Skip to main content

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

  • Article
  • Published:

High-throughput Oligopaint screen identifies druggable 3D genome regulators

Nature volumeΒ 620,Β pages 209–217 (2023)Cite this article

Subjects

Abstract

The human genome functions as a three-dimensional chromatin polymer, driven by a complex collection of chromosome interactions1,2,3. Although the molecular rules governing these interactions are being quickly elucidated, relatively few proteins regulating this process have been identified. Here, to address this gap, we developed high-throughput DNA or RNA labelling with optimized Oligopaints (HiDRO)β€”an automated imaging pipeline that enables the quantitative measurement of chromatin interactions in single cells across thousands of samples. By screening the human druggable genome, we identified more than 300 factors that influence genome folding during interphase. Among these, 43 genes were validated as either increasing or decreasing interactions between topologically associating domains. Our findings show that genetic or chemical inhibition of the ubiquitous kinase GSK3A leads to increased long-range chromatin looping interactions in a genome-wide and cohesin-dependent manner. These results demonstrate the importance of GSK3A signalling in nuclear architecture and the use of HiDRO for identifying mechanisms of spatial genome organization.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Development of HiDRO.
Fig. 2: HiDRO identifies regulators of genome folding.
Fig. 3: GSK3A has a noncanonical role in genome folding.
Fig. 4: GSK3A restricts chromatin looping to promote TAD insulation.
Fig. 5: GSK3A promotes WAPL recruitment to chromatin.

Data availability

Datasets reported in this paper are available at the Gene Expression Omnibus under accession number GSE199607.

Code availability

CellProfiler pipeline for image segmentation is available at Zenodo (https://doi.org/10.5281/zenodo.7699078).

References

  1. 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).

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  2. Beagan, J. A. & Phillips-Cremins, J. E. On the existence and functionality of topologically associating domains. Nat. Genet. 52, 8–16 (2020).

  3. Davidson, I. F. & Peters, J.-M. Genome folding through loop extrusion by SMC complexes. Nat. Rev. Mol. Cell Biol. 22, 445–464 (2021).

  4. Rao, S. S. P. et al. Cohesin Loss Eliminates All Loop Domains. Cell 171, 305–320.e24 (2017).

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

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

    ArticleΒ  ADSΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  6. Merkenschlager, M. & Nora, E. P. CTCF and Cohesin in Genome Folding and Transcriptional Gene Regulation. Annu. Rev. Genomics Hum. Genet. 17, 17–43 (2016).

    ArticleΒ  CASΒ  PubMedΒ  Google ScholarΒ 

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

  8. Kriz, A. J., Colognori, D., Sunwoo, H., Nabet, B. & Lee, J. T. Balancing cohesin eviction and retention prevents aberrant chromosomal interactions, Polycomb-mediated repression, and X-inactivation. Mol. Cell 81, 1970–1987.e9 (2021).

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

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

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  10. Liu, N. Q. et al. Rapid depletion of CTCF and cohesin proteins reveals dynamic features of chromosome architecture. Preprint at bioRxiv, https://www.biorxiv.org/content/10.1101/2021.08.27.457977v1 (2021).

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

    ArticleΒ  ADSΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  12. Ciosk, R. et al. Cohesin’s Binding to Chromosomes Depends on a Separate Complex Consisting of Scc2 and Scc4 Proteins. Mol. Cell 5, 243–254 (2000).

    ArticleΒ  CASΒ  PubMedΒ  Google ScholarΒ 

  13. Kueng, S. et al. Wapl Controls the Dynamic Association of Cohesin with Chromatin. Cell 127, 955–967 (2006).

    ArticleΒ  CASΒ  PubMedΒ  Google ScholarΒ 

  14. Haarhuis, J. H. I. et al. The Cohesin Release Factor WAPL Restricts Chromatin Loop Extension. Cell 169, 693–707.e14 (2017).

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  15. Overington, J. P., Al-Lazikani, B. & Hopkins, A. L. How many drug targets are there? Nat. Rev. Drug Discov. 5, 993–996 (2006).

    ArticleΒ  CASΒ  PubMedΒ  Google ScholarΒ 

  16. Sakharkar, M. K. & Sakharkar, K. R. Targetability of Human Disease Genes. Curr. Drug Discov. Technol. 4, 48–58 (2007).

    ArticleΒ  CASΒ  PubMedΒ  Google ScholarΒ 

  17. Boyle, S. et al. A central role for canonical PRC1 in shaping the 3D nuclear landscape. Genes Dev. 34, 931–949 (2020).

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  18. Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).

    ArticleΒ  CASΒ  PubMedΒ  Google ScholarΒ 

  19. Doble, B. W., Patel, S., Wood, G. A., Kockeritz, L. K. & Woodgett, J. R. Functional Redundancy of GSK-3Ξ± and GSK-3Ξ² in Wnt/Ξ²-Catenin Signaling Shown by Using an Allelic Series of Embryonic Stem Cell Lines. Dev. Cell 12, 957–971 (2007).

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  20. Sutherland, C. What are the bona fide GSK3 substrates? Int. J. Alzheimers Dis. 2011, e505607 (2011).

    Google ScholarΒ 

  21. Beurel, E., Grieco, S. F. & Jope, R. S. Glycogen synthase kinase-3 (GSK3): regulation, actions, and diseases. Pharmacol. Ther. 0, 114–131 (2015).

