Quantitative fluorescence resonance energy transfer measurements using microarray technology

2007 ◽  
Author(s):  
Jiang Zhu ◽  
Cheng Deng ◽  
Guoliang Huang ◽  
Yang Yang ◽  
Shukuan Xu ◽  
...  
Keyword(s):  
Energy Transfer ◽  
Fluorescence Resonance Energy Transfer ◽  
Resonance Energy Transfer ◽  
Resonance Energy ◽  
Microarray Technology ◽  
Fluorescence Resonance ◽  
Quantitative Fluorescence

scholarly journals Oligomeric Size of the M2Muscarinic Receptor in Live Cells as Determined by Quantitative Fluorescence Resonance Energy Transfer

2010 ◽  
Vol 285 (22) ◽  
pp. 16723-16738 ◽  
Author(s):  
Luca F. Pisterzi ◽  
David B. Jansma ◽  
John Georgiou ◽  
Michael J. Woodside ◽  
Judy Tai-Chieh Chou ◽  
...  
Keyword(s):  
Energy Transfer ◽  
Fluorescence Resonance Energy Transfer ◽  
Resonance Energy Transfer ◽  
Resonance Energy ◽  
Live Cells ◽  
Fluorescence Resonance ◽  
Quantitative Fluorescence

scholarly journals Highly-efficient quantitative fluorescence resonance energy transfer measurements based on deep learning

2020 ◽  
Vol 13 (06) ◽  
pp. 2050021
Author(s):  
Lin Ge ◽  
Fei Liu ◽  
Jianwen Luo
Keyword(s):  
Energy Transfer ◽  
Deep Learning ◽  
Fluorescence Resonance Energy Transfer ◽  
High Efficiency ◽  
Resonance Energy Transfer ◽  
Resonance Energy ◽  
Optical Diffraction ◽  
Fluorescence Resonance ◽  
Quantitative Fluorescence ◽  
Image Analyses

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.

Sign in / Sign up

Export Citation Format

Share Document