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Polycomb repression of Hox genes involves spatial feedback but not domain compaction or phase transition

Nature Genetics volume 56, pages 493–504 (2024)Cite this article

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Abstract

Polycomb group proteins have a critical role in silencing transcription during development. It is commonly proposed that Polycomb-dependent changes in genome folding, which compact chromatin, contribute directly to repression by blocking the binding of activating complexes. Recently, it has also been argued that liquid–liquid demixing of Polycomb proteins facilitates this compaction and repression by phase-separating target genes into a membraneless compartment. To test these models, we used Optical Reconstruction of Chromatin Architecture to trace the Hoxa gene cluster, a canonical Polycomb target, in thousands of single cells. Across multiple cell types, we find that Polycomb-bound chromatin frequently explores decompact states and partial mixing with neighboring chromatin, while remaining uniformly repressed, challenging the repression-by-compaction or phase-separation models. Using polymer simulations, we show that these observed flexible ensembles can be explained by ‘spatial feedback’—transient contacts that contribute to the propagation of the epigenetic state (epigenetic memory), without inducing a globular organization.

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Fig. 1: Polycomb-domain compaction by ORCA.
Fig. 2: Polycomb-repressed Hoxa can be decompact, controlling for correlated variation.
Fig. 3: Polycomb-repressed Hoxa can be decompact and intermixed with neighboring chromatin controlling for measurement uncertainty.
Fig. 4: The Hoxa locus in brain tissue is uniformly repressed but not uniformly compacted nor separated.
Fig. 5: Long-range interactions do not stabilize the Hoxa domain into a compact or phase-separated droplet within a larger Polycomb territory.
Fig. 6: A model with a single Polycomb state requires stable compaction to exhibit epigenetic memory.
Fig. 7: A model with three Polycomb states can exhibit epigenetic memory without stable compaction.
Fig. 8: Cell-type-specific spatial feedback allows for tunable domain stability.

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Data availability

The chromosome trace data have been converted into the NIH 4DN data standard, FOF-CT (FISH-omic Format, Chromosome Tracing) and submitted to the 4DN data portal, available with the following accessions: 4DNESYXWQD8L, 4DNES6LAM7TG, 4DNESKWHLKDQ, 4DNESTQ3UVYR and 4DNESKTTMWMD. A copy is also available in our repository for the project: https://github.com/BoettigerLab/Polycomb-ORCA-2022. Sequencing datasets produced in this study have been deposited in the NCBI Gene Expression Omnibus under accession number GSE237501. Due to the large size of raw imaging files, this data is available by request.

Image source data associated with Fig. 1g, Fig. 4a and Extended Data Fig. 4 are available at Zenodo (https://doi.org/10.5281/zenodo.10452916).

References

  1. Parreno, V., Martinez, A.-M. & Cavalli, G. Mechanisms of Polycomb group protein function in cancer. Cell Res. 32, 231–253 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Piunti, A. & Shilatifard, A. The roles of Polycomb repressive complexes in mammalian development and cancer. Nat. Rev. Mol. Cell Biol. 22, 326–345 (2021).

    Article  CAS  PubMed  Google Scholar 

  3. Yu, J.-R., Lee, C.-H., Oksuz, O., Stafford, J. M. & Reinberg, D. PRC2 is high maintenance. Genes Dev. 33, 903–935 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Blackledge, N. P. & Klose, R. J. The molecular principles of gene regulation by Polycomb repressive complexes. Nat. Rev. Mol. Cell Biol. 22, 815–833 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Laugesen, A., Højfeldt, J. W. & Helin, K. Molecular mechanisms directing PRC2 recruitment and H3K27 methylation. Mol. Cell 74, 8–18 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Mir, M., Bickmore, W., Furlong, E. E. M. & Narlikar, G. Chromatin topology, condensates and gene regulation: shifting paradigms or just a phase? Development 146, dev182766 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Chambeyron, S. & Bickmore, W. A. Chromatin decondensation and nuclear reorganization of the HoxB locus upon induction of transcription. Genes Dev. 18, 1119–1130 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Eskeland, R. et al. Ring1B compacts chromatin structure and represses gene expression independent of histone ubiquitination. Mol. Cell 38, 452–464 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Grimaud, C. et al. RNAi components are required for nuclear clustering of Polycomb group response elements. Cell 124, 957–971 (2006).

