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Regulation of single-cell genome organization into TADs and chromatin nanodomains

Nature Genetics volume 52, pages 1151–1157 (2020)Cite this article

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Abstract

The genome folds into a hierarchy of three-dimensional structures within the nucleus. At the sub-megabase scale, chromosomes form topologically associating domains (TADs)1,2,3,4. However, how TADs fold in single cells is elusive. Here, we reveal TAD features inaccessible to cell population analysis by using super-resolution microscopy. TAD structures and physical insulation associated with their borders are variable between individual cells, yet chromatin intermingling is enriched within TADs compared to adjacent TADs in most cells. The spatial segregation of TADs is further exacerbated during cell differentiation. Favored interactions within TADs are regulated by cohesin and CTCF through distinct mechanisms: cohesin generates chromatin contacts and intermingling while CTCF prevents inter-TAD contacts. Furthermore, TADs are subdivided into discrete nanodomains, which persist in cells depleted of CTCF or cohesin, whereas disruption of nucleosome contacts alters their structural organization. Altogether, these results provide a physical basis for the folding of individual chromosomes at the nanoscale.

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Fig. 1: TADs form heterogeneous structures in single cells, favoring intra-over inter-TAD chromatin intermingling.
Fig. 2: The spatial segregation of TADs increases during cell differentiation.
Fig. 3: Cohesin generates chromatin intermingling while CTCF prevents inter-TAD contacts.
Fig. 4: TADs are subdivided into discrete CNDs that depend on histone acetylation.

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Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding authors upon request. Publicly available datasets used in this study (GSE84646, ENCSR000CGO, ENCSR032JUI, ENCSR000CGQ, ENCSR059MBO, ENCSR253QPK, ENCSR857MYS, GSE96107, GSE74330 and GSE94489) are detailed in Supplementary Table 2. Source data are provided with this paper.

Code availability

The code used in the current study is available at https://github.com/QuentinSzabo/Szabo_NG_2020.

References

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

    CAS  Google Scholar 

  2. Hou, C., Li, L., Qin, Z. S. & Corces, V. G. Gene density, transcription, and insulators contribute to the partition of the Drosophila genome into physical domains. Mol. Cell 48, 471–484 (2012).

    CAS  Google Scholar 

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

    CAS  Google Scholar 

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

    CAS  Google Scholar 

  5. Ou, H. D. et al. ChromEMT: visualizing 3D chromatin structure and compaction in interphase and mitotic cells. Science 357, eaag0025 (2017).

    Google Scholar 

  6. Ricci, M. A., Manzo, C., García-Parajo, M. F., Lakadamyali, M. & Cosma, M. P. Chromatin fibers are formed by heterogeneous groups of nucleosomes in vivo. Cell 160, 1145–1158 (2015).

    CAS  Google Scholar 

  7. Cremer, T. & Cremer, M. Chromosome territories. Cold Spring Harb. Perspect. Biol. 2, a003889 (2010).

    Google Scholar 

  8. Robson, M. I., Ringel, A. R. & Mundlos, S. Regulatory landscaping: how enhancer-promoter communication is sculpted in 3D. Mol. Cell 74, 1110–1122 (2019).

    CAS  Google Scholar 

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

    CAS  Google Scholar 

  10. Haarhuis, J. H. I. et al. The cohesin release factor WAPL restricts chromatin loop extension. Cell 169, 693–707.e14 (2017).

    CAS  Google Scholar 

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

    CAS  Google Scholar 

  12. 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).

    CAS  Google Scholar 

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

    CAS  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  Google Scholar 

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

    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).

    CAS  Google Scholar 

  17. Flyamer, I. M. et al. Single-nucleus Hi-C reveals unique chromatin reorganization at oocyte-to-zygote transition. Nature 544, 110–114 (2017).

    CAS  Google Scholar 

  18. Nagano, T. et al. Cell-cycle dynamics of chromosomal organization at single-cell resolution. Nature 547, 61–67 (2017).

    CAS  Google Scholar 

  19. Stevens, T. J. 3D structures of individual mammalian genomes studied by single-cell Hi-C. Nature 544, 59–64 (2017).

    CAS  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  22. 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).

    CAS  Google Scholar 

  23. 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. USA 115, E2183–E2192 (2018).

