Dense Mapping Of Intracellular Diffusion And Drift From Single-Particle Tracking Data Analysis

It is of primary interest for biologists to be able to visualize diffusion and drift dynamics of proteins within the cell. In this paper, we propose a new mapping method to robustly estimate dynamics in the entire cell from particle tracks. To obtain satisfying diffusion and drift maps, we use a spatiotemporal kernel estimator. Trajectory classification data is used as input and allows to automatically label particle movements into three classes: confined motion (or subdiffusion), Brownian motion, and directed motion (or superdiffusion). We then use this information to calculate diffusion coefficient and drift maps separately on each class of motion.