ArticleNature communications2023
Deep-LASI: deep-learning assisted, single-molecule imaging analysis of multi-color DNA origami structures.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 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
- smDeepFLUOR: single-molecule deep learning fluorescence classification.Nature communications · 2026Article
- NanorulerQA: quantitative quality analysis of dual-color DNA nanorulers via single-molecule photobleaching step counting and spatio-temporal colocalization.Biomedical optics express · 2026Article
- Single-molecule dynamics reveal ATP binding alone powers substrate translocation by an ABC transporter.Nature communications · 2026Article
- Multiplexed single-molecule characterization at the library scale.Nature protocols · 2026Review
- Foundation model for efficient biological discovery in single-molecule time traces.Nature methods · 2025Article
- Bayesian Nonparametrics for FRET using Realistic Integrative Detectors.bioRxiv : the preprint server for biology · 2025Article
- Advancing Multicolor Super-Resolution Volume Imaging: Illuminating Complex Cellular Dynamics.JACS Au · 2025Review
- Artificial Intelligence-Enhanced Analysis of Genomic DNA Visualized with Nanoparticle-Tagged Peptides under Electron Microscopy.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Pretrained Deep Neural Network Kin-SiM for Single-Molecule FRET Trace Idealization.The journal of physical chemistry. B · 2025Article
- AI-based hardware and software tools in microscopy to boost research in immunology and virology.Frontiers in immunology · 2025Review
- Probing actin-activated ATP turnover kinetics of human cardiac myosin II by single molecule fluorescence.Cytoskeleton (Hoboken, N.J.) · 2024Article
- Deep learning based local feature classification to automatically identify single molecule fluorescence events.Communications biology · 2024Article
- Deep-LASI, single-molecule data analysis software.Biophysical journal · 2024Article
- Deep-LASI: deep-learning assisted, single-molecule imaging analysis of multi-color DNA origami structures.Nature communications · 2023Article
- Accelerating the characterization of dynamic DNA origami devices with deep neural networks.Scientific reports · 2023Article
- Reliability and accuracy of single-molecule FRET studies for characterization of structural dynamics and distances in proteins.Nature methods · 2023Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Single-molecule experiments have changed the way we explore the physical world, yet data analysis remains time-consuming and prone to human bias. Here, we introduce Deep-LASI (Deep-Learning Assisted Single-molecule Imaging analysis), a software suite powered by deep neural networks to rapidly analyze single-, two- and three-color single-molecule data, especially from single-molecule Förster Resonance Energy Transfer (smFRET) experiments. Deep-LASI automatically sorts recorded traces, determines FRET correction factors and classifies the state transitions of dynamic traces all in ~20-100 ms per trajectory. We benchmarked Deep-LASI using ground truth simulations as well as experimental data analyzed manually by an expert user and compared the results with a conventional Hidden Markov Model analysis. We illustrate the capabilities of the technique using a highly tunable L-shaped DNA origami structure and use Deep-LASI to perform titrations, analyze protein conformational dynamics and demonstrate its versatility for analyzing both total internal reflection fluorescence microscopy and confocal smFRET data.
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