| Summary: | Single particle tracking (SPT) trajectories are fundamentally stochastic, which makes the extraction of robust biological conclusions difficult. This is especially the case when trying to detect heterogeneous movement of molecules in the plasma membrane. This heterogeneity could be due to a number of biophysical processes such as: receptor clustering, traversing lipid microdomains or cytoskeletal barriers. Working in a Bayesian framework, we developed multiple hidden Markov models for heterogeneity, such as confinement in a harmonic potential well, switching between diffusion coefficients, and diffusion in a fenced environment (or "hop" diffusion). We implement these models using a Markov chain Monte Carlo (MCMC) methodology, developing algorithms that infer model parameters and hidden states from single trajectories. We also calculate model selection statistics, to determine the most likely model given the trajectory. For LFA-1 receptors diffusing on T cells we show that 12-26% of trajectories display clear switching between diffusive states, depending on treatment. We also demonstrated that allowing for measurement noise is essential, as otherwise false detection of heterogeneity may be observed. Analysis of the motion of GM1 lipids bound to the cholera toxin B subunit (CTxB) in model membranes confirmed transient confinement. On this dataset we also demonstrated a clear signature in the confinement shape for individual tagging molecules, and showed that confinement events are not exponentially distributed. Finally, we developed an algorithm which detects hopping diffusion, validating on simulated data. Rather than methods which rely on generic properties of Brownian motions, our approach allows us to test which biophysical model best fits a trajectory. Using a model-based approach we can also extract biophysical parameters, segment trajectories into different motion states, and hence analyse particle motion in high detail. With the continuing improvement in spatial and temporal resolution of trajectories, these methods will be important for biological interpretation of SPT experiments.
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