    ArticleΒ  CASΒ  Google ScholarΒ 

  22. Chen, X. et al. A chemical-genetic approach reveals the distinct roles of GSK3Ξ± and GSK3Ξ² in regulating embryonic stem cell fate. Dev. Cell 43, 563–576.e4 (2017).

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  23. Shinde, M. Y. et al. Phosphoproteomics reveals that glycogen synthase kinase-3 phosphorylates multiple splicing factors and is associated with alternative splicing. J. Biol. Chem. 292, 18240–18255 (2017).

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  24. Peifer, M., Pai, L.-M. & Casey, M. Phosphorylation of the Drosophila adherens junction protein Armadillo: roles for Wingless Signal and Zeste-white 3 kinase. Dev. Biol. 166, 543–556 (1994).

    ArticleΒ  CASΒ  PubMedΒ  Google ScholarΒ 

  25. Yost, C. et al. The axis-inducing activity, stability, and subcellular distribution of beta-catenin is regulated in Xenopus embryos by glycogen synthase kinase 3. Genes Dev. 10, 1443–1454 (1996).

    ArticleΒ  CASΒ  PubMedΒ  Google ScholarΒ 

  26. Wagner, F. F. et al. Exploiting an Asp-Glu β€œswitch” in glycogen synthase kinase 3 to design paralog-selective inhibitors for use in acute myeloid leukemia. Sci. Transl. Med. 10, eaam8460 (2018).

    ArticleΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  27. Engler, T. A. et al. Substituted 3-imidazo[1,2-a]pyridin-3-yl- 4-(1,2,3,4-tetrahydro-[1,4]diazepino-[6,7,1-hi]indol-7-yl)pyrrole-2,5-diones as highly selective and potent inhibitors of glycogen synthase kinase-3. J. Med. Chem. 47, 3934–3937 (2004).

    ArticleΒ  CASΒ  PubMedΒ  Google ScholarΒ 

  28. An, W. F. et al. Discovery of potent and highly selective inhibitors of GSK3b. In Probe Reports from the NIH Molecular Libraries Program (National Center for Biotechnology Information (US), 2010).

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

  30. Barrington, C. et al. Enhancer accessibility and CTCF occupancy underlie asymmetric TAD architecture and cell type specific genome topology. Nat. Commun. 10, 2908 (2019).

    ArticleΒ  ADSΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  31. Natsume, T., Kiyomitsu, T., Saga, Y. & Kanemaki, M. T. Rapid protein depletion in human cells by auxin-inducible degron tagging with short homology donors. Cell Rep. 15, 210–218 (2016).

  32. Tedeschi, A. et al. Wapl is an essential regulator of chromatin structure and chromosome segregation. Nature 501, 564–568 (2013).

    ArticleΒ  ADSΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  33. Branon, T. C. et al. Efficient proximity labeling in living cells and organisms with TurboID. Nat. Biotechnol. 36, 880–887 (2018).

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  34. Kikuchi, S., Borek, D. M., Otwinowski, Z., Tomchick, D. R. & Yu, H. Crystal structure of the cohesin loader Scc2 and insight into cohesinopathy. Proc. Natl Acad. Sci. 113, 12444–12449 (2016).

    ArticleΒ  ADSΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  35. Petela, N. J. et al. Scc2 is a potent activator of cohesin’s ATPase that promotes loading by binding Scc1 without Pds5. Mol. Cell 70, 1134–1148.e7 (2018).

  36. Kean, C. M. et al. Decreasing Wapl dosage partially corrects embryonic growth and brain transcriptome phenotypes in Nipbl+/βˆ’ embryos. Sci. Adv. 8, eadd4136 (2022).

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  37. Luppino, J. M. et al. Co-depletion of NIPBL and WAPL balance cohesin activity to correct gene misexpression. PLoS Genet. 18, e1010528 (2022).

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  38. Joyce, E. F., Williams, B. R., Xie, T. & Wu, C. -ting. Identification of genes that promote or antagonize somatic homolog pairing using a high-throughput FISH-based screen. PLoS Genet. 8, e1002667 (2012).

  39. Shachar, S., Voss, T. C., Pegoraro, G., Sciascia, N. & Misteli, T. Identification of gene positioning factors using high-throughput imaging mapping. Cell 162, 911–923 (2015).

  40. Finn, E. H. et al. Extensive heterogeneity and intrinsic variation in spatial genome organization. Cell 176, 1502–1515 (2019).

  41. Chin, C. V. et al. Cohesin mutations are synthetic lethal with stimulation of WNT signaling. eLife 9, e61405 (2020).

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  42. Grazioli, P. et al. Lithium as a possible therapeutic strategy for Cornelia de Lange syndrome. Cell Death Discov. 7, 1–11 (2021).

    ArticleΒ  Google ScholarΒ 

  43. Bottai, D. et al. Modeling Cornelia de Lange syndrome in vitro and in vivo reveals a role for cohesin complex in neuronal survival and differentiation. Hum. Mol. Genet. 28, 64–73 (2019).

    ArticleΒ  MathSciNetΒ  CASΒ  PubMedΒ  Google ScholarΒ 

  44. Kaidanovich-Beilin, O. & Woodgett, J. GSK-3: functional insights from cell biology and animal models. Front. Mol. Neurosci. 4, 40 (2011).

  45. Hegemann, B. et al. Systematic phosphorylation analysis of human mitotic protein complexes. Sci. Signal. https://doi.org/10.1126/scisignal.2001993 (2011).

