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Single-cell DNA replication profiling identifies spatiotemporal developmental dynamics of chromosome organization

Nature Genetics volume 51, pages 1356–1368 (2019)Cite this article

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Abstract

In mammalian cells, chromosomes are partitioned into megabase-sized topologically associating domains (TADs). TADs can be in either A (active) or B (inactive) subnuclear compartments, which exhibit early and late replication timing (RT), respectively. Here, we show that A/B compartments change coordinately with RT changes genome wide during mouse embryonic stem cell (mESC) differentiation. While A to B compartment changes and early to late RT changes were temporally inseparable, B to A changes clearly preceded late to early RT changes and transcriptional activation. Compartments changed primarily by boundary shifting, altering the compartmentalization of TADs facing the A/B compartment interface, which was conserved during reprogramming and confirmed in individual cells by single-cell Repli-seq. Differentiating mESCs altered single-cell Repli-seq profiles gradually but uniformly, transiently resembling RT profiles of epiblast-derived stem cells (EpiSCs), suggesting that A/B compartments might also change gradually but uniformly toward a primed pluripotent state. These results provide insights into how megabase-scale chromosome organization changes in individual cells during differentiation.

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Fig. 1: An mESC neural differentiation system in a defined medium.
Fig. 2: Coordinated developmental changes in RT, A/B compartments, subnuclear positioning and nuclear lamina binding.
Fig. 3: Genome organization of day 5 cells during differentiation resembles that of the EpiSCs.
Fig. 4: RT changes gradually but uniformly in differentiating cells, as assayed by scRepli-seq.
Fig. 5: Developmental regulation of A/B compartment boundaries and their relationship to TADs.
Fig. 6: RT profiles of compartment-switching TADs in single cells.
Fig. 7: Xist cloud formation precedes RT change of the inactive X.

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Data availability

All RT datasets (BrdU-IP (Repli-chip) and scRepli-seq), Hi-C, and RNA-seq datasets are deposited in the NCBI Gene Expression Omnibus (GEO) database under accession code GSE113985 (GEO; http://www.ncbi.nlm.nih.gov/geo/).

Code availability

Custom codes used in this study are available at https://github.com/kuzobuta/hic_paper_NG_2019.

References

  1. Cremer, T. & Cremer, C. Chromosome territories, nuclear architecture and gene regulation in mammalian cells. Nat. Rev. Genet. 2, 292–301 (2001).

    Article  CAS  PubMed  Google Scholar 

  2. Reinhart, M. & Cardoso, M. C. A journey through the microscopic ages of DNA replication. Protoplasma 254, 1151–1162 (2017).

    Article  CAS  PubMed  Google Scholar 

  3. Rivera-Mulia, J. C. & Gilbert, D. M. Replication timing and transcriptional control: beyond cause and effect - part III. Curr. Opin. Cell Biol. 40, 168–178 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Hiratani, I. et al. Global reorganization of replication domains during embryonic stem cell differentiation. PLoS Biol. 6, 2220–2236 (2008).

    Article  CAS  Google Scholar 

  5. Hiratani, I. et al. Genome-wide dynamics of replication timing revealed by in vitro models of mouse embryogenesis. Genome Res. 20, 155–169 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ryba, T. et al. Evolutionarily conserved replication timing profiles predict long-range chromatin interactions and distinguish closely related cell types. Genome Res. 20, 761–770 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Pope, B. D. et al. Topologically associating domains are stable units of replication-timing regulation. Nature 515, 402–405 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Guelen, L. et al. Domain organization of human chromosomes revealed by mapping of nuclear lamina interactions. Nature 453, 948–951 (2008).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Dixon, J. R. et al. Chromatin architecture reorganization during stem cell differentiation. Nature 518, 331–336 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Takahashi, S. et al. Genome-wide stability of the DNA replication program in single mammalian cells. Nat. Genet. 51, 529–540 (2019).

