Detecting structural heterogeneity in single-molecule localization microscopy data
Teun Huijben (ImPhys/Computational Imaging)
Hamidreza Heydarian (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)
B. Rieger (ImPhys/Computational Imaging)
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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.
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