Cytosine methylation is widespread among organisms and essential for mammalian development. In line with early postulations of an epigenetic role in gene regulation, symmetric CpG methylation can be mitotically propagated over many generations with extraordinarily high fidelity. Here, we combine BrdU labeling and immunoprecipitation with genome-wide bisulfite sequencing to explore the inheritance of cytosine methylation onto newly replicated DNA in human cells. Globally, we observe a pronounced lag between the copying of genetic and epigenetic information in embryonic stem cells that is reconsolidated within hours to accomplish faithful mitotic transmission. Populations of arrested cells show a global reduction of lag-induced intermediate CpG methylation when compared to proliferating cells, whereas sites of transcription factor engagement appear cell-cycle invariant. Alternatively, the cancer cell line HCT116 preserves global epigenetic heterogeneity independently of cell-cycle arrest. Taken together, our data suggest that heterogeneous methylation largely reflects asynchronous proliferation, but is intrinsic to actively engaged cis-regulatory elements and cancer.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Additional information

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

Change history

  • 26 March 2018

    Following online publication of this article, the Gene Expression Omnibus records corresponding to accession codes GSM2406773, MN-d6, and GSM2406772, MN-d14, listed in the data availability statement were deleted. The data are now available under accession codes GSM3039355, WGBS_hESC_WT_D6_R4 (MN day 6), and GSM3039351, WGBS_hESC_WT_D14_R4 (MN day 14), and the data availability statement has been updated with the new accession codes in the HTML and PDF versions of the article.


  1. 1.

    Holliday, R. & Pugh, J. E. DNA modification mechanisms and gene activity during development. Science 187, 226–232 (1975).

  2. 2.

    Riggs, A. D. X inactivation, differentiation, and DNA methylation. Cytogenet. Cell. Genet. 14, 9–25 (1975).

  3. 3.

    Smith, Z. D. & Meissner, A. DNA methylation: roles in mammalian development. Nat. Rev. Genet. 14, 204–220 (2013).

  4. 4.

    Prelich, G. & Stillman, B. Coordinated leading and lagging strand synthesis during SV40 DNA replication in vitro requires PCNA. Cell 53, 117–126 (1988).

  5. 5.

    Bestor, T. H. & Ingram, V. M. Two DNA methyltransferases from murine erythroleukemia cells: purification, sequence specificity, and mode of interaction with DNA. Proc. Natl Acad. Sci. USA 80, 5559–5563 (1983).

  6. 6.

    Hermann, A., Goyal, R. & Jeltsch, A. The Dnmt1 DNA-(cytosine-C5)-methyltransferase methylates DNA processively with high preference for hemimethylated target sites. J. Biol. Chem. 279, 48350–48359 (2004).

  7. 7.

    Pradhan, S. et al. Baculovirus-mediated expression and characterization of the full-length murine DNA methyltransferase. Nucleic. Acids. Res. 25, 4666–4673 (1997).

  8. 8.

    Bostick, M. et al. UHRF1 plays a role in maintaining DNA methylation in mammalian cells. Science 317, 1760–1764 (2007).

  9. 9.

    Sharif, J. et al. The SRA protein Np95 mediates epigenetic inheritance by recruiting Dnmt1 to methylated DNA. Nature 450, 908–912 (2007).

  10. 10.

    Qin, W. et al. DNA methylation requires a DNMT1 ubiquitin interacting motif (UIM) and histone ubiquitination. Cell. Res. 25, 911–929 (2015).

  11. 11.

    Chuang, L. S. et al. Human DNA-(cytosine-5) methyltransferase-PCNA complex as a target for p21WAF1. Science 277, 1996–2000 (1997).

  12. 12.

    Schermelleh, L. et al. Dynamics of Dnmt1 interaction with the replication machinery and its role in postreplicative maintenance of DNA methylation. Nucleic. Acids. Res. 35, 4301–4312 (2007).

  13. 13.

    Ziller, M. J. et al. Charting a dynamic DNA methylation landscape of the human genome. Nature 500, 477–481 (2013).

  14. 14.

    Adams, R. L. The relationship between synthesis and methylation of DNA in mouse fibroblasts. Biochim. Biophys. Acta. 254, 205–212 (1971).

