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:

Analyzing somatic mutations by single-cell whole-genome sequencing

Nature Protocols volume 19, pages 487–516 (2024)Cite this article

Subjects

Abstract

Somatic mutations are the cause of cancer and have been implicated in other, noncancerous diseases and aging. While clonally expanded mutations can be studied by deep sequencing of bulk DNA, very few somatic mutations expand clonally, and most are unique to each cell. We describe a detailed protocol for single-cell whole-genome sequencing to discover and analyze somatic mutations in tissues and organs. The protocol comprises single-cell multiple displacement amplification (SCMDA), which ensures efficiency and high fidelity in amplification, and the SCcaller software tool to call single-nucleotide variations and small insertions and deletions from the sequencing data by filtering out amplification artifacts. With SCMDA and SCcaller at its core, this protocol describes a complete procedure for the comprehensive analysis of somatic mutations in a single cell, covering (1) single-cell or nucleus isolation, (2) single-cell or nucleus whole-genome amplification, (3) library preparation and sequencing, and (4) computational analyses, including alignment, variant calling, and mutation burden estimation. Methods are also provided for mutation annotation, hotspot discovery and signature analysis. The protocol takes 12–15 h from single-cell isolation to library preparation and 3–7 d of data processing. Compared with other single-cell amplification methods or single-molecular sequencing, it provides high genomic coverage, high accuracy in single-nucleotide variation and small insertions and deletion calling from the same single-cell genome, and fewer processing steps. SCMDA and SCcaller require basic experience in molecular biology and bioinformatics. The protocol can be utilized for studying mutagenesis and genome mosaicism in normal and diseased human and animal tissues under various conditions.

Key points

  • Protocol for single-cell whole-genome sequencing to discover and analyze somatic mutations in tissues and organs, using single-cell multiple displacement amplification alongside the SCcaller software.

  • Compared with bulk sequencing approaches, single-cell whole-genome sequencing allows discovery of most, if not all, mutations in the same single-cell genome. This enables quantification of the mutation burden per cell, discovery of mutational hotspots and establishment of cell lineages.

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 schematic overview of the single-cell whole-genome sequencing protocol.
Fig. 2: A schematic illustration of the principle and protocol of SCMDA.
Fig. 3: Flowcharts of the SCcaller-pipeline.
Fig. 4: Flowchart of the SCcaller algorithm.
Fig. 5: Examples of single cells or nuclei on CytoArray under a microscope.
Fig. 6: Examples of gel electrophoresis result of SCMDA amplicons.
Fig. 7: An example of library size distribution of SCMDA amplicons.
Fig. 8: Violin plots of the η values of 262 single cells in three studies.

Similar content being viewed by others

Data availability

Raw sequencing data for SCMDA-amplified single cells can be obtained from our previous publications: human fibroblasts17, human B lymphocytes18, human hepatocytes19, human lung epithelium cells20, human mammary epithelial cells23 and fibroblasts of multiple rodent species21,22.

Code availability

The source code for the data analysis pipeline and its documentations is freely available on GitHub via ref. 24 and at https://github.com/XiaoDongLab/SCcaller-pipeline.

References

  1. Failla, G. The aging process and cancerogenesis. Ann. N. Y. Acad. Sci. 71, 1124–1140 (1958).

    Article  CAS  PubMed  ADS  Google Scholar 

  2. Szilard, L. On the nature of the aging process. Proc. Natl Acad. Sci. USA 45, 30–45 (1959).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  3. Vijg, J. & Dong, X. Pathogenic mechanisms of somatic mutation and genome mosaicism in aging. Cell 182, 12–23 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Nowell, P. C. The clonal evolution of tumor cell populations. Science 194, 23–28 (1976).

    Article  CAS  PubMed  ADS  Google Scholar 

  5. Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  6. The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).

    Article  CAS  ADS  Google Scholar 

  7. Jaiswal, S. et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med. 371, 2488–2498 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Kessler, M. D. et al. Common and rare variant associations with clonal haematopoiesis phenotypes. Nature 612, 301–309 (2022).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  9. Erickson, R. P. Somatic gene mutation and human disease other than cancer. Mutat. Res. 543, 125–136 (2003).

