compaction based on ‘loop-extrusion only’ polymer model simulation compared with experimental observations. The two-sided Mann-Whitney U test was applied.

Extended Data Fig. 4 Regulation of accessible chromatin domains involves BET family proteins.

(a) A schematic of multiple small molecule inhibitors to target highly enriched nuclear proteins in the enhancer and promoter regions within ACDs. Alpha-amanitin inhibits the RNA Polymerase II by selectively targeting its largest subunit Rpb1 for degradation. JQ1 specifically blocks BET proteins from binding to acetyl-lysine residuals on target proteins (for example, nucleosomes). 1,6-hexanediol (1,6-HD) targets the protein-protein interactions (for example, the Mediator complex) often mediated by low complexity domains (LCDs). (b-c) Immunofluorescence analysis of Rbp1 before and after 5 hours of alpha-amanitin treatment (independently repeated 3 times). (b) Cells were treated with alpha-amanitin at a final concentration of 100 µg/ml for 5 hours and then fixed for immunofluorescence analysis by using purified anti-RNA Polymerase II RPB1 antibody (H14, BioLegend). Scale bar, 5µm. (c) Quantification of fluorescence intensity values over 10 individual cells for each condition. (d) Disrupting BET-chromatin interactions or LCD mediated protein-protein interactions by JQ1 or 1,6HD, respectively, reduces accessible chromatin clustering measured by 3D ATAC-PALM after Cohesin depletion whereas alpha-amanitin treatment had no significant effect. g(r) curves were plotted for indicated conditions. (e) Treatment of JQ1, 1,6HD or alpha-amanitin does not reduces accessible chromatin clustering in the presence of Cohesin. g(r) curves were plotted for indicated conditions. In (d) and (e), the error bar represents SEM and the two-sided Mann-Whitney U test was used for statistical testing. n.s., not significant. (f-g) Violin plot of 3D volumes (the number of voxels) of two ATAC-rich segments R2 (f) and R5 (g) before (grey) and after (red) auxin-mediated RAD21 depletion with or without JQ1(1µM, 12 hours), 1,6-HD (2%, 1.5 hours) or alpha-amanitin (100µg/mL, 5 hours) treatment. The number of alleles analyzed is indicated at the bottom. The black bar indicates the median value for each data set and the Kruskal-Wallis test with multiple comparisons was applied for statistical test. n.s., not significant. (h) Representative iso-surfaces of an ATAC-rich segments R2 (green) and R5 (magenta) in control, JQ1 (1µM, 12 hours), and 1,6-HD (2%, 1.5 hours) treated cells before and after auxin-mediated RAD21 depletion. Scale bar, 1 µm.

Source data

Extended Data Fig. 5 BET family proteins shape ACD organization in the absence of Cohesin.

(a) Low-dose 1,6 Hexanediol (1,6-HD,2%) treatment for 1.5 hours with or without Cohesin depletion did not significantly affect chromatin accessibility in the linear genome. The heatmaps of ATAC-seq enrichment at CTCF sites, enhancers and promoters are shown for WT, Cohesin depletion, 1,6 HD treatment, or a combination of both. (b) Disrupting BET-chromatin interactions by JQ1 had no significant impact on accessible chromatin clustering after CTCF depletion. g(r) curves were plotted for indicated conditions. Specifically, 1µM JQ1 treatment for 12 hours was applied to both control and CTCF depleted cells (6-hour depletion). Data are presented as mean values +/- SEM. (c-d) Violin plot of 3D volumes (the number of voxels) of two ATAC-rich segments R2 (c) and R5 (d) before (grey) and after (red) auxin-mediated CTCF depletion with or without JQ1 or 1,6HD treatment. The number of alleles analyzed is indicated at the bottom. The black bar indicates the median value for each data set and Mann-Whitney U test was performed for the statistical analysis. (e) The disordered region predicted from PONDR program (Predictor of Natural Disordered Regions) for BET family proteins using the VSL2 algorithms. Except for the double bromodomain and the extra-terminal domain, other regions of BET proteins are predicted to be highly disordered with high disorder score. (f) RNA-seq analysis of the expression level of BET family genes in mouse ESCs and mouse embryonic fibroblasts (MEF). Except for the Brdt gene, Brd2/3/4 are all actively transcribed in ESCs. RNA-seq data was derived from GSM723776 and GSM723775.

