Biology subjects

Okimura, C.

Publications and source records attributed to Okimura, C..

2 recordsLinked to original sources

bioRxiv2023

Linear contraction of stress fibers generates cell body rotation

Wounds are healed by crawling migration of the epidermal cells around the injured area. Fish epidermal keratocytes that rapidly repair wounds comprise a frontal crescent-shaped lamellipodium and a rear rugby ball-shaped cell body. The cell body rotates like a wheel during migration. Stress fibers, which are bundles of contractile actomyosin filaments, are arranged along the seams of the rugby ball. Here we show the linear contraction of stress fibers to be the driving force for rotation. We constructed a mechanical model of the cell body that consisted of a soft cylinder with a contractile coil. From the motion of the model, it was predicted that contraction of the stress fibers would deform the soft cell body, as a result of which the deformed cell body would push against the substrate to generate torque. This prediction was confirmed by the observation of stress fiber dynamics in migrating cells. Linear-to-rotation conversion in migrating keratocytes is realized by simple soft-body mechanics. Conversion from linear motion to rotation is widely used in machines with moving parts, but requires somewhat complicated mechanics. An understanding of linear-to-rotation conversion in keratocytes has potential for use in the design of biomimetic soft robots.

biophysics↗

bioRxiv2020

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

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 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.

cell biology↗