Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol
  • Published:

Mapping replication timing domains genome wide in single mammalian cells with single-cell DNA replication sequencing

Nature Protocols volume 15, pages 4058–4100 (2020)Cite this article

Subjects

Abstract

Replication timing (RT) domains are stable units of chromosome structure that are regulated in the context of development and disease. Conventional genome-wide RT mapping methods require many S-phase cells for either the effective enrichment of replicating DNA through bromodeoxyuridine (BrdU) immunoprecipitation or the determination of copy-number differences during S-phase, which precludes their application to non-abundant cell types and single cells. Here, we provide a simple, cost-effective, and robust protocol for single-cell DNA replication sequencing (scRepli-seq). The scRepli-seq methodology relies on whole-genome amplification (WGA) of genomic DNA (gDNA) from single S-phase cells and next-generation sequencing (NGS)-based determination of copy-number differences that arise between replicated and unreplicated DNA. Haplotype-resolved scRepli-seq, which distinguishes pairs of homologous chromosomes within a single cell, is feasible by using single-nucleotide polymorphism (SNP)/indel information. We also provide computational pipelines for quality control, normalization, and binarization of the scRepli-seq data. The experimental portion of this protocol (before sequencing) takes 3 d.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: A stepwise overview of the scRepli-seq analysis of mid-S-phase cells.
Fig. 2: scRepli-seq analysis throughout S-phase.
Fig. 3: scRepli-seq of samples with a limited number of cells.
Fig. 4: Electropherogram of MultiNA DNA-1000 from the scRepli-seq experiment.
Fig. 5: A schematic overview of the data analysis.
Fig. 6: Examples of RT profiles obtained through Steps 92–97.
Fig. 7: X chromosome scRepli-seq profiles of a single female mESC and a day 7–differentiated cell.
Fig. 8: Directory structure.
Fig. 9: Distribution of the tag densities of single G1 and single mid-S-phase cells.
Fig. 10: Selection of control G1 cells.

Similar content being viewed by others

Data availability

The datasets used to generate the figures are deposited in the NCBI Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) under accession codes GSE138634, GSE108556, and GSE113985.

Code availability

The code used to analyze the scRepli-seq data in this study is available in Supplementary Software 1–4 and at https://github.com/kuzobuta/scRepliseq-Pipeline.

References

  1. Berezney, R., Dubey, D. D. & Huberman, J. A. Heterogeneity of eukaryotic replicons, replicon clusters, and replication foci. Chromosoma 108, 471–484 (2000).

    CAS  PubMed  Google Scholar 

  2. Hiratani, I. & Takahashi, S. DNA replication timing enters the single-cell era. Genes (Basel) 10, 221 (2019).

    CAS  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).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Fragkos, M., Ganier, O., Coulombe, P. & Méchali, M. DNA replication origin activation in space and time. Nat. Rev. Mol. Cell Biol. 16, 360–374 (2015).

    CAS  PubMed  Google Scholar 

  5. Prioleau, M. N. & MacAlpine, D. M. DNA replication origins—where do we begin? Genes Dev. 30, 1683–1697 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Takebayashi, S. I., Ogata, M. & Okumura, K. Anatomy of mammalian replication domains. Genes (Basel) 8, 110 (2017).

    Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Dileep, V., Rivera-Mulia, J. C., Sima, J. & Gilbert, D. M. Large-scale chromatin structure-function relationships during the cell cycle and development: insights from replication timing. Cold Spring Harb. Symp. quant. Biol. 80, 53–63 (2016).

    Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Azuara, V. Profiling of DNA replication timing in unsynchronized cell populations. Nat. Protoc. 1, 2171–2177 (2006).

    CAS  PubMed  Google Scholar 

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

    CAS  Google Scholar 

  13. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Marchal, C. et al. Genome-wide analysis of replication timing by next-generation sequencing with E/L Repli-seq. Nat. Protoc. 13, 819–839 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Takebayashi, S. I., Ogata, S., Ogata, M. & Okumura, K. Mapping mammalian replication domains using the ion torrent semiconductor sequencing platform. Biosci. Biotechnol. Biochem. 82, 2098–2100 (2018).

    CAS  PubMed  Google Scholar 

  16. Desprat, R. et al. Predictable dynamic program of timing of DNA replication in human cells. Genome Res. 19, 2288–2299 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Koren, A. & McCarroll, S. A. Random replication of the inactive X chromosome. Genome Res 24, 64–69 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Yehuda, Y. et al. Germline DNA replication timing shapes mammalian genome composition. Nucleic Acids Res 46, 8299–8310 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Selig, S., Okumura, K., Ward, D. C. & Cedar, H. Delineation of DNA replication time zones by fluorescence in situ hybridization. EMBO J. 11, 1217–1225 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Simon, I. et al. Asynchronous replication of imprinted genes is established in the gametes and maintained during development. Nature 401, 929–932 (1999).

