A Unified Platform for FCS and RICS Analysis with Advanced Statistical Inference
Hamed Karimi, Otto Gustavson, Irina Česnokova, Jelena Branovets, Rikke Birkedal, Martin Laasmaa, Marko Vendelin
ACS Omega, 2026 Mar 31; 11(12): 19201-19219.
- PMID: 41939380
- DOI: 10.1021/acsomega.5c12269
Significance
Understanding how molecules move inside cells is essential for studying normal biology and disease, but extracting reliable measurements from fluorescence microscopy is technically demanding. IOCBIO FCS makes this work more accessible by combining two widely used microscopy methods in one free, open-source tool. Its faster GPU-based calculations, realistic optical models, and rigorous uncertainty estimates help researchers to analyze the data. The platform also supports mapping molecular movement across an image and analyzing movement that differs by direction, enabling more reproducible studies of molecular transport in cells and other complex biological systems.
Abstract
Fluorescence correlation spectroscopy (FCS) and raster image correlation spectroscopy (RICS) are powerful techniques for measuring molecular diffusion, concentration, and dynamics in biological systems, yet current analysis tools lack unified frameworks that combine advanced statistical methods with high-performance computing. We present an open-source Python platform, IOCBIO FCS, that integrates FCS and RICS analysis with GPU-accelerated autocorrelation function calculation, robust statistical inference, and realistic optical modeling. The platform uniquely provides capabilities absent from existing open-source tools: direct incorporation of experimentally measured 3D point spread functions into fitting procedures, comprehensive statistical frameworks encompassing Bayesian inference alongside generalized, weighted, and ordinary least-squares methods for rigorous uncertainty quantification, and combined multiple-angle RICS analysis for characterizing anisotropic diffusion in complex biological systems. Additional features include image partitioning for spatial parameter mapping, advanced filtering strategies for data quality control, and comprehensive visualization of fitted results, residuals, posterior distributions, and parameter maps. This platform establishes a reproducible workflow bridging modern fluorescence microscopy with quantitative analysis of molecular transport across biophysics, biochemistry, and cell biology research.