    Article  CAS  PubMed  Google Scholar 

  10. Erickson, R. P. Somatic gene mutation and human disease other than cancer: an update. Mutat. Res. 705, 96–106 (2010).

    Article  CAS  PubMed  Google Scholar 

  11. Bae, T. et al. Analysis of somatic mutations in 131 human brains reveals aging-associated hypermutability. Science 377, 511–517 (2022).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  12. Bianconi, E. et al. An estimation of the number of cells in the human body. Ann. Hum. Biol. 40, 463–471 (2013).

    Article  PubMed  Google Scholar 

  13. Sender, R., Fuchs, S. & Milo, R. Revised estimates for the number of human and bacteria cells in the body. PLoS Biol. 14, e1002533 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Brunner, S. F. et al. Somatic mutations and clonal dynamics in healthy and cirrhotic human liver. Nature 574, 538–542 (2019).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  15. Blokzijl, F. et al. Tissue-specific mutation accumulation in human adult stem cells during life. Nature 538, 260–264 (2016).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  16. Martincorena, I. & Campbell, P. J. Somatic mutation in cancer and normal cells. Science 349, 1483–1489 (2015).

    Article  CAS  PubMed  ADS  Google Scholar 

  17. Dong, X. et al. Accurate identification of single-nucleotide variants in whole-genome-amplified single cells. Nat. Methods 14, 491–493 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zhang, L. et al. Single-cell whole-genome sequencing reveals the functional landscape of somatic mutations in B lymphocytes across the human lifespan. Proc. Natl Acad. Sci. USA 116, 9014–9019 (2019).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  19. Brazhnik, K. et al. Single-cell analysis reveals different age-related somatic mutation profiles between stem and differentiated cells in human liver. Sci. Adv. 6, eaax2659 (2020).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  20. Huang, Z. et al. Single-cell analysis of somatic mutations in human bronchial epithelial cells in relation to aging and smoking. Nat. Genet. 54, 492–498 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Milholland, B. et al. Differences between germline and somatic mutation rates in humans and mice. Nat. Commun. 8, 15183 (2017).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  22. Zhang, L. et al. Maintenance of genome sequence integrity in long- and short-lived rodent species. Sci. Adv. 7, eabj3284 (2021).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  23. Sun, S. et al. Single-cell analysis of somatic mutation burden in mammary epithelial cells of pathogenic BRCA1/2 mutation carriers. J. Clin. Invest. 132 https://doi.org/10.1172/jci148113 (2022).

  24. Zhang, L. et al. Single-cell whole-genome sequencing for discovering somatic mutations. GitHub https://doi.org/10.5281/zenodo.7826180 (2023).

    Article  Google Scholar 

  25. Luquette, L. J., Bohrson, C. L., Sherman, M. A. & Park, P. J. Identification of somatic mutations in single cell DNA-seq using a spatial model of allelic imbalance. Nat. Commun. 10, 3908 (2019).

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  26. Bohrson, C. L. et al. Linked-read analysis identifies mutations in single-cell DNA-sequencing data. Nat. Genet. 51, 749–754 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Min, S. et al. Absence of coding somatic single nucleotide variants within well-known candidate genes in late-onset sporadic Alzheimer’s disease based on the analysis of multi-omics data. Neurobiol. Aging 108, 207–209 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Zhang, L. et al. Analysis of somatic mutations in senescent cells using single-cell whole-genome sequencing. AgingBio 1, 1–6, https://doi.org/10.59368/agingbio.20230005 (2023).

    Article  Google Scholar 

  29. Schmitt, M. W. et al. Detection of ultra-rare mutations by next-generation sequencing. Proc. Natl Acad. Sci. USA 109, 14508–14513 (2012).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  30. Abascal, F. et al. Somatic mutation landscapes at single-molecule resolution. Nature 593, 405–410 (2021).

    Article  CAS  PubMed  ADS  Google Scholar 

  31. Maslov, A. Y. et al. Single-molecule, quantitative detection of low-abundance somatic mutations by high-throughput sequencing. Sci. Adv. 8, eabm3259 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zafar, H., Navin, N., Chen, K. & Nakhleh, L. SiCloneFit: Bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing data. Genome Res. 29, 1847–1859 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Wang, F. et al. MEDALT: single-cell copy number lineage tracing enabling gene discovery. Genome Biol. 22, 70 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Telenius, H. et al. Degenerate oligonucleotide-primed PCR: general amplification of target DNA by a single degenerate primer. Genomics 13, 718–725 (1992).

