Conformation of sister chromatids in the replicated human genome

Abstract

The three-dimensional organization of the genome supports regulated gene expression, recombination, DNA repair, and chromosome segregation during mitosis. Chromosome conformation capture (Hi-C)1,2 analysis has revealed a complex genomic landscape of internal chromosomal structures in vertebrate cells3,4,5,6,7, but the identical sequence of sister chromatids has made it difficult to determine how they topologically interact in replicated chromosomes. Here we describe sister-chromatid-sensitive Hi-C (scsHi-C), which is based on labelling of nascent DNA with 4-thio-thymidine and nucleoside conversion chemistry. Genome-wide conformation maps of human chromosomes reveal that sister-chromatid pairs interact most frequently at the boundaries of topologically associating domains (TADs). Continuous loading of a dynamic cohesin pool separates sister-chromatid pairs inside TADs and is required to focus sister-chromatid contacts at TAD boundaries. We identified a subset of TADs that are overall highly paired and are characterized by facultative heterochromatin and insulated topological domains that form separately within individual sister chromatids. The rich pattern of sister-chromatid topologies and our scsHi-C technology will make it possible to investigate how physical interactions between identical DNA molecules contribute to DNA repair, gene expression, chromosome segregation, and potentially other biological processes.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: scsHi-C methodology based on nascent DNA labelling in live cells.
Fig. 2: Genome-wide conformation maps of replicated human chromosomes.
Fig. 3: TAD topologies in replicated chromosomes.
Fig. 4: Organization of sister chromatids by distinct pools of cohesin complexes.

Data availability

All scsHi-C datasets generated in this study have been made available via the Gene Expression Omnibus (GEO) database under the series accession number GSE152373 and are also available from the authors upon request.

Code availability

The ipython notebooks used to perform all the sequencing data analysis of data generated within this work are available at https://github.com/gerlichlab/scshic_analysis, along with a detailed description of each script and the figures they produce. The programing environment used to perform this analysis is provided as a docker container (https://hub.docker.com/repository/docker/gerlichlab/scshic_docker) under the tag ‘release-1.0’. All the versions of the software packages used are noted within the dockerfile.

References

  1. 1.

    Dekker, J., Rippe, K., Dekker, M. & Kleckner, N. Capturing chromosome conformation. Science 295, 1306–1311 (2002).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  2. 2.

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

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

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

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    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  PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

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

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Gibcus, J. H. et al. A pathway for mitotic chromosome formation. Science 359, eaao6135 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  8. 8.

    Schoenfelder, S. & Fraser, P. Long-range enhancer-promoter contacts in gene expression control. Nat. Rev. Genet. 20, 437–455 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  9. 9.

    Hustedt, N. & Durocher, D. The control of DNA repair by the cell cycle. Nat. Cell Biol. 19, 1–9 (2016).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  10. 10.

    Batty, P. & Gerlich, D. W. Mitotic chromosome mechanics: how cells segregate their genome. Trends Cell Biol. 29, 717–726 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  11. 11.

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

    ADS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    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  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Gassler, J. et al. A mechanism of cohesin-dependent loop extrusion organizes zygotic genome architecture. EMBO J. 36, 3600–3618 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    van Ruiten, M. S. & Rowland, B. D. SMC complexes: universal DNA looping machines with distinct regulators. Trends Genet. 34, 477–487 (2018).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  16. 16.

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

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  17. 17.

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

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Kagey, M. H. et al. Mediator and cohesin connect gene expression and chromatin architecture. Nature 467, 430–435 (2010).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Franke, M. et al. Formation of new chromatin domains determines pathogenicity of genomic duplications. Nature 538, 265–269 (2016).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    Northcott, P. A. et al. Enhancer hijacking activates GFI1 family oncogenes in medulloblastoma. Nature 511, 428–434 (2014).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Gröschel, S. et al. A single oncogenic enhancer rearrangement causes concomitant EVI1 and GATA2 deregulation in leukemia. Cell 157, 369–381 (2014).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  22. 22.