  15. 15.

    Liang, G. et al. Cooperativity between DNA methyltransferases in the maintenance methylation of repetitive elements. Mol. Cell. Biol. 22, 480–491 (2002).

  16. 16.

    Woodcock, D. M., Adams, J. K. & Cooper, I. A. Characteristics of enzymatic DNA methylation in cultured cells of human and hamster origin, and the effect of DNA replication inhibition. Biochim. Biophys. Acta. 696, 15–22 (1982).

  17. 17.

    Woodcock, D. M. et al. Delayed DNA methylation is an integral feature of DNA replication in mammalian cells. Exp. Cell. Res. 166, 103–112 (1986).

  18. 18.

    Shirodkar, A. V. et al. A mechanistic role for DNA methylation in endothelial cell aECa-enriched gene expression: relationship with DNA replication timing. Blood 121, 3531–3540 (2013).

  19. 19.

    Jackson, D. A. & Pombo, A. Replicon clusters are stable units of chromosome structure: evidence that nuclear organization contributes to the efficient activation and propagation of S phase in human cells. J. Cell. Biol. 140, 1285–1295 (1998).

  20. 20.

    Pradhan, S., Bacolla, A., Wells, R. D. & Roberts, R. J. Recombinant human DNA (cytosine-5) methyltransferase. I. Expression, purification, and comparison of de novo and maintenance methylation. J. Biol. Chem. 274, 33002–33010 (1999).

  21. 21.

    Hansen, R. S. et al. Sequencing newly replicated DNA reveals widespread plasticity in human replication timing. Proc. Natl Acad. Sci. USA 107, 139–144 (2010).

  22. 22.

    Jeltsch, A. & Jurkowska, R. Z. New concepts in DNA methylation. Trends. Biochem. Sci. 39, 310–318 (2014).

  23. 23.

    Lei, H. et al. De novo DNA cytosine methyltransferase activities in mouse embryonic stem cells. Development 122, 3195–3205 (1996).

  24. 24.

    Liao, J. et al. Targeted disruption of DNMT1, DNMT3A and DNMT3B in human embryonic stem cells. Nat. Genet. 47, 469–478 (2015).

  25. 25.

    Jackson, M. et al. Severe global DNA hypomethylation blocks differentiation and induces histone hyperacetylation in embryonic stem cells. Mol. Cell. Biol. 24, 8862–8871 (2004).

  26. 26.

    Ziller, M. J. et al. Dissecting the functional consequences of de novo DNA methylation dynamics in human motor neuron differentiation and physiology. Cell Stem Cell (in the press).

  27. 27.

    Hansen, K. D. et al. Increased methylation variation in epigenetic domains across cancer types. Nat. Genet. 43, 768–775 (2011).

  28. 28.

    Landau, D. A. et al. Locally disordered methylation forms the basis of intratumor methylome variation in chronic lymphocytic leukemia. Cancer Cell 26, 813–825 (2014).

  29. 29.

    Stadler, M. B. et al. DNA-binding factors shape the mouse methylome at distal regulatory regions. Nature 480, 490–495, https://doi.org/10.1038/nature10716 (2011).

  30. 30.

    Elliott, G. et al. Intermediate DNA methylation is a conserved signature of genome regulation. Nat. Commun. 6, 6363 (2015).

  31. 31.

    Liu, Y., Oakeley, E. J., Sun, L. & Jost, J. P. Multiple domains are involved in the targeting of the mouse DNA methyltransferase to the DNA replication foci. Nucleic. Acids. Res. 26, 1038–1045 (1998).

  32. 32.

    Jones, P. A. & Baylin, S. B. The epigenomics of cancer. Cell 128, 683–692 (2007).

  33. 33.

    Smith, Z. D. et al. Epigenetic restriction of extraembryonic lineages mirrors the somatic transition to cancer. Nature 549, 543–547 (2017).

  34. 34.

    Witte, T., Plass, C. & Gerhauser, C. Pan-cancer patterns of DNA methylation. Genome Med. 6, 66 (2014).

  35. 35.

