Detecting structural heterogeneity in single-molecule localization microscopy data

Abstract Particle fusion for single molecule localization microscopy improves signal-to-noise ratio and overcomes underlabeling, but ignores structural heterogeneity or conformational variability. We present a-priori knowledge-free unsupervised classification of structurally different particles employing the Bhattacharya cost function as dissimilarity metric. achieve 96% accuracy on mixtures up to four DNA-origami structures, detect rare classes origami occuring at 2% rate, capture variation in ellipticity nuclear pore complexes.