Mechanosensing model of fibroblast cells adhered on a substrate with varying stiffness and thickness

Mechanosensing of cells to the surrounding material is crucial for their physiological and pathological processes. However, materials design to guide cellular responses is largely ad hoc due to the lack of comprehensive modelling techniques for quantitative understanding. In this paper, we propose a computational model to study the mechanosensing of fibroblast cells seeded on elastic hydrogel substrates with different stiffness and thickness. We consider the sensing mechanisms of cells to mechanical cues, including the rigidity and deformation of the substrate, and the traction forces of neighboring cells, which regulate the active changes of stress fibers and focal adhesions. This model allows us to predict the coupled effects of substrate stiffness and thickness on stress fiber formation and disassociation, and affinity integrin density. We also examine the combined effect of cell size and substrate thickness on the mechanosensing of fibroblast cells. The results reveal that a cell can sense its neighboring cell by deforming the underlying substrate. Our simulations also provide physical insights in the enhanced mechanosensing capacity of collective cells. The present modelling framework is not only important for profound understanding of cell mechanosensing, but also has the potential to guide the rationale design of biomaterials for tissue engineering and wound healing.

Automated summary

This paper proposes a computational model to study the mechanosensing mechanisms of fibroblast cells on elastic hydrogel substrates. By considering the sensing mechanisms of cells to the rigidity, deformation, and traction forces of the substrate and neighboring cells, the model predicts the effects of substrate stiffness and thickness on stress fiber formation, disassociation, and integrin density. The results show that cells can sense neighboring cells by deforming the underlying substrate, and collective cells have enhanced mechanosensing capacity. This model not only enhances our understanding of cell mechanosensing, but also has implications for the design of biomaterials for tissue engineering and wound healing.