    CAS  Google Scholar 

  24. Demmerle, J. et al. Strategic and practical guidelines for successful structured illumination microscopy. Nat. Protoc. 12, 988–1010 (2017).

    CAS  Google Scholar 

  25. Gustafsson, M. G. L. et al. Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination. Biophys. J. 94, 4957–4970 (2008).

    CAS  Google Scholar 

  26. Markaki, Y. et al. The potential of 3D-FISH and super-resolution structured illumination microscopy for studies of 3D nuclear architecture: 3D structured illumination microscopy of defined chromosomal structures visualized by 3D (immuno)-FISH opens new perspectives for studies of nuclear architecture. Bioessays 34, 412–426 (2012).

    Google Scholar 

  27. Bonev, B. et al. Multiscale 3D genome rewiring during mouse neural development. Cell 171, 557–572.e24 (2017).

    CAS  Google Scholar 

  28. Zhan, Y. et al. Reciprocal insulation analysis of Hi-C data shows that TADs represent a functionally but not structurally privileged scale in the hierarchical folding of chromosomes. Genome Res. 27, 479–490 (2017).

    CAS  Google Scholar 

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

    Google Scholar 

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

    CAS  Google Scholar 

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

    CAS  Google Scholar 

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

    CAS  Google Scholar 

  33. 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).

    CAS  Google Scholar 

  34. Matthews, N. E. & White, R. Chromatin architecture in the fly: living without CTCF/cohesin loop extrusion? Alternating chromatin states provide a basis for domain architecture in Drosophila. Bioessays 41, e1900048 (2019).

    Google Scholar 

  35. Szabo, Q., Bantignies, F. & Cavalli, G. Principles of genome folding into topologically associating domains. Sci. Adv. 5, eaaw1668 (2019).

    CAS  Google Scholar 

  36. Finn, E. H. et al. Extensive heterogeneity and intrinsic variation in spatial genome organization. Cell 176, 1502–1515.e10 (2019).

    CAS  Google Scholar 

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

    CAS  Google Scholar 

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

    CAS  Google Scholar 

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

    CAS  Google Scholar 

  40. Nozaki, T. et al. Dynamic organization of chromatin domains revealed by super-resolution live-cell imaging. Mol. Cell 67, 282–293.e7 (2017).

    CAS  Google Scholar 

  41. Kantidze, O. L. & Razin, S. V. Weak interactions in higher-order chromatin organization. Nucleic Acids Res. 48, 4614–4626 (2020).

    Google Scholar 

  42. Saldaña-Meyer, R. et al. RNA interactions are essential for CTCF-mediated genome organization. Mol. Cell 76, 412–422.e5 (2019).

    Google Scholar 

  43. Gaspard, N. et al. Generation of cortical neurons from mouse embryonic stem cells. Nat. Protoc. 4, 1454–1463 (2009).

    CAS  Google Scholar 

  44. 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).

    CAS  Google Scholar 

  45. Szabo, Q., Cavalli, G. & Bantignies, F. Higher-order chromatin organization using 3D DNA fluorescent in situ hybridization. Methods Mol. Biol. 2157, 221–237 (2021).

    Google Scholar 

  46. Ball, G. et al. SIMcheck: a toolbox for successful super-resolution structured illumination microscopy. Sci. Rep. 5, 15915 (2015).

    CAS  Google Scholar 

  47. Matsuda, A., Schermelleh, L., Hirano, Y., Haraguchi, T. & Hiraoka, Y. Accurate and fiducial-marker-free correction for three-dimensional chromatic shift in biological fluorescence microscopy. Sci. Rep. 8, 7583 (2018).

    Google Scholar 

  48. Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).

    CAS  Google Scholar 

  49. Kloet, S. L. et al. The dynamic interactome and genomic targets of Polycomb complexes during stem-cell differentiation. Nat. Struct. Mol. Biol. 23, 682–690 (2016).

    CAS  Google Scholar 

  50. Xu, J. et al. Landscape of monoallelic DNA accessibility in mouse embryonic stem cells and neural progenitor cells. Nat. Genet. 49, 377–386 (2017).