    Article  CAS  PubMed  Google Scholar 

  14. Hayashi, K., Ohta, H., Kurimoto, K., Aramaki, S. & Saitou, M. Reconstitution of the mouse germ cell specification pathway in culture by pluripotent stem cells. Cell 146, 519–532 (2011).

    Article  CAS  PubMed  Google Scholar 

  15. Eiraku, M. & Sasai, Y. Mouse embryonic stem cell culture for generation of three-dimensional retinal and cortical tissues. Nat. Protoc. 7, 69–79 (2012).

    Article  CAS  Google Scholar 

  16. Murakami, K., Araki, K., Ohtsuka, S., Wakayama, T. & Niwa, H. Choice of random rather than imprinted X inactivation in female embryonic stem cell-derived extra-embryonic cells. Development 138, 197–202 (2011).

    Article  CAS  PubMed  Google Scholar 

  17. Ryba, T. et al. Replication timing: A fingerprint for cell identity and pluripotency. PLoS Comput. Biol. 7, e1002225 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ryba, T., Battaglia, D., Pope, B. D., Hiratani, I. & Gilbert, D. M. Genome-scale analysis of replication timing: from bench to bioinformatics. Nat. Protoc. 6, 870–895 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Sofueva, S. et al. Cohesin-mediated interactions organize chromosomal domain architecture. EMBO J. 32, 3119–3129 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Rivera-Mulia, J. C. et al. Dynamic changes in replication timing and gene expression during lineage specification of human pluripotent stem cells. Genome Res. 25, 1091–1103 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Peric-Hupkes, D. et al. Molecular maps of the reorganization of genome-nuclear lamina interactions during differentiation. Mol. Cell 38, 603–613 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Selvaraj, S., Dixon, J. R., Bansal, V. & Ren, B. Whole-genome haplotype reconstruction using proximity-ligation and shotgun sequencing. Nat. Biotechnol. 31, 1111–1118 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Fraser, J. et al. Hierarchical folding and reorganization of chromosomes are linked to transcriptional changes in cellular differentiation. Mol. Syst. Biol. 11, 852 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Rivera-Mulia, J. C. et al. Allele-specific control of replication timing and genome organization during development. Genome Res. 28, 800–811 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Buecker, C. et al. Reorganization of enhancer patterns in transition from naive to primed pluripotency. Cell Stem Cell 14, 838–853 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Takahashi, S., Kobayashi, S. & Hiratani, I. Epigenetic differences between naïve and primed pluripotent stem cells. Cell. Mol. Life Sci. 75, 1191–1203 (2018).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Weinreb, C., Wolock, S. & Klein, A. M. Gene expression SPRING: a kinetic interface for visualizing high dimensional single-cell expression data. Bioinformatics 34, 1246–1248 (2018).

    Article  CAS  PubMed  Google Scholar 

  32. Dileep, V. & Gilbert, D. M. Single-cell replication profiling to measure stochastic variation in mammalian replication timing. Nat. Commun. 9, 427 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Crane, E. et al. Condensin-driven remodelling of X chromosome topology during dosage compensation. Nature 523, 240–244 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Schmitt, A. D. et al. A compendium of chromatin contact maps reveals spatially active regions in the human genome. Cell Rep. 17, 2042–2059 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hug, C. B., Grimaldi, A. G., Kruse, K. & Vaquerizas, J. M. Chromatin architecture emerges during zygotic genome activation independent of transcription. Cell 169, 216–228.e19 (2017).

    Article  CAS  PubMed  Google Scholar 

  36. Naumova, N. et al. Organization of the mitotic chromosome. Science 342, 948–953 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Stadhouders, R. et al. Transcription factors orchestrate dynamic interplay between genome topology and gene regulation during cell reprogramming. Nat. Genet. 50, 238–249 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Takahashi, K. & Yamanaka, S. A developmental framework for induced pluripotency. Development 142, 3274–3285 (2015).

