Biology subjects

Ogita, G.

Publications and source records attributed to Ogita, G..

3 recordsLinked to original sources

bioRxiv2026

Force transmission balance through adhesions determines multicellular handedness

Cell chirality has been implicated in left-right (LR) asymmetric morphogenesis, yet multicellular handedness is not a simple readout of single-cell chirality. Here, we show that multicellular LR asymmetry is determined by the balance of force transmission through cell-cell and cell-substrate adhesions. Combining experiments in human epithelial cells with theoretical modeling, we demonstrate that clockwise-rotating single cells can generate either clockwise or counterclockwise collective rotation depending on whether chiral forces are transmitted primarily through adherens junctions or focal adhesions, without altering single-cell chirality itself. Mechanistically, microtubules promoted junctional force transmission by recruiting the actin-microtubule crosslinker ACF7 to cell-cell contacts, where ACF7 stabilized F-actin and enabled intercellular mechanical coupling. Our results define a mechanical framework in which the routing of chiral forces through adhesions, rather than single-cell chirality alone, determines multicellular handedness.

cell biology↗

bioRxiv2025

Kalman-filter Force Inference: an estimation framework for cellular forces from temporal evolution of epithelial morphogenesis

Epithelial morphogenesis is orchestrated by cellular forces, such as cell junctional tension and cellular pressure. Therefore, elucidating the spatiotemporal distribution of these forces and their dynamics is paramount to understand morphogenesis during development. Over the past decade, various force inference methods have been proposed to estimate cellular forces based on the shape and geometry of cells within epithelial tissues. Most of these methods were developed under the assumption of static force balance, which neglects the cell deformation, thereby limiting their applicability to tissue undergoing dynamic deformation. To address this issue, we develop a novel method to accurately infer cellular forces from time-lapse imaging data of epithelial deformation, without relying on the static force balance assumption. Our method is based on two fundamental assumptions. First, the forces exerted by cells are balanced with dissipative forces (such as viscous and frictional forces) at each vertex arising from cell deformation, referred to as the dynamic force balance. Second, cellular forces vary smoothly over time. By formalizing these assumptions using a Bayesian framework, we construct a new approach of force inference, named Kalman-filter Force Inference (KFI). The effectiveness of our method is evaluated using synthetic data of a deforming tissue generated by numerical simulations of the cell vertex model. The results demonstrate the accurate estimation of cellular force dynamics. Furthermore, our systematic evaluation reveals the capability of our method to accurately estimate cellular forces across tissues with diverse mechanical parameters. Finally, we assessed the robustness of our method to observation noise. We anticipate that Kalman-filter Force Inference will broaden the applicability of force inference techniques and significantly contribute to our understanding of epithelial mechanics.

biophysics↗

bioRxiv2024

Bayesian parameter inference for epithelial mechanics

Cell-based mechanical models, such as the Cell Vertex Model (CVM), have proven useful for studying the mechanical control of epithelial tissue dynamics. We recently developed a statistical method called image-based parameter inference for formulating CVM model functions and estimating their parameters from image data of epithelial tissues. In this study, we employed Bayesian statistics to improve the utility and flexibility of image-based parameter inference. Tests on synthetic data confirmed that both our non-hierarchical and hierarchical Bayesian models provide accurate estimates of model parameters. By applying this method to Drosophila wings, we demonstrated that the reliability of parameter estimation is closely linked to the mechanical anisotropies present in the tissue. Moreover, we revealed that the cortical elasticity term is dispensable for explaining force-shape correlations in vivo. We anticipate that the flexibility of the Bayesian statistical framework will facilitate the integration of various types of information, thereby contributing to the quantitative dissection of the mechanical control of tissue dynamics.

biophysics↗