    CAS  Google Scholar 

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

    CAS  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  54. Kent, W. J., Zweig, A. S., Barber, G., Hinrichs, A. S. & Karolchik, D. BigWig and BigBed: enabling browsing of large distributed datasets. Bioinformatics 26, 2204–2207 (2010).

    CAS  Google Scholar 

  55. Roukos, V., Pegoraro, G., Voss, T. C. & Misteli, T. Cell cycle staging of individual cells by fluorescence microscopy. Nat. Protoc. 10, 334–348 (2015).

    Google Scholar 

Download references

Acknowledgements

We thank R. Saldana-Meyer for sharing the CTCF-AID cell line. We thank the Montpellier Ressources Imagerie facility (BioCampus Montpellier, Centre National de la Recherche Scientifique (CNRS), INSERM, University of Montpellier) and J. Mateos-Langerak for microscopy support. We thank L. Fritsch for help with the western blots. Q.S. was supported by the French Ministry of Higher Education and Research and La Ligue Nationale Contre le Cancer. A.D. was supported by the European Research Council (Advanced Grant 3DEpi). I.J. was supported by a European Molecular Biology Organization Long-Term Fellowship (no. ALTF 559-2018) and the Laboratory of Excellence EpiGenMed. G.L.P. was supported by a grant from the Agence Nationale de la Recherche (no. ANR-18-CE15-010). T.C. was supported by the CNRS. B.B. was supported by a Sir Henry Wellcome Postdoctoral Fellowship no. 100136MA/Z/12/Z. B.G.B. was supported by grants from the National Institutes of Health/National Heart, Lung, and Blood Institute (no. P01HL089707, Bench to Bassinet Program UM1HL098179). F.B. and G.C. were supported by the CNRS. Research in the Cavalli laboratory was supported by grants from the European Research Council (Advanced Grant 3DEpi, under grant agreement no. 788972), the European Union’s Horizon 2020 research and innovation programme (MuG, under grant agreement no. 676556), the Agence Nationale de la Recherche (no. ANR-15-CE12-0006 EpiDevoMath), the Fondation pour la Recherche Médicale (no. DEI20151234396), the MSDAVENIR foundation (Project GENE-IGH), INSERM and the French National Cancer Institute (INCa Project PLBIO18-362 PIT-MM).

Author information

Author notes

  1. Boyan Bonev

    Present address: Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg, Germany

  2. These authors contributed equally: Axelle Donjon, Ivana Jerković, Giorgio L. Papadopoulos.

Authors and Affiliations

  1. Institute of Human Genetics, Centre National de la Recherche Scientifique, University of Montpellier, Montpellier, France

    Quentin Szabo, Axelle Donjon, Ivana Jerković, Giorgio L. Papadopoulos, Thierry Cheutin, Boyan Bonev, Frédéric Bantignies & Giacomo Cavalli

  2. Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA, USA

    Elphège P. Nora & Benoit G. Bruneau

  3. Gladstone Institutes, San Francisco, CA, USA

    Benoit G. Bruneau

  4. Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA

    Benoit G. Bruneau

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  1. Quentin Szabo
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Contributions

Q.S., F.B. and G.C. initiated and led the project. Q.S. and F.B. designed the Oligopaint probes. Q.S. and A.D. produced the Oligopaint probes. Q.S. performed the FISH experiments, 3D-SIM image acquisition and image analysis. Q.S., A.D. and I.J. handled the ESC culture. A.D. and I.J. handled the CTCF-AID and RAD21-AID cultures and performed the NPC differentiation. A.D. performed the western blots. G.L.P. analyzed the sequencing data. T.C. performed the STED imaging. B.B. dissected the mouse neocortices. E.P.N. and B.G.B. generated the CTCF-AID and RAD21-AID cell lines. F.B. handled the flies. Q.S., F.B. and G.C. wrote the manuscript. All authors discussed the data and reviewed the manuscript.

Corresponding authors

Correspondence to Frédéric Bantignies or Giacomo Cavalli.

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Extended data

Extended Data Fig. 1 Oligopaint probe design.

Hi-C maps from ESCs and NPCs along with the positions of FISH probes. Gray ticks indicate TAD borders defined from visual inspection.

Extended Data Fig. 2 Oligopaint probe coverage.