    CAS  PubMed  Google Scholar 

  21. Nogami, M. et al. Intranuclear arrangement of human chromosome 12 correlates to large-scale replication domains. Chromosoma 108, 514–522 (2000).

    CAS  PubMed  Google Scholar 

  22. Azuara, V. et al. Heritable gene silencing in lymphocytes delays chromatid resolution without affecting the timing of DNA replication. Nat. Cell Biol. 5, 668–674 (2003).

    CAS  PubMed  Google Scholar 

  23. Kitamura, E., Blow, J. J. & Tanaka, T. U. Live-cell imaging reveals replication of individual replicons in eukaryotic replication factories. Cell 125, 1297–1308 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Ebrahimi, H. et al. Early initiation of a replication origin tethered at the nuclear periphery. J. Cell Sci. 123, 1015–1019 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Saner, N. et al. Stochastic association of neighboring replicons creates replication factories in budding yeast. J. Cell Biol. 202, 1001–1012 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Duriez, B., Chilaka, S., Bercher, J.-F., Hercul, E. & Prioleau, M.-N. Replication dynamics of individual loci in single living cells reveal changes in the degree of replication stochasticity through S phase. Nucleic Acids Res. 47, 5155–5169 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Norio, P. et al. Progressive activation of DNA replication initiation in large domains of the immunoglobulin heavy chain locus during B cell development. Mol. Cell 20, 575–587 (2005).

    CAS  PubMed  Google Scholar 

  28. Herrick, J. & Bensimon, A. Single molecule analysis of DNA replication. Biochimie 81, 859–871 (1999).

    CAS  PubMed  Google Scholar 

  29. Lebofsky, R., Heilig, R., Sonnleitner, M., Weissenbach, J. & Bensimon, A. DNA replication origin interference increases the spacing between initiation events in human cells. Mol. Biol. Cell 17, 5337–5345 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Letessier, A. et al. Cell-type-specific replication initiation programs set fragility of the FRA3B fragile site. Nature 470, 120–124 (2011).

    CAS  PubMed  Google Scholar 

  31. Hennion, M. et al. FORK-seq: replication landscape of the Saccharomyces cerevisiae genome by nanopore sequencing. Genome Biol. 21, 125 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Müller, C. A. et al. Capturing the dynamics of genome replication on individual ultra-long nanopore sequence reads. Nat. Methods 16, 429–436 (2019).

    PubMed  Google Scholar 

  33. Lacroix, J. et al. Analysis of DNA replication by optical mapping in nanochannels. Small 12, 5963–5970 (2016).

    CAS  PubMed  Google Scholar 

  34. Wang, W. et al. Genome-wide mapping of human DNA replication by optical replication mapping supports a stochastic model of eukaryotic replication timing. Preprint at bioRxiv https://doi.org/10.1101/2020.08.24.263459 (2020).

  35. De Carli, F. et al. High-throughput optical mapping of replicating DNA. Small Methods 2, 1800146 (2018).

    Google Scholar 

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

    CAS  PubMed  Google Scholar 

  37. Miura, H. et al. Single-cell DNA replication profiling identifies spatiotemporal developmental dynamics of chromosome organization. Nat. Genet. 51, 1356–1368 (2019).

    CAS  PubMed  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  39. Jackson, D. A. S-phase progression in synchronized human cells. Exp. Cell Res. 220, 62–70 (1995).

    CAS  PubMed  Google Scholar 

  40. Snyder, A. R., Zhou, J., Deng, Z. & Lieberman, P. M. Therapeutic doses of hydroxyurea cause telomere dysfunction and reduce TRF2 binding to telomeres. Cancer Biol. Ther. 8, 1136–1145 (2009).

    CAS  PubMed  Google Scholar 

  41. Courtot, L., Hoffmann, J.-S. & Bergoglio, V. The protective role of dormant origins in response to replicative stress. Int. J. Mol. Sci. 19, 3569 (2018).

    PubMed Central  Google Scholar 

  42. Baslan, T. et al. Optimizing sparse sequencing of single cells for highly multiplex copy number profiling. Genome Res 125, 714–724 (2015).

    Google Scholar 

  43. Kadota, M. et al. CTCF binding landscape in jawless fish with reference to Hox cluster evolution. Sci. Rep. 7, 4957 (2017).

