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

Author

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  • Simon Wanninger

    (Department of Chemistry and Center for NanoScience (CeNS) Ludwig-Maximilians-Universität München Butenandtstr. 5-13)

  • Pooyeh Asadiatouei

    (Department of Chemistry and Center for NanoScience (CeNS) Ludwig-Maximilians-Universität München Butenandtstr. 5-13)

  • Johann Bohlen

    (Department of Chemistry and Center for NanoScience (CeNS) Ludwig-Maximilians-Universität München Butenandtstr. 5-13)

  • Clemens-Bässem Salem

    (Department of Chemistry and Center for NanoScience (CeNS) Ludwig-Maximilians-Universität München Butenandtstr. 5-13)

  • Philip Tinnefeld

    (Department of Chemistry and Center for NanoScience (CeNS) Ludwig-Maximilians-Universität München Butenandtstr. 5-13)

  • Evelyn Ploetz

    (Department of Chemistry and Center for NanoScience (CeNS) Ludwig-Maximilians-Universität München Butenandtstr. 5-13)

  • Don C. Lamb

    (Department of Chemistry and Center for NanoScience (CeNS) Ludwig-Maximilians-Universität München Butenandtstr. 5-13)

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.

Suggested Citation

  • Simon Wanninger & Pooyeh Asadiatouei & Johann Bohlen & Clemens-Bässem Salem & Philip Tinnefeld & Evelyn Ploetz & Don C. Lamb, 2023. "Deep-LASI: deep-learning assisted, single-molecule imaging analysis of multi-color DNA origami structures," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42272-9
    DOI: 10.1038/s41467-023-42272-9
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    1. Markus Götz & Anders Barth & Søren S.-R. Bohr & Richard Börner & Jixin Chen & Thorben Cordes & Dorothy A. Erie & Christian Gebhardt & Mélodie C. A. S. Hadzic & George L. Hamilton & Nikos S. Hatzakis &, 2022. "A blind benchmark of analysis tools to infer kinetic rate constants from single-molecule FRET trajectories," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    2. Jieming Li & Leyou Zhang & Alexander Johnson-Buck & Nils G. Walter, 2020. "Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    3. Max Greenfeld & Dmitri S Pavlichin & Hideo Mabuchi & Daniel Herschlag, 2012. "Single Molecule Analysis Research Tool (SMART): An Integrated Approach for Analyzing Single Molecule Data," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-12, February.
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    1. Markus Götz & Anders Barth & Søren S.-R. Bohr & Richard Börner & Jixin Chen & Thorben Cordes & Dorothy A. Erie & Christian Gebhardt & Mélodie C. A. S. Hadzic & George L. Hamilton & Nikos S. Hatzakis &, 2022. "A blind benchmark of analysis tools to infer kinetic rate constants from single-molecule FRET trajectories," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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