Abstract
Super-resolution images reconstructed from single-molecule localizations can reveal cellular structures close to the macromolecular scale and are now being used routinely in many biomedical research applications. However, because of their coordinate-based representation, a widely applicable and unified analysis platform that can extract a quantitative description and biophysical parameters from these images is yet to be established. Here, we propose a conceptual framework for correlation analysis of coordinate-based super-resolution images using distance histograms. We demonstrate the application of this concept in multiple scenarios including image alignment, tracking of diffusing molecules, as well as for quantification of colocalization.
