Highly-efficient quantitative fluorescence resonance energy transfer measurements based on deep learning
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 ob...
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| Format: | Article |
| Language: | English |
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World Scientific Publishing
2020-11-01
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| Series: | Journal of Innovative Optical Health Sciences |
| Subjects: | |
| Online Access: | http://www.worldscientific.com/doi/epdf/10.1142/S1793545820500212 |
| _version_ | 1818159328847200256 |
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| author | Lin Ge Fei Liu Jianwen Luo |
| author_facet | Lin Ge Fei Liu Jianwen Luo |
| author_sort | Lin Ge |
| collection | DOAJ |
| description | 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 30s and demonstrate the same precision as manual image analyses. |
| first_indexed | 2024-12-11T15:44:15Z |
| format | Article |
| id | doaj.art-310029d8806441d09db0e750a80b505e |
| institution | Directory Open Access Journal |
| issn | 1793-5458 1793-7205 |
| language | English |
| last_indexed | 2024-12-11T15:44:15Z |
| publishDate | 2020-11-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Journal of Innovative Optical Health Sciences |
| spelling | doaj.art-310029d8806441d09db0e750a80b505e2022-12-22T00:59:43ZengWorld Scientific PublishingJournal of Innovative Optical Health Sciences1793-54581793-72052020-11-011362050021-12050021-1310.1142/S179354582050021210.1142/S1793545820500212Highly-efficient quantitative fluorescence resonance energy transfer measurements based on deep learningLin Ge0Fei Liu1Jianwen Luo2Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, ChinaBeijing Advanced Information & Industrial Technology Research Institute, Beijing Information Science & Technology University, Beijing, 100192, ChinaDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, ChinaIntensity-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 30s and demonstrate the same precision as manual image analyses.http://www.worldscientific.com/doi/epdf/10.1142/S1793545820500212resonance energy transferfluorescenceliving cellsphotoactivatabledeep network |
| spellingShingle | Lin Ge Fei Liu Jianwen Luo Highly-efficient quantitative fluorescence resonance energy transfer measurements based on deep learning Journal of Innovative Optical Health Sciences resonance energy transfer fluorescence living cells photoactivatable deep network |
| title | Highly-efficient quantitative fluorescence resonance energy transfer measurements based on deep learning |
| title_full | Highly-efficient quantitative fluorescence resonance energy transfer measurements based on deep learning |
| title_fullStr | Highly-efficient quantitative fluorescence resonance energy transfer measurements based on deep learning |
| title_full_unstemmed | Highly-efficient quantitative fluorescence resonance energy transfer measurements based on deep learning |
| title_short | Highly-efficient quantitative fluorescence resonance energy transfer measurements based on deep learning |
| title_sort | highly efficient quantitative fluorescence resonance energy transfer measurements based on deep learning |
| topic | resonance energy transfer fluorescence living cells photoactivatable deep network |
| url | http://www.worldscientific.com/doi/epdf/10.1142/S1793545820500212 |
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