    Rankin, S., Ayad, N. G. & Kirschner, M. W. Sororin, a substrate of the anaphase-promoting complex, is required for sister chromatid cohesion in vertebrates. Mol. Cell 18, 185–200 (2005).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  23. 23.

    Watrin, E. & Peters, J.-M. Cohesin and DNA damage repair. Exp. Cell Res. 312, 2687–2693 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  24. 24.

    Sjögren, C. & Nasmyth, K. Sister chromatid cohesion is required for postreplicative double-strand break repair in Saccharomyces cerevisiae. Curr. Biol. 11, 991–995 (2001).

    PubMed  Article  PubMed Central  Google Scholar 

  25. 25.

    Nasmyth, K. & Haering, C. H. Cohesin: its roles and mechanisms. Annu. Rev. Genet. 43, 525–558 (2009).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  26. 26.

    Herzog, V. A. et al. Thiol-linked alkylation of RNA to assess expression dynamics. Nat. Methods 14, 1198–1204 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Riml, C. et al. Osmium-mediated transformation of 4-thiouridine to cytidine as key to study RNA dynamics by sequencing. Angew. Chem. Int. Edn Engl. 56, 13479–13483 (2017).

    CAS  Article  Google Scholar 

  28. 28.

    Gasser, C. et al. Thioguanosine conversion enables mRNA-lifetime evaluation by RNA sequencing using double metabolic labeling (TUC-seq DUAL). Angew. Chem. Int. Edn Engl. 59, 6881–6886 (2020).

    CAS  Article  Google Scholar 

  29. 29.

    Vassilev, L. T. et al. Selective small-molecule inhibitor reveals critical mitotic functions of human CDK1. Proc. Natl Acad. Sci. USA 103, 10660–10665 (2006).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  30. 30.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Stanyte, R. et al. Dynamics of sister chromatid resolution during cell cycle progression. J. Cell Biol. 217, 1985–2004 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    An, L. et al. OnTAD: hierarchical domain structure reveals the divergence of activity among TADs and boundaries. Genome Biol. 20, 282 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Sheffield, N. C. & Bock, C. LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor. Bioinformatics 32, 587–589 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  34. 34.

    Jambhekar, A., Dhall, A. & Shi, Y. Roles and regulation of histone methylation in animal development. Nat. Rev. Mol. Cell Biol. 20, 625–641 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Erceg, J. et al. The genome-wide multi-layered architecture of chromosome pairing in early Drosophila embryos. Nat. Commun. 10, 4486 (2019).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  36. 36.

    ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    ADS  Article  CAS  Google Scholar 

  37. 37.

    Sandelin, A., Alkema, W., Engström, P., Wasserman, W. W. & Lenhard, B. JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res. 32, D91–D94 (2004).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Ladurner, R. et al. Sororin actively maintains sister chromatid cohesion. EMBO J. 35, 635–653 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Gerlich, D., Koch, B., Dupeux, F., Peters, J.-M. & Ellenberg, J. Live-cell imaging reveals a stable cohesin-chromatin interaction after but not before DNA replication. Curr. Biol. 16, 1571–1578 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  40. 40.

    Schmitz, J., Watrin, E., Lénárt, P., Mechtler, K. & Peters, J.-M. Sororin is required for stable binding of cohesin to chromatin and for sister chromatid cohesion in interphase. Curr. Biol. 17, 630–636 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  41. 41.

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

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  42. 42.

    Oomen, M. E., Hedger, A. K., Watts, J. K. & Dekker, J. Detecting chromatin interactions along and between sister chromatids with SisterC. Preprint at https://www.biorxiv.org/content/10.1101/2020.03.10.986208v1 (2020).

  43. 43.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Altmeyer, M. & Lukas, J. To spread or not to spread—chromatin modifications in response to DNA damage. Curr. Opin. Genet. Dev. 23, 156–165 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  45. 45.

    Bantignies, F. et al. Inheritance of Polycomb-dependent chromosomal interactions in Drosophila. Genes Dev. 19, 2406–2420 (2003).

