Methods for automating the analysis of live-cell single-molecule FRET data.

Autor:	Meszaros J; Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.; Division of Molecular Therapeutics, New York State Psychiatric Institute, New York, NY, United States., Geggier P; Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.; Division of Molecular Therapeutics, New York State Psychiatric Institute, New York, NY, United States., Manning JJ; Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.; Division of Molecular Therapeutics, New York State Psychiatric Institute, New York, NY, United States., Asher WB; Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.; Division of Molecular Therapeutics, New York State Psychiatric Institute, New York, NY, United States., Javitch JA; Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.; Division of Molecular Therapeutics, New York State Psychiatric Institute, New York, NY, United States.; Department of Molecular Pharmacology and Therapeutics, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.; Department of Physiology and Cellular Biophysics, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in cell and developmental biology [Front Cell Dev Biol] 2023 Aug 15; Vol. 11, pp. 1184077. Date of Electronic Publication: 2023 Aug 15 (Print Publication: 2023).
DOI:	10.3389/fcell.2023.1184077
Abstrakt:	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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Meszaros, Geggier, Manning, Asher and Javitch.)
Databáze:	MEDLINE
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