    Donaghey, J. et al. Genetic determinants and epigenetic effects of pioneer-factor occupancy. Nat. Genet. 50, 250–258 (2018).

  36. 36.

    Petruk, S. et al. Delayed accumulation of H3K27me3 on nascent DNA is essential for recruitment of transcription factors at early stages of stem cell differentiation. Mol. Cell 66, 247–257.e245 (2017).

  37. 37.

    Trombetta, J. J. et al. Preparation of single-cell RNA-seq libraries for next generation sequencing. Curr. Protoc. Mol. Biol. 107, 4.22.1–4.22.17 (2014).

  38. 38.

    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 30, 2114–2120 (2014).

  39. 39.

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

  40. 40.

    Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 25, 1105–1111 (2009).

  41. 41.

    Kowalczyk, M. S. et al. Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells. Genome Res. 25, 1860–1872 (2015).

  42. 42.

    Xi, Y. & Li, W. BSMAP: whole genome bisulfite sequence MAPping program. BMC Bioinformatics 10, 232 (2009).

  43. 43.

    Illingworth, R. et al. A novel CpG island set identifies tissue-specific methylation at developmental gene loci. PLoS Biol. 6, e22 (2008).

  44. 44.

    Saxonov, S., Berg, P. & Brutlag, D. L. A genome-wide analysis of CpG dinucleotides in the human genome distinguishes two distinct classes of promoters. Proc. Natl Acad. Sci. USA 103, 1412–1417 (2006).

  45. 45.

    Gifford, C. A. et al. Transcriptional and epigenetic dynamics during specification of human embryonic stem cells. Cell 153, 1149–1163 (2013).

Download references


We thank all members of the Meissner laboratory and in particular R. Karnik. We also thank A. Jeltsch for providing thoughtful feedback on the manuscript. T.L.D. was supported in part by postdoctoral fellowships from the Ford Foundation, UNCF/Merck Science Initiative, Harvard Medical School, and the Broad Institute Diversity Initiative. The Kiskinis lab gratefully acknowledges financial support from the Les Turner ALS Foundation, Muscular Dystrophy Association and the Feinberg School of Medicine. A.M. is a New York Stem Cell Foundation – Robertson Investigator. The Max Planck Society, the New York Stem Cell Foundation, the Broad Institute (SPARC funding to develop single cell RRBS) and NIH grants (1P50HG006193, P01GM099117, R01DA036898) supported this work.

Author information

Author notes

    • Timothy L. Downing

    Present address: Department of Biomedical Engineering, University of California-Irvine, Irvine, CA, USA

  1. These authors contributed equally: Jocelyn Charlton and Timothy L. Downing.


  1. Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany

    • Jocelyn Charlton
    • , Sven Klages
    • , Bernd Timmermann
    •  & Alexander Meissner
  2. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA

    • Jocelyn Charlton
    • , Timothy L. Downing
    • , Zachary D. Smith
    • , Kendell Clement
    • , Ramona Pop
    • , Veronika Akopian
    • , Alexander M. Tsankov
    •  & Alexander Meissner
  3. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    • Timothy L. Downing
    • , Zachary D. Smith
    • , Hongcang Gu
    • , Kendell Clement
    • , Alexander M. Tsankov
    • , Andreas Gnirke
    •  & Alexander Meissner
  4. The Ken & Ruth Davee Department of Neurology, Department of Physiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    • David P. Santos
    •  & Evangelos Kiskinis
  5. Department of Translational Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany

