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.

  • Brief Communication
  • Published:

Decoding cell lineage from acquired mutations using arbitrary deep sequencing

Nature Methods volume 9, pages 78–80 (2012)Cite this article

Subjects

Abstract

Because mutations are inevitable, the genome of each cell in a multicellular organism becomes unique and therefore encodes a record of its ancestry. Here we coupled arbitrary single primer PCR with next-generation DNA sequencing to catalog mutations and deconvolve the phylogeny of cultured mouse cells. This study helps pave the way toward construction of retrospective cell-fate maps based on mutations accumulating in genomes of somatic cells.

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

Figure 1: Cell lineages.
Figure 2: Sample-to-sample reproducibility.
Figure 3: Genome Browser snapshot.

Similar content being viewed by others

References

  1. Frumkin, D., Wasserstrom, A., Kaplan, S., Feige, U. & Shapiro, E. PLoS Comput. Biol. 1, e50 (2005).

    Article  Google Scholar 

  2. Salipante, S.J. & Horwitz, M.S. Proc. Natl. Acad. Sci. USA 103, 5448–5453 (2006).

    Article  CAS  Google Scholar 

  3. Frumkin, D. et al. Cancer Res. 68, 5924–5931 (2008).

    Article  CAS  Google Scholar 

  4. Wasserstrom, A. et al. PLoS ONE 3, e1939 (2008).

    Article  Google Scholar 

  5. Wasserstrom, A. et al. PLoS Comput. Biol. 4, e1000058 (2008).

    Article  Google Scholar 

  6. Salipante, S.J., Thompson, J.M. & Horwitz, M.S. Genetics 178, 967–977 (2008).

    Article  Google Scholar 

  7. Salipante, S.J., Kas, A., McMonagle, E. & Horwitz, M.S. Evol. Dev. 12, 84–94 (2010).

    Article  Google Scholar 

  8. Shibata, D. & Tavare, S. Stem Cell Rev. 3, 94–103 (2007).

    Article  CAS  Google Scholar 

  9. Dasgupta, S. et al. PLoS ONE 4, e6533 (2009).

    Article  Google Scholar 

  10. Fellous, T.G. et al. Stem Cells 27, 1410–1420 (2009).

    Article  CAS  Google Scholar 

  11. Navin, N. et al. Nature 472, 90–94 (2011).

    Article  CAS  Google Scholar 

  12. Sulston, J.E. ChemBioChem 4, 688–696 (2003).

    Article  CAS  Google Scholar 

  13. Preston, B.D., Albertson, T.M. & Herr, A.J. Semin. Cancer Biol. 20, 281–293 (2010).

    Article  CAS  Google Scholar 

  14. McClelland, M. & Welsh, J. PCR Methods Appl. 4, S59–S65 (1994).

    Article  CAS  Google Scholar 

  15. Li, H., Ruan, J. & Durbin, R. Genome Res. 18, 1851–1858 (2008).

    Article  CAS  Google Scholar 

  16. Nye, T.M., Lio, P. & Gilks, W.R. Bioinformatics 22, 117–119 (2006).

    Article  CAS  Google Scholar 

  17. Petit, A.C., Legue, E. & Nicolas, J.F. Reprod. Nutr. Dev. 45, 321–339 (2005).

    Article  Google Scholar 

  18. Baker, S.M. et al. Nat. Genet. 13, 336–342 10.1038/ng0796-336 (1996).

    Article  CAS  Google Scholar 

  19. Albertson, T.M. et al. Proc. Natl. Acad. Sci. USA 106, 17101–17104 (2009).

    Article  CAS  Google Scholar 

  20. Zheng, Q. Math. Biosci. 196, 198–214 (2005).

    Article  Google Scholar 

  21. Zheng, Q. Math. Biosci. 216, 150–153 (2008).

    Article  Google Scholar 

  22. Croyle, M.L., Woo, A.L. & Lingrel, J.B. Eur. J. Biochem. 248, 488–495 (1997).

    Article  CAS  Google Scholar 

  23. Fallows, D. et al. Mol. Cell. Biol. 7, 2985–2987 (1987).

    Article  CAS  Google Scholar 

  24. Price, E.M. & Lingrel, J.B. Biochemistry 27, 8400–8408 (1988).

    Article  CAS  Google Scholar 

  25. Cantley, L.G., Cunha, M.J. & Zhou, X.M. J. Biol. Chem. 269, 15358–15361 (1994).

    CAS  PubMed  Google Scholar 

  26. Li, H. et al. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  27. Felsenstein, J. Inferring Phylogenies (Sinauer Associates, 2004).

  28. Ronquist, F. & Huelsenbeck, J.P. Bioinformatics 19, 1572–1574 (2003).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank D. Anderson, J. Salk and L. Loeb for discussion and comments. This work was supported by US National Institutes of Health grants DP1OD003278 and R01DK078340 (to M.S.H.), R01CA111582 and R01CA098243 (to B.D.P.), T32HL007093 (for C.A.C.), F30AG030316 (to S.J.S.) and T32GM007266 (to M.S.H.), and Achievement Rewards for College Scientists Foundation fellowship grants to the University of Washington Medical Scientist Training Program (for S.J.S.).

Author information

Author notes

  1. Cheryl A Carlson & Laura E Hays

    Present address: Present addresses: Division of Hematology/Oncology, Department of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA (C.A.C.) and Division of Hematology/Medical Oncology, Department of Medicine, Oregon Health and Science University, Portland, Oregon, USA (L.E.H.).,

Authors and Affiliations

  1. Department of Pathology, University of Washington School of Medicine, Seattle, Washington, USA

    Cheryl A Carlson, Arnold Kas, Robert Kirkwood, Laura E Hays, Bradley D Preston & Marshall S Horwitz

  2. Departments of Laboratory Medicine and Genome Sciences, University of Washington School of Medicine, Seattle, Washington, USA

    Stephen J Salipante

Authors
  1. Cheryl A Carlson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arnold Kas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Robert Kirkwood
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Laura E Hays
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bradley D Preston
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Stephen J Salipante
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Marshall S Horwitz
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.A.C., B.D.P., L.E.H., S.J.S. and M.S.H. designed the experiments. C.A.C., S.J.S. and L.E.H. carried out the experiments. C.A.C., A.K., R.K. and M.S.H. contributed to analyzing the data. M.S.H., with input from other authors, wrote the paper.

Corresponding author

Correspondence to Marshall S Horwitz.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6, Supplementary Tables 1,3,4 (PDF 3453 kb)

Supplementary Table 2

Compilation of identified mutations. (XLS 167 kb)

Supplementary Software

PrimerDesign.pl is a Perl program for evaluation of arbitrary primers; ChromosomePrep.pl is a Perl program to prepare mouse reference sequence for PrimerDesign.pl; and Geno.pl is a Perl program to perform mutational analysis. (ZIP 9 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Carlson, C., Kas, A., Kirkwood, R. et al. Decoding cell lineage from acquired mutations using arbitrary deep sequencing. Nat Methods 9, 78–80 (2012). https://doi.org/10.1038/nmeth.1781

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.1781

This article is cited by

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