    Article  CAS  PubMed  Google Scholar 

  10. Lanzuolo, C., Roure, V., Dekker, J., Bantignies, F. & Orlando, V. Polycomb response elements mediate the formation of chromosome higher-order structures in the bithorax complex. Nat. Cell Biol. 9, 1167–1174 (2007).

    Article  CAS  PubMed  Google Scholar 

  11. Bantignies, F. et al. Polycomb-dependent regulatory contacts between distant Hox loci in Drosophila. Cell 144, 214–226 (2011).

    Article  CAS  PubMed  Google Scholar 

  12. Fabre, P. J. et al. Nanoscale spatial organization of the HoxD gene cluster in distinct transcriptional states. Proc. Natl Acad. Sci. USA 112, 13964–13969 (2015).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  14. Kundu, S. et al. Polycomb repressive complex 1 generates discrete compacted domains that change during differentiation. Mol. Cell 65, 432–446 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Szabo, Q. et al. TADs are 3D structural units of higher-order chromosome organization in Drosophila. Sci. Adv. 4, eaar8082 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  16. Weber, C. M. et al. mSWI/SNF promotes Polycomb repression both directly and through genome-wide redistribution. Nat. Struct. Mol. Biol. 28, 501–511 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. 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 

  18. Bonev, B. et al. Multiscale 3D genome rewiring during mouse neural development. Cell 171, 557–572. (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Francis, N. J., Kingston, R. E. & Woodcock, C. L. Chromatin compaction by a Polycomb group protein complex. Science 306, 1574–1577 (2004).

    Article  ADS  CAS  PubMed  Google Scholar 

  20. Margueron, R. et al. Ezh1 and Ezh2 maintain repressive chromatin through different mechanisms. Mol. Cell 32, 503–518 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Sanulli, S. & Narlikar, G. J. Liquid-like interactions in heterochromatin: implications for mechanism and regulation. Curr. Opin. Cell Biol. 64, 90–96 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Su, J.-H., Zheng, P., Kinrot, S. S., Bintu, B. & Zhuang, X. Genome-scale imaging of the 3D organization and transcriptional activity of chromatin. Cell 182, 1641–1659 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Takei, Y. et al. Integrated spatial genomics reveals global architecture of single nuclei. Nature 590, 344–350 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  24. Lafontaine, D. L. J., Riback, J. A., Bascetin, R. & Brangwynne, C. P. The nucleolus as a multiphase liquid condensate. Nat. Rev. Mol. Cell Biol. 22, 165–182 (2021).

    Article  CAS  PubMed  Google Scholar 

  25. Kraft, K. et al. Polycomb-mediated genome architecture enables long-range spreading of H3K27 methylation. Proc. Natl Acad. Sci. USA 119, e2201883119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Pirrotta, V. & Li, H.-B. A view of nuclear Polycomb bodies.Curr. Opin. Genet. Dev. 22, 101–109 (2012).

    Article  CAS  PubMed  Google Scholar 

  27. Plys, A.J. et al. Phase separation of Polycomb-repressive complex 1 is governed by a charged disordered region of CBX2. Genes Dev. 33, 799–813 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Tatavosian, R. et al. Nuclear condensates of the Polycomb protein chromobox 2 (CBX2) assemble through phase separation. J. Biol. Chem. 294, 1451–1463 (2019).

    Article  CAS  PubMed  Google Scholar 

  29. Seif, E. et al. Phase separation by the polyhomeotic sterile α motif compartmentalizes Polycomb group proteins and enhances their activity. Nat. Commun. 11, 5609 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  30. Sandholtz, S. H., MacPherson, Q. & Spakowitz, A. J. Physical modeling of the heritability and maintenance of epigenetic modifications. Proc. Natl Acad. Sci. USA 117, 20423–20429 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  31. Sandholtz, S. H., Kannan, D., Beltran, B. G. & Spakowitz, A. J. Chromosome structural mechanics dictates the local spreading of epigenetic marks. Biophys. J. 119, 1630–1639 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  32. Wakim, J. G., Sandholtz, S. H. & Spakowitz, A. J. Impact of chromosomal organization on epigenetic drift and domain stability revealed by physics-based simulations. Biophys. J. 120, 4932–4943 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  33. Owen, J. A., Osmanović, D. & Mirny, L. Design principles of 3D epigenetic memory systems. Science 382, eadg3053 (2023).

