ArticleNature methods2024
ECLiPSE: a versatile classification technique for structural and morphological analysis of 2D and 3D single-molecule localization microscopy data.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- O-SNAP uncovers nanoscale chromatin remodeling in dedifferentiation and stress responses.Nature communications · 2026Article
- Seeing the Unseen: Super-Resolution Microscopy in Protein Aggregation Research.Chemical & biomedical imaging · 2026Review
- Morphological and Hyperphosphorylation Transitions of Nanoscale Tau Aggregates in Alzheimer's Disease.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- 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
- A methodology to reduce the localization error in multi-loci microscopy provides new insights into enhancer biology.PLoS computational biology · 2025Article
- Super-resolution microscopy for structural biology.Nature methods · 2025Review
- From Biophysics to Biomedical Physics.ACS bio & med chem Au · 2025Review
- Heterogeneity of late endosome/lysosomes shown by multiplexed DNA-PAINT imaging.The Journal of cell biology · 2025Article
- Detecting continuous structural heterogeneity in single-molecule localization microscopy data.Scientific reports · 2023Article
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Abstract
Single-molecule localization microscopy (SMLM) has gained widespread use for visualizing the morphology of subcellular organelles and structures with nanoscale spatial resolution. However, analysis tools for automatically quantifying and classifying SMLM images have lagged behind. Here we introduce Enhanced Classification of Localized Point clouds by Shape Extraction (ECLiPSE), an automated machine learning analysis pipeline specifically designed to classify cellular structures captured through two-dimensional or three-dimensional SMLM. ECLiPSE leverages a comprehensive set of shape descriptors, the majority of which are directly extracted from the localizations to minimize bias during the characterization of individual structures. ECLiPSE has been validated using both unsupervised and supervised classification on datasets, including various cellular structures, achieving near-perfect accuracy. We apply two-dimensional ECLiPSE to classify morphologically distinct protein aggregates relevant for neurodegenerative diseases. Additionally, we employ three-dimensional ECLiPSE to identify relevant biological differences between healthy and depolarized mitochondria. ECLiPSE will enhance the way we study cellular structures across various biological contexts.
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