    Article  CAS  Google Scholar 

  46. 46.

    Ran, F. A. et al. Double nicking by RNA-guided CRISPR Cas9 for enhanced genome editing specificity. Cell 154, 1380–1389 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Samwer, M. et al. DNA cross-bridging shapes a single nucleus from a set of mitotic chromosomes. Cell 170, 956–972.e23 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Sommer, C., Hoefler, R., Samwer, M. & Gerlich, D. W. A deep learning and novelty detection framework for rapid phenotyping in high-content screening. Mol. Biol. Cell 28, 3428–3436 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Held, M. et al. CellCognition: time-resolved phenotype annotation in high-throughput live cell imaging. Nat. Methods 7, 747–754 (2010).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  51. 51.

    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  52. 52.

    Lusser, A. et al. Thiouridine-to-cytidine conversion sequencing (TUC-seq) to measure mRNA transcription and degradation rates. Methods Mol. Biol. 2062, 191–211 (2020).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  53. 53.

    Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Abdennur, N. & Mirny, L. Cooler: scalable storage for Hi-C data and other genomically-labeled arrays. Bioinformatics 36, 311–316 (2020).

    PubMed  PubMed Central  Google Scholar 

  55. 55.

    Imakaev, M. et al. Iterative correction of Hi-C data reveals hallmarks of chromosome organization. Nat. Methods 9, 999–1003 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    Zufferey, M., Tavernari, D., Oricchio, E. & Ciriello, G. Comparison of computational methods for the identification of topologically associating domains. Genome Biol. 19, 217 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  58. 58.

    Zhong, J. et al. Purification of nanogram-range immunoprecipitated DNA in ChIP-seq application. BMC Genomics 18, 985 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  59. 59.

    Sathyan, K. M. et al. An improved auxin-inducible degron system preserves native protein levels and enables rapid and specific protein depletion. Genes Dev. 33, 1441–1455 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Yesbolatova, A., Natsume, T., Hayashi, K. I. & Kanemaki, M. T. Generation of conditional auxin-inducible degron (AID) cells and tight control of degron-fused proteins using the degradation inhibitor auxinole. Methods 164-165, 73–80 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

Download references

Acknowledgements

We acknowledge technical support by the IMBA/IMP/GMI BioOptics and Molecular Biology Services facilities, and the Vienna BioCenter Metabolomics and Next Generation Sequencing facilities. We thank I. Patten, P. Batty, M. W. G. Schneider, and M. Voichek for comments on the manuscript, and Life Science Editors for editing assistance. Research in the laboratory of D.W.G. is supported by the Austrian Academy of Sciences, an ERC Starting (Consolidator) Grant (281198), the Austrian Science Fund (FWF; Doktoratskolleg ‘Chromosome Dynamics’ DK W1238), and the Vienna Science and Technology Fund (WWTF; project LS17-003). Research in the laboratory of R.M. is supported by the Austrian Science Fund (P27947, P31691, F8011), the Austrian Research Promotion Agency FFG (West-Austrian BioNMR 858017), and the Vienna Science and Technology Fund (WWTF; project no. LS17-003). Research in the laboratory of S.L.A. is supported by the Austrian Academy of Sciences, the European Research Council (ERC-CoG-866166 and ERC-PoC-825710), the Vienna Science and Technology Fund (WWTF; project LS17-003), and the Austrian Science Fund (FWF; SFB F8002). Research in the laboratory of J.-M.P. is supported by Boehringer Ingelheim, the Austrian Research Promotion Agency (Headquarter grant FFG-852936), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme GA No 693949, the Human Frontiers Science Programme (RGP0057/2018) and the Vienna Science and Technology Fund (WWTF; grant LS19-029). M.M. received a PhD fellowship from the Boehringer Ingelheim Fonds. The VBCF Metabolomics Facility is funded by the City of Vienna through the Vienna Business Agency.