  46. Liang, C. et al. A kinase-dependent role for Haspin in antagonizing Wapl and protecting mitotic centromere cohesion. EMBO Rep. 19, 43–56 (2018).

    ArticleΒ  CASΒ  PubMedΒ  Google ScholarΒ 

  47. Beliveau, B. J. et al. OligoMiner provides a rapid, flexible environment for the design of genome-scale oligonucleotide in situ hybridization probes. Proc. Natl Acad. Sci. 115, E2183–E2192 (2018).

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  48. Bintu, B. et al. Super-resolution chromatin tracing reveals domains and cooperative interactions in single cells. Science 362, eaau1783 (2018).

    ArticleΒ  ADSΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  49. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    ArticleΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

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

    ArticleΒ  ADSΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  51. Saito, Y. & Kanemaki, M. T. Targeted Protein Depletion Using the Auxin-Inducible Degron 2 (AID2) System. Curr. Protoc. 1, e219 (2021).

    ArticleΒ  CASΒ  PubMedΒ  Google ScholarΒ 

  52. Beckwith, K. S. et al. Visualization of loop extrusion by nanoscale 3D DNA tracing in single human cells. Preprint at bioRxiv, https://doi.org/10.1101/2021.04.12.439407 (2022).

  53. Shah, P. P. et al. Pathogenic LMNA variants disrupt cardiac lamina-chromatin interactions and de-repress alternative fate genes. Cell Stem Cell 28, 938–954.e9 (2021).

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  54. Rhodes, J., Mazza, D., Nasmyth, K. & Uphoff, S. Scc2/Nipbl hops between chromosomal cohesin rings after loading. eLife 6, e30000 (2017).

    ArticleΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  55. Cho, K. F. et al. Proximity labeling in mammalian cells with TurboID and split-TurboID. Nat. Protoc. 15, 3971–3999 (2020).

    ArticleΒ  CASΒ  PubMedΒ  Google ScholarΒ 

  56. Ran, F. A. et al. Genome engineering using the CRISPR-Cas9 system. Nat. Protoc. 8, 2281–2308 (2013).

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  57. Yesbolatova, A. et al. The auxin-inducible degron 2 technology provides sharp degradation control in yeast, mammalian cells, and mice. Nat. Commun. 11, 5701 (2020).

    ArticleΒ  ADSΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  58. McQuin, C. et al. CellProfiler 3.0: Next-generation image processing for biology. PLoS Biol. 16, e2005970 (2018).

    ArticleΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  59. Li, C. H. & Lee, C. K. Minimum cross entropy thresholding. Pattern Recognit. 26, 617–625 (1993).

    ArticleΒ  ADSΒ  Google ScholarΒ 

  60. Otsu, N. A Threshold Selection Method from Gray-Level Histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979).

    ArticleΒ  Google ScholarΒ 

  61. Drasgow, F. in Encyclopedia of Statistical Sciences (eds. Kotz, S. et al.) https://doi.org/10.1002/0471667196.ess2014.pub2 (John Wiley & Sons, 2006).

  62. Ollion, J., Cochennec, J., Loll, F., EscudΓ©, C. & Boudier, T. TANGO: a generic tool for high-throughput 3D image analysis for studying nuclear organization. Bioinformatics 29, 1840–1841 (2013).

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  63. Ollion, J., Cochennec, J., Loll, F., EscudΓ©, C. & Boudier, T. in The Nucleus (ed. Hancock, R.) 203–222 (Springer, 2015).

  64. Stirling, D. R. et al. CellProfiler 4: improvements in speed, utility and usability. BMC Bioinf. 22, 433 (2021).

    ArticleΒ  Google ScholarΒ 

  65. Babraham Bioinformatics. FastQC a quality control tool for high throughput sequence data, https://www.bioinformatics.babraham.ac.uk/projects/fastqc/.

  66. Schneider, V. A. et al. Evaluation of GRCh38 and de novo haploid genome assemblies demonstrates the enduring quality of the reference assembly. Genome Res. 27, 849–864 (2017).

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

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

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

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

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

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

    ArticleΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

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

    ArticleΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  71. Wu, D.-Y., Bittencourt, D., Stallcup, M. R. & Siegmund, K. D. Identifying differential transcription factor binding in ChIP-seq. Front. Genet. 6, 169 (2015).

  72. 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).

    ArticleΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  73. Lopez-Delisle, L. et al. pyGenomeTracks: reproducible plots for multivariate genomic datasets. Bioinformatics 37, 422–423 (2021).

    ArticleΒ  CASΒ  PubMedΒ  Google ScholarΒ 

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

    ArticleΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  75. Yang, T. et al. HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient. Genome Res. 27, 1939–1949 (2017).

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  76. Fernandez, L. R., Gilgenast, T. G. & Phillips-Cremins, J. E. 3DeFDR: statistical methods for identifying cell type-specific looping interactions in 5C and Hi-C data. Genome Biol. 21, 219 (2020).

    ArticleΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  77. Emerson, D. J. et al. Cohesin-mediated loop anchors confine the locations of human replication origins. Nature 606, 812–819 (2022).

    ArticleΒ  ADSΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  78. Knight, P. A. & Ruiz, D. A fast algorithm for matrix balancing. IMA J. Numer. Anal. 33, 1029–1047 (2013).

