Maps of variability in cell lineage trees

PLoS Comput Biol. 2019 Feb 12;15(2):e1006745. doi: 10.1371/journal.pcbi.1006745. eCollection 2019 Feb.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Body Patterning / genetics*
  • Caenorhabditis elegans / genetics
  • Cell Differentiation / genetics
  • Cell Lineage / genetics*
  • Computational Biology / methods*
  • Gene Expression Regulation, Developmental / genetics
  • Genetic Variation / genetics
  • Models, Biological
  • Phenotype

Grants and funding

This work was supported in part by Australian Research Council (ARC) grant FT140101104 to DGH, the National Health and Medical Research Council of Australia (NHMRC) Program Grant 1054618 to TPS, and NHMRC grants 620500 and APP1099140 and ARC grant FT0990405 to SMR. The sponsors or funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. ARC - https://www.arc.gov.au/ NHMRC - https://www.nhmrc.gov.au/