Physical limits to biomechanical sensing

TL;DR

Cells sense their mechanical environment within disordered fiber networks, which exhibit a broad range of local stiffnesses; understanding this helps explain how cells adapt to heterogeneous tissues.

Contribution

This study models cell mechanosensing in disordered fiber networks, revealing the extensive variability in local stiffness and how cells can optimize sensing by integrating multiple measurements.

Findings

Local stiffness measurements vary over two decades within the same network.

Network features like long fibers and elastic transitions increase stiffness heterogeneity.

Cells can improve global tissue stiffness estimates by integrating multiple local measurements.

Abstract

Cells actively probe and respond to the stiffness of their surroundings. Since mechanosensory cells in connective tissue are surrounded by a disordered network of biopolymers, their in vivo mechanical environment can be extremely heterogeneous. Here, we investigate how this heterogeneity impacts mechanosensing by modeling the cell as an idealized local stiffness sensor inside a disordered fiber network. For all types of networks we study, including experimentally-imaged collagen and fibrin architectures, we find that measurements applied at different points throughout a given network yield a strikingly broad range of local stiffnesses, spanning roughly two decades. We verify via simulations and scaling arguments that this broad range of local stiffnesses is a generic property of disordered fiber networks, and show that the range can be further increased by tuning specific network features, including the presence of long fibers and the proximity to elastic transitions. These features additionally allow for a highly tunable dependence of stiffness on probe length. Finally, we show that to obtain optimal, reliable estimates of global tissue stiffness, a cell must adjust its size, shape, and position to integrate multiple stiffness measurements over extended regions of space.