    Article  CAS  PubMed  Google Scholar 

  39. Briggs, J. A. et al. Mouse embryonic stem cells can differentiate via multiple paths to the same state. eLife 6, e26945 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Deng, X. et al. Bipartite structure of the inactive mouse X chromosome. Genome Biol. 16, 152 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Minajigi, A. et al. A comprehensive Xist interactome reveals cohesin repulsion and an RNA-directed chromosome conformation. Science 349, aab2276 (2015).

    Article  Google Scholar 

  42. Giorgetti, L. et al. Structural organization of the inactive X chromosome in the mouse. Nature 535, 575–579 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Darrow, E. M. et al. Deletion of DXZ4 on the human inactive X chromosome alters higher-order genome architecture. Proc. Natl Acad. Sci. USA 113, E4504–E4512 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Taylor, J. H. Asynchronous duplication of chromosomes in cultured cells of Chinese hamster. J. Biophys. Biochem. Cytol. 7, 455–464 (1960).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Chaligné, R. & Heard, E. X-chromosome inactivation in development and cancer. FEBS Lett. 588, 2514–2522 (2014).

    Article  PubMed  Google Scholar 

  46. Dimitrova, D. S. & Gilbert, D. M. The spatial position and replication timing of chromosomal domains are both established in early G1 phase. Mol. Cell 4, 983–993 (1999).

    Article  CAS  PubMed  Google Scholar 

  47. Dileep, V. et al. Topologically associating domains and their long-range contacts are established during early G1 coincident with the establishment of the replication timing program. Genome Res. 25, 1104–1113 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Smith, A. Formative pluripotency: the executive phase in a developmental continuum. Development 144, 365–373 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Lupianez, D. G. et al. Disruptions of topological chromatin domains cause pathogenic rewiring of gene-enhancer interactions. Cell 161, 1012–1025 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Ragoczy, T., Telling, A., Scalzo, D., Kooperberg, C. & Groudine, M. Functional redundancy in the nuclear compartmentalization of the late-replicating genome. Nucleus 5, 626–635 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Battulin, N. et al. Comparison of the 3D organization of sperm and fibroblast genomes using the Hi-C approach. Genome Biol. 16, 77 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Krijger, P. H. L. et al. Cell-of-origin-specific 3D genome structure acquired during somatic cell reprogramming. Cell Stem Cell 18, 597–610 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Hayashi, K. & Saitou, M. Generation of eggs from mouse embryonic stem cells and induced pluripotent stem cells. Nat. Protoc. 8, 1513–1524 (2013).

    Article  CAS  PubMed  Google Scholar 

  54. Rathjen, J. & Rathjen, P. D. Lineage specific differentiation of mouse ES cells: formation and differentiation of early primitive ectoderm-like (EPL) cells. Methods Enzymol. 365, 3–25 (2003).

    PubMed  Google Scholar 

  55. Tesar, P. J. et al. New cell lines from mouse epiblast share defining features with human embryonic stem cells. Nature 448, 196–199 (2007).

    Article  CAS  PubMed  Google Scholar 

  56. Nasu, M. et al. Robust formation and maintenance of continuous stratified cortical neuroepithelium by laminin-containing matrix in mouse ES cell culture. PLoS ONE 7, 13–14 (2012).

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  58. Ikeda, T. et al. Srf destabilizes cellular identity by suppressing cell-type-specific gene expression programs. Nat. Commun. 9, 1387 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Sakata, Y. et al. Defects in dosage compensation impact global gene regulation in the mouse trophoblast. Development 144, 2784–2797 (2017).

    Article  CAS  PubMed  Google Scholar 

  60. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10 (2011).

    Article  Google Scholar 

  65. Sirén, J., Välimäki, N. & Mäkinen, V. HISAT2—fast and sensitive alignment against general human population. IEEE/ACM Trans. Comput. Biol. Bioinform. 11, 375–388 (2014).

    Article  PubMed  Google Scholar 

  66. Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  68. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  69. de Hoon, M. J. L., Imoto, S., Nolan, J. & Miyano, S. Open source clustering software. Bioinformatics 20, 1453–1454 (2004).

