Regression Analysis of Confocal FRAP and its Application to Diffusion in Membranes

In most biological processes, diffusion plays a critical role in transferring various bio-molecules to transfer desirable locations in an effective and energy-efficient manner. How fast molecules are transferred is measured by diffusion coefficients. Since each bio-molecules, in particular, signaling molecules have their unique diffusion coefficients and quantifying the diffusion coefficients help us to understand various time scales of both physiological and pathological processes in biological systems. Moreover, since diffusion profiles of a diffusant vary in different micro-environments of cell membranes, accurate diffusion coefficient also can provide a good picture of membrane landscapes as well as interactions of different membrane constituents. Currently, only a few experimental methods are available to assess the diffusion coefficient of a biomolecule of interest in live cells including Fluorescence Recovery After Photobleaching (FRAP). FRAP was developed to study diffusion processes of biomolecules in the cell membranes in the 1970s. Albeit its long history, the main principle of FRAP analysis has remained unchanged since its inception: fitting FRAP data to a theoretical diffusion model for the best fitting diffusion coefficient or using the relation between the half time of recovery and ROI size. In this study, we developed a flexible yet versatile confocal FRAP data analysis framework based on linear regression analysis which allows FRAP users to determine the diffusion from either single or multiple FRAP data points without data fitting. We also validated this approach for a series of fluorescently labeled soluble and membrane-bound proteins and lipids.