Detecting structural heterogeneity in single-molecule localization microscopy data
Published in Nature Communications, 2021
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. We achieve 96% classification accuracy on mixtures of up to four different DNA-origami structures, detect rare classes of origami occuring at 2% rate, and capture variation in ellipticity of nuclear pore complexes.
Recommended citation: Huijben, T.A., Heydarian, H., Auer, A. et al. Detecting structural heterogeneity in single-molecule localization microscopy data. Nat Commun 12, 3791 (2021). https://www.nature.com/articles/s41467-021-24106-8