Decoding cell lineage from acquired mutations using arbitrary deep sequencing

Journal name:
Nature Methods
Year published:
Published online

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.

At a glance


  1. Cell lineages.
    Figure 1: Cell lineages.

    (a) A single mouse fibroblast was seeded onto a Petri dish. After approximately 20 doublings, a single cell was used to seed each of the next tier of dishes and so on. (b) Lineage reconstructed from 592 mutations identified from sequencing of singleprimer 'arbitrary PCR' products from DNA extracted from all 15 dishes. (c) Simplified lineage tree, similar to that in a but showing only the terminal nodes. (d) Lineage reconstructed from the 667 mutations present in only the terminal nodes. Numbers in deduced trees are Bayesian posterior probabilities. Numbers inside boxes identify unique nodes; colors are arbitrary.

  2. Sample-to-sample reproducibility.
    Figure 2: Sample-to-sample reproducibility.

    Total quantity of unique genomic sequence in common for all samples, at various minimum depths of coverage, as number of samples increased from 1 to 15 (for example, at ≥1× depth of coverage, there were ~10,000,000 unique genomic positions that were sequenced in common for all 15 samples).

  3. Genome Browser snapshot.
    Figure 3: Genome Browser snapshot.

    For a representative example, shown are histogram plots of an ~3 kb amplicon on chromosome 1 corresponding to mapped reads from arbitrary PCR for the first six samples (bottom six graphs). Other tracks include (from top to bottom): known genes (coverage overlaps with exons and introns of Il19), position of identified mutations (vertical red line at right end of plots) that are found in at least one sample, minimum fold coverage common to all 15 samples and mean fold coverage for all 15 samples. Note that PCR results were highly consistent from one sample to the next. Also note low depth of coverage reads unique to each sample (flanking the amplicon).


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Author information


  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
  3. 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.).

    • Cheryl A Carlson &
    • Laura E Hays


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.

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The authors declare no competing financial interests.

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Supplementary information

PDF files

  1. Supplementary Text and Figures (4 MB)

    Supplementary Figures 1–6, Supplementary Tables 1,3,4

Excel files

  1. Supplementary Table 2 (172 KB)

    Compilation of identified mutations.

Zip files

  1. Supplementary Software (12 KB) is a Perl program for evaluation of arbitrary primers; is a Perl program to prepare mouse reference sequence for; and is a Perl program to perform mutational analysis.

Additional data