    PubMed  PubMed Central  Google Scholar 

  44. Li, H. & Durbin, R. Making the leap: Maq to BWA. Mass Genomics 25, 1754–1760 (2009).

    CAS  Google Scholar 

  45. Bakker, B. et al. Single-cell sequencing reveals karyotype heterogeneity in murine and human malignancies. Genome Biol. 17, 115 (2016).

    PubMed  PubMed Central  Google Scholar 

  46. Huang, L., Ma, F., Chapman, A., Lu, S. & Xie, X. S. Single-cell whole-genome amplification and sequencing: methodology and applications. Annu. Rev. Genomics Hum. Genet. 16, 79–102 (2015).

    CAS  PubMed  Google Scholar 

  47. Navin, N. E. Cancer genomics: one cell at a time. Genome Biol. 15, 452 (2014).

    PubMed  PubMed Central  Google Scholar 

  48. Spits, C. et al. Whole-genome multiple displacement amplification from single cells. Nat. Protoc. 1, 1965–1970 (2006).

    CAS  PubMed  Google Scholar 

  49. Chen, C. et al. Single-cell whole-genome analyses by linear amplification via transposon insertion (LIANTI). Science 356, 189–194 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Baslan, T. et al. Genome-wide copy number analysis of single cells. Nat. Protoc. 7, 1024–1041 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Koberna, K. et al. Nuclear organization studied with the help of a hypotonic shift: its use permits hydrophilic molecules to enter into living cells. Chromosoma 108, 325–335 (1999).

    CAS  PubMed  Google Scholar 

  52. Salic, A. & Mitchison, T. J. A chemical method for fast and sensitive detection of DNA synthesis in vivo. Proc. Natl Acad. Sci. USA 105, 2415–2420 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Takahashi, S., Kobayashi, S. & Hiratani, I. Epigenetic differences between naïve and primed pluripotent stem cells. Cell. Mol. Life Sci. 75, 1–13 (2017).

    Google Scholar 

  54. Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37, 547–554 (2019).

    CAS  PubMed  Google Scholar 

  55. Dey, S. S., Kester, L., Spanjaard, B., Bienko, M. & Van Oudenaarden, A. Integrated genome and transcriptome sequencing of the same cell. Nat. Biotechnol. 33, 285–289 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Macaulay, I. C. et al. Separation and parallel sequencing of the genomes and transcriptomes of single cells using G&T-seq. Nat. Protoc. 11, 2081–2103 (2016).

    CAS  PubMed  Google Scholar 

  57. Yamazaki, S. et al. Rif1 regulates the replication timing domains on the human genome. EMBO J. 31, 3667–3677 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Cornacchia, D. et al. Mouse Rif1 is a key regulator of the replication-timing programme in mammalian cells. EMBO J. 31, 3678–3690 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882.e21 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Dixit, A. et al. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866.e17 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Jaitin, D. A. et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167, 1883–1896.e15 (2016).

    CAS  PubMed  Google Scholar 

  62. Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  64. Sasagawa, Y. et al. Quartz-seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals nongenetic gene-expression heterogeneity. Genome Biol. 14, 3097 (2013).

    Google Scholar 

  65. R Development Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, 2017). http://www.R-project.org.

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

    Google Scholar 

  67. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Google Scholar 

  68. Lassmann, T., Hayashizaki, Y. & Daub, C. O. SAMStat: monitoring biases in next generation sequencing data. Bioinformatics 27, 130–131 (2011).

    CAS  PubMed  Google Scholar 

  69. Beaulieu-Jones, B. K. & Greene, C. S. Reproducibility of computational workflows is automated using continuous analysis. Nat. Biotechnol. 35, 342–346 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/arXiv:1303.3997 (2013).

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

    CAS  PubMed  Google Scholar 

  72. Degner, J. F. et al. Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data. Bioinformatics 25, 3207–3212 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Manders, E. M. M., Kimura, H. & Cook, P. R. Direct imaging of DNA in living cells reveals the dynamics of chromosome formation. J. Cell Biol. 144, 813–821 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Dimitrova, D. S. & Berezney, R. The spatio-temporal organization of DNA replication sites is identical in primary, immortalized and transformed mammalian cells. J. Cell Sci. 115, 4037–4051 (2002).

    CAS  PubMed  Google Scholar 

  75. Wu, R., Terry, A. V., Singh, P. B. & Gilbert, D. M. Differential subnuclear localization and replication timing of histone H3 lysine 9 methylation states. Mol. Biol. Cell 16, 2872–2881 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Kuriya, K. et al. Direct visualization of DNA replication dynamics in zebrafish cells. Zebrafish 12, 432–439 (2015).

