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Register a new userShape-velocity correlation defines polarization in migrating cell simulations
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Cell migration plays essential roles in development, wound healing, diseases, and in the maintenance of a complex body. Experiments in collective cell migration generally measure quantities such as cell displacement and velocity. The observed short-time diffusion regime for mean square displacement in single-cell migration experiments on flat surfaces calls into question the definition of cell velocity and the measurement protocol. Theoretical results in stochastic modeling for single-cell migration have shown that this fast diffusive regime is explained by a white noise acting on displacement on the direction perpendicular to the migrating cell polarization axis (not on velocity). The prediction is that only the component of velocity parallel to the polarization axis is a well-defined quantity, with a robust measurement protocol. Here, we ask whether we can find a definition of a migrating-cell polarization that is able to predict the cells subsequent displacement, based on measurements of its shape. Supported by experimental evidence that cell nucleus lags behind the cell center of mass in a migrating cell, we propose a robust parametrization for cell migration where the distance between cell nucleus and the cells center of mass defines cell shape polarization. We tested the proposed methods by applying to a simulation model for three-dimensional cells performed in the CompuCell3D environment, previously shown to reproduce biological cells kinematics migrating on a flat surface.
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Key to collective cell migration is the ability of cells to rearrange their position with respect to their neighbors. Recent theory and experiments demonstrated that cellular rearrangements are facilitated by cell shape, with cells having more elongated shapes and greater perimeters more easily sliding past their neighbors within the cell layer. Though it is thought that cell perimeter is controlled primarily by cortical tension and adhesion at each cells periphery, experimental testing of this hypothesis has produced conflicting results. Here we studied collective cell migration in an epithelial monolayer by measuring forces, cell perimeters, and motion, and found all three to decrease with either increased cell density or inhibition of cell contraction. In contrast to previous understanding, the data suggest that cell shape and rearrangements are controlled not by cortical tension or adhesion at the cell periphery but rather by the stress fibers that produce tractions at the cell-substrate interface. This finding is confirmed by an experiment showing that increasing tractions reverses the effect of density on cell shape and rearrangements. Our study therefore reduces the focus on the cell periphery by establishing cell-substrate traction as a major physical factor controlling cell shape and motion in collective cell migration.
In embryonic development, programmed cell shape changes are essential for building functional organs, but in many cases the mechanisms that precisely regulate these changes remain unknown. We propose that fluid-like drag forces generated by the motion of an organ through surrounding tissue could generate changes to its structure that are important for its function. To test this hypothesis, we study the zebrafish left-right organizer, Kupffers vesicle (KV), using experiments and mathematical modeling. During development, monociliated cells that comprise the KV undergo region-specific shape changes along the anterior-posterior axis that are critical for KV function: anterior cells become long and thin, while posterior cells become short and squat. Here, we develop a mathematical vertex-like model for cell shapes, which incorporates both tissue rheology and cell motility, and constrain the model parameters using previously published rheological data for the zebrafish tailbud [Serwane et al.] as well as our own measurements of the KV speed. We find that drag forces due to dynamics of cells surrounding the KV could be sufficient to drive KV cell shape changes during KV development. More broadly, these results suggest that cell shape changes could be driven by dynamic forces not typically considered in models or experiments.
Cell polarization underlies many cellular processes, such as differentiation, migration, and budding. Many living cells, such as budding yeast and fission yeast, use cytoskeletal structures to actively transport proteins to one location on the membrane and create a high density spot of membrane-bound proteins. Yet, the thermodynamic constraints on filament-based cell polarization remain unknown. We show by mathematical modeling that cell polarization requires detailed balance to be broken, and we quantify the free-energy cost of maintaining a polarized state of the cell. Our study reveals that detailed balance cannot only be broken via the active transport of proteins along filaments, but also via a chemical modification cycle, allowing detailed balance to be broken by the shuttling of proteins between the filament, membrane, and cytosol. Our model thus shows that cell polarization can be established via two distinct driving mechanisms, one based on active transport and one based on non-equilibrium binding. Furthermore, the model predicts that the driven binding process dissipates orders of magnitude less free-energy than the transport-based process to create the same membrane spot. Active transport along filaments may be sufficient to create a polarized distribution of membrane-bound proteins, but an additional chemical modification cycle of the proteins themselves is more efficient and less sensitive to the physical exclusion of proteins on the transporting filaments, providing insight in the design principles of the Pom1/Tea1/Tea4 system in fission yeast and the Cdc42 system in budding yeast.
Cell crawling is critical to biological development, homeostasis and disease. In many cases, cell trajectories are quasi-random-walk. In vitro assays on flat surfaces often described such quasi-random-walk cell trajectories as approximations to a solution of a Langevin process. However, experiments show quasi-diffusive behavior at small timescales, indicating that instantaneous velocity and velocity autocorrelations are not well-defined. We propose to characterize mean-squared cell displacement using a modified Furth equation with three temporal and spatial regimes: short- and long-time/range diffusion and intermediate time/range ballistic motion. This analysis collapses mean-squared displacements of previously published experimental data onto a single-parameter family of curves, allowing direct comparison between movement in different cell types, and between experiments and numerical simulations. Our method also show that robust cell-motility quantification requires an experiment with a maximum interval between images of a few percent of the cell-motion persistence time or less, and a duration of a few orders-of-magnitude longer than the cell-motion persistence time or more.
We investigate the mechanical interplay between the spatial organization of the actin cytoskeleton and the shape of animal cells adhering on micropillar arrays. Using a combination of analytical work, computer simulations and in vitro experiments, we demonstrate that the orientation of the stress fibers strongly influences the geometry of the cell edge. In the presence of a uniformly aligned cytoskeleton, the cell edge can be well approximated by elliptical arcs, whose eccentricity reflects the degree of anisotropy of the cells internal stresses. Upon modeling the actin cytoskeleton as a nematic liquid crystal, we further show that the geometry of the cell edge feeds back on the organization of the stress fibers by altering the length scale at which these are confined. This feedback mechanism is controlled by a dimensionless number, the anchoring number, representing the relative weight of surface-anchoring and bulk-aligning torques. Our model allows to predict both cellular shape and the internal structure of the actin cytoskeleton and is in good quantitative agreement with experiments on fibroblastoid (GD$beta$1,GD$beta$3) and epithelioid (GE$beta$1, GE$beta$3) cells.
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