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

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 timescales. In this work, we considered an alternative analysis regime that focused on shorter timescales and relied on automated segmentation of paths. Due to intrinsic uncertainty in changepoint analysis, we highlighted the importance of statistical summaries that were robust with respect to the performance of segmentation algorithms. In contrast to effective diffusivity, which averaged over multiple behaviors, we emphasized tools that highlighted the different motor-cargo states, with an eye toward identifying biophysical mechanisms that determined emergent whole-cell transport properties. By developing a Markov chain model for noisy, continuous, piecewise-linear microparticle movement, and associated mathematical analysis, we provided insight into a common question posed by experimentalists: how does the choice of observational frame rate affect what is inferred about transport properties?