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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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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. |
format | Online Article Text |
id | pubmed-5910415 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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