Author information

Affiliations

Authors

Contributions

M.M., D.W.G., and R.M. conceived the project and designed experiments. C.G., E.N., and R.M. developed the chemistry to convert 4sT into 5mC. M.M. developed the scsHi-C methodology and together with Z.T. performed all experiments, except some scsHi-C experiments that were performed by C.T.B. S.L.A. provided advice on DNA labelling methodology. C.C.H.L. developed the computational pipeline for DNA sequencing pre-processing. M.M. developed computational procedures for scsHi-C analysis, with help from C.C.H.L. M.M. and D.W.G. analysed and interpreted the data, with help from J.-M.P. and A.G. W.T. and G.J. generated AID-tagged cell lines. D.W.G., R.M., S.L.A., J.-M.P., and M.M. acquired funding. D.W.G. and R.M. supervised the project. D.W.G. and M.M. wrote the manuscript.

Corresponding authors

Correspondence to Michael Mitter or Daniel W. Gerlich.

Ethics declarations

Competing interests

R.M. and C.G. are listed as inventors on a patent application that has been filed concerning the nucleoside conversion chemistry of this work (Osmiumtetroxide-based conversion of RNA and DNA containing thiolated nucleotides, US Patent App. 16/533,988). The other authors declare no competing interests.

Additional information

Peer review information Nature thanks David Gilbert, Benjamin Rowland and Hongtao Yu for their contribution to the peer review of this work. Peer reviewer reports are available.

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 Characterization of 4sT.

a, Synthetic hairpin-oligonucleotide used to probe 4sT conversion by OsO4. The theorized reaction educts and products are highlighted in red. b, High performance liquid chromatography (HPLC) trace at 260 nm of the oligos depicted in a before and after the conversion by OsO4/NH4Cl. The peak position of the oligo before conversion is indicated by a dashed line. c, Melting point analysis of a synthetic DNA hairpin containing 4sT or thymidine in the Watson–Crick base-paired stem (see Methods for details). d, Native mass spectrum of the oligonucleotide shown in a before and after OsO4/NH4Cl conversion measured in negative ion mode.

Extended Data Fig. 2 Characterization of the cellular response to 4sT.

a, Percentage of live cells determined via Topro-3-Iodide staining of dead cells after 24 h incubation with the indicated compounds. Bars indicate mean of live cell percentages of individual wells from n = 2 biologically independent experiments. b, DNA damage assay performed after 24 h incubation with the indicated compounds. Scale bars indicate 5 μm. c, Quantification of mean fluorescence in cell nuclei stained by anti-p-γ-H2A.X antibody shown in b. Bars indicate mean of individual cells from n = 2 biologically independent experiments. d, Flow cytometry (FACS) analysis of cells progressing through S-phase in the presence or absence of 2 mM 4sT. DNA was stained using propidium iodide and kernel density estimation of signal in the PE-channel is shown. Cells were pre-synchronized to G1/S by thymidine and released into S-phase by removal of thymidine. The G2 sample was arrested by RO3306 and the G1 sample was arrested after progression through mitosis using aphidicolin. This experiment was repeated one more time with similar results.

Extended Data Fig. 3 Preparation of cell cycle stage-specific scsHi-C samples.

a, Quantification of 4sT incorporation into genomic DNA of HeLa cells using mass spectrometry. Cells were grown in medium containing 4sT for 5 days and purified genomic DNA digested to single nucleotides. Indicated values reflect the percentage of 4sT in total measured thymidine. Bars indicate mean of n = 6 biologically independent experiments. b Quantification of 4sT incorporation using DNA sequencing. Cells were 4sT labelled as in a and purified genomic DNA chemically converted as in Fig. 1c. Indicated values are the sum of the A-to-G-mutation rate and the T-to-C mutation rate, normalized to the total amount of adenosine and thymidine measured respectively. Bars indicate mean of n = 3 biologically independent experiments. c, Overview of experimental procedure for differential labelling of sister chromatids using 4sT. d, Procedure of cell synchronization for scsHi-C sample preparation of cells synchronized to G2, prometaphase and the subsequent G1 phase. The different compounds were added to the cell culture medium as indicated by the colored bars. e, Cell cycle analysis of WT HeLa cells synchronized to G2, prometaphase and G1 as indicated in d. Anti-pH3S10 antibody was used to detect the mitotic state and propidium iodide to measure DNA content. Gates for different cell cycle stages are shown and the indicated numbers reflect percentage of cells that were measured. This experiment was repeated one time with similar results. See Supplementary Fig. 2 for the exact gating strategy. f, Average contact probability over different genomic distances of HeLa cells synchronized to G2 that were either labelled with 4sT or unlabelled. Experiment was repeated one time with similar results. g, Hi-C interaction matrices at example regions of HeLa cells synchronized to G2 that were either labelled with 4sT or unlabelled.

