Super-resolution fluorescence microscopy, and specifically DNA-PAINT, provides localization precision down to ~2 nm enabling molecular-resolution imaging. To produce molecular maps of single biomolecules, their positions must be inferred from localizations stemming from single fluorescent molecules. Current clustering methods fail to exploit the full potential of the imaging method. Here, we introduce G5M, a modified Gaussian Mixture Modeling algorithm tailored to DNA-PAINT data. By incorporating prior knowledge of localization precision, spatial constraints, and DNA hybridization kinetics, G5M accurately infers true molecular positions while avoiding overfitting. In realistic simulations of dimers, G5M resolves molecules at the Rayleigh limit with a 27-fold higher recovery rate than current methods and <0.1% false positives. Applied to experimental datasets, G5M recovers full nuclear pore complex structures and detects higher-order CD20 oligomers induced by antibody treatment, outperforming conventional DNA-PAINT analysis. G5M is implemented in the open-source Picasso platform, offering an accessible solution for high-resolution, high-accuracy molecular mapping in super-resolution microscopy.
Kowalewski et al. (Mon,) studied this question.