Quantitative Characterization of Changes in Dynamical Behavior for Single-Particle Tracking Studies

Abstract

Single-particle tracking experiments have been used widely to study the heterogeneity of a sample. Segments with dissimilar diffusive behaviors are associated with different intermediate states, usually by visual inspection of the tracking trace. A likelihood-based, systematic approach is presented to remove this incertitude. Maximum likelihood estimators are derived for the determination of diffusion coefficients. A likelihood ratio test is applied to the localization of the changes in them. Simulations suggest that the proposed procedure is statistically robust and is able to quantitatively recover time-dependent changes in diffusion coefficients even in the presence of large measurement noise.

The influence of movement on the localization precision of sub‐resolution particles in fluorescence microscopy

“…In conclusion, by tracking a diffusing particle in two channels and calculating the variance on the difference between the two positions, the effective localization precision in both channels can be readily calculated with the dual-channel method according to Eq. (16) or (18). Note that the dual-channel method does not make any explicit assumption on the type of motion.…”

“…It should be noted that the effect of particle diffusion during image acquisition was already studied on the level of motion quantification in SPT experiments. A correction on the classical expression for the mean squared displacement (MSD) was proposed, but the influence on the localization precision was not considered [15][16][17][18].…”

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

“…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).…”