Methods for automating the analysis of live-cell single-molecule FRET data
Single-molecule FRET (smFRET) is a powerful imaging platform capable of revealing dynamic changes in the conformation and proximity of biological molecules. The expansion of smFRET imaging into living cells creates both numerous new research opportunities and new challenges. Automating dataset curat...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Cell and Developmental Biology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcell.2023.1184077/full |
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| author | Jozsef Meszaros Jozsef Meszaros Peter Geggier Peter Geggier Jamie J. Manning Jamie J. Manning Wesley B. Asher Wesley B. Asher Jonathan A. Javitch Jonathan A. Javitch Jonathan A. Javitch Jonathan A. Javitch |
| author_facet | Jozsef Meszaros Jozsef Meszaros Peter Geggier Peter Geggier Jamie J. Manning Jamie J. Manning Wesley B. Asher Wesley B. Asher Jonathan A. Javitch Jonathan A. Javitch Jonathan A. Javitch Jonathan A. Javitch |
| author_sort | Jozsef Meszaros |
| collection | DOAJ |
| description | Single-molecule FRET (smFRET) is a powerful imaging platform capable of revealing dynamic changes in the conformation and proximity of biological molecules. The expansion of smFRET imaging into living cells creates both numerous new research opportunities and new challenges. Automating dataset curation processes is critical to providing consistent, repeatable analysis in an efficient manner, freeing experimentalists to advance the technical boundaries and throughput of what is possible in imaging living cells. Here, we devise an automated solution to the problem of multiple particles entering a region of interest, an otherwise labor-intensive and subjective process that had been performed manually in our previous work. The resolution of these two issues increases the quantity of FRET data and improves the accuracy with which FRET distributions are generated, increasing knowledge about the biological functions of the molecules under study. Our automated approach is straightforward, interpretable, and requires only localization and intensity values for donor and acceptor channel signals, which we compute through our previously published smCellFRET pipeline. The development of our automated approach is informed by the insights of expert experimentalists with extensive experience inspecting smFRET trajectories (displacement and intensity traces) from live cells. We test our automated approach against our recently published research on the metabotropic glutamate receptor 2 (mGluR2) and reveal substantial similarities, as well as potential shortcomings in the manual curation process that are addressable using the algorithms we developed here. |
| first_indexed | 2024-03-12T14:40:35Z |
| format | Article |
| id | doaj.art-e79b0c9b51604bc2ad840e75fa536cfc |
| institution | Directory Open Access Journal |
| issn | 2296-634X |
| language | English |
| last_indexed | 2024-03-12T14:40:35Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cell and Developmental Biology |
| spelling | doaj.art-e79b0c9b51604bc2ad840e75fa536cfc2023-08-16T11:43:20ZengFrontiers Media S.A.Frontiers in Cell and Developmental Biology2296-634X2023-08-011110.3389/fcell.2023.11840771184077Methods for automating the analysis of live-cell single-molecule FRET dataJozsef Meszaros0Jozsef Meszaros1Peter Geggier2Peter Geggier3Jamie J. Manning4Jamie J. Manning5Wesley B. Asher6Wesley B. Asher7Jonathan A. Javitch8Jonathan A. Javitch9Jonathan A. Javitch10Jonathan A. Javitch11Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United StatesDivision of Molecular Therapeutics, New York State Psychiatric Institute, New York, NY, United StatesDepartment of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United StatesDivision of Molecular Therapeutics, New York State Psychiatric Institute, New York, NY, United StatesDepartment of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United StatesDivision of Molecular Therapeutics, New York State Psychiatric Institute, New York, NY, United StatesDepartment of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United StatesDivision of Molecular Therapeutics, New York State Psychiatric Institute, New York, NY, United StatesDepartment of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United StatesDivision of Molecular Therapeutics, New York State Psychiatric Institute, New York, NY, United StatesDepartment of Molecular Pharmacology and Therapeutics, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United StatesDepartment of Physiology and Cellular Biophysics, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United StatesSingle-molecule FRET (smFRET) is a powerful imaging platform capable of revealing dynamic changes in the conformation and proximity of biological molecules. The expansion of smFRET imaging into living cells creates both numerous new research opportunities and new challenges. Automating dataset curation processes is critical to providing consistent, repeatable analysis in an efficient manner, freeing experimentalists to advance the technical boundaries and throughput of what is possible in imaging living cells. Here, we devise an automated solution to the problem of multiple particles entering a region of interest, an otherwise labor-intensive and subjective process that had been performed manually in our previous work. The resolution of these two issues increases the quantity of FRET data and improves the accuracy with which FRET distributions are generated, increasing knowledge about the biological functions of the molecules under study. Our automated approach is straightforward, interpretable, and requires only localization and intensity values for donor and acceptor channel signals, which we compute through our previously published smCellFRET pipeline. The development of our automated approach is informed by the insights of expert experimentalists with extensive experience inspecting smFRET trajectories (displacement and intensity traces) from live cells. We test our automated approach against our recently published research on the metabotropic glutamate receptor 2 (mGluR2) and reveal substantial similarities, as well as potential shortcomings in the manual curation process that are addressable using the algorithms we developed here.https://www.frontiersin.org/articles/10.3389/fcell.2023.1184077/fullGPCRFRETsingle-molecule imagingsingle-particle trackingautomationmachine vision |
| spellingShingle | Jozsef Meszaros Jozsef Meszaros Peter Geggier Peter Geggier Jamie J. Manning Jamie J. Manning Wesley B. Asher Wesley B. Asher Jonathan A. Javitch Jonathan A. Javitch Jonathan A. Javitch Jonathan A. Javitch Methods for automating the analysis of live-cell single-molecule FRET data Frontiers in Cell and Developmental Biology GPCR FRET single-molecule imaging single-particle tracking automation machine vision |
| title | Methods for automating the analysis of live-cell single-molecule FRET data |
| title_full | Methods for automating the analysis of live-cell single-molecule FRET data |
| title_fullStr | Methods for automating the analysis of live-cell single-molecule FRET data |
| title_full_unstemmed | Methods for automating the analysis of live-cell single-molecule FRET data |
| title_short | Methods for automating the analysis of live-cell single-molecule FRET data |
| title_sort | methods for automating the analysis of live cell single molecule fret data |
| topic | GPCR FRET single-molecule imaging single-particle tracking automation machine vision |
| url | https://www.frontiersin.org/articles/10.3389/fcell.2023.1184077/full |
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