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.

中文翻译：

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基于深度学习的高效定量荧光共振能量转移测量

基于强度的定量荧光共振能量转移 (FRET) 是一种在几纳米尺度上测量分子距离的技术，远远超出光学衍射极限。这种广泛使用的技术需要复杂的实验过程和手动图像分析才能获得精确的结果，这需要很长时间并且限制了定量 FRET 的应用，特别是在活细胞中的应用。在本文中，提出了一种高效的简化和自动定量 FRET (saqFRET) 方法。在 saqFRET 中，光激活受体 PA-m​​Cherry 和供体增强绿色荧光蛋白 (EGFP) 的优化激发波长用于简化 FRET 串扰消除。当数据集很大时，传统的手动图像分析非常耗时。