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.
Original language | English |
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Article number | 3791 |
Number of pages | 8 |
Journal | Nature Communications |
Volume | 12 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2021 |
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Dive into the research topics of 'Detecting structural heterogeneity in single-molecule localization microscopy data'. Together they form a unique fingerprint.Datasets
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Software for Detecting Structural Heterogeneity in Single-Molecule Localization Microscopy Data
Heydarian, H. (Creator), Huijben, T. A. P. M. (Creator), Rieger, B. (Creator) & Stallinga, S. (Creator), TU Delft - 4TU.ResearchData, 2 Mar 2021
DOI: 10.4121/14135849, https://github.com/imphys/smlm_classification2d
Dataset/Software: Software