    ArticleΒ  MathSciNetΒ  MATHΒ  Google ScholarΒ 

  79. Norton, H. K. et al. Detecting hierarchical genome folding with network modularity. Nat. Methods 15, 119–122 (2018).

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

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

  81. Wolff, J. et al. Galaxy HiCExplorer 3: a web server for reproducible Hi-C, capture Hi-C and single-cell Hi-C data analysis, quality control and visualization. Nucleic Acids Res. 48, W177–W184 (2020).

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  82. Open2C et al. Cooltools: enabling high-resolution Hi-C analysis in Python. Preprint at bioRxiv, https://doi.org/10.1101/2022.10.31.514564 (2022).

  83. Roayaei Ardakany, A., Gezer, H. T., Lonardi, S. & Ay, F. Mustache: multi-scale detection of chromatin loops from Hi-C and Micro-C maps using scale-space representation. Genome Biol. 21, 256 (2020).

    ArticleΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

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

    ArticleΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  85. Yoon, S., Chandra, A. & Vahedi, G. Stripenn detects architectural stripes from chromatin conformation data using computer vision. Nat. Commun. 13, 1602 (2022).

    ArticleΒ  ADSΒ  CASΒ  PubMedΒ  PubMed CentralΒ  Google ScholarΒ 

  86. Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).

Download references

Acknowledgements

We thank the members of the Jain and Joyce laboratories for discussions and reading the manuscript; J. Smith, J. Nale, R. Liu and D. Wrobel of the Harvard ICCB-Longwood Screening Facility for RNAi libraries, bioinformatics tools and other support for the screen; B. Freedman and G. Ruthel for assistance with high-content imaging; and A. Stout and X. Zhao for assistance with imaging. Funding was provided by NIGMS R35GM128903 (to E.F.J.), NICHD R21HD107261 (to E.F.J.), NSF 2207050 (to E.F.J.), NHLBI R35HL166663 and the Burroughs Wellcome Foundation (to R.J.), 4D Nucleome Common Fund grants U01DA052715 (to G.V., J.E.P.-C., R.J. and E.F.J.) and U01DK127405 (to J.E.P.-C. and E.F.J.), NIMH R01MH120269 (to J.E.P.-C.), NINDS R01NS114226 (to J.E.P.-C.), NICHD F30HD104360 (to D.S.P.), the Blavatnik Family Foundation fellowship (to D.S.P.), the American Heart Association (to W.K.), NICHD F31HD102084 (to J.M.L.), JSPS Kakenhi JP21H0419 and JST CREST JPMJCR21E6 (to M.T.K.).

Author information

Authors and Affiliations

  1. Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Daniel S. Park,Β Son C. Nguyen,Β Randi Isenhart,Β R. Jordan Barnett,Β Aditi Chandra,Β Jennifer M. Luppino,Β Jailynn Harke,Β May Wai,Β Patrick J. Walsh,Β Richard J. Abdill,Β Sora Yoon,Β Rebecca Yunker,Β Golnaz Vahedi,Β Jennifer E. Phillips-CreminsΒ &Β Eric F. Joyce

  2. Penn Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Daniel S. Park,Β Son C. Nguyen,Β Randi Isenhart,Β Parisha P. Shah,Β Wonho Kim,Β R. Jordan Barnett,Β Aditi Chandra,Β Jennifer M. Luppino,Β Jailynn Harke,Β May Wai,Β Patrick J. Walsh,Β Richard J. Abdill,Β Rachel Yang,Β Yemin Lan,Β Sora Yoon,Β Rebecca Yunker,Β Golnaz Vahedi,Β Jennifer E. Phillips-Cremins,Β Rajan JainΒ &Β Eric F. Joyce

  3. Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Parisha P. Shah,Β Wonho Kim,Β Rachel YangΒ &Β Rajan Jain

  4. Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Parisha P. Shah,Β Wonho Kim,Β Rachel YangΒ &Β Rajan Jain

  5. Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Parisha P. Shah,Β Wonho Kim,Β Rachel YangΒ &Β Rajan Jain

  6. Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA

    R. Jordan BarnettΒ &Β Jennifer E. Phillips-Cremins

  7. Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Aditi Chandra,Β Sora YoonΒ &Β Golnaz Vahedi

  8. Department of Chromosome Science, National Institute of Genetics, Research Organization of Information and Systems (ROIS), Shizuoka, Japan

    Masato T. Kanemaki

  9. Department of Genetics, The Graduate University for Advanced Studies (SOKENDAI), Shizuoka, Japan

    Masato T. Kanemaki

  10. Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan

    Masato T. Kanemaki

Authors
  1. Daniel S. Park
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  2. Son C. Nguyen
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  3. Randi Isenhart
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  4. Parisha P. Shah
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  5. Wonho Kim
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  6. R. Jordan Barnett
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  7. Aditi Chandra
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  8. Jennifer M. Luppino
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  9. Jailynn Harke
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  10. May Wai
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  11. Patrick J. Walsh
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  12. Richard J. Abdill
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  13. Rachel Yang
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  14. Yemin Lan
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  15. Sora Yoon
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  16. Rebecca Yunker
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  17. Masato T. Kanemaki
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  18. Golnaz Vahedi
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  19. Jennifer E. Phillips-Cremins
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  20. Rajan Jain
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  21. Eric F. Joyce
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