    Article  PubMed  Google Scholar 

  70. Oliphant, T. E. SciPy: open source scientific tools for Python. Comput. Sci. Eng. 9, 10–20 (2007).

    Article  CAS  Google Scholar 

  71. Saldanha, A. J. Java Treeview–extensible visualization of microarray data. Bioinformatics 20, 3246–3248 (2004).

    Article  CAS  PubMed  Google Scholar 

  72. Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 99–104 (2007).

    Article  Google Scholar 

  73. R Core team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2015).

  74. Hart, T., Komori, H. K., LaMere, S., Podshivalova, K. & Salomon, D. R. Finding the active genes in deep RNA-seq gene expression studies. BMC Genom. 14, 778 (2013).

    Article  CAS  Google Scholar 

  75. Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank S. Kuraku and members of his laboratory, especially S. Keeley, for assistance with NGS, I. Fujita for technical advice and assistance with EB sectioning and immunofluorescence staining and Y. Kondo for technical assistance. We also thank H. Niwa and K. Araki for CBMS1 mESCs, P.J. Tesar for EpiSCs, T. Sado for the Xist plasmid (pXist cDNA-SS12.9), K. Hayashi for suggestions on mESC differentiation, A. Eritano for suggestions on immunofluorescence image analysis and K. Kawaguchi for suggestions on clustering analysis. This work was supported by JST-PRESTO and a RIKEN CDB/BDR intramural grant (to I.H.), a fellowship from the Special Postdoctoral Researcher (SPDR) Program of RIKEN (to S.T.), MEXT KAKENHI grant numbers JP18H05530 (to I.H.) and JP16H01405 (to S.-i.T.) and JSPS KAKENHI grant number JP18K14681 (to S.T.).

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Authors and Affiliations

  1. Laboratory for Developmental Epigenetics, RIKEN Center for Developmental Biology and Center for Biosystems Dynamics Research, Kobe, Japan

    Hisashi Miura, Saori Takahashi, Rawin Poonperm, Akie Tanigawa & Ichiro Hiratani

  2. Laboratory of Molecular & Cellular Biology, Graduate Schoold of Bioresources, Mie University, Tsu, Japan

    Shin-ichiro Takebayashi

  3. Japan Science and Technology Agency, PRESTO, Kawaguchi, Japan

    Ichiro Hiratani

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Contributions

H.M. and I.H. conceived the project. I.H. performed the mESC culture, differentiation and sample collection. H.M. performed the Hi-C experiments. I.H. performed the RNA-seq and BrdU-IP RT experiments. S.T. and S-i.T. developed and conducted the single-cell RT (scRepli-seq) experiments. A.T. conducted the immunofluorescence staining experiments. H.M. performed all of the bioinformatic analyses. R.P. performed the RNA/DNA FISH experiments. H.M. and I.H. wrote the manuscript.

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Correspondence to Ichiro Hiratani.

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Integrated supplementary information

Supplementary Figure 1 RNA-seq analysis of lineage markers during the neural differentiation of mESCs.

(a) Expression profiles of ICM, epiblast, and pluripotency markers in day-0 mESCs, day-2 EpiLCs, and day-7 differentiated cells as assayed by RNA-seq. (b) Expression profiles of ectoderm and mesoendoderm markers in days 0, 2, and 7 cells, as in (a). (c) Changes in the expression patterns of representative markers of pluripotency, epiblast, and ectoderm during 7-day mESC differentiation. Bars represent the mean of three (d0, d3–6) or five (d2, d7) independent experiments, which are shown as open circles. Error bars represent ± 1 s.d. from the mean. Fpkm, fragments per kilobase of exon per million mapped reads.

Supplementary Figure 2 Immunofluorescence staining of mESCs, EpiLCs, and EBs and their quantification.