Oligopaint coverage for each probe. X axes represent the labeled regions (from their start to their end coordinates), each dot represents the number of oligos in a 10 kb bin.

Extended Data Fig. 3 Spatial segregation of TADs in ESCs.

a, 3D-SIM images of a control (Ctl) probe (5d2) simultaneously labeled by Alexa-488 and ATTO-565 fluorophores (85 alleles were analyzed). Maximum projections, scale bar = 500 nm. b, Distances between the centroids of the two segmented channels. n = 85 alleles. c, Pearson’s correlation coefficient (PCC) between probe intensities. Boxplots represent median, interquartile ranges, and Tukey-style whiskers. n = 48 and 50 for the pair 51a-51b and 51b-52, respectively; ***P = 1.14e-5, two-sided Wilcoxon rank sum test. d, Mean (±s.d.) population fraction with OF < 0.1. n = 7 and 8 probe pairs within and between TADs, respectively; ***P = 3.11e-4, two-sided Wilcoxon rank sum test. e, OFs and 3D distances between centroids. Boxplots represent median, interquartile ranges and Tukey-style whiskers. A mean of 65 alleles was analyzed per probe pair. f, Mean (±s.d.) PCC between probe intensities measured from each probe pair. n = 7 and 8 probe pairs within and between TADs, respectively; ***P = 6.22e-4, two-sided Wilcoxon rank sum test. g, Representation of the crossing contact fraction, defined as the Hi-C contacts measured between the labeled regions (A) divided by the sum of contacts measured in regions A, B and C. h, scHi-C maps of a locus labeled by FISH probes (color-coded). Yellow lines indicate TADs detected from cell-population Hi-C. i, Crossing contact fraction from individual cells for each probe pair. Boxplots represent median, interquartile ranges and Tukey-style whiskers. n = 165 cells. j, Crossing contact fraction from individual cells for all probe pairs used in FISH. Boxplots represent median, interquartile ranges and Tukey-style whiskers. n = 1155 and 1320 for probe pairs within and between TADs, respectively; ***P = 3.84e-158, two-sided Wilcoxon rank sum test.

Extended Data Fig. 4 Spatial segregation of TADs in NPCs.

a, Pax6 immunostaining (in all FISH experiments performed in NPCs, each analyzed nucleus was positive for Pax6 staining). Conventional wide field (WF) microscopy, scale bar = 10 µm. b, Mean (±s.d.) population fraction with OF < 0.1. n = 10 and 5 probe pairs within and between TADs, respectively; ***P = 6.66e-4, two-sided Wilcoxon rank sum test. c, OFs and 3D distances between centroids. Boxplots represent median, interquartile ranges and Tukey-style whiskers. A mean of 36 alleles was analyzed per probe pair. d, Mean (±s.d.) OF fold change (NPC/ESC, probe pairs that do not change their TAD borders between ESCs and NPCs). n = 7 and 5 probe pairs within and between TADs, respectively; two-sided t-test. e, Mean probe volume as a function of genomic size. A mean of 70 and 47 alleles was analyzed per probe in ESCs and NPCs, respectively. f, Mean (±s.d.) of coefficient of variations (CV) for OFs and 3D distances (probe pairs that do not change their TAD borders between ESCs and NPCs). n = 7 and 5 probe pairs within and between TADs, respectively. g, Crossing contact fraction measured for the probe pair shown in Fig. 2e. h, Hi-C maps in ESCs, NPCs, and ncxNPCs along with probe pair location. Arrowhead indicates the expressed Zfp608 gene. i, 3D-SIM images of the probes shown in h (62, 34 and 48 alleles were analyzed in ESCs, NPCs and ncxNPCs, respectively). Maximum projections, scale bar = 500 nm. j, OFs and 3D distances between the centroids of the probes shown in h. Boxplots represent median, interquartile ranges and Tukey-style whiskers. n = 62, 34 and 48 for ESCs, NPCs, and ncxNPCs, respectively. k, Crossing contact fraction measured for the probe pair shown in i. Despite the appearance of a border within the locus labeled by the probe pair 11-12 in differentiated cells (panel h and Extended Data Fig. 1), and considering the absence of changes in crossing contact fraction neither in intermingling (j), we considered this probe pair as ‘Within TAD’ in NPCs.