Contributions

D.S.P. and E.F.J. conceptualized and initiated the project. D.S.P., S.C.N., J.M.L., J.H. and E.F.J. developed the HiDRO protocol. G.V., J.E.P.-C., R.J. and E.F.J. supervised the project and acquired funding. D.S.P., S.C.N., M.W. and R. Yang performed and analysed the imaging experiments. D.S.P. and S.C.N. performed the Hi-C experiments. D.S.P., S.C.N., R.J.B., A.C., S.Y., G.V., J.E.P.-C. and E.F.J. analysed the Hi-C data. D.S.P., R.I. and P.P.S. performed the ChIP–seq experiments. D.S.P., R.I., P.P.S., R.J.A. and Y.L. analysed the ChIP–seq data. D.S.P., W.K. and P.J.W. performed and analysed the biochemistry experiments. S.C.N. and R. Yunker designed and generated Oligopaints for this study. M.T.K. generated and validated the WAPL-AID cell line. D.S.P. and E.F.J. wrote the original draft. All of the authors reviewed and edited the manuscript.

Corresponding author

Correspondence to Eric F. Joyce.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks David Gilbert and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data figures and tables

Extended Data Fig. 1 Additional DNA HiDRO spot efficiency and RNA HiDRO workflow, Related to Fig. 1.

a) Labelling efficiency for D1 locus as measured by percent of nuclei with at least one signal for different Oligopaint probe designs including dye-conjugated 80-mers (DC 80), secondary 80-mers (SL 80) and secondary labelled 42-mers (SL 42, conventional Oligopaints used in ref. 7). Each bar is mean +/βˆ’ SD. [n = 12 biological replicate wells for all conditions except DC80 1pmol/well and 2pmol/well (n = 24) and UL80 25 pmol/well (n = 11)]. b) Labelling efficiency for D2 locus for different Oligopaint probe designs. Each bar is mean +/βˆ’ SD. [n = 12 biological replicate wells for all conditions except DC80 1pmol/well and 2pmol/well (n = 24) and UL80 25 pmol/well (n = 11)]. c) Ideograms showing chromosomal locations of Oligopaint probes to 42 DNA regions tested by HiDRO. d) Labelling efficiency as measured by percent of nuclei in a well with one or more signals detected. Chr22 D1 (green) and D2 (magenta) highlighted. Each data point represents mean +/βˆ’ SEM of six biological replicates. e) – (p) Hi-C contact matrices for boundaries tested by DNA HiDRO. Hi-C from ref. 4; tracks below are Oligopaint design, RAD21 ChIP-seq peaks (ENCODE ENCFF001UEG), CTCF ChIP-seq peaks with directionality (GEO GSM1022652), Genes, Compartment designation by eigenvector4, lamina associated domains (LADs) (4DN Data Portal: 4DNFI2BGIZ5F), superenhancers86, and insulation scores7. Percentages above each domain represent the probe efficiency for that domain as measured by the percentage of nuclei with at least one spot detected. q) Schematic for RNA HiDRO. Probes can be designed to introns and/or exons and RNA FISH is performed in 384-well plates. Wells are imaged on a high-content microscope, and nascent signals in the nucleus are segmented and measured computationally. Solid white line indicates nuclear edge. Scale bar field, 10 Β΅m; Scale bar nucleus, 5 Β΅m. r) Bursting frequency of three genes MCM5, LRCC20, and CHPF as measured by 3D RNA FISH on slides and RNA HiDRO shown. Each dot represents one biological replicate of bursting frequency calculated from greater than 100 nuclei. ns = P > 0.05, two-tailed t-test. [Biological replicate wells for MCM5 Slides and HiDRO: n = 4; LRCC20 Slides n = 6, HiDRO n = 4; CHPF Slides n = 6, HiDRO n = 10].

Extended Data Fig. 2 HiDRO screen validated hits are region non-specific regulators of TAD boundaries, Related to Fig. 2.

