Local temporal image correlation spectroscopy and Bayesian simulation technique for sparse estimation of diffusion in fluorescence imaging

The dynamics and fusion of vesicles during the last steps of exocytosis are not well established yet in cell biology. An open issue is the characterization of the diffusion process at the plasma membrane. Total Internal Reflection Fluorescence Microscopy (TIRFM) has been successfully used to analyze the coordination of proteins involved in this mechanism. It enables to capture dynamics of proteins with with high frame rate and reasonable SNR values. Nevertheless, we lack methodological approaches that can analyze and estimate diffusion in local small area at the scale of a single diffusing spot within cells. Accordingly, we propose a novel correlation-based method for local diffusion estimation. s starting point, we consider the diffusion model well established in biophysics. We derive an explicit parametric model which is further fitted to time-correlation signals computed from regions of interest (ROI) containing individual spots. This modeling and the proposed Bayesian estimation framework is well appropriate to represent isolated diffusion events and is robust to noise, ROI sizes and localizaiton of spots in ROIs. In contrast to previous fluorescence correlation methods focused on the characterization of the mobility of a population of molecules, no pre-treatment and little data are required in our approach. The performance of our method is shown on both synthetic and real TIRM images depicting Transferrin Receptor proteins