Extended Data Fig. 4 scsHi-C classification procedure.

a, Depiction of all possible Hi-C ligation products of a sample where one strand of each sister chromatid has been labelled with a synthetic nucleotide. Only ligation products that carry two continuous halves that are labelled (“double-labelled”) can be used to discriminate cis sister contacts from trans sister contacts. This is because 4sT incorporation density is not high enough to allow detection of unlabelled reads based on the absence of signature mutations. If a given read exhibits signature mutations, however, it is possible to know with high confidence (see panel b and c) that it comes from the labelled strand. The ligation products that do not contain two halves with signature mutations are thus discarded during analysis. b, Histograms of signature point mutations per read (AG or TC) of conventional sequencing libraries constructed from cells that were grown for 5 days in the presence (“4sT-labelled”) or absence (“unlabelled”) of 4sT and treated with OsO4 as described in Fig. 1c. Note that at sites of 4sT-incorporation only 50% of PCR amplicons will contain the signature mutations, as 4sT base-pairs with unmodified A on the opposing DNA strand. Bar indicates mean of n = 3 biologically independent experiments. c, To assess how many signature mutations are required to confidently detect double-labelled reads, we analysed Hi-C libraries from cells grown in the absence of 4sT and calculated the false-positive rate of double-labelled read detection. Double-labelled reads were assigned based on different required signature point mutations on both read-halves. The samples shown was grown without 4sT, suggesting that every detected double-labelled read should be a false positive. Points indicate mean of n = 2 biologically independent experiments. When considering reads that contained at least two signature mutations, less than 0.2% were classified as “double-labelled”, indicating very low rates of misclassification. When cells were released into S-phase in the presence of 4sT, the percentage of double-labelled reads increased to 12% in the subsequent G2 phase (f). Thus, double-labelled reads are detected with a very low false positive rate and at sufficient yield to construct Hi-C-maps. d, Percentage of Hi-C contacts in HeLa wildtype cells synchronized to G2 in the presence of 4sT that can be used to assign sister-chromatid identity (“double-labelled reads”) based on a classification scheme that requires an equal amount or more than the shown number of signature mutations. The number used in this paper (2) is highlighted in red. Points indicate mean of n = 9 biologically independent experiments. e, Quantification of wrongly assigned trans sister contacts based on different signature mutation thresholds. To calculate the false-positive rate with which a cis sister contact is wrongly assigned as a trans sister contact, we adapted the scheme used to quantify the false-positive rate of trans-homologue contact assignment from ref. 35. Briefly, all contacts that exhibit a genomic separation smaller than 1 kb are assumed to be Hi-C artefacts that arise from uncut continuous pieces of chromatin. Such contacts should be exclusively classified as cis sister contacts and thus all trans sister contacts in this range are assumed to be false positives. To then quantify the percentage of incorrect trans sister Hi-C contacts among all trans sister Hi-C contacts, the calculated false-positive rate was multiplied by the number of cis sister contacts exhibiting separation larger than 1 kb and the resulting percentage of all trans sister contacts exhibiting separation larger than 1 kb plotted in this figure. Points indicate mean of n = 9 biologically independent experiments. We thus estimate that - at the number of required point mutations used in this paper (2) - the wrongly assigned trans sister contacts are below 2%. f, Quantification of Hi-C reads that are labelled on both sides for sister-specific contact classification, as a percentage of all reads. Cells were synchronized to the G1/S boundary and released into S-Phase in the presence of 4sT for the indicated times. The G2 sample was arrested using RO3306; the prometaphase sample was arrested using nocodazole; the control sample refers to unlabelled DNA and the G1/S 4sT sample refers to a sample that was treated with 4sT, but not released into S-Phase. Bars show the mean of n = 2 biologically independent replicates. g, Percentage of trans sister contacts based on all double-labelled reads that exhibit a genomic separation larger than 10 kb. Cells were released from G1/S block into medium containing 4sT and then arrested in G2 using RO3306, in prometaphase using nocodazole, or the following G1 using thymidine. Bars show mean of n = 2 biologically independent replicates.