Source data

Extended Data Fig. 6 Characterization of acute BET family protein depletion on the spatial organization of the accessible genome.

(a) WB test of time and dose-dependent degradation of protein levels of endogenous BRD2-dTAG (performed once). (b-c) Quantification of single cell fluorescence of endogenously labeled HaloTag-BRD2/3/4-using HaloTag ligand JF646 before and after dTAG13 treatment for 6 hours (b). RAD21 GFP signal serves as the control (c). (d-f) Representative images of acute degradation of BRD2 (d), BRD3 (e) and BRD4 (f) after 6 hours of dTAG13 treatment (independently repeated 3 times). Endogenous BET proteins were labeled with 500nM JF646. The RAD21 level was monitored by GFP. Scale bar,5 µm. (g-i) Cell cycle analysis after 6 hours depletion of BRD2, BRD3 or BRD4 by propidium iodide staining. Histograms of the DNA content distribution measured by propidium iodide staining are shown. (j) WB analysis of protein levels of endogenous BRD3-dTAG or RAD21-AID individually or in combination after 6 hours of dTAG13 or auxin treatment, respectively (independently repeated 2 times). (k) Depletion of BRD3 for 6 hours did not significantly reduce the accessible chromatin clustering after RAD21 depletion. g(r) curves were plotted for indicated conditions. (l) Violin plot of 3D volumes (the number of voxels) of two ATAC-rich segments R2 (left) and R5 (right) before (grey) and after (red) BRD3 depletion, RAD21 depletion or in combination. (m) WB analysis of protein levels of endogenous BRD4-dTAG or RAD21-AID individually or in combination after 6 hours of dTAG13 or auxin treatment, respectively (independently repeated 3 times). (n) Degradation of BRD4 for 6 hours did not significantly reduce but instead had a trend to increase the accessible chromatin clustering after RAD21 depletion. g(r) curves were plotted for indicated conditions. For (k) and (n), data are presented as mean values +/- SEM. (o) Violin plot of 3D volumes (the number of voxels) of two ATAC-rich segments R2 (left) and R5 (right) before (grey) and after (red) BRD4 depletion, RAD21 depletion or in combination. For (i) and (o), the number of alleles analyzed is indicated at the bottom. The black bar indicates the median value and the two-sided Mann-Whitney U test was performed for the statistical analysis. n.s.,not significant.

Source data

Extended Data Fig. 7 The BET family protein BRD2 contributes to chromatin compartmentalization.

(a) Snapshots of Micro-C contact maps at different size scales (whole chromosome, 10Mbp, 2Mbp) for Control, Cohesin depletion, BRD2 depletion or in their combination. The number of reads for each condition analyzed are indicated inside the box region in the left panel (M, million reads). Whole chromosome, chromosome 3; 10-Mb region, chromosome1:184M-194M; 2-Mb region, chromosome1:119M-121M. (b-d) Micro-C analysis of loops (b), TADs (c) and compartments (d) after Cohesin depletion, BRD2 depletion or in their combination. See more details in the Micro-C analysis in the methods section. (e) Differential saddle plot analysis of Cohesin depletion, BRD2 depletion or in their combination compared to non-treated control cells. (f) Live cell analysis of cells stably expressing the V5-SNAP tagged full length (FL), N-terminus double bromodomain (BD) and the C-terminus low complexity domain (LCD) of mouse BRD2. Cells were stained with SNAP JF552 ligand and counterstained with Hoechst 33342. First column indicates the zoomed-in view of cells marked by the while box in the second column. (g) WB analysis of cells from (f) analyzed by ant-V5 antibody and the anti-histone H4 antibody (independently repeated 2 times). (h-i) Micro-C analysis of loops (h) and TADs (i) for cells stably expressing FL BRD2 with or without Cohesin depletion. Ectopic expression of FL BRD2 does not restore the loss of Cohesin-dependent chromatin loops or architectural domains. (j) Micro-C analysis of compartments by the Pearson correlation matrix (chromosome 17) for cells stably expressing FL BRD2 with or without Cohesin depletion. (k) Quantification of the digitalized A-A compartmental interactions (log10 value of observed/expected) after ectopically expressing FL BRD2 with or without RAD21 depletion from Micro-C experiments. The black solid line in each violin plot represents the median value. The Kruskal-Wallis test with multiple comparisons was used for statistic test with the adjusted p value reported.

