Open AccessHighly-efficient quantitative fluorescence resonance energy transfer measurements based on deep learning
Author(s) -
Lin Ge,
Fei Liu,
Jianwen Luo
Publication year - 2020
Publication title -
journal of innovative optical health sciences/journal of innovation in optical health science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 24
eISSN - 1793-5458
pISSN - 1793-7205
DOI - 10.1142/s1793545820500212
Subject(s) - förster resonance energy transfer , mcherry , fluorescence , computer science , acceptor , excitation , biological system , chemistry , optics , physics , green fluorescent protein , biology , biochemistry , gene , condensed matter physics , quantum mechanics
Intensity-based quantitative fluorescence resonance energy transfer (FRET) is a technique to measure the distance of molecules in scale of a few nanometers which is far beyond optical diffraction limit. This widely used technique needs complicated experimental process and manual image analyses to obtain precise results, which take a long time and restrict the application of quantitative FRET especially in living cells. In this paper, a simplified and automatic quantitative FRET (saqFRET) method with high efficiency is presented. In saqFRET, photoactivatable acceptor PA-mCherry and optimized excitation wavelength of donor enhanced green fluorescent protein (EGFP) are used to simplify FRET crosstalk elimination. Traditional manual image analyses are time consuming when the dataset is large. The proposed automatic image analyses based on deep learning can analyze 100 samples within 30[Formula: see text]s and demonstrate the same precision as manual image analyses.