Considering experimental frame rates and robust segmentation analysis of piecewise-linear microparticle trajectories

TL;DR

This paper introduces a robust segmentation-based analysis of microparticle trajectories to better understand intracellular transport, emphasizing short-term behavior and the impact of frame rate on inferred properties.

Contribution

It develops a Markov chain model for noisy, piecewise-linear particle movement and analyzes how observational frame rate influences transport property inference.

Findings

Robust segmentation methods improve analysis of short-term particle trajectories.

Frame rate significantly affects the interpretation of transport dynamics.

Markov chain models provide insights into intracellular cargo movement.

Abstract

The movement of intracellular cargo transported by molecular motors is commonly marked by switches between directed motion and stationary pauses. The predominant measure for assessing movement is effective diffusivity, which predicts the mean-squared displacement of particles over long time scales. In this work, we consider an alternative analysis regime that focuses on shorter time scales and relies on automated segmentation of paths. Due to intrinsic uncertainty in changepoint analysis, we highlight the importance of statistical summaries that are robust with respect to the performance of segmentation algorithms. In contrast to effective diffusivity, which averages over multiple behaviors, we emphasize tools that highlight the different motor-cargo states, with an eye toward identifying biophysical mechanisms that determine emergent whole-cell transport properties. By developing a Markov chain model for noisy, continuous, piecewise-linear microparticle movement, and associated mathematical analysis, we provide insight into a common question posed by experimentalists: how does the choice of observational frame rate affect what is inferred about transport properties?