    Article  CAS  PubMed  Google Scholar 

  35. Dean, F. B., Nelson, J. R., Giesler, T. L. & Lasken, R. S. Rapid amplification of plasmid and phage DNA using Phi 29 DNA polymerase and multiply-primed rolling circle amplification. Genome Res. 11, 1095–1099 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Zong, C., Lu, S., Chapman, A. R. & Xie, X. S. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 338, 1622–1626 (2012).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  38. 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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  40. Xing, D., Tan, L., Chang, C. H., Li, H. & Xie, X. S. Accurate SNV detection in single cells by transposon-based whole-genome amplification of complementary strands. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2013106118 (2021).

  41. Sarangi, V. et al. SCELLECTOR: ranking amplification bias in single cells using shallow sequencing. BMC Bioinforma. 21, 521 (2020).

    Article  CAS  Google Scholar 

  42. Motyer, A. et al. The mutational landscape of single neurons and oligodendrocytes reveals evidence of inflammation-associated DNA damage in multiple sclerosis. Preprint at bioRxiv https://doi.org/10.1101/2022.04.30.490132 (2022).

  43. Bloom, J. C. & Schimenti, J. C. Sexually dimorphic DNA damage responses and mutation avoidance in the mouse germline. Genes Dev. 34, 1637–1649 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Gundry, M., Li, W., Maqbool, S. B. & Vijg, J. Direct, genome-wide assessment of DNA mutations in single cells. Nucleic Acids Res. 40, 2032–2040 (2012).

    Article  CAS  PubMed  Google Scholar 

  45. Gravina, S., Dong, X., Yu, B. & Vijg, J. Single-cell genome-wide bisulfite sequencing uncovers extensive heterogeneity in the mouse liver methylome. Genome Biol. 17, 150 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Ailshire, J. A., Beltran-Sanchez, H. & Crimmins, E. M. Becoming centenarians: disease and functioning trajectories of older US Adults as they survive to 100. J. Gerontol. Ser. A, Biol. Sci. Med. Sci. 70, 193–201 (2015).

    Article  Google Scholar 

  47. Van der Auwera, G. A. & O'Connor, B. D. Genomics in the Cloud: Using Docker, GATK, and WDL in Terra 1st edn (O'Reilly Media, 2020).

  48. Lodato, M. A. et al. Aging and neurodegeneration are associated with increased mutations in single human neurons. Science 359, 555–559 (2018).

    Article  CAS  PubMed  ADS  Google Scholar 

  49. Sherman, M. A. et al. PaSD-qc: quality control for single cell whole-genome sequencing data using power spectral density estimation. Nucleic Acids Res. 46, e20 (2018).

    Article  CAS  PubMed  Google Scholar 

  50. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Chang, X. & Wang, K. wANNOVAR: annotating genetic variants for personal genomes via the web. J. Med. Genet. 49, 433–436 (2012).

    Article  PubMed  Google Scholar 

  52. Yang, H. & Wang, K. Genomic variant annotation and prioritization with ANNOVAR and wANNOVAR. Nat. Protoc. 10, 1556–1566 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  ADS  Google Scholar 

  54. Mejia-Ramirez, E. & Florian, M. C. Understanding intrinsic hematopoietic stem cell aging. Haematologica 105, 22–37 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Wang, J. et al. Evidence for mutation showers. Proc. Natl Acad. Sci. USA 104, 8403–8408 (2007).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  56. Nik-Zainal, S. et al. Mutational processes molding the genomes of 21 breast cancers. Cell 149, 979–993 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Lora, D. Location and visualization of clustered somatic mutations. v1.0.1. CRAN-R Project https://cran.r-project.org/web/packages/ClusteredMutations/ClusteredMutations.pdf (2016).

