Skip to main content

Thank you for visiting 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.

  • Letter
  • Published:

Genome sequencing of normal cells reveals developmental lineages and mutational processes



The somatic mutations present in the genome of a cell accumulate over the lifetime of a multicellular organism. These mutations can provide insights into the developmental lineage tree1, the number of divisions that each cell has undergone and the mutational processes that have been operative2. Here we describe whole genomes of clonal lines3 derived from multiple tissues of healthy mice. Using somatic base substitutions, we reconstructed the early cell divisions of each animal, demonstrating the contributions of embryonic cells to adult tissues. Differences were observed between tissues in the numbers and types of mutations accumulated by each cell, which likely reflect differences in the number of cell divisions they have undergone and varying contributions of different mutational processes. If somatic mutation rates are similar to those in mice, the results indicate that precise insights into development and mutagenesis of normal human cells will be possible.

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

Access options

Buy this article

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

Figure 1: Reconstructed phylogenetic trees of cells from early mouse embryos.
Figure 2: Contributions of early embryonic cells to adult tail cell populations.
Figure 3: The number and spectrum of substitution mutations in individual organoids.

Similar content being viewed by others

Accession codes


European Nucleotide Archive

Data deposits

Sequencing data have been deposited at the European Nucleotide Archive ( under accession numbers ERP002057 (Illumina data) and ERP005717 (SOLiD data).


  1. Shapiro, E., Biezuner, T. & Linnarsson, S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nature Rev. Genet. 14, 618–630 (2013)

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Sato, T. & Clevers, H. Growing self-organizing mini-guts from a single intestinal stem cell: mechanism and applications. Science 340, 1190–1194 (2013)

    ADS  CAS  PubMed  Google Scholar 

  4. De, S. Somatic mosaicism in healthy human tissues. Trends Genet. 27, 217–223 (2011)

    Article  CAS  PubMed  Google Scholar 

  5. Carlson, C. A. et al. Decoding cell lineage from acquired mutations using arbitrary deep sequencing. Nature Methods 9, 78–80 (2012)

    Article  CAS  Google Scholar 

  6. Kennedy, S. R., Loeb, L. A. & Herr, A. J. Somatic mutations in aging, cancer and neurodegeneration. Mech. Ageing Dev. 133, 118–126 (2012)

    Article  CAS  PubMed  Google Scholar 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  8. Salipante, S. J. & Horwitz, M. S. A phylogenetic approach to mapping cell fate. Curr. Top. Dev. Biol. 79, 157–184 (2007)

    Article  CAS  PubMed  Google Scholar 

  9. Shibata, D. & Tavare, S. Counting divisions in a human somatic cell tree: how, what and why? Cell Cycle 5, 610–614 (2006)

    Article  CAS  PubMed  Google Scholar 

  10. Wasserstrom, A. et al. Reconstruction of cell lineage trees in mice. PLoS ONE 3, e1939 (2008)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  11. Zhou, W. et al. Use of somatic mutations to quantify random contributions to mouse development. BMC Genomics 14, 39 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Lasken, R. S. Single-cell sequencing in its prime. Nature Biotechnol. 31, 211–212 (2013)

    Article  CAS  Google Scholar 

  13. Barker, N. et al. Lgr5+ve stem cells drive self-renewal in the stomach and build long-lived gastric units in vitro. Cell Stem Cell 6, 25–36 (2010)

    Article  CAS  PubMed  Google Scholar 

  14. Barker, N. et al. Identification of stem cells in small intestine and colon by marker gene Lgr5. Nature 449, 1003–1007 (2007)

    Article  ADS  CAS  PubMed  Google Scholar 

  15. Sato, T. et al. Long-term expansion of epithelial organoids from human colon, adenoma, adenocarcinoma, and Barrett’s epithelium. Gastroenterology 141, 1762–1772 (2011)

    Article  CAS  PubMed  Google Scholar 

  16. Sato, T. et al. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche. Nature 459, 262–265 (2009)

    Article  ADS  CAS  PubMed  Google Scholar 

  17. Snippert, H. J. et al. Intestinal crypt homeostasis results from neutral competition between symmetrically dividing Lgr5 stem cells. Cell 143, 134–144 (2010)

    Article  CAS  PubMed  Google Scholar 

  18. Plusa, B. et al. The first cleavage of the mouse zygote predicts the blastocyst axis. Nature 434, 391–395 (2005)

    Article  ADS  CAS  PubMed  Google Scholar 

  19. Bruce, A. W. & Zernicka-Goetz, M. Developmental control of the early mammalian embryo: competition among heterogeneous cells that biases cell fate. Curr. Opin. Genet. Dev. 20, 485–491 (2010)

    Article  CAS  PubMed  Google Scholar 

  20. Barker, N. et al. Very long-term self-renewal of small intestine, colon, and hair follicles from cycling Lgr5+ve stem cells. Cold Spring Harb. Symp. Quant. Biol. 73, 351–356 (2008)

