Mapping molecular diffusion across the whole cell with spatial statistics-based FRAP

Abstract

Understanding molecular diffusion within cells is crucial for gaining insights into cellular biophysical mechanisms. Fluorescence recovery after photobleaching (FRAP) is a powerful technique for assessing molecular diffusion, yet its localized measurement approach hinders whole-cell analysis. To overcome this limitation, we developed Probabilistic FRAP (Pro-FRAP), a novel approach that integrates FRAP with sequential Gaussian simulation (SGS), an advanced spatial statistical method incorporating probabilistic modeling to estimate diffusion in unmeasured regions. Pro-FRAP applies SGS to standardize measured FRAP data, perform conditional simulations based on spatial correlations, and generate statistically robust estimates. In separate analyses, numerical simulations were conducted to optimize the spatial arrangement of measurement points, enhancing data accuracy and coverage. Unlike deterministic interpolation methods, Pro-FRAP captures spatial variability and quantifies uncertainty in intracellular diffusion, providing a more detailed representation of molecular transport. Thus, this framework extends FRAP beyond localized measurements, offering a refined approach for mapping intracellular diffusion with improved spatial coverage and statistical reliability. Significance statementUnderstanding molecular diffusion is essential for deciphering cellular functions and regulation. However, conventional fluorescence recovery after photobleaching (FRAP) techniques are constrained to localized measurements, limiting their ability to capture diffusion dynamics across entire cells. Here, we describe Probabilistic FRAP (Pro-FRAP), a novel framework that integrates FRAP with sequential Gaussian simulation (SGS), a spatial statistical method that enables the estimation of molecular diffusion in unmeasured regions. By incorporating probabilistic modeling, Pro-FRAP generates high-resolution diffusion maps with enhanced statistical reliability. This approach provides a more comprehensive perspective on intracellular transport, allowing for deeper insights into the spatial organization of cellular processes.