  The movement of molecules inside living cells is a fundamental feature of
biological processes. The ability to both observe and analyse the details of
molecular diffusion in vivo at the single molecule and single cell level can
add significant insight into understanding molecular architectures of diffusing
molecules and the nanoscale environment in which the molecules diffuse. The
tool of choice for monitoring dynamic molecular localization in live cells is
fluorescence microscopy, especially so combining total internal reflection
fluorescence (TIRF) with the use of fluorescent protein (FP) reporters in
offering exceptional imaging contrast for dynamic processes in the cell
membrane under relatively physiological conditions compared to competing single
molecule techniques. There exist several different complex modes of diffusion,
and discriminating these from each other is challenging at the molecular level
due to underlying stochastic behaviour. Analysis is traditionally performed
using mean square displacements of tracked particles, however, this generally
requires more data points than is typical for single FP tracks due to
photophysical instability. Presented here is a novel approach allowing robust
Bayesian ranking of diffusion processes (BARD) to discriminate multiple complex
modes probabilistically. It is a computational approach which biologists can
use to understand single molecule features in live cells.
