Detecting structural heterogeneity in single-molecule localization microscopy data

Teun A.P.M. Huijben, Hamidreza Heydarian, Alexander Auer, Florian Schueder, Ralf Jungmann, Sjoerd Stallinga and Bernd Rieger ()
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Teun A.P.M. Huijben: Department of Imaging Physics, Delft University of Technology
Hamidreza Heydarian: Department of Imaging Physics, Delft University of Technology
Alexander Auer: Faculty of Physics and Center for Nanoscience, Ludwig Maximilian University
Florian Schueder: Faculty of Physics and Center for Nanoscience, Ludwig Maximilian University
Ralf Jungmann: Faculty of Physics and Center for Nanoscience, Ludwig Maximilian University
Sjoerd Stallinga: Department of Imaging Physics, Delft University of Technology
Bernd Rieger: Department of Imaging Physics, Delft University of Technology

Nature Communications, 2021, vol. 12, issue 1, 1-8

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-021-24106-8

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