a) Protein classes of genes in the Druggable Genome library. Targeted genes encode proteins across diverse classes including kinases, membrane and extracellular matrix proteins, and proteases. n = 3083. b) Protein class designations of all primary hits from Druggable Genome HiDRO screen (n = 58). c) Protein class designations of primary hits that increase inter-TAD interactions (n = 33). d) Protein class designations of primary hits that decrease inter-TAD interactions (n = 25). e) Representative images of chr22:D1-D2 for hits altering inter-TAD interactions. Solid white line indicates nuclear edge. Scale bar nucleus, 5 Β΅m; Scale bar spots, 1 Β΅m. Each gene was tested in two biological replicates. f) Correlation heatmap of 21 image-based phenotypes with colour-coded squares outlining the five measurement categories used for the phenotypic tree in Fig. 2f. The five categories are overlap metrics, domain area, domain shape, nuclear area and nuclear shape. g) Histogram of nuclear area for non-targeting control wells from replicate 1 of Fig. 1b data. Black lines denote the 20th (142 Β΅m2) and 30th percentiles (170 Β΅m2) of nuclear area, representing G1 nuclei. Red lines denote the 70th (283 Β΅m2) and 80th percentiles (354 Β΅m2) of nuclear area, representing G2 nuclei. n = 128 wells. h) Violin plot of D1 spots detected per nucleus per well in G1 and G2 nuclei. Solid line is median, dotted lines are 25th and 75th percentiles. [Data from n = 128 wells for each bar.] i) Violin plot of mean CCD per well in G1 and G2 nuclei. Solid line is median, dotted lines are 25th and 75th percentiles. [Data from n = 128 wells for each bar.] ns P-value > 0.05, two-tailed t-test. j)Violin plot of mean D1 overlap per well in G1 and G2 nuclei. Solid line is median, dotted lines are 25th and 75th percentiles. [Data from n = 128 wells for each bar.] ns P-value > 0.05, two-tailed t-test. k) Validation HiDRO screen workflow tests each primary hit with four independent siRNA duplexes in separate wells, then applies DNA FISH to chr22 domains D1 and D2. l) Hi-C contact matrix and Oligopaint design for three adjacent TADs on chr3. Hi-C data from ref. 4. Fold change in spatial overlap between chr3 D1 and D2 for top hits. [Data from one well per condition of HiDRO experiment; Number of Alleles for Control (n = 1,075), WAPL (n = 426), GSK3A (n = 855), CALM1 (n = 1,321), FBXL14 (n = 1,184)]. *** P-value < 0.001, **** P-value < 0.0001, two-tailed Mann-Whitney U test. m) Heatmap displaying fold change in CCD at 13 boundaries across the human genome for WAPL, GSK3A, CALM1 and FBXL14 KD. Each boundary was tested with 3-4 biological replicates per gene KD. n) Fold change in CCD versus insulation score at boundary for top siRNA KD. For each graph, x-axis is insulation score of the boundary. Insulations scores from ref. 4. Y-axis is fold change in centre-centre distance between domains relative to control. Each point is the mean of 4 biological replicates except WAPL KD at insulation scores 91, 119, 142, 149, 195 (n = 3 biological replicates) error bars are +/βˆ’ SEM. o) Number of significantly altered boundaries as measured by D1 overlap and D2 overlap for different gene KD.

Extended Data Fig. 3 Validation of a non-canonical role for GSK3A in inter-TAD interactions, Related to Fig. 3.

a) Representative fields of control and GSK3A KD HCT-116 cells after IF to GSK3A and GSK3B. Scale bar, 25 Β΅m. b) Western blot of whole cell lysate after KD of GSK3A, GSK3B, or GSK3A+GSK3B. Proteins were labelled with HRP-linked antibodies. [Images in order top to bottom. Blot 1: Beta-catenin total, GAPDH; Blot 2: phospho-Beta-catenin Ser675 (activated), GSK3A, GAPDH; Blot 3: phospho-Beta-catenin Ser33/37/Thr41 (marked for degradation), GSK3B, GAPDH]. c) Representative 3D DNA FISH images of chr22 domains after KD of GSK3A using four independent siRNA constructs. Dotted white line indicates nuclear edge. Scale bar nucleus, 5 Β΅m; Scale bar spots, 1 Β΅m. d) Fold change in spatial overlap between chr22:D1 and D2 after KD of GSK3A using four independent siRNA constructs and a pool of all four constructs. * P-value < 0.05, *** P-value < 0.001, *** P-value < 0.0001, two-tailed Mann-Whitney U-test. [Alleles for RNAi Control (n = 961), GSK3A Pool (n = 383), GSK3A #1 (n = 807), GSK3A #2 (n = 586), GSK3A #3 (n = 314), GSK3A #4 (n = 571)]. e) Western blot of whole cell lysate after GSK3A KD using four independent siRNA constructs and pool of all four constructs leads to selective depletion of GSK3A. Proteins were labelled with HRP-linked antibodies. All lanes cropped from same blot to show only relevant lanes. Data shown represents one independent experiment. f) Representative 3D DNA FISH images of chr22 domains after 24 h DMSO- and BRD0705- (GSK3Ai, 20 Β΅M) treated HCT-116 cells. Dotted white line indicates nuclear edge. Scale bar nucleus, 5 Β΅m; Scale bar spots, 1 Β΅m. Data shown represents one independent experiment. g) Fold change in spatial overlap after 24 h 20 Β΅M BRD0705 treatment for four TAD boundaries of varying insulation scores. ** P-value < 0.01, **** P-value < 0.0001, two-tailed Mann-Whitney U-test. [Number of alleles for chr3:D1-D2: DMSO Control (n = 556), BRD0705 (n = 502); chr3:D2-D3: Control (n = 542), BRD0705 (n = 491); chr22:D1-D2: Control (n = 732), BRD0705 (n = 440); chr22:D2-D3: Control (n = 687), BRD0705 (n = 427)].

Extended Data Fig. 4 Hi-C reveals long-range looping interactions are gained in GSK3A KD, Related to Fig. 4.

