How to Follow a Traveler with a Brownian Swagger

Main Text

In 1827, the botanist Robert Brown made the curious observation that particles trapped in pollen grains exhibit stochastic motion (1). At the time, there was no physical explanation for the mysterious phenomenon. Now we know this Brownian random walk is driven by thermal energy and provides rich information about the molecular world (2). Observations of single diffusing particles have helped to clarify membrane organization, cellular compartment composition, and numerous active transport mechanisms, among many other processes (3, 4, 5). Nevertheless, reliable classification of transitions between random and directed movement has remained a significant challenge due to the intrinsic noise in single-particle observations. Current approaches suffer from numerous shortcomings. Some depend on prior knowledge about the number of motion states, and others on empirically optimized rules and parameters that lack a statistical foundation. As a consequence, current methods are ideal for the characterization of expected behaviors, but lack generality, and risk obscuring unexpected phenomena.

In this issue, Yin et al. (6) provide a general approach to detect changes in velocity and diffusion coefficient from noisy single-particle observations. The algorithm presented builds on past success, leveraging a robust statistical framework first developed to detect transitions between diffusive regions in the absence of any kinetic information (7). However, whereas the previous approach is only appropriate for random motion, the contribution in this issue adds considerable analytical power with the addition of a velocity parameter. At the heart of the method are two maximum likelihood estimators for the velocity and diffusion coefficient that only depend on the time resolution, displacement in position, and number of position observations. Using these estimators, particle motion is systematically segmented and statistical testing is applied to distinguish real transitions over background in a highly reproducible manner without the need to bin or filter the raw data.

This approach represents a major step forward, enabling unbiased analysis of complex single-particle datasets that involve an undefined number of diffusive and directed microstates (Fig. 1). Exhaustive simulations benchmark the performance and demonstrate robust detection of transitions among directed, confined, and free diffusion in 3D, as well as transitions on complex membrane surfaces. Application of this approach will undoubtedly help to clarify the complex lifecycle of biological assemblies as they travel through the cellular environment. To promote general use of the algorithm, source code and detailed installation instructions have been deposited on GitHub. We can only hope this contribution brings us one step closer to a standard analysis method for single-particle tracking observations, which would greatly facilitate comparative analysis and reproducibility.

Figure 1.

Random and directed particle motion through a complex cellular environment. Simultaneous detection of changes in velocity $\left(\stackrel{\rightharpoonup }{\nu }\right)$ and diffusion coefficient (D) provides a rich multidimensional view of dynamic events in the molecular world. To see this figure in color, go online.

Editor: Antoine van Oijen.