    Article  CAS  PubMed  Google Scholar 

  34. Liu, M. et al. Chromatin tracing and multiplexed imaging of nucleome architectures (MINA) and RNAs in single mammalian cells and tissue. Nat. Protoc. 16, 2667–2697 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Cardozo Gizzi, A. M. et al. Direct and simultaneous observation of transcription and chromosome architecture in single cells with Hi-M. Nat. Protoc. 15, 840–876 (2020).

    Article  CAS  PubMed  Google Scholar 

  36. Mateo, L. J., Sinnott-Armstrong, N. & Boettiger, A. N. Tracing DNA paths and RNA profiles in cultured cells and tissues with ORCA. Nat. Protoc. 16, 1647–1713 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Nicodemi, M. & Bianco, S. Chromosomes phase transition to function. Biophys. J. 119, 724–725 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  38. Haddad, N., Jost, D. & Vaillant, C. Perspectives: using polymer modeling to understand the formation and function of nuclear compartments. Chromosome Res. 25, 35–50 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Lesage, A., Dahirel, V., Victor, J.-M. & Barbi, M. Polymer coil-globule phase transition is a universal folding principle of Drosophila epigenetic domains. Epigenetics Chromatin. 12, 28 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Schuettengruber, B., Bourbon, H.-M., Di Croce, L. & Cavalli, G. Genome regulation by Polycomb and Trithorax: 70 years and counting. Cell 171, 34–57 (2017).

    Article  CAS  PubMed  Google Scholar 

  41. Pengelly, A. R., Copur, O., Jackle, H., Herzig, A. & Muller, J. A histone mutant reproduces the phenotype caused by loss of histone-modifying factor Polycomb. Science 339, 698–699 (2013).

    Article  ADS  CAS  PubMed  Google Scholar 

  42. 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 

  43. Wani, A. H. et al. Chromatin topology is coupled to Polycomb group protein subnuclear organization. Nat. Commun. 7, 10291 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  44. Lau, M. S. et al. Mutation of a nucleosome compaction region disrupts Polycomb-mediated axial patterning. Science 355, 1081–1084 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  45. Cheutin, T. & Cavalli, G. Loss of PRC1 induces higher-order opening of Hox loci independently of transcription during Drosophila embryogenesis. Nat. Commun. 9, 3898 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  46. Mateo, L. J. et al. Visualizing DNA folding and RNA in embryos at single-cell resolution. Nature 568, 49–54 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  47. Mirny, L. A. The fractal globule as a model of chromatin architecture in the cell. Chromosome Res. 19, 37–51 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002).

    Article  ADS  CAS  PubMed  Google Scholar 

  49. Kim, J. & Kingston, R. E. The CBX family of proteins in transcriptional repression and memory. J. Biosci. 45, 16 (2020).

    Article  CAS  PubMed  Google Scholar 

  50. Guo, Y., Zhao, S. & Wang, G. G. Polycomb gene silencing mechanisms: PRC2 chromatin targeting, H3K27me3 ‘readout’, and phase-separation-based compaction. Trends Genet. 37, 547–565 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. 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 

  52. Gurgo, J. et al. Multiplexed chromatin imaging reveals predominantly pairwise long-range coordination between Drosophila Polycomb genes. Preprint at bioRxiv 10.1101/2022.05.16.492046 (2022).

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. 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  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  55. Gabriele, M. et al. Dynamics of CTCF- and cohesin-mediated chromatin looping revealed by live-cell imaging. Science 376, 496–501 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  56. Guo, Y. et al. Chromatin jets define the properties of cohesin-driven in vivo loop extrusion. Mol. Cell 82, 3769–3780.e5 (2022).

