How to find structurally different molecules before they disappear in the average?
Published today in Nature Communications a study about finding heterogeneity in SMLM data. Particle fusion for single molecule localization microscopy improves signal-to-noise ratio and overcomes underlabeling, but ignores structural heterogeneity or conformational variability. This study presents a-priori knowledge-free unsupervised classification of structurally different particles employing the Bhattacharya cost function as dissimilarity metric.
Title: Detecting structural heterogeneity in single-molecule localization microscopy data
Authors: Teun A.P.M. Huijben, Hamidreza Heydarian, Alexander Auer, Florian Schueder, Ralf Jungmann, Sjoerd Stallinga & Bernd Rieger