Extended Data Fig. 5 Spatial segregation of TADs in CTCF- and RAD21-depleted cells.

a, Western Blot of CTCF and Vinculin (loading control) in CTCF-AID, CTCF-AID+ auxin (2 days), and wild-type ESCs. 4 reproducible western blots were performed from different biological replicates. b, OFs and 3D distances between centroids measured for each probe pair located within or between TADs as a function of the genomic distance separating their centers. Graph represents medians and interquartile ranges. A mean of 57 and 51 alleles was analyzed per probe pair in CTCF-AID and CTCF-AID+ auxin cells, respectively. c, Mean OF measured from all probe pairs within TADs divided by the mean OF from all probe pairs between TADs. d, Western Blot of RAD21 and Vinculin (loading control) in wild-type ESCs, RAD21-AID and RAD21-AID+ auxin (6 hours). 3 reproducible western blots were performed from different biological replicates. e, OFs and 3D distances between centroids measured for each probe pair located within or between TADs as a function of the genomic distance separating their centers. Graph represents medians and interquartile ranges. A mean of 75 and 47 alleles was analyzed per probe pair in RAD21-AID and RAD21-AID+ auxin cells, respectively. f, Mean OF measured from all probe pairs within TADs divided by the mean OF from all probe pairs between TADs.

Extended Data Fig. 6 TAD and CND structures revealed by super-resolution imaging.

a, Hi-C map from ESCs along with probe position. b. Individual TADs labeled by the probe shown in a imaged with conventional WF microscopy (top) or with 3D-SIM (bottom). 23 and 33 alleles were analyzed with conventional WF and 3D-SIM microscopy, respectively. White lines represent the boundaries of probe segmentations (2D projections). Maximum projections, scale bar = 500 nm. c, Volumes, sphericities and principal axis lengths of individual TADs. Boxplots represent median, interquartile ranges and Tukey-style whiskers. n = 23 and 33 for conventional WF microscopy and 3D-SIM, respectively; ***P < 0.001, two-sided Wilcoxon rank sum tests. d, Sphericity as a function of principal axis length for individual TADs. A mean of 73 TADs was analyzed per probe. e, 3D-SIM image of TAD #22 (top, maximum projection, scale bar = 500 nm), CND identification using 3D watershed segmentation (middle) or using local fluorescence intensity maxima (bottom) and number of CNDs per TAD. 51 alleles were analyzed. f, Stimulated emission depletion (STED) images of TAD #22 (single z-slice, scale bar = 500 nm) and extrapolated diameters of individual TADs and of CNDs within them. Bins represent 50 nm, n = 21 and 96 for TADs and CNDs, respectively. 21 alleles were analyzed. g, DAPI staining in ESC and watershed segmentation showing CNDs. 3 nuclei were analyzed. Single z-slice, scale bar = 5 µm. h, 3D-SIM image of DAPI staining in Drosophila male embryonic cell (single z-slice, scale bar = 5 µm or 500 nm in the magnification) and of an X-linked 110 kb Drosophila TAD (maximum projection, scale bar = 500 nm). 20 and 52 nuclei were analyzed with DAPI staining and FISH, respectively. i, Extrapolated diameters of the Drosophila TAD, of CNDs within it, and of CNDs measured with DAPI staining. Bins represent 50 nm, n = 52, 54 and 2250, respectively.

Extended Data Fig. 7 Structural organization of TADs and CNDs in CTCF- and RAD21-depleted cells.

