Comparative mapping of crawling-cell morphodynamics in deep learning-based feature space.

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

SUBMITTER: Imoto D 

PROVIDER: S-EPMC8360578 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature