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Quantitative diffusion measurements using the open-source software PyFRAP

Fluorescence Recovery After Photobleaching (FRAP) and inverse FRAP (iFRAP) assays can be used to assess the mobility of fluorescent molecules. These assays measure diffusion by monitoring the return of fluorescence in bleached regions (FRAP), or the dissipation of fluorescence from photoconverted re...

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Autores principales: Bläßle, Alexander, Soh, Gary, Braun, Theresa, Mörsdorf, David, Preiß, Hannes, Jordan, Ben M., Müller, Patrick
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5910415/
https://www.ncbi.nlm.nih.gov/pubmed/29679054
http://dx.doi.org/10.1038/s41467-018-03975-6
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author Bläßle, Alexander
Soh, Gary
Braun, Theresa
Mörsdorf, David
Preiß, Hannes
Jordan, Ben M.
Müller, Patrick
author_facet Bläßle, Alexander
Soh, Gary
Braun, Theresa
Mörsdorf, David
Preiß, Hannes
Jordan, Ben M.
Müller, Patrick
author_sort Bläßle, Alexander
collection PubMed
description Fluorescence Recovery After Photobleaching (FRAP) and inverse FRAP (iFRAP) assays can be used to assess the mobility of fluorescent molecules. These assays measure diffusion by monitoring the return of fluorescence in bleached regions (FRAP), or the dissipation of fluorescence from photoconverted regions (iFRAP). However, current FRAP/iFRAP analysis methods suffer from simplified assumptions about sample geometry, bleaching/photoconversion inhomogeneities, and the underlying reaction-diffusion kinetics. To address these shortcomings, we developed the software PyFRAP, which fits numerical simulations of three-dimensional models to FRAP/iFRAP data and accounts for bleaching/photoconversion inhomogeneities. Using PyFRAP we determined the diffusivities of fluorescent molecules spanning two orders of magnitude in molecular weight. We measured the tortuous effects that cell-like obstacles exert on effective diffusivity and show that reaction kinetics can be accounted for by model selection. These applications demonstrate the utility of PyFRAP, which can be widely adapted as a new extensible standard for FRAP analysis.
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spelling pubmed-59104152018-04-23 Quantitative diffusion measurements using the open-source software PyFRAP Bläßle, Alexander Soh, Gary Braun, Theresa Mörsdorf, David Preiß, Hannes Jordan, Ben M. Müller, Patrick Nat Commun Article Fluorescence Recovery After Photobleaching (FRAP) and inverse FRAP (iFRAP) assays can be used to assess the mobility of fluorescent molecules. These assays measure diffusion by monitoring the return of fluorescence in bleached regions (FRAP), or the dissipation of fluorescence from photoconverted regions (iFRAP). However, current FRAP/iFRAP analysis methods suffer from simplified assumptions about sample geometry, bleaching/photoconversion inhomogeneities, and the underlying reaction-diffusion kinetics. To address these shortcomings, we developed the software PyFRAP, which fits numerical simulations of three-dimensional models to FRAP/iFRAP data and accounts for bleaching/photoconversion inhomogeneities. Using PyFRAP we determined the diffusivities of fluorescent molecules spanning two orders of magnitude in molecular weight. We measured the tortuous effects that cell-like obstacles exert on effective diffusivity and show that reaction kinetics can be accounted for by model selection. These applications demonstrate the utility of PyFRAP, which can be widely adapted as a new extensible standard for FRAP analysis. Nature Publishing Group UK 2018-04-20 /pmc/articles/PMC5910415/ /pubmed/29679054 http://dx.doi.org/10.1038/s41467-018-03975-6 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Bläßle, Alexander
Soh, Gary
Braun, Theresa
Mörsdorf, David
Preiß, Hannes
Jordan, Ben M.
Müller, Patrick
Quantitative diffusion measurements using the open-source software PyFRAP
title Quantitative diffusion measurements using the open-source software PyFRAP
title_full Quantitative diffusion measurements using the open-source software PyFRAP
title_fullStr Quantitative diffusion measurements using the open-source software PyFRAP
title_full_unstemmed Quantitative diffusion measurements using the open-source software PyFRAP
title_short Quantitative diffusion measurements using the open-source software PyFRAP
title_sort quantitative diffusion measurements using the open-source software pyfrap
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5910415/
https://www.ncbi.nlm.nih.gov/pubmed/29679054
http://dx.doi.org/10.1038/s41467-018-03975-6
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