We observed that all MSDs show a quasilinear dependence at short times, enabling us to calculate an instantaneous diffusion coefficient (D) to characterize mobility. To analyze the global mobility, the software computed the instantaneous diffusion coefficient from the linear regression on the first three points of all trajectories longer than six consecutive frames. The frequency distribution of log D was calculated with a bin width of 0.2 in GraphPad Prism 5 and plotted in Origin Pro7 to generate the histogram of diffusion coefficients and the cumulative probability distributions (Figures 3, B and C , and 6, B and C ).
To study the behavior of vesicle trajectories, MSDs for each trajectory greater than six consecutive frames (and <50 frames to avoid clusters) were fitted with a general model MSD = 〈r2〉 = tα + k, where D is the diffusion coefficient, t is the time lag, and α is the scaling exponent (Saxton, 1993 (link), 1995 (link); Sibarita, 2014 (link)). The MSD values were imported into Origin Pro7 to calculate the mean values and standard errors corresponding to each time point (Figures 3D and 6D ). The trajectories were sorted according to the value of the scaling exponent α into confined, diffusive, and directed fractions and their respective MSDs plotted (Figures 3E and Supplemental Figure S6).
To study the behavior of vesicle trajectories, MSDs for each trajectory greater than six consecutive frames (and <50 frames to avoid clusters) were fitted with a general model MSD = 〈r2〉 = tα + k, where D is the diffusion coefficient, t is the time lag, and α is the scaling exponent (Saxton, 1993 (link), 1995 (link); Sibarita, 2014 (link)). The MSD values were imported into Origin Pro7 to calculate the mean values and standard errors corresponding to each time point (