Schirripa Spagnolo, C.; Luin, S. Trajectory Analysis in Single-Particle Tracking: From Mean Squared Displacement to Machine Learning Approaches. Int. J. Mol. Sci.2024, 25, 8660.
Schirripa Spagnolo, C.; Luin, S. Trajectory Analysis in Single-Particle Tracking: From Mean Squared Displacement to Machine Learning Approaches. Int. J. Mol. Sci. 2024, 25, 8660.
Schirripa Spagnolo, C.; Luin, S. Trajectory Analysis in Single-Particle Tracking: From Mean Squared Displacement to Machine Learning Approaches. Int. J. Mol. Sci.2024, 25, 8660.
Schirripa Spagnolo, C.; Luin, S. Trajectory Analysis in Single-Particle Tracking: From Mean Squared Displacement to Machine Learning Approaches. Int. J. Mol. Sci. 2024, 25, 8660.
Abstract
Single particle tracking is a powerful technique to investigate the motion of molecules or particles. Here we review methods for analyzing the reconstructed trajectories, step needed for deciphering the underlying mechanisms driving the motion.
First, we review the traditional analysis based on the mean square displacement (MSD), highlighting sometimes-neglected factors potentially affecting results accuracy. We then report methods that exploit distributions of parameters other than displacements, e.g. angles, velocities, and times and probabilities of reaching a target, discussing how they are more sensitive in characterizing heterogeneities and transient behaviors masked in MSD analysis. Hidden Markov Models are also used for this purpose, and these allow identifying different states, their populations and the switching kinetics.
Finally, we discuss a rapidly expanding field: trajectory analysis based on machine learning. Various approaches, from random forest to deep learning, are used to classify trajectory motions, which can be identified by motion models or by model-free sets of trajectory features, either previously defined or automatically identified by the algorithms.
We also review free software available for some of the analysis methods. We emphasize that approaches based on combined different methods, including classical statistics and machine learning, may be the way to obtain the most informative and accurate results.
Keywords
Particle Dynamics; Molecular Diffusion; Molecular Trajectory Statistics; Single-Molecule Analysis; Single Molecule Tracking; Machine Learning in Biology; Quantitative Microscopy; Quantitative Biology; Hidden Markov Models; Moment Scaling Spectrum
Subject
Physical Sciences, Biophysics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