Source data

Extended Data Fig. 8 BRD2 interplays with Cohesin.

(a) Quantification of compartmental changes after BRD2 and RAD21 depletion individually or in combination. A, active compartments; I, intermediated mixed compartments; B, inactive compartments. The mouse genome is binned into 25480 segments (1Mbp bin size) and the percentage of each fraction is shown. (b) Browser track view of the eigenvector values for compartmental score in various perturbation conditions on chromosome 17. The genomic regions containing B to A switches after BRD2 depletion (green line) are highlighted with black arrows. (c) Endogenous co-immunoprecipitation between BRD2 and CTCF in mouse ESCs. Benzonase (BZ) treatment did not abrogate the BRD2-CTCF interaction as compared to untreated control (UT) (independently repeated 3 times). (d) (Upper panel) Schematic of genome engineering of BET proteins with HaloTag and labeled with Janelia photoactivatable fluorophore PA-JF549 for single molecule tracking. (Lower panel) Illustration of stroboscopic single molecule imaging of BET protein dynamics in live cells. After activation by a 405 nm pulse, single BET molecules were excited by 561 nm laser pulse (2 ms) to suppress motion blurring and images were captured with ~5ms exposure times. (e) The probability distribution function of jump length was fit over multiple camera integration time scales by the two-state model estimated by Spot-On program (See methods). The two-state diffusive vs bound model does not well account for the dynamics of BRD2 or BRD3/4 (not shown) as compared to the three-state model in Fig.3c. (f-g) The diffusive fraction (f) and its diffusion coefficient (g) analysis for BRD2, BRD3 and BRD4 from SMT analysis after Cohesin depletion. Cohesin depletion significantly decreased the diffusive fraction of BRD2 accompanied with an increase of the chromatin bound fraction. (h-i) The slow bound fraction (h) and its diffusion coefficient (i) analysis for BRD2, BRD3 and BRD4 from SMT analysis after Cohesin depletion. Cohesin depletion also significantly decreased the diffusion coefficient of slow bound BRD2. In (f-i), the number of cells analyzed are the same as Fig.3d-e. Data are presented as mean values +/- SEM from 3 biologically independent experiments. The ordinary one-way ANOVA with Tukey’s multiple comparisons test was used for statistical testing. n.s., not significant.

Source data

Extended Data Fig. 9 BRD4 regulates BRD2 chromatin binding and function.

(a) Enrichment profile and heatmap of BRD2/3/4, CTCF, P300 ChIP-seq signal at their respective binding peaks before and after BRD2 depletion. (b) Enrichment profile and heatmap of ChIP-seq signal of BRD2, BRD3, BRD4 and CTCF at their cognate binding peaks after 6 hours of BRD3 depletion. (c) Heatmap of BRD2 binding signal specifically at BRD2/BRD4 co-bound regions before and after acute BRD4 depletion for 6 hours. (d) Representative images of BRD2 immunofluorescence before and after acute RAD21 or BRD4 depletion in the dual RAD21-mAID-eGFP and BRD4-Halo-dTAG mESC line (independently repeated 3 times). (e) Quantification of BRD2 puncta size (left panel) and puncta mean intensity (right panel) in control (n = 1502), RAD21 depletion (n = 2314), BRD4 depletion (n = 2861) conditions, n represent the number of BRD2 puncta analyzed. The Kruskal-Wallis test with multiple comparisons was used for statistical testing with n.s. indicating not significant. (f-h) Micro-C analysis of loops (f), TADs (g) and compartments (h) after BRD4 depletion or dual BRD2/BRD4 depletion in the presence or absence of Cohesin. In h, saddle plots of compartmental interactions are shown. (i-j) Micro-C analysis of active compartmental interactions (A-A interactions) (i) and histogram of eigenvalues from the Pearson’s correlation matrix (j) for BRD4 depletion alone or dual BRD4/BRD2 depletion. (k-l) RNA-seq analysis of differentially expressed genes (DEGs) after BRD4 (k) or BRD2 (l) depletion. Volcano plot of significantly (p<0.05) expressed genes after 6 hours depletion of BRD4 or BRD2 are shown. Red dots indicate significant expressed genes with p<0.01. False positive rate adjusted p value in the -log10 form are shown in the y axis. We detected 133 DEGs for BRD2 depletion and 1579 genes for BRD4 depletion. (m) Cumulative distribution function of DEGs after BRD2 depletion over control relative to compartmental switches. A-A indicates no switch within the A compartment. A-I/B, switches from A to intermediate (I) or B compartment. B-I/A, switches from B to I or A compartment. The overlapping curves suggest no obvious association of DEGs with compartmental changes. (n) Profiling the chromatin accessibility by ATAC-seq before and after acute BRD2 depletion (6 hours) over DEGs (upregulated and downregulated in Fig. (l)).

