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Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning

Author

Listed:
  • Jieming Li

    (The University of Michigan
    Bristol-Myers Squibb Company)

  • Leyou Zhang

    (The University of Michigan
    Google)

  • Alexander Johnson-Buck

    (The University of Michigan
    Division of Hematology/Oncology, University of Michigan)

  • Nils G. Walter

    (The University of Michigan)

Abstract

Traces from single-molecule fluorescence microscopy (SMFM) experiments exhibit photophysical artifacts that typically necessitate human expert screening, which is time-consuming and introduces potential for user-dependent expectation bias. Here, we use deep learning to develop a rapid, automatic SMFM trace selector, termed AutoSiM, that improves the sensitivity and specificity of an assay for a DNA point mutation based on single-molecule recognition through equilibrium Poisson sampling (SiMREPS). The improved performance of AutoSiM is based on accepting both more true positives and fewer false positives than the conventional approach of hidden Markov modeling (HMM) followed by hard thresholding. As a second application, the selector is used for automated screening of single-molecule Förster resonance energy transfer (smFRET) data to identify high-quality traces for further analysis, and achieves ~90% concordance with manual selection while requiring less processing time. Finally, we show that AutoSiM can be adapted readily to novel datasets, requiring only modest Transfer Learning.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19673-1
    DOI: 10.1038/s41467-020-19673-1
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    Cited by:

    1. 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.

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