ArticleNature communications2024
SEMORE: SEgmentation and MORphological fingErprinting by machine learning automates super-resolution data analysis.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- O-SNAP uncovers nanoscale chromatin remodeling in dedifferentiation and stress responses.Nature communications · 2026Article
- Review of Progress of AI in Biomimetics: From Biological Patterns to Closed-Loop Discovery.Biomimetics (Basel, Switzerland) · 2026Review
- Image-to-molecule benchmarking dataset with fractal pattern and hierarchical morphology recognition.Scientific data · 2026Article
- Seeing the Unseen: Super-Resolution Microscopy in Protein Aggregation Research.Chemical & biomedical imaging · 2026Review
- Diverse intracellular trafficking of insulin analogs by machine learning-based colocalization and diffusion analysis.iScience · 2026Article
- ClusterNet: Classifying Single-Molecule Localization Microscopy Datasets with Graph-Based Deep Learning of Supracluster Structure.Small science · 2025Article
- Enhanced spatial clustering of single-molecule localizations with graph neural networks.Nature communications · 2025Article
- Deep learning in chromatin organization: from super-resolution microscopy to clinical applications.Cellular and molecular life sciences : CMLS · 2025Review
- Advancing Multicolor Super-Resolution Volume Imaging: Illuminating Complex Cellular Dynamics.JACS Au · 2025Review
- From Biophysics to Biomedical Physics.ACS bio & med chem Au · 2025Review
- Modulation of insulin aggregation by betaine and proline directly observed via real-time super-resolution microscopy.Protein science : a publication of the Protein Society · 2025Article
- ECLiPSE: a versatile classification technique for structural and morphological analysis of 2D and 3D single-molecule localization microscopy data.Nature methods · 2024Article
- CsgA gatekeeper residues control nucleation but not stability of functional amyloid.Protein science : a publication of the Protein Society · 2024Article
- Guardians of memory: The urgency of early dementia screening in an aging society.Intractable & rare diseases research · 2024Review
- AI analysis of super-resolution microscopy: Biological discovery in the absence of ground truth.The Journal of cell biology · 2024Review
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5 authors.
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Abstract
The morphology of protein assemblies impacts their behaviour and contributes to beneficial and aberrant cellular responses. While single-molecule localization microscopy provides the required spatial resolution to investigate these assemblies, the lack of universal robust analytical tools to extract and quantify underlying structures limits this powerful technique. Here we present SEMORE, a semi-automatic machine learning framework for universal, system- and input-dependent, analysis of super-resolution data. SEMORE implements a multi-layered density-based clustering module to dissect biological assemblies and a morphology fingerprinting module for quantification by multiple geometric and kinetics-based descriptors. We demonstrate SEMORE on simulations and diverse raw super-resolution data: time-resolved insulin aggregates, and published data of dSTORM imaging of nuclear pore complexes, fibroblast growth receptor 1, sptPALM of Syntaxin 1a and dynamic live-cell PALM of ryanodine receptors. SEMORE extracts and quantifies all protein assemblies, their temporal morphology evolution and provides quantitative insights, e.g. classification of heterogeneous insulin aggregation pathways and NPC geometry in minutes. SEMORE is a general analysis platform for super-resolution data, and being a time-aware framework can also support the rise of 4D super-resolution data.
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