A Jump Distance-based Bayesian analysis method to unveil fine single molecule transport features

Single-molecule tracking (SMT) methods are under considerable expansion in many fields of cell biology, as the dynamics of cellular components in biological mechanisms becomes increasingly relevant. Despite the development of SMT technologies, it is still difficult to reconcile a sparse signal at all times (required to distinguish single molecules) with long individual trajectories, within confined regions of the cell and given experimental limitations. This strongly reduces the performance of current data analysis methods in extracting meaningful transport features from single molecules trajectories. In this work, we develop and implement a new mathematical analysis method of SMT data, which takes advantage of the large number of (short) trajectories that are typically obtained with cellular systems in vivo. The method is based on the fitting of the jump distance distribution, e.g. the distribution that represents how far molecules travel in a set time interval; it uses a Bayesian approach to compare plausible molecule motion models and extract both qualitative and quantitative information. Finally, the method is tested on in silico trajectories simulated using Monte Carlo algorithms, and ranges of parameters for which the method yields accurate results are determined.

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