    Article  CAS  PubMed  Google Scholar 

  57. Jia, B.B. et al. A spatial genome aligner for resolving chromatin architectures from multiplexed DNA FISH. Nat. Biotechnol. 41, 1004–1017 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Chen, L.-F. et al. Structural elements promote architectural stripe formation and facilitate ultra-long-range gene regulation at a human disease locus. Mol. Cell 83, 1446–1461 (2023).

    Article  CAS  PubMed  Google Scholar 

  59. Hafner, A. et al. Loop stacking organizes genome folding from TADs to chromosomes. Mol. Cell 83, 1377–1392 (2023).

    Article  CAS  PubMed  Google Scholar 

  60. Goychuk, A., Kannan, D., Chakraborty, A. K. & Kardar, M. Polymer folding through active processes recreates features of genome organization. Proc. Natl Acad. Sci. USA 120, e2221726120 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Polovnikov, K. E. et al. Crumpled polymer with loops recapitulates key features of chromosome organization. Phys. Rev. X 13, 041029 (2023).

    MathSciNet  CAS  Google Scholar 

  62. Jost, D., Carrivain, P., Cavalli, G. & Vaillant, C. Modeling epigenome folding: formation and dynamics of topologically associated chromatin domains. Nucleic Acids Res. 42, 9553–9561 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Cheutin, T. & Cavalli, G. Progressive Polycomb assembly on H3K27me3 compartments generates polycomb bodies with developmentally regulated motion. PLoS Genet. 8, e1002465 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Huseyin, M. K. & Klose, R. J. Live-cell single particle tracking of PRC1 reveals a highly dynamic system with low target site occupancy. Nat. Commun. 12, 887 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  65. Chory, E. J. et al. Nucleosome turnover regulates histone methylation patterns over the genome. Mol. Cell 73, 61–72 (2019).

    Article  CAS  PubMed  Google Scholar 

  66. Dodd, I. B., Micheelsen, M. A., Sneppen, K. & Thon, G. Theoretical analysis of epigenetic cell memory by nucleosome modification. Cell 129, 813–822 (2007).

    Article  CAS  PubMed  Google Scholar 

  67. Hathaway, N.A. et al. Dynamics and memory of heterochromatin in living cells. Cell 149, 1447–60 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Müller-Ott, K. et al. Specificity, propagation, and memory of pericentric heterochromatin. Mol. Syst. Biol. 10, 746 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Berry, S., Dean, C. & Howard, M. Slow chromatin dynamics allow Polycomb target genes to filter fluctuations in transcription factor activity. Cell Syst. 4, 445–457 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Reinig, J., Ruge, F., Howard, M. & Ringrose, L. A theoretical model of Polycomb/Trithorax action unites stable epigenetic memory and dynamic regulation. Nat. Commun. 11, 4782 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  71. Ancona, M., Michieletto, D. & Marenduzzo, D. Competition between local erasure and long-range spreading of a single biochemical mark leads to epigenetic bistability. Phys. Rev. E 101, 042408 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  72. Newar, K., Abdulla, A. Z., Salari, H., Fanchon, E. & Jost, D. Dynamical modeling of the H3K27 epigenetic landscape in mouse embryonic stem cells. PLoS Comput. Biol. 18, e1010450 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  73. Katava, M., Shi, G. & Thirumalai, D. Chromatin dynamics controls epigenetic domain formation. Biophys. J. 121, 2895–2905 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  74. Cortini, R. et al. The physics of epigenetics. Rev. Mod. Phys. 88, 025002 (2016).

    Article  ADS  Google Scholar 

  75. Lövkvist, C. & Howard, M. Using computational modelling to reveal mechanisms of epigenetic Polycomb control. Biochem. Soc. Trans. 49, 71–77 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Denell, R. E. & Frederick, R. D. Homoeosis in Drosophila: a description of the Polycomb lethal syndrome. Dev. Biol. 97, 34–47 (1983).

    Article  CAS  PubMed  Google Scholar 

  77. Kuroda, M. I., Kang, H., De, S. & Kassis, J. A. Dynamic competition of Polycomb and Trithorax in transcriptional programming. Annu. Rev. Biochem. 89, 235–253 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. D Michieletto E Orlandini D Marenduzzo Polymer model with epigenetic recoloring reveals a pathway for the de novo establishment and 3D organization of chromatin domains. Phys. Rev. 6 1–15 (2016).

