RRC ID 67493
Author Imoto D, Saito N, Nakajima A, Honda G, Ishida M, Sugita T, Ishihara S, Katagiri K, Okimura C, Iwadate Y, Sawai S.
Title Comparative mapping of crawling-cell morphodynamics in deep learning-based feature space.
Journal PLoS Comput Biol
Abstract Navigation of fast migrating cells such as amoeba Dictyostelium and immune cells are tightly associated with their morphologies that range from steady polarized forms that support high directionality to those more complex and variable when making frequent turns. Model simulations are essential for quantitative understanding of these features and their origins, however systematic comparisons with real data are underdeveloped. Here, by employing deep-learning-based feature extraction combined with phase-field modeling framework, we show that a low dimensional feature space for 2D migrating cell morphologies obtained from the shape stereotype of keratocytes, Dictyostelium and neutrophils can be fully mapped by an interlinked signaling network of cell-polarization and protrusion dynamics. Our analysis links the data-driven shape analysis to the underlying causalities by identifying key parameters critical for migratory morphologies both normal and aberrant under genetic and pharmacological perturbations. The results underscore the importance of deciphering self-organizing states and their interplay when characterizing morphological phenotypes.
Volume 17(8)
Pages e1009237
Published 2021-8-1
DOI 10.1371/journal.pcbi.1009237
PMID 34383753
PMC PMC8360578
MeSH Animals Cell Movement / physiology* Cell Polarity / physiology Cell Shape / physiology Cell Surface Extensions / physiology Cells, Cultured Cichlids Computational Biology Computer Simulation Deep Learning* Dictyostelium / cytology Dictyostelium / physiology Fibroblasts / cytology Fibroblasts / physiology HL-60 Cells Humans Models, Biological*
IF 4.7
Human and Animal Cells HL60(RCB0041)