Dynamic remodeling of the actin cytoskeleton allows cells to migrate, change shape, and exert mechanical forces on their surroundings. How the complex dynamical behavior of the cytoskeleton arises from the interactions of its molecular components remains incompletely understood. Tracking the movement of individual actin filaments in living cells can in principle provide a powerful means of addressing this question. However, single-molecule fluorescence imaging measurements that could provide this information are limited by low signal-to-noise ratios, with the result that the localization errors for individual fluorophore fiducials attached to filamentous (F)-actin are comparable to the distances traveled by actin filaments between measurements. In this study we tracked the movement F-actin labeled with single-molecule densities of the fluorogenic label SiR-actin in primary fibroblasts and endothelial cells. We then used a Bayesian statistical approach to estimate true, underlying actin filament velocity distributions from the tracks of individual actin-associated fluorophores along with quantified localization uncertainties. This analysis approach is broadly applicable to inferring statistical pairwise distance distributions arising from noisy point localization measurements such as occur in superresolution microscopy. We found that F-actin velocity distributions were better described by a statistical jump process, in which filaments exist in mechanical equilibria punctuated by abrupt, jump-like movements, than by models incorporating combinations of diffusive motion and drift. A model with exponentially distributed time- and length-scales for filament jumps recapitulated F-actin velocity distributions measured for the cell cortex, integrin-based adhesions, and actin stress fibers, indicating that a common physical model can potentially describe F-actin dynamics in a variety of cellular contexts.
Correlation analysis framework for localization-based superresolution microscopy