a) Tukey box plots for stratum-adjusted correlation coefficient per chromosome for replicates of Hi-C in control, GSK3A KD, WAPL KD and PDS5A KD. Lower whisker = 25th percentile – 1.5*IQR, lower box bound = 25th percentile, middle of box = median, upper box bound = 75th percentile, upper whisker = 75th percentile + 1.5*IQR. [n = 23 chromosomes for each condition]. b) Log2 of difference (GSK3A KD replicate 2 – control replicate 2) in contact probability as a function of genomic distance (log scale). Dotted line at 500 kb indicates divide between short-range intra-TAD and long-range inter-TAD interactions. c) Domain counts for TAD [Control (n = 2,253), GSK3A KD (n = 2,271)] and subTADs [Control (n = 18,117), GSK3A KD (n = 19,632)]. d) Violin plot of TAD length in kb. Solid line is median, dotted lines are 25th and 75th percentiles. [Control (n = 2,253), GSK3A KD (n = 2,271)]. e) Violin plot of subTAD length in kb. [Control (n = 18,117), GSK3A KD (n = 19,632)]. f) Insulation score pileup at control TAD boundaries in control replicate 1, control replicate 2, GSK3A KD replicate 1 and GSK3A KD replicate 2 Hi-C. g) 3D pileup plots of Hi-C interactions in control and GSK3A KD at control subTAD boundaries, as well as log2 fold change in interactions across those boundaries. h) 3D pileup plots of Hi-C interactions in control and WAPL KD at control TAD boundaries, as well as log2 fold change in interactions across those boundaries. i) 3D pileup plots of Hi-C interactions in control and PDS5A KD at control TAD boundaries, as well as log2 fold change in interactions across those boundaries. j) Upset plot of chromatin loop intersections between control, GSK3A KD, PDS5A KD and WAPL KD. [Control (n = 11,895), GSK3A KD (n = 7,721), PDS5A KD (n = 8,294), WAPL (n = 23,278)]. k) Aggregate peak analysis of control and GSK3A KD at union set of loops across control and GSK3A KD (n = 13,491). l) 3D pileup plots of Hi-C interactions in control, GSK3A KD, WAPL KD and PDS5A KD at stripes detected in control. m) Hi-C contact matrices for control, WAPL KD, and WAPL KD – Control subtraction at chr7:81.5-85 Mbp. Looping interactions highlighted with squares on contact map and arcs below contact map. For subtraction map, red loops are gained in WAPL KD, and blue loops are lost. n) Hi-C contact matrices for control, PDS5A KD, and PDS5A KD – Control subtraction at chr7:81.5-85 Mbp. Looping interactions highlighted with squares on contact map and arcs below contact map. For subtraction map, red loops are gained in PDS5A KD, and blue loops are lost.

Extended Data Fig. 5 GSK3A regulates genome folding in a cohesin-dependent manner.

(a) Western blot of whole cell lysate after six hour auxin treatment in HCT-116-RAD21-mClover-AID cells and depletion of GSK3A after 72 h RNAi KD. Proteins are labelled with HRP-linked antibodies. All bands and lanes from same blot. Data shown represent one independent experiment. (b) Representative 3D DNA FISH images of chr22 domains following six-hour RAD21 auxin-inducible degradation, 72 h GSK3A KD or both. Dotted white line indicates nuclear edge. Scale bar for nucleus, 5 Β΅m; scale bar for spots, 1 Β΅m. Data shown represent one independent experiment. (c) Fold change in spatial overlap between chr22:D1-D2 after GSK3A KD, RAD21 degradation or both. ns P-value > 0.05, **** P-value < 0.0001, two-tailed Mann-Whitney U-test. [n = biologically independent alleles for Control (n = 669); GSK3A KD (n = 712); +Auxin (n = 680); GSK3A KD + Auxin (n = 560)]. (d) Representative 3D DNA FISH images of chr22 domains after auxin-inducible degradation of RAD21, 24 h treatment with BRD0705 (GSK3Ai) or both. Dotted white line indicates nuclear edge. Scale bar nucleus, 5 Β΅m; Scale bar spots, 1 Β΅m. Data shown represent one independent experiment. (e) Fold change in mean spatial overlap at chr22:D1-D2. ns P-value > 0.05, * P-value < 0.05, **** P-value < 0.0001, two-tailed Mann-Whitney U-test. [n = biologically independent alleles for control (n = 350), BRD0705 (n = 302), +Auxin (n = 525), BRD0705+Auxin (n = 280)].

Extended Data Fig. 6 GSK3A regulates levels of cohesin on chromatin.

a) RT-qPCR of architectural proteins after GSK3A, GSK3B or GSK3A+B KD. Bar is mean of two biological replicates. Each biological replicate data point is the average of three technical replicates. b) Western blots to cohesin components and CTCF in whole cell lysate after GSK3A, GSK3B or GSK3A+B KD. Proteins are labelled with fluorescent antibodies. Two biological replicates shown, run on the same gel with ladder lanes cropped out. [Images in order top to bottom. From Blot 1: NIPBL, WAPL, GSK3A, CTCF, GAPDH; Blot 2: RAD21, GSK3B, GAPDH]. c) Representative images from half-nuclear FRAP of RAD21-mClover in control and GSK3A KD HCT-116-RAD21-mClover-AID cells. Scale bar, 5 Β΅m. Data shown represent two independent experiments. d) Half-nuclear FRAP curves for RAD21-mClover in control and GSK3A KD HCT-116-RAD21-mClover-AID cells. [Control (n = 22 nuclei), GSK3A KD (n = 21 nuclei)]. Each point is median of all nuclei for that condition +/βˆ’ 95% CI. e) Model of core cohesin components bound to chromatin: RAD21 (yellow) and SMC1A (blue). f) Chromatin-bound to nucleoplasmic ratio of RAD21 or SMC1A, normalized to loading control (HDAC2). Dotted line represents control. Bar is mean +/– SD of three biological replicates. Western blots in Extended Data Fig. 6g. * P-value < 0.05, ** P-value < 0.01, *** P-value < 0.001, two-tailed t-test of each condition vs negative control. P-values left to right: 0.0236, 0.0425, 0.0167, 0.0045, 0.0002, 0.0263. g) Western blots of nuclear and chromatin-bound fractions of cohesin components following GSK3A KD, PDS5A KD and WAPL KD. Proteins are labelled using fluorescent antibodies. Corresponds to Extended Data Fig. 6f. Three total biological replicates. h) Representative genome browser image of chr3:44.5-45.2Mbp with RAD21 ChIP signal for control and GSK3A KD and CTCF ChIP signal for control. * = significantly increased RAD21 sites. i) Genome browser track chr22:16.8-18.1 Mbp with RAD21 and CTCF ChIP-seq signal shown. * = significantly gained RAD21 site. j) RAD21 occupancy for control and GSK3A KD at retained (n = 50,874) and significantly gained (n = 4,069) RAD21 sites. k) CTCF occupancy for control at retained (n = 50,874) and gained (n = 4,069) RAD21 sites. l) Percentage of retained and gained RAD21 sites that overlap with at least one CTCF peak.