    Article  Google Scholar 

  79. Brackley, C. A. et al. Ephemeral protein binding to DNA shapes stable nuclear bodies and chromatin domains. Biophys. J. 112, 1085–1093 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  80. Eeftens, J. M., Kapoor, M., Michieletto, D. & Brangwynne, C. P. Polycomb condensates can promote epigenetic marks but are not required for sustained chromatin compaction. Nat. Commun. 12, 5888 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  81. Sood, A., Schuette, G. & Zhang, B. Coupling chromatin folding with histone modifications reveals dynamical asymmetry in the epigenetic landscape. Preprint at bioRxiv 10.1101/2022.11.02.514881 (2022).

  82. Hodges, C. & Crabtree, G. R. Dynamics of inherently bounded histone modification domains. Proc. Natl Acad. Sci. USA 109, 13296–13301 (2012).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  83. Nicodemi, M. & Prisco, A. Thermodynamic pathways to genome spatial organization in the cell nucleus. Biophys. J. 96, 2168–2177 (2009).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  84. Jaensch, E. S. et al. A Polycomb domain found in committed cells impairs differentiation when introduced into PRC1 in pluripotent cells. Mol. Cell 81, 4677–4691 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Shin, Y. & Brangwynne, C. P. Liquid phase condensation in cell physiology and disease. Science 357, eaaf4382 (2017).

    Article  PubMed  Google Scholar 

  86. Strom, A. R. & Brangwynne, C. P. The liquid nucleome–phase transitions in the nucleus at a glance. J. Cell Sci. 132, jcs235093 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Poepsel, S., Kasinath, V. & Nogales, E. Cryo-EM structures of PRC2 simultaneously engaged with two functionally distinct nucleosomes. Nat. Struct. Mol. Biol. 25, 154–162 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Grau, D. et al. Structures of monomeric and dimeric PRC2:EZH1 reveal flexible modules involved in chromatin compaction. Nat. Commun. 12, 714 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  89. Denholtz, M. et al. Long-range chromatin contacts in embryonic stem cells reveal a role for pluripotency factors and polycomb proteins in genome organization. Cell Stem Cell 13, 602–616 (2013).

    Article  CAS  PubMed  Google Scholar 

  90. Galaxy Community The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update. Nucleic Acids Res. 50, W345–W351 (2022).

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Rigano, A. et al. Micro-Meta App: an interactive tool for collecting microscopy metadata based on community specifications. Nat. Methods 18, 1489–1495 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  94. Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021).

    Article  CAS  PubMed  Google Scholar 

  95. Imakaev, M. et al. BoettigerLab/polychrom: polymer simulations (v0.0.0). Zenodo. https://doi.org/10.5281/zenodo.7698987 (2023).

  96. Eastman, P. et al. OpenMM 7: rapid development of high performance algorithms for molecular dynamics. PLoS Comput. Biol. 13, e1005659 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Dempsey, G. T., Vaughan, J. C., Chen, K. H., Bates, M. & Zhuang, X. Evaluation of fluorophores for optimal performance in localization-based super-resolution imaging. Nat. Methods 8, 1027–1036 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Boettiger, A. BoettigerLab/Polycomb-ORCA-2022: v0.0.0 (v0.0.0). Zenodo. https://doi.org/10.5281/zenodo.10258161 (2023).

  99. Boettiger, A. BoettigerLab/ORCA-public: v1.1 (v1.1). Zenodo. https://doi.org/10.5281/zenodo.7698979 (2023).

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Acknowledgements

The authors would like to thank J. Wakim and A. Spakowitz for detailed discussions and feedback on modeling chromatin polymers and Polycomb interactions; C. Weber (Stanford University) for gifting the Polycomb protein degradation mES cell lines used in this study; R. Kingston (Massachusetts General Hospital) for providing the CaPS and mCaPS cell lines; T.-C. Hung and M. Koska (Stanford University) for generously gifting the mouse brain and embryo tissues imaged in this study; B. Doughty and E. Metzl-Raz for technical assistance with the CUT&RUN sequencing; and A. Fire, A. Villeneuve and L. Bintu for critical feedback during these investigations and critical reading of the manuscript. This study was supported by a Stanford Bio-X SIGF Fellowship (to S.E.M.); National Science Foundation (NSF) and National Institutes of Health (NIH) under grants EF2022182 and U01DK127419, respectively; and a Beckman Young Investigator Award and Packard Fellowship (to A.N.B.).