    • Michael J. Ziller


  1. Search for Jocelyn Charlton in:

  2. Search for Timothy L. Downing in:

  3. Search for Zachary D. Smith in:

  4. Search for Hongcang Gu in:

  5. Search for Kendell Clement in:

  6. Search for Ramona Pop in:

  7. Search for Veronika Akopian in:

  8. Search for Sven Klages in:

  9. Search for David P. Santos in:

  10. Search for Alexander M. Tsankov in:

  11. Search for Bernd Timmermann in:

  12. Search for Michael J. Ziller in:

  13. Search for Evangelos Kiskinis in:

  14. Search for Andreas Gnirke in:

  15. Search for Alexander Meissner in:


J.C., T.L.D. and A.M. designed the study with input from Z.D.S. T.L.D., J.C., R.P. and V.A. performed the experiments. H.G. and A.G. developed the multiplexed single-cell RRBS protocol, generated the sequencing libraries and helped with experimental design and analysis. K.C., S.K., B.T. and M.J.Z. assisted in data processing. J.C. performed bioinformatics analyses. A.M.T. performed the single-cell RNA-seq cell cycle analysis. D.P.S. and E.K. performed the MN differentiation, characterization and sample collection. J.C., T.L.D., Z.D.S., A.G. and A.M. interpreted the data. J.C., T.L.D., Z.D.S. and A.M. wrote the manuscript with assistance from the other authors.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Alexander Meissner.

Integrated supplementary information

  1. Supplementary Figure 1 Characteristics of nascent strand methylation.

    (a) Simplified schematic of BrdU incorporation into the nascent strands of asynchronously replicating cells (adapted from Hansen, R. S. et al. Proc Natl Acad Sci U S A 107, 139–144, 2010). Based on estimated fork speeds of ~ 0.035 nucleotides/second (Shirodkar, A. V. et al. Blood 121, 3531–3540, 2013) we expect to label ~ 100 kb of single stranded DNA for each lagging or leading strand at every replication fork during a 1 hour pulse. (b) Genome browser tracks of read distribution for Repli-seq data from ENCODE (dark blue tracks, BG02 ESCs) and from our nascent DNA data using Repli-BS (light purple, HUES64 ESCs). All samples were auto-scaled to display the maximum range in the data track. An inverted “V” shows the typical pattern for Repli-seq data where read density at the peak corresponds to early replicating domains from which DNA is replicated bi-directionally to generate segregated peaks across S-phase. (c) Methylation levels for individual CpGs show more CpGs with intermediate methylation status in nascent DNA compared to bulk. (d) Methylation levels for matched nascent and bulk CpGs separated into early (S1 + S2; n = 10,351,838), mid (S3 + S4; n = 9,099,483) or late (S5 + S6; n = 3,937,510) S-phase fractions. White dots: median. (e) For CpGs located within specific genomic features, the ratio (nascent to bulk methylation) is shown on the x-axis and bulk mean methylation on the y-axis. CpG islands (CGI), high CpG-dense promoters (HCP), low CpG-dense promoters (LCP), long terminal repeats (LTR), long interspersed nuclear elements (LINE) and short interspersed nuclear elements (SINE). The shaded area highlights the small difference between genomic features.(f) Gene body methylation levels for genes classified as not expressed (FPKM < 1), expressed (FPKM >10) or highly expressed (FPKM > 100). Both bulk and nascent methylation levels are not affected by gene expression level. Bold line: median; box displays interquartile range and whiskers extend to the most extreme data point that is no more than 1.5 times the interquartile range. (g) Boxplots show methylation levels for CpGs with methylation >0.8 in bulk DNA. CpGs were grouped according to the CpG density of their surrounding genomic regions. Bold black lines: median; box displays interquartile range and whiskers extend to the most extreme data point that is no more than 1.5 times the interquartile range. Pie charts (right) show the proportion of CpGs within high CpG dense regions that overlap with promoters (defined as 1 kb upstream to 1 kb downstream of transcription start site), genomic repeats and biologically determined CGIs. (h) Representative CGI with high methylation levels. (i) H3K27me3 (red) and EZH2 ChIP-seq data (blue, top, ENCODE data for human ESC H1 cells) are displayed for a region on chromosome 15 as an example of a site where nascent DNA shows highly reduced methylation compared to bulk (shaded in grey). (j) Boxplot display methylation levels for bulk WT ESCs and DKO (DNMT3A−/− and DNMT3B−/−) cells for all CpGs (left) and for only CpGs located in methylated EZH2 sites (right). While globally, only a ~ 10% reduction in methylation is observed in DKO cells, EZH2 sites show almost complete loss of methylation in the DKO line indicating their dependence on DNMT3A and 3B for methylation. Bold line: median; box displays interquartile range and whiskers extend to the most extreme data point that is no more than 1.5 times the interquartile range. Note that as only ~ 10,000 CpGs are located within these particular EZH2-eniched sites and their functional relevance is unkown. (k) Mean CpG (left) and CpA (right) nascent (S1) and bulk methylation in ESCs. (l) For CpAs located within specific genomic features, the ratio (nascent to bulk methylation) is shown on the x-axis with bulk mean methylation on the y-axis for features as defined in e.

