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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.

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.

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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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  1. Hisashi Miura
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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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