ArticleNature communications2021
Detecting structural heterogeneity in single-molecule localization microscopy data.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Adversarial Erasing Enhanced Multiple Instance Learning (siMILe): Discriminative Identification of Oligomeric Protein Structures in Single Molecule Localization Microscopy.Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) · 2026Article
- High-speed 3D DNA PAINT and unsupervised clustering for unlocking 3D DNA origami cryptography.Nature communications · 2025Article
- Detecting continuous structural heterogeneity in single molecule localization microscopy data with a point cloud variational auto-encoder.Scientific reports · 2025Article
- ClusterNet: Classifying Single-Molecule Localization Microscopy Datasets with Graph-Based Deep Learning of Supracluster Structure.Small science · 2025Article
- Closing the multichannel gap through computational reconstruction of interaction in super-resolution microscopy.Patterns (New York, N.Y.) · 2025Review
- ECLiPSE: a versatile classification technique for structural and morphological analysis of 2D and 3D single-molecule localization microscopy data.Nature methods · 2024Article
- Insights into protein structure using cryogenic light microscopy.Biochemical Society transactions · 2023Review
- Detecting continuous structural heterogeneity in single-molecule localization microscopy data.Scientific reports · 2023Article
- Simulating structurally variable nuclear pore complexes for microscopy.Bioinformatics (Oxford, England) · 2023Article
- Particle fusion of super-resolution data reveals the unit structure of Nup96 in Nuclear Pore Complex.Scientific reports · 2023Article
- Maximum-likelihood model fitting for quantitative analysis of SMLM data.Nature methods · 2023Article
- Mapping molecular complexes with super-resolution microscopy and single-particle analysis.Open biology · 2022Review
- Joint registration of multiple point clouds for fast particle fusion in localization microscopy.Bioinformatics (Oxford, England) · 2022Article
- Detecting structural heterogeneity in single-molecule localization microscopy data.Nature communications · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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