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Extended Data Fig. 10 Robustness of the polymer model that incorporates both loop extrusion and scaffold protein-mediated chromatin interactions.

(a-c) Testing the robustness of the polymer model by varying the scaffold protein concentrations. Simulations show minimal impact of scaffold concentrations passing 56.9nM on the pair auto-correlation function g(r) (a), the cumulative distribution function (CDF) of 3D distance between neighboring active segments (b), and distributions for the volume of the convex hull for individual ATAC-rich segment (c). The yellow bar represents mean value. (d-g) Testing the robustness of the polymer model by changing the distribution of CTCF sites on the ATAC-rich segment. (d) Contact probability map for the WT condition in which the CTCF beads are randomized. Simulations demonstrate the impact of randomized CTCF site distribution on g(r) (e), CDF of 3D distance between neighboring active segments after Cohesin loss (f) and distributions for the volume of the convex hull for individual ATAC-rich segments (g). The yellow bar represents mean value. (h-j) Simulation results are robust with respect to the varying model setups demonstrating g(r) for systems with different sizes A-B-A (h), A-B-A-B-A (i), A-B-A-B-A-B-A-B-A-B-A (j) and chromatin volume fraction (15% with lighter color, 30% with denser color) for WT and Cohesin depletion conditions. (k) g(r) showing various protein concentration for WT and Cohesin depletion of system A-B-A-B-A-B-A-B-A-B-A with 15% chromatin volume fraction. (l) Testing the robustness of the polymer model by varying system setup from A-B-A to A-B-A-B-A-B-A-B-A-B-A with chromatin volume fraction of 15%, and various protein concentration. (m) Simulation for the volume of the convex hull of ATAC-rich segments under different perturbation conditions. The panels from left to right show the results of (a) Wild type, and Cohesin depletion with (b) current model setup, (c) no protein-DNA interaction, (d) no protein-protein interaction, (e) scaffold protein with only one interaction segment (lacking multivalency), (f) reduced protein-protein interaction strength (2 kT), (g) increased protein-protein interaction strength (4 kT), (h) reduced protein-DNA interaction strength (2 kT), (i) increased protein-DNA interaction strength (6 kT), (j) reduced protein-protein interaction strength (2 kT) with higher protein concentration, (k) increased protein-protein interaction strength (4 kT) with low protein concentration. k is the Boltzmann constant and T is the temperature.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2 and associated legends, methods and associated references.

Reporting Summary

Supplementary Video 1

3D representation of 3D ATAC-PALM localizations in untreated control (left) or RAD21 (right) depleted mESCs. The color bar indicates localization density calculated by using a canopy radius of 250 nm.

Supplementary Video 2

3D representation of SOX2 stable binding sites localizations in live mESCs for untreated control (left) or RAD21 depletion (right) by the lattice light-sheet microscope. HaloTag labeled SOX2 was stably expressed in Rad21-mAID-eGFP cells and detected by low concentration of JF549 HaloTag ligand. The stable SOX2 binding events with long residence time (>4 s) were reconstructed in 3D, with the localization density color coded.

Supplementary Tables

Supplementary Tables 1–4 combined into one xlsx file.

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Xie, L., Dong, P., Qi, Y. et al. BRD2 compartmentalizes the accessible genome. Nat Genet 54, 481–491 (2022). https://doi.org/10.1038/s41588-022-01044-9

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