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  1. Sedona Eve Murphy

    Present address: Department of Cell Biology, Yale University, New Haven, CT, USA

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  1. Department of Genetics, Stanford University, Stanford, CA, USA

    Sedona Eve Murphy

  2. Department of Developmental Biology, Stanford University, Stanford, CA, USA

    Sedona Eve Murphy & Alistair Nicol Boettiger

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Contributions

S.E.M. conducted all of the experiments. S.E.M. and A.N.B. designed experiments, analyzed the data, conducted and analyzed the simulations and wrote the paper.

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Correspondence to Alistair Nicol Boettiger.

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

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Extended data

Extended Data Fig. 1 Comparison of STORM and ORCA approaches for estimating compaction using the radius of gyration.

(a) Example of STORM data of a Pc-region (from ref. 13), showing the challenge in distinguishing foreground and background. Purple “+” marks the output of two different parameter sets for density-based filtering of background. (b) Example of ORCA data of a Pc region from this work. Note decompact portions of the polymer (regions “23” and “24”) can still be reliably linked to the rest of the domain based on the expectation each chromosome contains 1 and only 1 copy of the region. (c) Simulation of super-resolution imaging of a small chromosome region using STORM. Parameters for the photon counts, labeling density, photo-cycling background, taken from refs. 13,97. (d) Simulation of the same region in (c) imaged by ORCA, with labeling density, detection efficiency, and measurement errors taken from this work (see ED Fig. 2). (e) Histogram of the error from 100 simulated traces, imaged by simulated STORM or simulated ORCA.

Extended Data Fig. 2 Barcode detection.

(a) Genome browser track annotating the Pc domains, H3K27me3 repressive mark, 5 kb ORCA probes, and genes (ch6:52090001-52270036) (top), like above but for the alternative non-Pc control region (chr3:35001901-35103454). (b) Fraction of barcodes detected per cell in non-Pc neighboring region (ch6:52090001-52150013) and Pc-region (chr6:52150013-52270036) (n=2027). Red lines denote median, blue box extends from 1st to 3rd quartile, whiskers extend the furthest point within 1.5x the interquartile range. Outliers are represented with red ‘+’. (c) Barcode detection efficiency per barcode across the measured neighboring non-Pc and Pc region. (d) As in (b) but for an alternative non-Pc control region (chr3:35001901-35103454) (n=2609). (e) As in (c) but for the alternate non-Pc control region and the Pc-region.

Extended Data Fig. 3 Comparison of replicates, detection efficiency, and effect of PcG protein degradation.

(a) log scale correlation plots (Pearson’s) of 3D pairwise contact frequency (d<150 nm considered a contact) across 3 biological replicates of non-treated (NT), left, and Pc-knockdown (KD), right, cells. (b) Correlation plots (Pearson’s) of 3D pairwise distance (in nm) of NT (left) and Pc-KD (right) cells. We note that the correlation coefficient in these plots is lower than that in Extended Data Fig. 7a due to fewer cells sampled. (c) Pairwise contact frequency measured by ORCA in Pc-KD cells (contact threshold 150 nm). (d) Map of the statistical significance of the observed decompaction/compaction, plotted as two-sided Wilcoxon rank sum p-values for pairwise comparisons for the distribution of distances measured in NT and KD cells. Color marks −log10 p-value. Gray p = > 0.05.

Extended Data Fig. 4 Analysis of efficiency of induced protein degradation.