Extended Data Fig. 7 GSK3A genetically interacts with WAPL to regulate cohesin levels.

a) Plasmid maps for inserting WAPL-mAID2-mClover3-Hygromycin and WAPL-mAID2-mClover3-Neomycin into parental HCT-116-OsTir1(F74G) cell line to create HCT-116-WAPL-AID2 cell line. DNA electrophoresis below confirms insert of both cassettes. b) Western blot of WAPL from HCT116-WAPL-AID lysate +/βˆ’ WAPL KD and +/βˆ’ auxin-inducible degradation of WAPL. LE = long exposure, SE = short exposure. Data shown represent one independent experiment. c) Representative images of RAD21 immunofluorescence after auxin-induced degradation of WAPL and/or siRNA treatment. Median RAD21 signal granularity noted in red. Quantification in Extended Data Fig. 5. Scale bar nucleus, 5 Β΅m. d) Median RAD21 signal granularity for +/βˆ’ siRNA treatment and +/βˆ’ degradation of WAPL. Each bar is median. [Replicate wells for -Auxin, Control (n = 13), GSK3A (n = 14), WAPL (n = 9), GSK3A + WAPL (n = 14). For +Auxin, Control (n = 12), GSK3A (n = 14), WAPL (n = 12), GSK3A +WAPL (n = 14)] **** P-value < 0.0001, two-tailed t-test.

Extended Data Fig. 8 Additional data supporting role of GSK3A in regulating cohesin unloading through WAPL, Related to Fig. 5.

a) Additional biological replicates of western blot of nucleoplasmic and chromatin-bound WAPL in control and GSK3A KD. Proteins labelled with HRP-linked antibodies. b) Additional biological replicates of co-immunoprecipitation of chromatin fractions in control and GSK3A KD with blotting of WAPL in SMC1A-IP. c) Quantification of WAPL normalized intensity in SMC1A-IP. *** P-value = 0.0007, two-tailed t-test. d) Additional biological replicate of GSK3A Turbo-ID. Western blots for cohesin components input lysate or lysate from with 24 h biotin incubation with either control construct (V5-BirA) or GSK3A TurboID (GSK3A-V5-BirA). Proteins labelled with HRP-linked antibodies. e) Western blot of RAD21 in chromatin fractions of control and 72 h NIPBL KD. Proteins are labelled with fluorescent antibodies. All bands from same blot, cropped for clarity. Two biological replicates represented. f) Quantification of western blot in Extended Data Fig. 6e. Protein intensity was adjusted for background, normalized to H3 volume and then normalized to the control lane for two biological replicates shown. g) Representative 3D DNA FISH images of chr22 domains after 72 h NIPBL KD, 24 h 20 Β΅M BRD0705 treatment, or both. Dotted white line indicates nuclear edge. Scale bar nucleus, 5 Β΅m; Scale bar spots, 1 Β΅m. h) Fold change in mean spatial overlap between chr22:D1-D2. * P-value < 0.05, **** P-value < 0.0001, two-tailed Mann-Whitney U-test. [Alleles for Control+DMSO (n = 732); Control+BRD0705 (n = 440); NIPBL+DMSO (n = 543); NIPBL+BRD0705 (n = 373)].

Supplementary information

Supplementary Information

This file contains Supplementary Figure 1: Uncropped western blots; Supplementary Table 1: Coordinates of domains labelled by Oligopaint probes, related to Figure 1 and Extended Data Figure 1; and Supplementary Table 2: Available siRNA sequences used in this study.

Reporting Summary

Supplementary Table 3

Primary HiDRO screen data, related to Figure 2. This table contains data for all genes tested in the primary HiDRO screen and robust z-scores for each of 29 image phenotypes including overlap measurements, TAD size and shape, and nuclear size and shape. β€œPrimary_screen_hit” is denoted as β€œ1” if this gene was considered a hit (see Methods).

Supplementary Table 4

Phenotypic tree of primary HiDRO screen data, related to Figure 2. This table contains data for the five different phenotype categories for each gene in the phenotypic tree in Figure 2. Further detail on how the tree was constructed in Methods.

Supplementary Table 5

Validation HiDRO screen data, related to Figure 2 and Extended Data Figure 2. This table contains data for the five different phenotype categories for each gene in the phenotypic tree in Figure 2. Further detail on how the tree was constructed in Methods.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Park, D.S., Nguyen, S.C., Isenhart, R. et al. High-throughput Oligopaint screen identifies druggable 3D genome regulators. Nature 620, 209–217 (2023). https://doi.org/10.1038/s41586-023-06340-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-023-06340-w

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

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

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