Nakashima, O. | Search bioRxiv

bioRxiv2025

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

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.

biophysics↗

bioRxiv2025

Decoupling diffusion, turnover, and advection in long-term FRAP

Intracellular molecular turnover is a dynamic process governed by diffusion, biochemical reactions, and intracellular transport dynamics. While fluorescence recovery after photobleaching (FRAP) has been widely used to quantify fluorescence recovery mechanisms, conventional models primarily focus on diffusion and reaction kinetics, often overlooking the influence of intracellular advection. However, in cytoskeletal structures such as stress fibers, myosin-driven actin retrograde flow generates a significant advective component, which complicates the interpretation of FRAP data, particularly in long-term observations where advective effects become sufficiently pronounced. Here, we develop an analytical framework that extends FRAP modeling to incorporate the coupled effects of diffusion, turnover, and intracellular advection within the photobleached region of interest. By deriving exact solutions to a reaction-diffusion-advection system, we identify three key dimensionless parameters that govern fluorescence recovery dynamics: the turnover-to-diffusion ratio, the monomer-to-filament ratio, and the advection magnitude. Our results demonstrate that, even in the absence of biochemical reactions, fluorescence recovery in a fixed region can occur due to advection, leading to potential misinterpretations of molecular exchange rates. The model provides a theoretical foundation for distinguishing these effects and offers a practical tool for long-term FRAP analysis, where the interplay of diffusion, turnover, and advection becomes increasingly relevant over extended timescales. By systematically characterizing the interplay between molecular diffusion, reaction kinetics, and intracellular transport, our framework provides deeper insight into protein turnover in complex biological environments.

biophysics↗