Extended Data Fig. 5 Reproducibility of scsHi-C.

a, Hi-C interaction matrices of the long arm of chromosome 1 of all contacts, cis sister, and trans sister contacts shown for two of the 11 G2 WT replicates. The all-contacts matrix was normalized to the total number of corrected contacts in the region of interest (ROI), whereas cis sister and trans sister contacts were normalized to the total amount of cis sister contacts and trans sister contacts in the ROI. Bin size of the matrix is 500 kb. b, HiCrep57 analysis of all, cis sister and trans sister contacts of all n = 11 biologically independent G2 replicates. Bars show the mean of all comparisons. c, Hi-C interaction matrix of the long arm of chromosome 1 of all, cis sister, and trans sister contacts of the two prometaphase replicates. Contacts were normalized as in a. d, HiCrep57 analysis of all, cis sister and trans sister contacts of n = 2 biologically independent prometaphase replicates. Bars show the mean of all comparisons.

Extended Data Fig. 6 Sister-chromatid conformation analysis by scsHi-C and microscopy.

a, All contacts, cis sister and trans sister contacts, as well as the ratio of trans sister observed/expected to cis sister observed/expected of n = 11 biologically independent, merged G2 samples at a representative region on chromosome 3 is displayed alongside the location of TAD boundaries and the trans sister pairing score (see Methods for details). Bin size is 30 kb. b, Comparison of sister-chromatid separation at 5 genomic loci measured by fluorescence in situ hybridization (FISH) and scsHi-C. Microscopy image shows examples for split and unsplit genomic sister loci, from G2-synchronized HeLa cell data reported in ref. 31. scsHi-C quantification of sister locus distance was done by calculating (1 – average trans sister contacts) in a region spanning 600 kb around each FISH target site and standardizing the resulting value (see Methods for more details). Each dot indicates one target locus, measured in n = 11 biologically independent HeLa WT G2 samples by scsHi-C. The points indicate the mean and the error indicates the standard deviation of the Hi-C measurements. Two-sided p-value for a Wald-test with t-distribution of the test statistic is shown with the null hypothesis being a zero slope (calculated using the scipy.stats.linregress function). P-value = 1 * 10−14. c, Comparison of sister-chromatid separation at 16 genomic loci measured by live cell microscopy and scsHi-C. Microscopy analysis was by live-cell imaging of 16 HeLa cell lines expressing dCas9-EGFP with different locus-specific gRNAs, using automated detection of merged or split sister loci in G2 cells, as reported in ref. 31. scsHi-C quantification of sister locus distance was done by calculating (1 – average trans sister contacts) in a region spanning 600 kb around each gRNA target site and standardizing the resulting value. Each dot indicates one target locus, measured in n = 11 biologically independent HeLa WT G2 samples by scsHi-C. The points indicate the mean and the error indicates the standard deviation of the Hi-C measurements. Two-sided p-value for a Wald-test with t-distribution of the test statistic is shown with the null hypothesis being a zero slope (calculated using the scipy.stats.linregress function). P-value = 3 * 10−27. d, Histogram of average trans sister contact frequency for annotated TADs (see Methods for details). Vertical lines indicate the cut-offs for “highly paired” and “highly unpaired” TADs. e, Average contact probability over different genomic distances for cis sister and trans sister contacts at “highly paired”, “highly unpaired” regions as well as the genome-wide average for the n = 11 biologically independent HeLa WT G2 samples. Trans sister contacts were evenly increased or decreased over variable genomic distances in these highly paired or unpaired domains, respectively. Curves were normalized to the contact frequency of doubly labelled reads below 1 kb. f, Average H3K27me3 ChIP-seq signal (fold-control) at “highly paired” domains. Within highly paired domains, H3K27me3 distributed relatively evenly, without marked enrichment at the edges. The domains of different size were scaled to range from arbitrary genomic units -0.5 to 0.5. The bars represent average ChIP-seq signal (fold-control). TAD boundaries are marked with dashed, grey vertical lines.