a, TAD volumes, TAD sphericities, CND volumes, and number of CNDs per TAD (mean ±s.d.). Boxplots represent median, interquartile ranges, and Tukey-style whiskers. A mean of 84 and 64 alleles was analyzed per probe in CTCF-AID and CTCF-AID+ auxin cells, respectively; ***P < 0.001, **P < 0.01, *P < 0.05, two-sided Wilcoxon rank sum tests. b, 3D-SIM images of TAD #62 (59 and 41 alleles were analyzed in CTCF-AID and CTCF-AID+ auxin, respectively). White lines represent the boundaries of probe segmentations (2D projections). Maximum projections, scale bar = 500 nm. c, TAD volumes, TAD sphericities, CND volumes, and number of CNDs per TAD (mean ±s.d.). Boxplots represent median, interquartile ranges and Tukey-style whiskers. A mean of 176 and 116 alleles was analyzed per probe in in RAD21-AID and RAD21-AID + auxin cells, respectively; ***P < 0.001, **P < 0.01, two-sided Wilcoxon rank sum tests. d, 3D-SIM images of TAD #112 showing alleles segmented as one (top) or two (bottom) objects. Lines represent the boundaries of probe segmentations (2D projections). Maximum projections, scale bar = 500 nm. e, Cell cycle profiling using DAPI staining (with examples of nucleus segmentation, scale bar = 10 µm). As nucleus size and DAPI intensity reflect cell cycle stage55, cutoff values for nucleus area and DAPI integrated intensity were applied to define G1 ESCs. 27% of the population was defined as G1, consistently with flow cytometry measurements27. 164 nuclei were analyzed. f, TAD volumes, TAD sphericities, CND volumes, and number of CNDs per TAD (mean ±s.d.). Boxplots represent median, interquartile ranges and Tukey-style whiskers. n = 48, 77 and 48 for ESC-G1, RAD21-AID+ auxin and RAD21-AID+ auxin single chromatids, respectively; ***P < 0.001, two-sided Wilcoxon rank sum tests.

Extended Data Fig. 8 Structural organization of TADs and CNDs relates to chromatin state and histone acetylation.

a, Hierarchical clustering of the 26 labeled loci (Extended Data Figs. 1 and 9) using ATAC-seq, RNA-seq and ChIP-seq of the indicated histone post-translational modifications in ESCs. b, Mean (±s.d.) density (genomic size/mean volume) of the probes in ESCs. A mean of 70 alleles was analyzed per probe; **P = 0.0019, two-sided Wilcoxon rank sum test. c, Mean (±s.d.) number of CNDs per probe divided by their genomic size (in Mb) in ESCs. A mean of 70 alleles was analyzed per probe; ***P = 6.05e-4, two-sided Wilcoxon rank sum test. d, Hierarchical clustering of the 26 labeled loci in NPCs. e, Mean (±s.d.) density of the probes in NPCs. A mean of 47 alleles was analyzed per probe; **P = 0.0013, two-sided Wilcoxon rank sum test. f, Mean (±s.d.) number of CNDs per probe divided by their genomic size (in Mb) in NPCs. A mean of 47 alleles was analyzed per probe; **P = 0.001, two-sided Wilcoxon rank sum test. g, Western Blot of H4 (loading control) and H4-acetyl (H4ac). Two different protein concentrations were loaded. 3 reproducible western blots were performed from different biological replicates. h, Mean OF measured from probe pairs within TADs (102a-102b and 121a-121b) divided by the mean OF from probe pairs between TADs (101-102a, 101-103 and 111-112). A mean of 76 and 69 alleles was analyzed in ESCs and ESCs + TSA, respectively. i, Mean OF fold change (ESC+TSA/ESC) for each probe pair within TADs (n = 2; 102a-102b and 121a-121b) or between TADs (n = 3; 101-102a, 101-103 and 111-112). j, TAD volumes and sphericities. Boxplots represent median, interquartile ranges and Tukey-style whiskers. A mean of 93 and 95 alleles was analyzed in ESCs and ESCs + TSA, respectively; ***P < 0.001, *P < 0.05, two-sided Wilcoxon rank sum tests.

Extended Data Fig. 9 Oligopaint probe information.

Oligopaint IDs, coordinates, sizes, numbers of oligos, density of oligos, primer codes (see Supplementary Table 1), and clusters (1 for active or 0 for repressed; ESC/NPC; see Extended Data Fig. 8a, d).

Supplementary information

Source data

Source Data Fig. 1

Unprocessed western blots corresponding to Extended Data Fig. 5a.

Source Data Fig. 2

Unprocessed western blots corresponding to Extended Data Fig. 5d.

Source Data Fig. 3

Unprocessed western blots corresponding to Extended Data Fig. 8g.

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Szabo, Q., Donjon, A., Jerković, I. et al. Regulation of single-cell genome organization into TADs and chromatin nanodomains. Nat Genet 52, 1151–1157 (2020). https://doi.org/10.1038/s41588-020-00716-8

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