Detecting structural heterogeneity in single-molecule localization microscopy data.
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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.
SUBMITTER: Huijben TAPM
PROVIDER: S-EPMC8213809 | biostudies-literature |
REPOSITORIES: biostudies-literature
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