(a) Immunofluorescence staining of representative cell colonies (days 0 and 2) and EB sections (days 3–7) during mESC differentiation with two antibodies, Oct4/Sox1 (top) and Nanog/Sox1 (bottom) (n=2 independent experiments with similar results). Nuclei were counterstained with DAPI. (b) Typical EB sections on days 3 and 7. Cell debris frequently appeared after day 4 of differentiation in the center of EBs (day-7 EB, white dashed line), which were observed as DAPI-positive fragments smaller than normal cell nuclei and were excluded from the counting/analysis of marker-positive cells (bottom cartoon) (n=2 independent experiments with similar results). (c) Percentages of Oct4-, Nanog-, Sox1-, and Eomes-expressing cells in EB sections during differentiation (days 3–7). Percentages of expressed cells were counted in each EB section and were averaged (at least 3 sections per marker; see Supplementary Table 2 for the exact percentages of positive cells and the detailed statistics). For Eomes-expressing cells, we analyzed only days 3–5 EBs. Because we empirically set the threshold intensity to 256 to define marker-positive cells (see Methods), even the weakly-expressed cells were counted as positive, such as in the case of Oct4, Nanog or Sox1 on days 3–5. Nevertheless, Oct4 and Nanog expressing cells clearly decreased after day 5, while cells with strong Sox1 expression clearly increased after day 6 by visual inspection (a), leading us to conclude that the cells are responding to differentiation cues in a timely and relatively uniform manner. Data represent mean (points) ± 1 s.d. (error bars) from at least 3 EB sections per marker (see Supplementary Table 2 for detailed statistics).

Supplementary Figure 3 Method description for RT and Hi-C profiling, as well as gene expression data comparison between two differentiation protocols.

(a) Protocol for genome-wide RT-profiling by the BrdU immunoprecipitation (BrdU-IP) method using CGH microarrays. (b) Comparison of fold changes in gene expression values between the two mESC differentiation protocols described under Fig. 1c by RNA-seq (CBMS1) or expression microarrays (D3). Pearson’s R values are shown (total gene number n shown between brackets). See also Fig. 1d and Methods. (c) An outline of the Hi-C protocol. Briefly, cells were fixed and cross-linked with formaldehyde and then subjected to restriction digestion in-nucleus as opposed to in-solution, and the protruding ends were biotinylated through a fill-in reaction by Klenow and ligated to each other in-nucleus. After the removal of proteins and biotins from the unligated ends, the ligated, biotinylated products were sonicated, size-selected, and pulled down by streptavidin beads. Then, the end products were subjected to next-generation sequencing (NGS) library construction and paired-end sequencing. See Methods for details. (d) A schematic representation of A/B compartment calculation from Hi-C contact matrix data. Chromosome 1 data are shown. From a raw Hi-C contact matrix (Observed; Obs), a distance-dependent Hi-C average contact probability matrix (Expected; Exp) and an observed/expected (Obs/Exp) matrix were calculated. The resultant Obs/Exp matrices were then converted to Pearson’s correlation matrices, in which element Cij in the matrix represents the similarity of the interaction profiles of the ith and jth bin to the rest of the chromosome. Then, these matrices were subjected to PCA by the hiclib pipeline (Imakaev, M. et al., Nat. Methods 9, 999–1003, 2012). The eigenvector of the first principal component, PC1, defines the A/B compartment profile, with regions of positive and negative values corresponding to A (green) and B (red) compartments, respectively (Lieberman-Aiden, E. et al., Science 326, 289–293, 2009).

Supplementary Figure 4 Analysis of the relationship between RT and A/B compartments using 4-state HMM.

