Abstract
Cells exhibit various morphological characteristics due to their physiological activities, and changes in cell morphology are inherently accompanied by the assembly and disassembly of the actin cytoskeleton. Stress fibers are a prominent component of the actin-based intracellular structure and are highly involved in numerous physiological processes, e.g., mechanotransduction and maintenance of cell morphology. Although it is widely accepted that variations in cell geometry interact with the distribution and localization of stress fibers, it remains unclear if there are underlying geometric principles between the cell morphology and actin cytoskeleton. Here we present a machine learning system, which uses the diffusion model, that can convert the cell shape to the distribution of stress fibers. By training with corresponding datasets of cell shape and stress fibers, our system learns the conversion to generate the stress fiber images from its corresponding cell shape. The predicted stress fiber distribution has good agreement with the experimental data, and the overlap region of predicted and experimentally observed stress fibers reaches 79.3 ±12.4%. We then found some unknown natures such as a linear relation relationship between the stress fiber length and cell area. With this “installed” conversion relation between cellular morphology and corresponding stress fibers’ localization, our system could perform virtual experiments that provide a visual map showing the probability of stress fiber distribution from the virtual cell shape. Our system provides a powerful approach to seek further hidden geometric principles between the cell morphologies and actin cytoskeletons.
Competing Interest Statement
The authors have declared no competing interest.
