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

Jieming Li1,2, Leyou Zhang3,4, Alexander Johnson-Buck5,6

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|November 18, 2020
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Summary

Deep learning automates single-molecule fluorescence microscopy (SMFM) trace selection, improving DNA mutation assays and single-molecule Förster resonance energy transfer (smFRET) analysis. AutoSiM enhances accuracy and reduces processing time compared to traditional methods.

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Area of Science:

  • Biophysics
  • Computational Biology
  • Molecular Biology

Background:

  • Single-molecule fluorescence microscopy (SMFM) experiments generate data with photophysical artifacts requiring manual expert screening.
  • Manual screening is time-consuming and prone to user-dependent bias, impacting assay reliability.
  • Existing automated methods like hidden Markov modeling (HMM) followed by hard thresholding have limitations in sensitivity and specificity.

Purpose of the Study:

  • To develop a rapid, automated deep learning-based trace selector (AutoSiM) for SMFM data.
  • To improve the sensitivity and specificity of single-molecule recognition through equilibrium Poisson sampling (SiMREPS) assays for DNA point mutations.
  • To enable automated screening of single-molecule Förster resonance energy transfer (smFRET) data for high-quality trace identification.

Main Methods:

  • Development of a deep learning model, AutoSiM, for automatic selection of high-quality SMFM traces.
  • Application of AutoSiM to an assay for DNA point mutations using SiMREPS.
  • Utilizing AutoSiM for automated screening of smFRET data.
  • Demonstration of AutoSiM's adaptability to new datasets using Transfer Learning.

Main Results:

  • AutoSiM significantly improves sensitivity and specificity in SiMREPS DNA mutation assays compared to HMM and hard thresholding.
  • The deep learning selector accepts more true positives and fewer false positives.
  • AutoSiM achieves approximately 90% concordance with manual selection for smFRET data, with reduced processing time.
  • AutoSiM demonstrates effective adaptation to novel datasets with modest Transfer Learning.

Conclusions:

  • AutoSiM provides a rapid and accurate automated solution for SMFM trace selection.
  • The deep learning approach enhances the performance of single-molecule assays, including SiMREPS and smFRET.
  • AutoSiM offers a versatile and adaptable tool for analyzing complex single-molecule data, overcoming limitations of manual screening and traditional methods.