IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1006745.html
   My bibliography  Save this article

Maps of variability in cell lineage trees

Author

Listed:
  • Damien G Hicks
  • Terence P Speed
  • Mohammed Yassin
  • Sarah M Russell

Abstract

New approaches to lineage tracking have allowed the study of differentiation in multicellular organisms over many generations of cells. Understanding the phenotypic variability observed in these lineage trees requires new statistical methods. Whereas an invariant cell lineage, such as that for the nematode Caenorhabditis elegans, can be described by a lineage map, defined as the pattern of phenotypes overlaid onto the binary tree, a traditional lineage map is static and does not describe the variability inherent in the cell lineages of higher organisms. Here, we introduce lineage variability maps which describe the pattern of second-order variation in lineage trees. These maps can be undirected graphs of the partial correlations between every lineal position, or directed graphs showing the dynamics of bifurcated patterns in each subtree. We show how to infer these graphical models for lineages of any depth from sample sizes of only a few pedigrees. This required developing the generalized spectral analysis for a binary tree, the natural framework for describing tree-structured variation. When tested on pedigrees from C. elegans expressing a marker for pharyngeal differentiation potential, the variability maps recover essential features of the known lineage map. When applied to highly-variable pedigrees monitoring cell size in T lymphocytes, the maps show that most of the phenotype is set by the founder naive T cell. Lineage variability maps thus elevate the concept of the lineage map to the population level, addressing questions about the potency and dynamics of cell lineages and providing a way to quantify the progressive restriction of cell fate with increasing depth in the tree.Author summary: Multicellular organisms develop from a single fertilized egg by sequential cell divisions. The progeny from these divisions adopt different traits that are transmitted and modified through many generations. By tracking how cell traits change with each successive cell division throughout the family, or lineage, tree, it has been possible to understand where and how these modifications are controlled at the single-cell level. This helps address questions about, for example, the developmental origin of tissues, the sources of differentiation in immune cells, or the relationship between primary tumors and metastases. Such lineages often show large variability, with apparently similar founder cells giving rise to different patterns of descendants. In addition, questions about the range of accessible cell types at different stages of the lineage tree are actually questions about lineage variability. To characterize this variation, and thus understand the lineage at the population level, we introduce lineage variability maps. Using data from worm and mammalian cell lineages we show how these maps provide quantifiable answers to questions about any developing lineage, such as the potency of progenitor cells and the restriction of cell fate at different stages of the tree.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pcbi00:1006745
    DOI: 10.1371/journal.pcbi.1006745
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006745
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1006745&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1006745?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Oded Sandler & Sivan Pearl Mizrahi & Noga Weiss & Oded Agam & Itamar Simon & Nathalie Q. Balaban, 2015. "Lineage correlations of single cell division time as a probe of cell-cycle dynamics," Nature, Nature, vol. 519(7544), pages 468-471, March.
    2. Anna Alemany & Maria Florescu & Chloé S. Baron & Josi Peterson-Maduro & Alexander van Oudenaarden, 2018. "Whole-organism clone tracing using single-cell sequencing," Nature, Nature, vol. 556(7699), pages 108-112, April.
    3. Nicola Gritti & Simone Kienle & Olga Filina & Jeroen Sebastiaan van Zon, 2016. "Long-term time-lapse microscopy of C. elegans post-embryonic development," Nature Communications, Nature, vol. 7(1), pages 1-9, November.
    4. Kirsten L. Frieda & James M. Linton & Sahand Hormoz & Joonhyuk Choi & Ke-Huan K. Chow & Zakary S. Singer & Mark W. Budde & Michael B. Elowitz & Long Cai, 2017. "Synthetic recording and in situ readout of lineage information in single cells," Nature, Nature, vol. 541(7635), pages 107-111, January.
    5. de Saporta, Benoîte & Gégout-Petit, Anne & Marsalle, Laurence, 2014. "Statistical study of asymmetry in cell lineage data," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 15-39.
    6. Andreas Hilfinger & Johan Paulsson, 2015. "Defiant daughters and coordinated cousins," Nature, Nature, vol. 519(7544), pages 422-423, March.
    7. Paul W. Sternberg, 2017. "Forty years of cellular clues from worms," Nature, Nature, vol. 543(7647), pages 628-630, March.
    8. 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.
    9. Ewen Callaway, 2017. "The trickiest family tree in biology," Nature, Nature, vol. 547(7661), pages 20-22, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Speed, Terence P. & Hicks, Damien G., 2022. "Spectral PCA for MANOVA and data over binary trees," Journal of Multivariate Analysis, Elsevier, vol. 188(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xinhai Pan & Hechen Li & Pranav Putta & Xiuwei Zhang, 2023. "LinRace: cell division history reconstruction of single cells using paired lineage barcode and gene expression data," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    2. Klement Stojanovski & Ioana Gheorghe & Peter Lenart & Anne Lanjuin & William B. Mair & Benjamin D. Towbin, 2023. "Maintenance of appropriate size scaling of the C. elegans pharynx by YAP-1," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    3. Yelyzaveta Shlyakhtina & Bianca Bloechl & Maximiliano M. Portal, 2023. "BdLT-Seq as a barcode decay-based method to unravel lineage-linked transcriptome plasticity," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    4. Vincent Bansaye & S. Valère Bitseki Penda, 2021. "A Phase Transition for Large Values of Bifurcating Autoregressive Models," Journal of Theoretical Probability, Springer, vol. 34(4), pages 2081-2116, December.
    5. Bercu, Bernard & Blandin, Vassili, 2015. "A Rademacher–Menchov approach for random coefficient bifurcating autoregressive processes," Stochastic Processes and their Applications, Elsevier, vol. 125(4), pages 1218-1243.
    6. 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.
    7. S. Valère Bitseki Penda & Adélaïde Olivier, 2017. "Autoregressive functions estimation in nonlinear bifurcating autoregressive models," Statistical Inference for Stochastic Processes, Springer, vol. 20(2), pages 179-210, July.
    8. Bernard Bercu & Vassili Blandin, 2015. "Limit theorems for bifurcating integer-valued autoregressive processes," Statistical Inference for Stochastic Processes, Springer, vol. 18(1), pages 33-67, April.
    9. Klement Stojanovski & Helge Großhans & Benjamin D. Towbin, 2022. "Coupling of growth rate and developmental tempo reduces body size heterogeneity in C. elegans," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    10. Zhu, Guang & Lin, Zhenhua, 2021. "Commentary on statistical mechanical models of cancer," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    11. A. S. Eisele & M. Tarbier & A. A. Dormann & V. Pelechano & D. M. Suter, 2024. "Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    12. 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.
    13. Scharf, Yael, 2017. "A chaotic outlook on biological systems," Chaos, Solitons & Fractals, Elsevier, vol. 95(C), pages 42-47.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1006745. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.