A Unified Platform for FCS and RICS Analysis with Advanced Statistical Inference

Area of Science:

• Biophysics
• Biochemistry
• Cell Biology
• Fluorescence Microscopy

Background:

• Fluorescence correlation spectroscopy (FCS) and raster image correlation spectroscopy (RICS) are vital for studying molecular diffusion and dynamics.
• Existing analysis tools lack unified frameworks combining advanced statistics and high-performance computing.

Purpose of the Study:

• To introduce IOCBIO FCS, an open-source Python platform for integrated FCS and RICS analysis.
• To provide advanced capabilities including GPU acceleration, robust statistical inference, and realistic optical modeling.

Main Methods:

• Developed a Python platform integrating FCS and RICS analysis.
• Implemented GPU-accelerated autocorrelation function calculation.
• Incorporated experimentally measured 3D point spread functions and advanced statistical frameworks (Bayesian inference, weighted/ordinary least-squares).
• Enabled combined multiple-angle RICS analysis for anisotropic diffusion.

Main Results:

• IOCBIO FCS offers unique features like direct PSF incorporation and comprehensive uncertainty quantification.
• The platform supports spatial parameter mapping via image partitioning and advanced data filtering.
• It provides visualization tools for fitted results, residuals, and parameter maps.

Conclusions:

• IOCBIO FCS establishes a reproducible workflow for quantitative molecular transport analysis.
• The platform bridges modern fluorescence microscopy with advanced computational analysis.
• It enhances biophysical, biochemical, and cell biology research by providing a unified analysis framework.