Detecting structural heterogeneity in single-molecule localization microscopy data
Teun A.P.M. Huijben (TU Delft - ImPhys/Computational Imaging)
Hamidreza Heydarian (TU Delft - ImPhys/Computational Imaging)
Alexander Auer (Ludwig Maximilians University)
Florian Schueder (Ludwig Maximilians University)
Ralf Jungmann (Ludwig Maximilians University)
S Stallinga (TU Delft - ImPhys/Imaging Physics)
Bernd Rieger (TU Delft - ImPhys/Computational Imaging)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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. 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.
help_outline