(a) Immunofluorescence of untreated mES cells labeled with antibodies against v5 tag (RING1b-v5 tagged) and HA tag (EED-HA tagged) (n=3). (b) Immunofluorescence of cells after 8 hours of treatment with dTAG-13 ligand labeled with antibodies against v5 tag (RING1b-v5 tagged) and HA tag (EED-HA tagged). (c) Western blot of untreated vs. 8 hours of dTAG-13 treatment (n=1). (d) Median Rg of vehicle treated DMSO (0 hr) and 1, 2, 4, 6, 8, and 24 hr addition of dTAG to knock down RING1b and EED by induced protein degradation. Time points are not significant (two-sided Wilcoxon rank sum p > 0.05) between 1 and 24 hr dTAG treatment. Error bars show confidence intervals from bootstrapping, such that two error bars which do not overlap have a 95% confidence of being distinct. (e) Example of a cell with an individual detection event for the hoxa1 mRNA probe. Colors as in Fig. 1g. (f) As in Fig. 1h showing the distributions of Hoxa1 and Sox2 mRNA counts per cell at the 4-hr and 24-hr KD time points.

Extended Data Fig. 5 Further genomic analyses of Hoxa locus in non-treated and Pc-KD cells.

(a) CUT&RUN tracks from non-treated cells (top) and 8 hour Pc-KD cells (bottom) for H3k4me3, Ring1b, and IgG input. (b) Genome browser tracks of RNA-seq data from ref. 16 comparing the NT (bottom) and 8-hour Pc knockdown (top). Left browser tracks show zoom out with Hoxa locus highlighted in blue box. Right is the zoom-in view of the Hoxa locus.

Extended Data Fig. 6 Additional analysis of Hoxa domains in ES cells.

(a) Example traces from mES cells following Pc KD. The Hoxa region is shown in blue, other regions are shown in gray. For scale, the tube radius is 50 nm and the sphere radius is 70 nm. (b) Distributions of Rg for non-Pc region 1 (neighboring) and non-Pc region 2 (secondary) in untreated (right) and Pc-knockdown (left) (p-value determined by two-sided Wilcoxon rank sum test). (c) Median values from the distributions shown in (a). Error bars defined by 95% confidence intervals. (d) A 2D histogram of the Pc Rg and neighboring non-Pc Rg from each trace in the Pc-KD cells (left) and the log2 fold change of the Rg of the non-Pc region 1 and Pc region. (e) Pearson’s correlation between compaction and separation measurements in untreated (NT) and knockdown (KD) samples showing these are distinct properties though they remain weakly correlated. (f) Distribution of normalized distance of Pc-to-Pc loci (blue) compared to the distribution of normalized distances of the Pc-to-non-Pc loci from ORCA imaging (two-sided Wilcoxon rank sum p=2.3e-16). (g) Fraction of voxels in containing both Pc and non-Pc regions compared to the total number of voxels containing Pc regions “overlap fraction” for Pc region (blue) and a non-Pc region (gray). Three separate regions of similar length to the Hoxa domain from prior STORM imaging are combined 13 (two-sided Wilcoxon rank sum p=5.3e-16). (h) Comparison of the distribution of normalized distances of Pc-to-Pc loci from non-treated cells (blue) to Pc-KD cells (gray). (i) The distribution of normalized distances of Pc-to-non-Pc loci from non-treated cells (blue) to Pc-KD cells (gray). (j) 2D-histogram plot comparing the median normalized distances per trace (all Pc-to-Pc vs all Pc-to-non-Pc). Color bar indicates trace count. (k) Histogram of the log2 fold change in normalized distance (non-Pc to Pc) for purely separated traces and the same data with added noise.

Extended Data Fig. 7 Brain data reproducibility.

(a) Correlation plot of median distances for brain replicates, consisting of 12,333 traces in replicate one and 13,802 traces in replicate 2 (r=0.99). (b) Violin plots showing the distribution of all pairwise distances (x,y,z) in the trace for all brain data, compared to the distribution of pairwise distances between repeat labels of the same point in the trace. Data for two different repeat labels are shown. Black dots show the median distance (nm) for each violin. Median distance values: data (x,y,z) = (156, 158, 145) nm, repeat 1 (x,y,z) = (27, 55, 57) nm, repeat 2 (x,y,z) = (41, 60, 50) nm.

Extended Data Fig. 8 Additional analysis of model behavior.