  2. Supplementary Figure 2 Effect of lag on single-cell methylation levels and nascent strand methylation dynamics.

    a) Individual cells are binned into M-G1, G1-S, S and G2-M based on expression of known cell cycle regulated genes (Kowalczyk, M.S. et al. Genome Res 25, 1860–72, 2015). Of the 30,311 genes that were ranked according to their differential regulation throughout G1-S and S-phase, only 1,085 show notable dynamics (Supplementary Data Set 2; Methods, FDR < 0.05). However, no difference in expression for the key DNA methylation regulators is found in ESCs. Expression values displayed were averaged using a moving window of 20 cells. (b) All single cells (n = 455) ordered by mean methylation (based on single cell RRBS) and colored by sorted cell cycle phase. Most cells in G1 (blue) or G2-M (green) show higher mean methylation than cells in S-phase (grey). (c) Using single cell RRBS data, CpGs were binned according to whether they were replicated in early or late S-phase (x-axis). Mean methylation for each CpG is displayed as a heat map for cells sorted into early or late S-phase (y-axis), and shows that cells in early S-phase (bottom row) have reduced methylation for early-replicating CpGs, while late-replicating CpGs are still highly methylated. The reverse is observed for cells in late S-phase (top row). Together, our single cell analysis shows that post-replication methylation delay reduces mean methylation of individual cells in S-phase. (d) Heat map of individual CpGs after 1 hour (h) BrdU pulse with varying lengths of chase time (displayed beneath each sample) which shows that methylation levels gradually increase over time. (e) Violin plots display methylation levels for CpGs that are replicated in early (S1 + S2), mid (S3 + S4) and late (S5 + S6) S-phase; see Fig. 1b. For each subset of CpGs, the methylation levels for 0, 1, 4 and 16 h are shown. (f) Bars display all matched CpGs between replicates across the time course. The color indicates the absolute difference in methylation between matched CpGs. In contrast to the variable correlation between individual CpGs, the global mean methylation differences between replicates are very low (0.04, <0.01 and <0.01 between the 1, 4 and 16 h replicates, respectively; shown in Fig. 1e). (g) Relationship between methylation values of neighboring CpGs that were captured in phase on the same sequencing read. The frequency of “transitions” in methylation states between neighboring CpGs is calculated and a transition score is computed by dividing the number of transitions by the number of CpGs. Reads were categorized as “low” if all CpGs showed the same methylation state (number of transitions = 0, score = 0), “medium” for few transitions (score ≤ 0.4), or “high” for frequent transitions (score > 0.4) according to the schematic shown. Examples of reads with their respective score are shown with filled and empty circles representing methylated and unmethylated CpGs respectively. (h) Using the metric described in g, bar plots show the proportion of sequencing reads categorized as low (dark grey), medium (dark blue) and high (light blue) across the time course. Only reads containing ≥3 CpGs were used in this analysis. Several previous studies have reported that DNMT1 is a highly processive enzyme that remains engaged with the same DNA molecule throughout consecutive methylation events (Jeltsch, A. & Jurkowska, R. Z. Trends Biochem Sci 39, 310–318, 2014, Jeltsch, A. & Jurkowska, R. Z. Nucleic Acids Res 44, 8556–8575, 2016). The decrease in the number of transitions between methylation states in our genome-wide read level analysis therefore provides further support for this type of activity during the post-replication window. (i) Correlation between neighboring CpGs on individual sequencing reads according to distance between them is shown for bulk and nascent DNA. The size of the data point is proportional to the number of CpGs.

