ArticleNature methods2025
Foundation model for efficient biological discovery in single-molecule time traces.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Automated kinetic-scheme-free sorting of single-molecule fluorescence events using a deep learning based hidden-state method.Nature communications · 2026Article
- Segment any tumour: an uncertainty-aware vision foundation model for whole-body analysis.Nature communications · 2026Article
- FINDER converts zero-background kinetic fingerprinting into area-scalable attomolar biomarker detection.Research square · 2026Article
- FINDER converts zero-background kinetic fingerprinting into area-scalable attomolar biomarker detection.bioRxiv : the preprint server for biology · 2026Article
- NanorulerQA: quantitative quality analysis of dual-color DNA nanorulers via single-molecule photobleaching step counting and spatio-temporal colocalization.Biomedical optics express · 2026Article
- Chromato-kinetic fingerprinting enables multiomic digital counting of single disease biomarker molecules.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
- Erratum issued
- Update of
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Authors and funding
4 authors.
Funding
Abstract
Single-molecule fluorescence microscopy (SMFM) can reveal important biological insights. However, uncovering rare but critical intermediates often demands manual inspection of time traces and iterative ad hoc approaches. To facilitate systematic and efficient discovery from SMFM time traces, we introduce META-SiM, a transformer-based foundation model pretrained on diverse SMFM analysis tasks. META-SiM rivals best-in-class algorithms on a broad range of tasks including trace classification, segmentation, idealization and stepwise photobleaching analysis. Additionally, the model produces embeddings that encapsulate detailed information about each trace, which the web-based META-SiM Projector ( https://www.simol-projector.org ) casts into lower-dimensional space for efficient whole-dataset visualization, labeling, comparison and sharing. Combining this Projector with the objective metric of local Shannon entropy enables rapid identification of condition-specific behaviors, even if rare or subtle. Applying META-SiM to an existing single-molecule Förster resonance energy transfer dataset, we discover a previously undetected intermediate state in pre-mRNA splicing. META-SiM removes bottlenecks, improves objectivity and both systematizes and accelerates biological discovery in single-molecule data.
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Registered trials
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