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Genomic Variability within an Organism Exposes Its Cell Lineage Tree

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  • Dan Frumkin
  • Adam Wasserstrom
  • Shai Kaplan
  • Uriel Feige
  • Ehud Shapiro

Abstract

What is the lineage relation among the cells of an organism? The answer is sought by developmental biology, immunology, stem cell research, brain research, and cancer research, yet complete cell lineage trees have been reconstructed only for simple organisms such as Caenorhabditis elegans. We discovered that somatic mutations accumulated during normal development of a higher organism implicitly encode its entire cell lineage tree with very high precision. Our mathematical analysis of known mutation rates in microsatellites (MSs) shows that the entire cell lineage tree of a human embryo, or a mouse, in which no cell is a descendent of more than 40 divisions, can be reconstructed from information on somatic MS mutations alone with no errors, with probability greater than 99.95%. Analyzing all ~1.5 million MSs of each cell of an organism may not be practical at present, but we also show that in a genetically unstable organism, analyzing only a few hundred MSs may suffice to reconstruct portions of its cell lineage tree. We demonstrate the utility of the approach by reconstructing cell lineage trees from DNA samples of a human cell line displaying MS instability. Our discovery and its associated procedure, which we have automated, may point the way to a future “Human Cell Lineage Project” that would aim to resolve fundamental open questions in biology and medicine by reconstructing ever larger portions of the human cell lineage tree.: The human body is made of about 100 trillion cells, all of which are descendants of a single cell, the fertilized egg. The quest to understand their path of descent, called a cell lineage tree, is shared by many branches of biology and medicine, including developmental biology, immunology, stem cell research, brain research, and cancer research. So far, science has been able to determine the cell lineage tree of tiny organisms only, worms with a thousand cells or so. Our team has discovered that the mutations accumulated in each cell in our body during its normal development from the zygote carry sufficient information to reconstruct, in principle, cell lineage trees for large organisms, including humans. Inspired by this discovery, we developed an automated procedure for the reconstruction of cell lineage trees from DNA samples. A direct application of these results may include the analysis of the development of cancer. The results may also inspire a future “Human Cell Lineage Project,” whose aim would be to reconstruct an entire human cell lineage tree.

Suggested Citation

  • Dan Frumkin & Adam Wasserstrom & Shai Kaplan & Uriel Feige & Ehud Shapiro, 2005. "Genomic Variability within an Organism Exposes Its Cell Lineage Tree," PLOS Computational Biology, Public Library of Science, vol. 1(5), pages 1-13, October.
  • Handle: RePEc:plo:pcbi00:0010050
    DOI: 10.1371/journal.pcbi.0010050
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    References listed on IDEAS

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    1. Yuval Dor & Juliana Brown & Olga I. Martinez & Douglas A. Melton, 2004. "Adult pancreatic β-cells are formed by self-duplication rather than stem-cell differentiation," Nature, Nature, vol. 429(6987), pages 41-46, May.
    2. Stephen C. Noctor & Alexander C. Flint & Tamily A. Weissman & Ryan S. Dammerman & Arnold R. Kriegstein, 2001. "Neurons derived from radial glial cells establish radial units in neocortex," Nature, Nature, vol. 409(6821), pages 714-720, February.
    3. Leroy Hood & David Galas, 2003. "The digital code of DNA," Nature, Nature, vol. 421(6921), pages 444-448, January.
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    Cited by:

    1. Damien G Hicks & Terence P Speed & Mohammed Yassin & Sarah M Russell, 2019. "Maps of variability in cell lineage trees," PLOS Computational Biology, Public Library of Science, vol. 15(2), pages 1-32, February.
    2. Noa Chapal-Ilani & Yosef E Maruvka & Adam Spiro & Yitzhak Reizel & Rivka Adar & Liran I Shlush & Ehud Shapiro, 2013. "Comparing Algorithms That Reconstruct Cell Lineage Trees Utilizing Information on Microsatellite Mutations," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-17, November.
    3. Sara Ballouz & Risa Karakida Kawaguchi & Maria T. Pena & Stephan Fischer & Megan Crow & Leon French & Frank M. Knight & Linda B. Adams & Jesse Gillis, 2023. "The transcriptional legacy of developmental stochasticity," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

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