Extended Data Fig. 7 TAD conformations in G2 chromosomes.

a, Cis sister- and trans sister contacts of n = 11 biologically independent, merged G2 samples at a representative region on chromosome 1 are displayed alongside the location of TAD boundaries (see Methods for details) and average trans sister and cis sister contact amount within a sliding window of 200 kb (see Methods for details). Bin size of matrix is 40 kb. b, Cis sister and trans sister contacts of n = 11 biologically independent, merged G2 samples at a representative region on chromosome 3 are displayed alongside the location of TAD boundaries (see Methods for details) and average trans sister and cis sister contact amount within a sliding window of 200 kb (see Methods for details). Bin size of matrix is 40 kb. c, Cis sister and trans sister contacts of n = 11 biologically independent, merged G2 samples at a representative region on chromosome 5 are displayed alongside the location of TAD boundaries (see Methods for details) and average trans sister and cis sister contact amount within a sliding window of 200 kb (see Methods for details). Bin size of matrix is 40 kb. These examples show that the high-resolution maps of G2 chromosomes reveal many fine structures inside TADs, which we currently cannot attribute to specific genomic features. d, Stack-up of corrected interaction frequency within sliding windows of 100 kb around CTCF Chip-seq peaks overlapping CTCF motifs of n = 11 biologically independent, merged G2 wildtype samples. The panel shows windows of 900 kb. The rows are sorted based on the centre enrichment. e, Quantification of trans-contact enrichment at CTCF Chip-seq peaks. The average observed/expected values for cis sister and trans sister contacts within a 80 kb window surrounding all (n = 60,929) annotated CTCF Chip-seq peaks overlapping a CTCF motif are displayed as a histogram. P-value was calculated using a two-sided Mann–Whitney U test. P-value = 2 * 10−296.

Extended Data Fig. 8 Characterization of HeLa Sororin-AID and HeLa NIPBL-AID cells.

a, Western blot for Sororin and GAPDH of HeLa Sororin-AID cells synchronized to G2 and either treated with auxin (+) or H20 (-) as well as western blot for NIPBL and GAPDH of HeLa NIPBL-AID cells synchronized to G2 and either treated with auxin (+) or H2O (-). This experiment was repeated two (Sororin-AID samples) and three (NIPBL-AID samples) more times with similar results. Uncropped images are displayed in Supplementary Fig. 1. b, Cell cycle analysis of HeLa Sororin-AID and HeLa NIPBL-AID cells synchronized to G2 as indicated in Extended Data Fig. 3d, treated with auxin. Panel shows a FACS plot of cells stained for pH3S10 to mark mitotic cells and propidium iodide to measure DNA content. Gates for different cell cycle stages are shown and the indicated numbers reflect percentage of cells that were measured. This experiment was repeated three more times with similar results. c, Contact probability of all contacts at different genomic distances of HeLa NIPBL-AID cells synchronized to G2 (n = 4 biologically independent experiments) that were treated with auxin and HeLa WT cells synchronized to G2 (n = 11 biologically independent experiments). d, Chromosome congression analysis of NIPBL- and Sororin-depleted cells. AID-tagging often reduces protein levels already before the addition of auxin59,60, which for NIPBL might impair sister-chromatid cohesion establishment during S-phase. We therefore performed metaphase congression analysis by time-lapse microscopy of WT HeLa cells, HeLa Sororin-AID cells and HeLa NIPBL-AID cells stained with SiR-DNA. HeLa Sororin-AID cells were treated with auxin before the final S-phase and HeLa NIPBL-AID cells were treated with auxin after the final S-phase. Panel shows the cumulative frequency of cells congressing their chromosomes in metaphase after entering mitosis in a RO3306 wash-out. Pooled replicates are shown from n = 2 biologically independent experiments. e, Cis sister and trans sister contacts of n = 11 biologically independent, merged G2 wildtype samples, n = 4 biologically independent, merged G2 NIPBL-degraded samples and n = 3 biologically independent, merged Sororin-degraded samples at a representative region on chromosome 5 are displayed alongside the location of TAD boundaries (see Methods for details). The strong accumulation of trans sister contacts close to the diagonal in NIPBL-depleted cells indicates frequent contacts between sister chromatids and a tighter alignment. Owing to normalization of contacts to the marginal count per row, trans sister contacts appear less frequent at larger genomic distances. Bin size of matrix is 150 kb. f, HiCrep57 analysis of all, cis sister and trans sister contacts of all replicates of HeLa NIPBL-AID (n = 4 biologically independent experiments) or Sororin-AID (n = 3 biologically independent experiments) cells treated with auxin. Bars show the mean of all comparisons.

