InferenceMAP: Mapping of Single-Molecule Dynamics with Bayesian Inference

Single-particle tracking (SPT) grants unprecedented insight into cellular function at the molecular scale [1]. Throughout the cell, the movement of single-molecules is generally heterogeneous and complex. Hence, there is an imperative to understand the multi-scale nature of single-molecule dynamics in biological systems. We have previously shown that with high-density SPT, spatial maps of the parameters that dictate molecule motion can be generated to intricately describe cellular environments [2,3,4]. To date, however, there exist no publically available tools that reconcile trajectory data to generate the aforementioned maps. We address this void in the SPT community with InferenceMAP: an interactive software package that uses a powerful Bayesian method to map the dynamic cellular space experienced by individual biomolecules.Comment: 56 page

Mapping the energy and diffusion landscapes of membrane proteins at the cell surface using high-density single-molecule imaging and Bayesian inference: application to the multi-scale dynamics of glycine receptors in the neuronal membrane

Protein mobility is conventionally analyzed in terms of an effective diffusion. Yet, this description often fails to properly distinguish and evaluate the physical parameters (such as the membrane friction) and the biochemical interactions governing the motion. Here, we present a method combining high-density single-molecule imaging and statistical inference to separately map the diffusion and energy landscapes of membrane proteins across the cell surface at ~100 nm resolution (with acquisition of a few minutes). When applying these analytical tools to glycine neurotransmitter receptors (GlyRs) at inhibitory synapses, we find that gephyrin scaffolds act as shallow energy traps (~3 kBT) for GlyRs, with a depth modulated by the biochemical properties of the receptor-gephyrin interaction loop. In turn, the inferred maps can be used to simulate the dynamics of proteins in the membrane, from the level of individual receptors to that of the population, and thereby, to model the stochastic fluctuations of physiological parameters (such as the number of receptors at synapses). Overall, our approach provides a powerful and comprehensive framework with which to analyze biochemical interactions in living cells and to decipher the multi-scale dynamics of biomolecules in complex cellular environments.Comment: 23 pages, 4 figure