Detecting structural heterogeneity in single-molecule localization microscopy data

Huijben, Teun A. P. M.; Heydarian, Hamidreza; Auer, Alexander; Schueder, Florian; Jungmann, Ralf; Stallinga, Sjoerd and Rieger, Bernd (2021): Detecting structural heterogeneity in single-molecule localization microscopy data. In: Nature Communications, Vol. 12, No. 1, 3791

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. Particle fusion can improve signal-to-noise ratio in single molecule localization microscopy, but is limited by structural heterogeneity. Here, the authors demonstrate an unsupervised classification method that differentiates structurally different DNA origami structures without prior knowledge.

• Item Type: Journal article
• Faculties: Physics
• Subjects: 500 Science > 530 Physics
• ISSN: 2041-1723
• Language: English