(a) 3D plot comparing the initialization condition (black) to the steady-state relaxed polymer configurations used to initialize the model simulations. Blue and Red traces denote two independent starting configurations. (b) Correlation analysis, showing the mean correlation coefficient across 70 simulations for the displacement of each monomer from the centroid, shown as a function of the time interval from the start of the simulation. Coefficients = 0 indicate the structure is uncorrelated with the starting configuration. (c) Simulated contact frequency (using an 8 monomer radii cut-off for contact) from the all simulations in the 1 Pc-state model with no-adhesion, weak adhesion (#2 in Fig. 6e) or droplet-level adhesion (#3 in Fig. 6e). (d) 2D histogram of comparing the Rg in units of monomer radii for the 3 states shown in (c). (e) 2D histogram comparing the normalized distance between blue monomers to the normalized distance from blue to gray monomers. (f-h) As in (c-e) but for the simulations with the 3 Pc-state model, showing the 3 parameter regimes enumerated in Fig. 7e. (i) Snapshots of simulations run in the weak adhesion-regime, but where 3D motion of the polymer was much slower than the kinetics of epigenetic spreading. In the top panels, the epigenetic state is indicated as in Fig. 6 and 7 - blue is on-target Pc, red is off-target Pc, yellow is missing Pc. The lower panels show the epigenetic state as a function of polymer position. Note that the 3D structure has changed in only minor ways between t=12 and t=600, whereas the Pc-state has changed substantially. (j) Kymographs from two simulations where polymer motion was much slower than epigenetic spreading, which give variable behaviors even in the weak adhesion regime.

Extended Data Fig. 9 Kymographs.

Panels show kymographs, as in Fig. 7, for examples from several independent simulations under the different conditions shown in Fig. 8: free polymer (no Pc), simulated ESCs (lowest stickiness), simulated brain (higher stickiness), simulated cNPCs (shorter domains) simulated droplets. As in Figs. 6 and 7, the y-axis is time (here 2000 steps), and the x-axis is positions along the simulated chromatin polymer.

Extended Data Fig. 10 Model benchmarking and additional cell type analyses.

(a) Histograms of the relative compaction (left) and relative separation (right) in simulations of free polymers (gray) compared to droplet-forming polymer simulations (blue). (b) Histograms of the relative compaction (left) and relative separation (right) for simulations with 0.3 KT affinity (pink) and 0.4 KT affinity (blue). The former give a more ES-like distribution and the latter a more brain-cell like distribution (see (c)), though neither match the droplet regime or free polymer regime (a). (c) Histograms of the relative compaction of E10.5 brain cells with higher affinity CBX subunit (blue) and ESCs with lower affinity CBX subunit (pink) (left) (two-sided Wilcoxon rank sum p=4.6e-15). Histograms of the relative separation of E10.5 brain cells with higher affinity CBX subunit (blue) and ESCs with lower affinity CBX subunit (gray) (right) (two-sided Wilcoxon rank sum p=2.5e-64). (d) Rg distributions of the Pc region and non-Pc region from left to right of ESC-CaPS (two-sided Wilcoxon rank sum p=1.3e-19), ESC-mCaPS (two-sided Wilcoxon rank sum p=1.2e-16), E10.5 brain (two-sided Wilcoxon rank sum p~0 to numerical precision), and cNP cells (two-sided Wilcoxon rank sum p=0.00015). (e) Rg distributions of Pc-regions of ESC-CaPS and ESC-mCaPS cells (left) (two-sided Wilcoxon rank sum p=0.0028) and non-Pc regions (right) (two-sided Wilcoxon rank sum p=0.49).

Supplementary information

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Supplementary Notes 1–3 and Supplementary Table 1.

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Supplementary Video 1

Polymer simulation with no attraction.

Supplementary Video 2

Polymer simulation with spatial feedback attraction.

Supplementary Video 3

Polymer simulation with droplet attraction.

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Murphy, S.E., Boettiger, A.N. Polycomb repression of Hox genes involves spatial feedback but not domain compaction or phase transition. Nat Genet 56, 493–504 (2024). https://doi.org/10.1038/s41588-024-01661-6

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