Deep-LASI: deep-learning assisted, single-molecule imaging analysis of multi-color DNA origami structures

Wanninger, Simon; Asadiatouei, Pooyeh; Bohlen, Johann; Salem, Clemens-Bässem; Tinnefeld, Philip; Ploetz, Evelyn; Lamb, Don C. (2023): Deep-LASI: deep-learning assisted, single-molecule imaging analysis of multi-color DNA origami structures. Nature Communications, 14 (1). ISSN 2041-1723

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.

• Dokumententyp: Artikel (LMU)
• Organisationseinheit (Fakultäten): 18 Chemie und Pharmazie > Department Chemie
• DFG-Fachsystematik der Wissenschaftsbereiche: Naturwissenschaften
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 491502892
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 201269156
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 267681426