Detecting structural heterogeneity in single-molecule localization microscopy data

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...

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Detalles Bibliográficos
Autores principales: Huijben, Teun A.P.M., Heydarian, Hamidreza, Auer, Alexander, Schueder, Florian, Jungmann, Ralf, Stallinga, Sjoerd, Rieger, Bernd
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8213809/
https://www.ncbi.nlm.nih.gov/pubmed/34145284
http://dx.doi.org/10.1038/s41467-021-24106-8
Descripción
Sumario: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.