Extended Data Fig. 9 Effect of NIPBL degradation on distribution of trans sister contacts.

a, Stack-up of average cis sister and trans sister contacts for NIPBL-degraded cells (n = 4 biologically independent, merged experiments) within sliding windows of 100 kb along TADs; lines represent 6 Mb genomic windows of individual TADs, aligned at the centre and sorted by size. Compare Fig. 3e for WT data. b, Stack-up of average trans sister observed/expected values for NIPBL-degraded (n = 4 biologically independent, merged experiments) and WT cells (n = 11 biologically independent, merged experiments) within sliding windows of 100 kb along TADs, individual TADs aligned at centre and sorted by size. c, Stack-up of trans sister corrected interaction frequency (see Methods for details) along TADs that are defined as highly paired or highly unpaired in WT cells (Fig. 2f; Extended Data Fig. 6d), sorted by the size of TADs for NIPBL-degraded cells (n = 4 biologically independent, merged experiments) an WT cells (n = 11 biologically independent, merged experiments). Shown are windows of 6 Mb around the centre of the respective TADs. Pairing scores were calculated within a sliding window of 200 kb on a Hi-C matrix with 20 kb bin size. d, Stack-up of trans sister pairing score (see Methods for details) along TADs that are highly paired or highly unpaired in WT cells (Fig. 2f; Extended Data Fig. 6d), sorted by the size of TADs for NIPBL-degraded cells (n = 4 biologically independent, merged experiments). Shown are windows of 6 Mb around the centre of the respective TADs. Pairing scores were calculated within a sliding window of 200 kb on a Hi-C matrix with 20 kb bin size.

Extended Data Fig. 10 Synthesis of a 4sT-phosphoramidite building block.

a, Synthesis of 5′-O-(4,4′-Dimethoxytrityl)-S-(2-cyanoethyl)-4-thiothymidine 3′-O-[(2-cyanoethyl)-(N,N-diisopropyl)]-phosphoramidite. b, 1H-NMR (700 MHz, CDCl3) of 4sT phosphoramidite (diastereomeric mixture). c, 31P-NMR (282 MHz, CDCl3) of 4sT phosphoramidite (diastereomeric mixture).

Supplementary information

Supplementary Information

This file contains Supplementary Figures 1 and 2 and Supplementary Tables S1-S7.

Reporting Summary

Peer Review File

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mitter, M., Gasser, C., Takacs, Z. et al. Conformation of sister chromatids in the replicated human genome. Nature 586, 139–144 (2020). https://doi.org/10.1038/s41586-020-2744-4

Download citation

Further reading

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

Nature Briefing

Sign up for the Nature Briefing newsletter — 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