    Article  CAS  PubMed  Google Scholar 

  21. Schepers, A. G., Vries, R., van den Born, M., van de Wetering, M. & Clevers, H. Lgr5 intestinal stem cells have high telomerase activity and randomly segregate their chromosomes. EMBO J. 30, 1104–1109 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  23. Schuster-Bockler, B. & Lehner, B. Chromatin organization is a major influence on regional mutation rates in human cancer cells. Nature 488, 504–507 (2012)

    Article  ADS  PubMed  Google Scholar 

  24. 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 

  25. van Loon, B., Markkanen, E. & Hubscher, U. Oxygen as a friend and enemy: how to combat the mutational potential of 8-oxo-guanine. DNA Repair (Amst.) 9, 604–616 (2010)

    Article  CAS  Google Scholar 

  26. Nik-Zainal, S. et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Leushacke, M., Ng, A., Galle, J., Loeffler, M. & Barker, N. Lgr5+ gastric stem cells divide symmetrically to effect epithelial homeostasis in the pylorus. Cell Rep. 5, 349–356 (2013)

    Article  CAS  PubMed  Google Scholar 

  28. 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 

  29. Felsenstein, J. PHYLIP—Phylogeny Inference Package (Version 3.2). Cladistics 5, 164–166 (1989)

    Google Scholar 

  30. Yang, Z. & Rannala, B. Molecular phylogenetics: principles and practice. Nature Rev. Genet. 13, 303–314 (2012)

    Article  CAS  PubMed  Google Scholar 

Download references


We thank M. Zernicka-Goetz and the Goldman group for discussion of our findings. This work was supported by funding from the Wellcome Trust (grant reference 077012/Z/05/Z), the Kadoorie Charitable Foundation and the Louis-Jeantet Foundation. Individual authors were supported as follows: M.H., Marie Curie IEF fellowship (EU/236954) and ERC grant (232814); R.B. and E.C., Zenith grant of the Netherlands Genomics Initiative (935.12.003); W.K., Centre for Biomedical Genetics, Utrecht; I.M., EMBO Long Term Fellowship (ALTF-1287-2012); S.N.Z., Wellcome Trust Intermediate Clinical Fellowship (WT100183MA) and Wellcome-Beit Prize Fellowship 2013; S.B., Wellcome Trust Research Training Fellowship for Clinicians; and P.C., Wellcome Trust Senior Research Fellowship in Clinical Science.

Author information

Authors and Affiliations



S.B. and M.R.S. analysed sequencing data. R.B. and E.C. contributed data and data analyses. S.R., M.P. and P.S.T. contributed to data analysis. I.M. assessed the association of mutation density with genomic features. L.A. performed analysis of mutational signatures. D.W. and P.C. performed statistical analyses. S.B, S.N.Z., P.C. and M.R.S. contributed to data interpretation. M.H., W.K. and M.W. generated organoids. M.M., L.M. and B.R. performed technical investigations. A.T. and N.G. performed phylogenetic analyses. H.C. and M.R.S. directed the research. M.R.S. wrote the manuscript.

Corresponding author

Correspondence to Michael R. Stratton.

Ethics declarations

Competing interests

H.C. is an inventor of several patents involving the organoid culture system. The remaining authors declare no competing interests.

Extended data figures and tables

Extended Data Figure 1 Mutant read count frequencies and clonality of organoids.

Using the Dirichlet process to analyse the mutant read count frequencies, we determined the clonality of each organoid. In every organoid only one clone can be found, with no evidence of subclonality. The single clone of each organoid is presented with variants that are heterozygous (mutation copy number of 1). The shaded area represents the 95% confidence interval around each peak. The y axis shows probability density; the x axis shows mutation copy number. the identifier in the top left corner of each graph is the sample ID. St, stomach; SB, small bowel; LB, large bowel; P, prostate.

Extended Data Figure 2 Example of an embryonic mutation.

a, Mutation 13 of mouse 1, defining an embryological lineage that ultimately gives rise to four organoids from large and small bowel. b, The mutation was called from next-generation sequencing data. c, The mutation was then validated by capillary sequencing. d, In addition, the absence of the variant in all other organoids was confirmed by capillary sequencing (representative trace shown). WT, wild type; MT, mutant.

