Mapping intracellular dynamics across the whole cell with spatial statistics.

Understanding molecular diffusion within cells is crucial for gaining insights into cellular biophysical mechanisms. Despite its importance, achieving versatile mapping of such diffusion across whole cells remains challenging, as many live-cell measurement techniques including fluorescence recovery after photobleaching (FRAP) provide data only at discrete spatial locations due to experimental constraints. 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. While this study focuses on molecular diffusion, the proposed approach is applicable to other intracellular dynamics measurable at limited spatial points such as molecular turnover, thus offering a generalizable tool for whole-cell biophysical analysis under sparse sampling conditions.