    CAS  PubMed  Google Scholar 

  77. Wutz, A. Haploid animal cells. Development 141, 1423–1426 (2014).

    CAS  PubMed  Google Scholar 

  78. Kuhn, R. M., Haussler, D. & James Kent, W. The UCSC Genome Browser and associated tools. Brief. Bioinform. 14, 144–161 (2013).

    CAS  PubMed  Google Scholar 

  79. Krueger, F. & Andrews, S. R. SNPsplit: allele-specific splitting of alignments between genomes with known SNP genotypes. F1000Res. 5, 1479 (2016).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank the Center for Molecular Biology and Genetics of Mie University for NGS services. We also thank S. Kuraku and members of his laboratory for assistance with NGS, F. Matsuzaki for the use of the FACS instrument, A. Tanigawa and Y. Kondo for technical assistance, S-i. Hiraga for technical assistance in the Docker analysis and helpful comments, and L. Choubani for helpful comments. This work was supported by a RIKEN CDB/BDR intramural grant to I.H.; an award from the Special Postdoctoral Researcher (SPDR) Program of RIKEN to S.T.; an award from the RIKEN ‘Epigenome Manipulation Project’ of the All-RIKEN Projects to I.H.; MEXT KAKENHI grants JP16H01405 (to S.-i.T.), JP18H05530 (to I.H.), and JP15H01462 and JP17H06426 (to K.N.); and JSPS KAKENHI grants JP19K06610 (to S.-i.T.), JP18K14681 (to S.T.), and JP15K06942 (to K.N.).

Author information

Author notes

  1. These authors contributed equally to this work: Hisashi Miura, Saori Takahashi, Takahiro Shibata.

Authors and Affiliations

  1. Laboratory for Developmental Epigenetics, RIKEN Center for Biosystems Dynamics Research (BDR), Kobe, Japan

    Hisashi Miura, Saori Takahashi & Ichiro Hiratani

  2. Department of Biochemistry and Proteomics, Graduate School of Medicine, Mie University, Tsu, Japan

    Takahiro Shibata, Masato Ogata & Shin-ichiro Takebayashi

  3. Laboratory of Molecular & Cellular Biology, Graduate School of Bioresources, Mie University, Tsu, Japan

    Takahiro Shibata, Katsuzumi Okumura & Shin-ichiro Takebayashi

  4. Department of Biological Sciences, Graduate School of Science, Osaka University, Toyonaka, Japan

    Koji Nagao & Chikashi Obuse

Authors
  1. Hisashi Miura
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saori Takahashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Takahiro Shibata
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Koji Nagao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chikashi Obuse
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Katsuzumi Okumura
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Masato Ogata
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ichiro Hiratani
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Shin-ichiro Takebayashi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

H.M., S.T., T.S., I.H., and S.-i.T. conceived the project. S.T., T.S., and S.-i.T. developed and conducted scRepli-seq. H.M. established the scRepli-seq data analysis pipeline. S.T., T.S., I.H. and S.-i.T. performed cell culture and sample collection. K.N. and C.O. constructed a diploid reference genome and helped with the haplotype-resolved analysis pipeline setup. K.O. and M.O. supported the design and execution of the project. H.M., S.T., I.H. and S.-i.T. wrote the manuscript.

Corresponding authors

Correspondence to Ichiro Hiratani or Shin-ichiro Takebayashi.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks Chongyi Chen, Marie-Noëlle Prioleau and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Related links

Key references using this protocol

Takahashi, S. et al. Nat. Genet. 51, 529–540 (2019): https://doi.org/10.1038/s41588-019-0347-5

Miura, H. et al. Nat. Genet. 51, 1356–1368 (2019): https://doi.org/10.1038/s41588-019-0474-z

Supplementary information

Supplementary Information

Supplementary Figs. 1–3 and Supplementary Note.

Reporting Summary

Supplementary Software 1

Supplementary Software 1

Supplementary Software 2

Supplementary Software 2

Supplementary Software 3

Supplementary Software 3

Supplementary Software 4

Supplementary Software 4

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Miura, H., Takahashi, S., Shibata, T. et al. Mapping replication timing domains genome wide in single mammalian cells with single-cell DNA replication sequencing. Nat Protoc 15, 4058–4100 (2020). https://doi.org/10.1038/s41596-020-0378-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-020-0378-5

This article is cited by

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

Advanced search

Quick links

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