Detecting structural heterogeneity in single-molecule localization microscopy data.

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