Novel metrics reveal new structure and unappreciated heterogeneity in Caenorhabditis elegans development

• Published in: PLoS computational biology Vol. 19; no. 12; p. e1011733
• Main Authors: Natesan, Gunalan, Hamilton, Timothy, Deeds, Eric J, Shah, Pavak K
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.12.2023; Public Library of Science (PLoS)
• Subjects: Analysis; Animals; Biology and Life Sciences; Caenorhabditis elegans; Caenorhabditis elegans Proteins - metabolism; Cell cycle; Cell Cycle - genetics; Cell Differentiation - genetics; Cell fate; Cell Lineage - genetics; Decisions; Distillation; Embryos; Gene expression; Genetic aspects; Growth; Heterogeneity; Hypothesis testing; Nematodes; Phenotype; Phenotypes; Quantitative analysis; Quantitative research; Research and Analysis Methods; RNA; RNA Interference; RNA-mediated interference; United States
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1011733

High throughput experimental approaches are increasingly allowing for the quantitative description of cellular and organismal phenotypes. Distilling these large volumes of complex data into meaningful measures that can drive biological insight remains a central challenge. In the quantitative study of development, for instance, one can resolve phenotypic measures for single cells onto their lineage history, enabling joint consideration of heritable signals and cell fate decisions. Most attempts to analyze this type of data, however, discard much of the information content contained within lineage trees. In this work we introduce a generalized metric, which we term the branch edit distance, that allows us to compare any two embryos based on phenotypic measurements in individual cells. This approach aligns those phenotypic measurements to the underlying lineage tree, providing a flexible and intuitive framework for quantitative comparisons between, for instance, Wild-Type (WT) and mutant developmental programs. We apply this novel metric to data on cell-cycle timing from over 1300 WT and RNAi-treated Caenorhabditis elegans embryos. Our new metric revealed surprising heterogeneity within this data set, including subtle batch effects in WT embryos and dramatic variability in RNAi-induced developmental phenotypes, all of which had been missed in previous analyses. Further investigation of these results suggests a novel, quantitative link between pathways that govern cell fate decisions and pathways that pattern cell cycle timing in the early embryo. Our work demonstrates that the branch edit distance we propose, and similar metrics like it, have the potential to revolutionize our quantitative understanding of organismal phenotype.