Dynamic range and background filtering in raster image correlation spectroscopy

Raster-scan image correlation spectroscopy (RICS) enables researchers to measure molecular translational diffusion constants and concentrations from standard confocal laser scanning microscope images and is suitable for measuring a wide range of mobility, especially fast-diffusing molecules. However, as RICS analysis is based on the spatial autocorrelation function of fluorescence images, it is sensitive to the presence of fluorescent structures within the image. In this study, we investigate methods to filter out immobile or slow moving background structures and their impact on RICS results. Both the conventional moving-average subtraction-based method and cross-correlation subtraction-based method are rationalized and quantified. Simulated data and experimental measurements in living cells stress the importance of optimizing the temporal resolution of background filtering for reliable RICS measurements. Finally, the capacity of RICS analysis to separate two species is studied.