title: Detection of Diffusion Heterogeneity in Single Particle Tracking Trajectories Using a Hidden Markov Model with Measurement Noise Propagation.
abstract: We develop a Bayesian analysis framework to detect heterogeneity in the diffusive behaviour of single particle trajectories on cells, implementing model selection to classify trajectories as either consistent with Brownian motion or with a two-state (diffusion coefficient) switching model. The incorporation of localisation accuracy is essential, as otherwise false detection of switching within a trajectory was observed and diffusion coefficient estimates were inflated. Since our analysis is on a single trajectory basis, we are able to examine heterogeneity between trajectories in a quantitative manner. Applying our method to the lymphocyte function-associated antigen 1 (LFA-1) receptor tagged with latex beads (4 s trajectories at 1000 frames s(-1)), both intra- and inter-trajectory heterogeneity were detected; 12-26% of trajectories display clear switching between diffusive states dependent on condition, whilst the inter-trajectory variability is highly structured with the diffusion coefficients being related by D1 = 0.68D0 - 1.5 × 10(4) nm2 s(-1), suggestive that on these time scales we are detecting switching due to a single process. Further, the inter-trajectory variability of the diffusion coefficient estimates (1.6 × 10(2) - 2.6 × 10(5) nm2 s(-1)) is very much larger than the measurement uncertainty within trajectories, suggesting that LFA-1 aggregation and cytoskeletal interactions are significantly affecting mobility, whilst the timescales of these processes are distinctly different giving rise to inter- and intra-trajectory variability. There is also an 'immobile' state (defined as D < 3.0 × 103 nm2 s-1) that is rarely involved in switching, immobility occurring with the highest frequency (47%) under T cell activation (phorbol-12-myristate-13-acetate (PMA) treatment) with enhanced cytoskeletal attachment (calpain inhibition). Such 'immobile' states frequently display slow linear drift, potentially reflecting binding to a dynamic actin cortex. Our methods allow significantly more information to be extracted from individual trajectories (ultimately limited by time resolution and time-series length), and allow statistical comparisons between trajectories thereby quantifying inter-trajectory heterogeneity. Such methods will be highly informative for the construction and fitting of molecule mobility models within membranes incorporating aggregation, binding to the cytoskeleton, or traversing membrane microdomains.
authors.affiliation: Systems Biology Centre, University of Warwick, Coventry, United Kingdom; Systems Biology Doctoral Training Centre, University of Warwick, Coventry, United Kingdom.
authors.forename: Paddy J
authors.initials: PJ
authors.lastname: Slator
authors.affiliation: Department of Chemistry, University of Alberta, Edmonton, Alberta, Canada.
authors.forename: Christopher W
authors.initials: CW
authors.lastname: Cairo
authors.affiliation: Systems Biology Centre, University of Warwick, Coventry, United Kingdom.
authors.forename: Nigel J
authors.initials: NJ
authors.lastname: Burroughs
mesh_terms: D000818:Animals; D003599:Cytoskeleton / Q000276:immunology; D006801:Humans; D008213:Lymphocyte Activation / Q000187:drug effects / Q000276:immunology*; D016169:Lymphocyte Function-Associated Antigen-1 / Q000276:immunology; D018448:Models, Immunological; D013601:T-Lymphocytes / Q000276:immunology*; D013755:Tetradecanoylphorbol Acetate / Q000494:pharmacology
chemical_list: D016169:Lymphocyte Function-Associated Antigen-1; D013755:Tetradecanoylphorbol Acetate