Cellular Contact Guidance Emerges from Gap Avoidance.

In the presence of anisotropic biochemical or topographical patterns, cells tend to align in the direction of these cues-a widely reported phenomenon known as "contact guidance." To investigate the origins of contact guidance, here, we created substrates micropatterned with parallel lines of fibronectin with dimensions spanning multiple orders of magnitude. Quantitative morphometric analysis of our experimental data reveals two regimes of contact guidance governed by the length scale of the cues that cannot be explained by enforced alignment of focal adhesions. Adopting computational simulations of cell remodeling on inhomogeneous substrates based on a statistical mechanics framework for living cells, we show that contact guidance emerges from anisotropic cell shape fluctuation and "gap avoidance," i.e., the energetic penalty of cell adhesions on non-adhesive gaps. Our findings therefore point to general biophysical mechanisms underlying cellular contact guidance, without the necessity of invoking specific molecular pathways.

Entropic Forces Drive Cellular Contact Guidance.

Contact guidance-the widely known phenomenon of cell alignment induced by anisotropic environmental features-is an essential step in the organization of adherent cells, but the mechanisms by which cells achieve this orientational ordering remain unclear. Here, we seeded myofibroblasts on substrates micropatterned with stripes of fibronectin and observed that contact guidance emerges at stripe widths much greater than the cell size. To understand the origins of this surprising observation, we combined morphometric analysis of cells and their subcellular components with a, to our knowledge, novel statistical framework for modeling nonthermal fluctuations of living cells. This modeling framework is shown to predict not only the trends but also the statistical variability of a wide range of biological observables, including cell (and nucleus) shapes, sizes, and orientations, as well as stress-fiber arrangements within the cells with remarkable fidelity with a single set of cell parameters. By comparing observations and theory, we identified two regimes of contact guidance: 1) guidance on stripe widths smaller than the cell size (w ≤ 160 µm), which is accompanied by biochemical changes within the cells, including increasing stress-fiber polarization and cell elongation; and 2) entropic guidance on larger stripe widths, which is governed by fluctuations in the cell morphology. Overall, our findings suggest an entropy-mediated mechanism for contact guidance associated with the tendency of cells to maximize their morphological entropy through shape fluctuations.

Thermodynamic Modeling of the Statistics of Cell Spreading on Ligand-Coated Elastic Substrates.

Biological spread cells exist in a perpetually fluctuating state and therefore cannot be described in terms of a unique deterministic system. For modeling approaches to provide novel insight and uncover new mechanisms that drive cell behavior, a framework is required that progresses from traditional deterministic methods (whereby simulation of an experiment predicts a single outcome). In this study, we implement a new, to our knowledge, modeling approach for the analysis of cell spreading on ligand-coated substrates, extending the framework for nonequilibrium thermodynamics of cells developed by Shishvan et al. to include active focal adhesion assembly. We demonstrate that the model correctly predicts the coupled influence of surface collagen density and substrate stiffness on cell spreading, as reported experimentally by Engler et al. Low surface collagen densities are shown to result in a high probability that cells will be restricted to low spread areas. Furthermore, elastic free energy induced by substrate deformation lowers the probability of observing a highly spread cell, and, consequentially, lower cell tractions affect the assembly of focal adhesions. Experimentally measurable observables such as cell spread area and aspect ratio can be directly postprocessed from the computed homeostatic ensemble of (several million) spread states. This allows for the prediction of mean and SDs of such experimental observables. This class of cell mechanics modeling presents a significant advance on conventional deterministic approaches.