A Primer on the Bayesian Approach to High-Density Single-Molecule Trajectories Analysis

Abstract

Tracking single molecules in living cells provides invaluable information on their environment and on the interactions that underlie their motion. New experimental techniques now permit the recording of large amounts of individual trajectories, enabling the implementation of advanced statistical tools for data analysis. In this primer, we present a Bayesian approach toward treating these data, and we discuss how it can be fruitfully employed to infer physical and biochemical parameters from single-molecule trajectories.

Figure 1

Bayesian approach to mapping single-molecule dynamics of a transmembrane protein that interacts with gephyrin clusters via an intracellular β-loop inside a HeLa cell: (A) phase contrast image of a HeLa cell; (B) fluorescent image of gephyrin clusters, visible as bright spots; (C) whole-cell diffusivity; (D) force norm; and (E) potential energy. (F) Example of posterior distributions of the diffusion and potential is shown. Positions are indicated by labeled arrows in (C) and (E). To see this figure in color, go online.