  58. Bhagat, T. D. et al. miR-21 mediates hematopoietic suppression in MDS by activating TGF-beta signaling. Blood 121, 2875–2881 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Lee, B. T. et al. The UCSC Genome Browser database: 2022 update. Nucleic Acids Res. 50, D1115–d1122 (2022).

    Article  CAS  PubMed  Google Scholar 

  61. Alexandrov, L. B. et al. Mutational signatures associated with tobacco smoking in human cancer. Science 354, 618–622 (2016).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  62. Alexandrov, L. B., Nik-Zainal, S., Wedge, D. C., Campbell, P. J. & Stratton, M. R. Deciphering signatures of mutational processes operative in human cancer. Cell Rep. 3, 246–259 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Sun, S., Wang, Y., Maslov, A. Y., Dong, X. & Vijg, J. SomaMutDB: a database of somatic mutations in normal human tissues. Nucleic Acids Res. 50, D1100–D1108 (2022).

    Article  CAS  PubMed  Google Scholar 

  65. Denkinger, M. D., Leins, H., Schirmbeck, R., Florian, M. C. & Geiger, H. HSC aging and senescent immune remodeling. Trends Immunol. 36, 815–824 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Osorio, F. G. et al. Somatic mutations reveal lineage relationships and age-related mutagenesis in human hematopoiesis. Cell Rep. 25, 2308–2316.e2304 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Mitchell, E. et al. Clonal dynamics of haematopoiesis across the human lifespan. Nature 606, 343–350 (2021).

  68. Rodrigues-Moreira, S. et al. Low-dose irradiation promotes persistent oxidative stress and decreases self-renewal in hematopoietic stem cells. Cell Rep. 20, 3199–3211 (2017).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

L.Z. is supported by the American Federation for Aging Research (the Sagol Network GerOmic Award for Junior Faculty). J.V. is supported by the US National Institutes of Health awards (P01 AG017242, P01 AG047200, P30 AG038072, U01 ES029519, U01 HL145560 and U19 AG056278). X.D. is supported by the NIH awards (R00 AG056656, P01 HL160476, U54 AG076041 and U54 AG079754).

Author information

Authors and Affiliations

  1. Institute on the Biology of Aging and Metabolism, University of Minnesota, Minneapolis, MN, USA

    Lei Zhang & Xiao Dong

  2. Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA

    Lei Zhang

  3. Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA

    Moonsook Lee, Alexander Y. Maslov & Jan Vijg

  4. Laboratory of Applied Genomic Technologies, Voronezh State University of Engineering Technology, Voronezh, Russia

    Alexander Y. Maslov

  5. Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA

    Cristina Montagna

  6. Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Jan Vijg

  7. Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN, USA

    Xiao Dong

Authors
  1. Lei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Moonsook Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexander Y. Maslov
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cristina Montagna
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jan Vijg
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiao Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.Z. and J.V. designed the wet-laboratory protocol. L.Z., M.L. and A.Y.M. validated the wet-laboratory protocol. X.D. designed and validated the dry-laboratory protocol. L.Z., C.M., J.V. and X.D. wrote the manuscripts with input from M.L. and A.Y.M.

Corresponding authors

Correspondence to Lei Zhang or Xiao Dong.

Ethics declarations

Competing interests

L.Z., M.L., A.Y.M., J.V. and X.D. are co-founders and shareholders of SingulOmics Corp. C.M. declares no conflict of interest.

Peer review

Peer review information

Nature Protocols thanks Isidro Cortes-Ciriano, Thomas Mitchell and Jie Qiao for their contribution to the peer review of this work.

Additional information

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

Dong, X. et al. Nat. Methods 14, 491–493 (2017): https://doi.org/10.1038/nmeth.4227

Zhang, L. et al. Proc. Natl Acad. Sci. USA 116, 9014–9019 (2019): https://doi.org/10.1073/pnas.1902510116

Zhang, L. et al. Sci. Adv. 7, eabj3284 (2021): https://doi.org/10.1126/sciadv.abj3284

Supplementary information

Supplementary Tables 1–5

Supplementary Tables 1–5.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, L., Lee, M., Maslov, A.Y. et al. Analyzing somatic mutations by single-cell whole-genome sequencing. Nat Protoc 19, 487–516 (2024). https://doi.org/10.1038/s41596-023-00914-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-023-00914-8

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: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research