Mapping intracellular dynamics across the whole cell with spatial statistics.

Area of Science:

• Cellular biophysics
• Molecular dynamics
• Quantitative biology

Background:

• Mapping intracellular molecular diffusion is vital for understanding cellular mechanisms.
• Techniques like fluorescence recovery after photobleaching (FRAP) offer limited spatial data due to experimental constraints.
• Deterministic interpolation methods fail to capture spatial variability in diffusion.

Purpose of the Study:

• To develop a novel method for versatile whole-cell mapping of molecular diffusion.
• To overcome the limitations of sparse data acquisition in live-cell measurements.
• To provide a statistically robust estimation of diffusion in unmeasured cellular regions.

Main Methods:

• Integration of fluorescence recovery after photobleaching (FRAP) with sequential Gaussian simulation (SGS).
• Application of SGS for probabilistic modeling and estimation of diffusion in unmeasured regions.
• Numerical simulations to optimize measurement point distribution for enhanced data accuracy.

Main Results:

• Probabilistic FRAP (Pro-FRAP) enables statistically robust estimation of intracellular diffusion.
• Pro-FRAP captures spatial variability and quantifies uncertainty, unlike deterministic methods.
• Optimized measurement point arrangements improve data accuracy and coverage for diffusion mapping.

Conclusions:

• Pro-FRAP offers a generalizable tool for whole-cell biophysical analysis under sparse sampling.
• The approach enhances the detailed representation of molecular transport within cells.
• Applicable to other intracellular dynamics like molecular turnover measurable at limited points.