Super-resolution algorithms for imaging FCS enhancement: A comparative study

Understanding the structure and dynamics of biological systems is often limited by the trade-off between spatial and temporal resolution. Imaging fluorescence correlation spectroscopy (ImFCS) is a powerful technique for capturing molecular dynamics with high temporal precision but remains diffraction limited. This constraint poses challenges for quantifying dynamics of subcellular structures like membrane-proximal cortical actin fibers. Computational super-resolution microscopy (CSRM) presents an accessible strategy for enhancing spatial resolution without specialized instrumentation, enabling compatibility with ImFCS. In this study, we evaluated various CSRM techniques, including super-resolution radial fluctuations, mean-shift super-resolution, and multiple signal classification imaging, using total internal reflection fluorescence datasets of actin fibers labeled with F-tractin-mApple. By combining structural masks from total internal reflection fluorescence and CSRM, we distinguished off-fiber, mixed, and on-fiber regions for region-specific diffusion analyses. Although all CSRM algorithms improve ImFCS data analysis, super-resolution radial fluctuations demonstrated superior performance in identifying cortical actin fibers, showing minimal variance in on-fiber diffusion coefficients. These findings establish a framework for integrating CSRM with ImFCS to achieve high-resolution spatial and dynamic characterization of subcellular structures from single measurements.