(a) Genome-wide comparison of ∆RT and ∆Hi-C PC1 as in Fig. 2a in human cells. Left, human mesenchymal stem cells (hMSCs) vs. H1 (Hi-C) or H9 (RT) hESCs. Right, IMR90 vs. H1 hESCs. Pearson’s R values are based on top 10% EtoL and LtoE ΔRT values (n=2640 each). When hESCs were compared with hESC-derived hMSCs, ΔRT and ΔPC1 correlated well, although not as strongly as in mice (Fig. 2a and Supplementary Table 4). This weaker correlation could be either because the relationship is indeed weaker in humans, or due to intrinsic differences between the hESC lines used for Hi-C and RT or between hMSCs generated in different laboratories. To distinguish between these possibilities, we compared the same hESC line (H1) and IMR90 cells, reasoning that the use of the same cells should lead to less interlaboratory variability. This analysis revealed a high correlation between ΔRT and ΔPC1, comparable to what we observed in mice. (b) Classifying RT and Hi-C PC1 into 4 groups by 4-state HMM. For RT, we named the 4 categories E-I, E-II, L-II, and L-I, from early to late S phase. For A/B compartments (Hi-C PC1), we named the 4 categories A-I, A-II, B-II, and B-I, from A to B compartments. See also Fig. 2b, in which E, L, A, and B represent E-I/E-II, L-I/L-II, A-I/A-II, and B-I/B-II states, respectively, and ‘switched’ bins traverse the A-II/B-II boundary. (c) The top panel bar plots show the frequencies of the 4 Hi-C PC1 categories in each RT category in day-0 mESCs and day-7 cells. Similarly, the lower panel bar plots show the RT category frequencies in each Hi-C PC1 category in day-0 and day-7 cells. (d) Bar plots similar to those in (c), showing the relationship of the 4 RT and Hi-C PC1 groups in H1 hESCs and human IMR90 cells. (e) Summary of the relationship between RT and A/B compartments.

Supplementary Figure 5 Relationships between RT changes and subnuclear positioning, gene expression and RT/compartments, and RT regulation and gene ontology.

(a) Changes in radial positioning (Δradial positioning) and changes in RT (ΔRT) during neural differentiation of mESCs. Radial positioning refers to the relative radial distance to the nuclear periphery, where 0 and 1 represent the periphery and the center of the nucleus, respectively. n=8 loci (∆median), based on two independent DNA-FISH experiments (Hiratani, I. et al., Genome Res. 20, 155–169, 2010). R, Pearson’s R. (b) For transiently RT-switching k-means clusters no. 12–14 (transient L) and clusters no. 15–17 (transient E), centroid RT (blue), average Hi-C PC1 (red), average expression level difference from day 0 (orange; by RNA-seq) are plotted as a function of time. (c) Distribution of Pearson’s R values between gene expression values (RNAseq) and RT or Hi-C PC1 values for a total of 9,066 genes. The comparison was made for all samples from days 0, 2–7 during CBMS1 mESC differentiation. The 9,066 genes were chosen based on the criteria of showing more than 2-fold difference during differentiation between the minimum and maximum gene expression values by RNA-seq (FDR<0.01 by “nbinomLRT” in DEseq2). (d) Gene ontology (GO) terms of genes enriched in E to L and L to E k-means clusters from Fig. 2i and 2j, respectively. FDR (q-value) shows the false discovery rate analog of hypergeometric p-value after correction for multiple hypothesis testing according to Benjamini and Hochberg, as shown previously (Liberzon, A. et al., Cell Systems 1, 417–425, 2015).

Supplementary Figure 6 Prediction of cell-type specific A/B compartment organization from single-cell RT (scRepli-seq) profiles.

(a) Binarized mid-S (45–65% replication) scRepli-seq data sets from day-0 mESCs and day-7 differentiated cells on mouse chromosome 16 are shown, along with percentage replication scores. Hi-C PC1, 2-state HMM Hi-C PC-1, and BrdU-IP RT ensemble data sets are also shown. (b) Phi coefficient values from comparing day-0 or day-7 scRepli-seq data sets to 2-state HMM Hi-C PC1 derived from day-0 and day-7 cells are plotted. Higher correlations were observed between day-0 scRepli-seq data and day-0 Hi-C PC1 than day-7 PC1, and between day-7 scRepli-seq data and day-7 PC1 than day-0 PC1. For reference, day-0 and day-7 BrdU-IP ensemble RT data sets and their Phi coefficient values are also shown. (c) A genome-wide heatmap depicting binarized scRepli-seq data sets from day-0 mESCs and day-7 differentiated cells. Single-cell data sets are sorted according to their percentage replication scores. Different classes of RT regulation were identified by MCA K-means clustering (k=5). (d, e) Heatmaps showing RT (d) and Hi-C PC1 (e) values of corresponding genomic bins in Fig. 7c. See Supplementary Note 2 for detailed description of the analysis.

