Temporal Analysis of Active and Passive Transport in Living Cells

Abstract

The cellular cytoskeleton is a fascinating active network, in which Brownian motion is intercepted by distinct phases of active transport. We present a time-resolved statistical analysis dissecting phases of directed motion out of otherwise diffusive motion of tracer particles in living cells. The distribution of active lifetimes is found to decay exponentially with a characteristic time "A ¼ 0:65 s. The velocity distribution of active events exhibits several peaks, in agreement with a discrete number of motor proteins acting collectively.

Segmentation of 3D Trajectories Acquired by TSUNAMI Microscope: An Application to EGFR Trafficking

“…The first step of the test is to differentiate directed motion (active transport) from Brownian diffusion, confined diffusion, and transient immobilization (passive motion) by evaluating two parameters from the segmented trajectories and their derived MSD curves-scaling exponent (a) (34) and directional persistence (Df) (36). By fitting each segmented MSD curve with the following power-law equation (41), a single a value is obtained and is assigned to each time point s along the trajectory as follows:…”

“…As a wealth of information about membrane structure, interior organization, and receptor biology can be derived from the long 3D trajectories acquired by TSUNAMI, a sophisticated tool is needed to segment and classify these trajectories according to their motional modes (34)(35)(36)(37), extract physical parameters of the motion (30,38), and correlate that motion to the surrounding environment (39), all with the goal of understanding the physical scenarios behind the observed motion (40,41). Considerable effort has been devoted to the identification of change points in motion (36) or diffusivity (38) along the same trajectory and to the visualization of spatial regions with different dynamic behaviors (34,35,38,42).…”

“…As a wealth of information about membrane structure, interior organization, and receptor biology can be derived from the long 3D trajectories acquired by TSUNAMI, a sophisticated tool is needed to segment and classify these trajectories according to their motional modes (34)(35)(36)(37), extract physical parameters of the motion (30,38), and correlate that motion to the surrounding environment (39), all with the goal of understanding the physical scenarios behind the observed motion (40,41). Considerable effort has been devoted to the identification of change points in motion (36) or diffusivity (38) along the same trajectory and to the visualization of spatial regions with different dynamic behaviors (34,35,38,42). Such an analysis is called trajectory segmentation and classification (11), which is often carried out by calculating a number of classification parameters over the trajectory using methods such as rolling window analysis (34,36,43), supervised segmentation (44), mean-squareddisplacement (MSD) curvature (34,35,45,46), maximum likelihood estimator (38), Bayesian methods (47,48), F-statistics (49), hidden Markov model (50,51), and wavelet analysis (42,52).…”

“…Considerable effort has been devoted to the identification of change points in motion (36) or diffusivity (38) along the same trajectory and to the visualization of spatial regions with different dynamic behaviors (34,35,38,42). Such an analysis is called trajectory segmentation and classification (11), which is often carried out by calculating a number of classification parameters over the trajectory using methods such as rolling window analysis (34,36,43), supervised segmentation (44), mean-squareddisplacement (MSD) curvature (34,35,45,46), maximum likelihood estimator (38), Bayesian methods (47,48), F-statistics (49), hidden Markov model (50,51), and wavelet analysis (42,52).…”

“…In this study, we demonstrate an integrated trajectory analysis tool that adopts a combination of analytical strategies (34)(35)(36)(37)39) and classification parameters (MSD scaling exponent (53)(54)(55)(56), directional persistence (36,57), and confinement index (37,58)) in achieving effective segmentation and classification of both simulated and real-world trajectories. Our algorithm has achieved 81% accuracy in classifying segments along simulated 3D trajectories.…”