  3. Supplementary Figure 3 Substrate/enzyme ratio may affect post-replication dynamics.

    (a) Genome browser tracks for bulk wild-type (WT) and double knock out (DKO; DNMT3A−/− and DNMT3B−/−) human ESCs for a 165 kb region on chromosome 17. Globally, DKO cells are hypomethylated in comparison to WT (mean methylation shown on top left). Only CpGs with ≥10X coverage are displayed. (b) Mean methylation levels for WT and DKO nascent and bulk DNA is shown. Samples were pulsed with BrdU for 1 h then chased for variable lengths of time (x-axis). For WT, n = 24.5 M, 8.9 M, 7.6 M; for DKO, n = 2.5 M, 459 K, 511 K for chase times of 0, 1 and 4 h respectively. Bulk levels for WT and DKO cells are displayed as dashed lines. DKO cells show a higher proportion of methylated CpGs for nascent DNA with respect to bulk and reach their respective bulk methylation level more quickly than WT cells. (c) Histograms display the distribution of CpG methylation for bulk (top) and nascent (bottom) WT and DKO DNA at 0 h chase time (n = 2,422,761 matched CpGs). After a 1 h BrdU pulse, the majority of nascent CpGs in WT cells display methylation levels of around 0.7 while DKO cells display a more bimodal distribution. This suggests that, with less CpGs to methylate, DKO cells are able to fully methylate a greater number of CpGs within the same time frame. (d) The proportion of cells in different cell cycle phases is displayed for WT and DKO cells based on single cell RNA-seq data (see Methods). Also note that expression of DNMT1 in both cell lines is similar, as shown in Liao, J. et al. Nat Genet 47, 469–478, 2015.

  4. Supplementary Figure 4 Arrested ESCs display globally fewer intermediately methylated CpGs but maintain TF target-specific intermediate methylation.

    (a) Bisulfite sequencing was performed on nascent DNA collected from cells pulsed with BrdU for 1 h and then arrested with nocodazole for 16 h (Supplementary Data Set 1). We compared nascent DNA in arrested cells to nascent DNA in cells with 16 h chase time (generated in our time course experiment, Fig. 1e) to confirm that prometaphase arrest did not result in aberrant methylation. The smoothed scatter plot comparing nascent DNA 16 h post-synthesis in arrested and proliferating conditions shows high correlation. (b) All CpGs with methylation level ≥0.9 in arrested cells (right). The methylation distribution of these same CpGs for proliferating cells (left). (c) Loess-smoothed regression analysis for CpG methylation levels in arrested, proliferating and nascent samples. The bold line indicates the mean value and shading shows +/− one standard deviation. (d) The percentage of CpGs with coverage ranging from 5X to 20X that have methylation = 0 (light), intermediate (medium), or = 1 (dark) for proliferating bulk and arrested samples. A clear shift in the proportion of methylated and intermediate CpGs between proliferating (top) and arrested (bottom) samples is observed that is independent of coverage. (e) FACS plots for proliferating and arrested HCT116 cells. Arrested cells were cultured with nocodazole for 16 h. PI = propidium iodide. (f) Doubling time and percentage of cells in S-phase for ESCs and HCT116 cells. For ESCs 35% cells in S-phase is an average derived from Hoechst staining and FACS-based analysis as well as single cell RNA-seq experiments where the number of cells in S-phase are estimated based on expression of known cell cycle markers (range: 29–45%). For HCT116 the number is based on the PI staining in panel e. (g) All CpGs in both proliferating and arrested ESCs that overlap with OCT4 binding sites (defined by ChIP-seq, ENCODE H1 human ESCs) are displayed. Instead of CpGs becoming fully methylated in the arrested cells as observed for the majority of the genome, the distributions are seemingly unchanged between proliferating and arrested cells. (h) For proliferating and arrested ESCs: the proportion of CpGs that overlap with GATA6 binding sites in mesoderm, EOMES binding sites in mesendoderm and OCT4 binding sites in ESCs that have methylation = 0 (light), intermediate (medium) or = 1 (dark) are displayed. Peaks = total number of ChIP-seq peaks called by MACS2, ratio = average number of CpGs per peak.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–4

  2. Life Sciences Reporting Summary

  3. Supplementary Dataset 1

    Sample details including experimental and sequencing data

  4. Supplementary Dataset 2

    Single cell RNA-seq data: gene ranking according to cell cycle regulation

About this article

Publication history