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

Nature Communications
Jieming LiNils G Walter

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.

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Citations

May 18, 2021·Frontiers in Chemistry·Xiaoyu Cheng, Wei Yin
Jan 1, 2021·Accounts of Chemical Research·Shankar MandalNils G Walter
Sep 4, 2021·Beilstein Journal of Nanotechnology·Ido AzuriSidney R Cohen
Sep 7, 2021·Biophysical Journal·Argha Bandyopadhyay, Marcel P Goldschen-Ohm

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Methods Mentioned

BETA
fluorescence microscopy
FRET
deamination

Software Mentioned

ADAM
SiMREPS
SPARTAN
SMFM
GLOROT
MATLAB
smFRET
AutoSiM

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