Supplementary Figure 7 Defining TAD boundary positions by the insulation score method.

(a–c) The relationship between the boundary score threshold and the fractions of boundaries that are called TAD boundaries. The plots show the relationship in human foreskin fibroblasts (HFF1) (a), HeLaS3 cells and K562 erythroleukemia cells (b) (Naumova, N. et al., Science 342, 948–953, 2013), and CBMS1 mESCs before and after differentiation (c) (this study). Boundary scores were generated as reported (Hug, C. B. et al., Cell 169, 216–228.e19, 2017) using publicly available Hi-C data (Naumova, N. et al., 2013). Genomic regions with scores >0 were defined as ‘potential’ TAD boundaries. The fractions of TAD boundaries among the ‘potential’ TAD boundaries at different threshold scores were plotted. In (a), blue and red represent nonsynchronous (NS) and mitotic (M) HFF1 cells, respectively. In (b), blue, red, and green represent HeLaS3 at mid-G1, HeLaS3 at mitosis, and K562 at mitosis, respectively. (d) A Venn diagram depicting the number of day-0 and day-7 TAD boundaries and their overlap. (e) A cumulative probability analysis of the distance between A/B compartment boundaries and the nearest TAD boundaries. Red, green, gold, and blue represent A/B compartment boundaries that are day-0 specific, day-7 specific, shared between days 0 and 7, and randomly permutated (control), respectively, with the former three significantly closer to TAD boundaries than control (p<2.2e-16, two-sided Wilcoxon rank sum test). (f) A cumulative probability analysis similar to (e). Red and dark red represent day-0 and day-7 A/B compartment (Hi-C PC1) boundaries, respectively. Blue and dark blue represent day-0 and day-7 RT boundaries, respectively. Gray lines represent randomly permutated controls. A/B compartment boundaries and RT boundaries were significantly closer to TAD boundaries than control (two-sided Wilcoxon rank sum test): day-0 Hi-C PC1, day-7 Hi-C PC1, and day-7 RT, p< 2.2e-16; day-0 RT, p=2.903e-13. (g) Representative AtoB (left) and BtoA (right) compartment switches by ‘isolation’ (see also Fig. 5b, d, e).

Supplementary information

Supplementary Information

Supplementary Figs. 1–7, Supplementary Tables 1–6 and Supplementary Notes

Reporting Summary

Supplementary Table 1

FPKM, zFPKM values and vsd counts from RNA-seq data (related to Figs. 1–3, 5 and 7 and Supplementary Figs. 1, 3 and 5)

Supplementary Table 2

Percentage of Sox1-, Oct4-, Nanog-, Eomes-positive cells (related to Supplementary Fig. 2c)

Supplementary Table 3

List of sequencing and microarray experiments (related to Figs. 1–7)

Supplementary Table 4

A/B compartment and RT data at 200-kb resolution in mouse and human (related to Figs. 1–5 and Supplementary Figs. 4 and 5)

Supplementary Table 5

A/B compartment, RT, boundary scores and scRepli-seq data at 40-kb resolution (related to Figs. 5 and 6 and Supplementary Figs. 6 and 7)

Supplementary Table 6

X-chromosome RT profiles (related to Fig. 7)

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Miura, H., Takahashi, S., Poonperm, R. et al. Single-cell DNA replication profiling identifies spatiotemporal developmental dynamics of chromosome organization. Nat Genet 51, 1356–1368 (2019). https://doi.org/10.1038/s41588-019-0474-z

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