Extended Data Figure 3 Principles of parsimonious construction of cell division trees.

a, First two-cell division of mouse 1. Each white-filled large circle represents an embryo cell defined by a unique combination of mutations. Each mutation is represented by a number next to the white circles, and yellow highlighting shows mutation(s) acquired during the most recent mitosis. Letters next to the white circles are identifiers of each embryonic precursor cell. The proportional contribution of each embryonic precursor cell to the population of cells in the tail is represented by the proportion of the circle area coloured red, assessed by read counts, in the tail, of the most recently acquired mutation(s) in each embryo cell. b, Note that cells ‘c’, ‘d’ and ‘e’ are defined by more than one mutation that may have occurred in successive cell divisions rather than in a single-cell division, as illustrated here for cell ‘c’. Because the allele fractions of these mutations ‘2’ and ‘3’ in the tail are not significantly different, the mutations can be interpreted as having occurred during the same cell division. The limited depth of these mutations in the tail, however, may not provide sufficient power to detect small, but real, significant differences, so we cannot exclude the possibility that these mutations occurred in successive cell divisions. c, Possibility of ‘silent’ division and cell death. It is possible that any number of ‘silent’ cell divisions (in which no mutation took place) occurred that we did not capture because of the lack of detectable mutations. In the hypothetical scenario illustrated here, two such silent divisions took place between precursor ‘b’ and ‘d’/‘e’. The existence of ‘silent’ cell divisions can be tested by comparing the tail allele frequencies of a precursor cell with the sum of the allele frequencies of the two derivative cells, which should be similar. In the example illustrated here, the combined tail allele fractions of mutations defining ‘d’ and ‘e’ are not significantly different from mutation 1 of cell ‘b’. The tail allele fraction of mutation 1 is 37.5% and the combined allele fraction of mutations 4–9 is 39.8%. These observations are therefore compatible with our hypothesis that cell ‘b’ is the immediate precursor of cells ‘d’ and ‘e’. Again, however, lack of statistical power through limited coverage may limit our ability to detect these differences. Moreover, if a cell division results in one daughter with no mutations and the other daughter dies and does not contribute to the adult, we will be unable to detect the existence of this cell division, illustrated here in cells ‘z1’ and ‘z2’.

Extended Data Figure 4 Hypothetical lineage trees.

Red numbers show the count of cell divisions, and black numbers the count of organoids. On the assumption that two bifurcating lineage trees led to all the cells in the two mice, we constructed two hypothetical trees to include the minimum number of bifurcations required to place all 25 organoid lines singly at the end of the branches of each tree: 13 organoids in mouse 1 and 12 in mouse 2. Together the two trees comprised 23 cell divisions.

Extended Data Figure 5 Association of mutation density with genome features.

a, Association of mutation density with chromatin states. Shown is the relative density of mutations in regions of open chromatin (green bars) or repressed chromatin (grey bars). Chromatin states are inferred from histone modifications (as in the labels of the bars), according to publicly available ChIP-seq data. Mutation rates were normalized to the average genomic rate. Error bars denote 95% confidence intervals using an Exact Poisson test. Significance was obtained for every pairwise comparison (n = 10) using an Exact Poisson test for the ratio between two rate parameters (r = 1 in the null hypothesis) and adjusted for multiple testing using Benjamini–Hochberg’s False Discovery Rate. #q < 10−5. b, Density of mutations in early (white bar) versus late (blue bar) replicating regions. The mouse genome was segmented into early or late replicating regions based on 16 publicly available Repli-chip data sets. Statistical analysis as in a. *P = 3.4 × 10−5.

Extended Data Figure 6 Mutational signatures extracted from mouse organoids.

The vertical axis depicts the contribution of each mutation type at each context for the two identified mutational signatures. The horizontal axis shows the six base substitutions including the bases immediately 5′ and 3′ to the mutation.

Extended Data Figure 7 In vitro mutations of murine small-bowel organoids.

a, Mutational spectra of small-bowel in vitro mutations. Small-bowel organoids were isolated and expanded from a third mouse (mouse 3). After 56 days in culture, single Lgr5-positive stem cells were isolated from the parental organoid cultures and expanded to obtain sufficient quantities of DNA for sequencing. Both subclones exhibit a different mutational spectrum, compared to parental cultures, characterized by a decrease in C→A and an increase in T→G mutations. b, Mutation burden of in vitro mutations. The absolute number of mutations unique to each organoid is shown. Mutations that were found in more than one organoid were excluded from the count. In vitro the small-bowel organoid cells, subclones A and B, acquired 507 and 739 mutations, respectively. c, Non-negative matrix factorization extracted a distinct mutational signature, termed in vitro signature, from the mutations of the subclones, which is characterized by an excess of T→X mutations at XpTpT trinucleotides. The vertical axis shows the contribution of each mutation type at each context to the overall mutation burden; the horizontal axis shows the six classes of base substitutions including the bases immediately 5′ and 3′ to the mutation.

Supplementary information

Supplementary Information

This file contains a Supplementary Discussion and Supplementary Tables 1-2. (PDF 280 kb)

Supplementary Data

This file contains Supplementary Tables 3 and 4. (XLS 1537 kb)

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Behjati, S., Huch, M., van Boxtel, R. et al. Genome sequencing of normal cells reveals developmental lineages and mutational processes. Nature 513, 422–425 (2014).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


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.


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