Revealing Hidden Dynamics within Living Soft Matter

Abstract

In the study of living soft matter, we often seek to understand the mechanisms underlying the motion of a single molecule, an organelle, or some other tracer. The experimentally observed signature of the tracer is masked by its thermal fluctuations, inherent drift of the system, and instrument noise. In addition, the timing or length scales of the events of interest are often unknown. In the current issue of ACS Nano, Chen et al. present a general method for extracting the underlying dynamics from time series. Here, we provide an easily accessible introduction to the method, put it into perspective with the field, and exemplify how it can be used to answer important out-standing questions within soft matter and living systems.

Elucidating distinct ion channel populations on the surface of hippocampal neurons via single-particle tracking recurrence analysis

“…Single-particle tracking provides unique advantages for the investigation of the dynamics of individual molecules [16–18]. However, observing heterogeneous dynamics can be challenging due to the inherent thermal fluctuations and experimental noise [19]. Some types of heterogeneous dynamics that have been recognized in trajectories in the plasma membrane include hop-diffusion between actin-delimited membrane compartments [20–22], confinement in nanoscale membrane domains [23–25], and transient tethering to intracellular scaffolds [26,27].…”

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

“…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).…”

Elucidating distinct ion channel populations on the surface of hippocampal neurons via single-particle tracking recurrence analysis

“…This number is much smaller than originally estimated [1,2]. For example, human and mouse genomes have merely 19,817 and 21,968 protein-coding genes (GENCODE 26 and GENCODE M13 [3]). In order to reach the diversity and complexity required by cells in any mammal, genes can produce multiple protein forms, which can further be chemically modified at several locations.…”