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FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis

In the field of fluorescence microscopy, there is continued demand for dynamic technologies that can exploit the complete information from every pixel of an image. One imaging technique with proven ability for yielding additional information from fluorescence imaging is Fluorescence Lifetime Imaging...

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Autores principales: Gao, Dasong, Barber, Paul R., Chacko, Jenu V., Kader Sagar, Md. Abdul, Rueden, Curtis T., Grislis, Aivar R., Hiner, Mark C., Eliceiri, Kevin W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773231/
https://www.ncbi.nlm.nih.gov/pubmed/33378370
http://dx.doi.org/10.1371/journal.pone.0238327
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author Gao, Dasong
Barber, Paul R.
Chacko, Jenu V.
Kader Sagar, Md. Abdul
Rueden, Curtis T.
Grislis, Aivar R.
Hiner, Mark C.
Eliceiri, Kevin W.
author_facet Gao, Dasong
Barber, Paul R.
Chacko, Jenu V.
Kader Sagar, Md. Abdul
Rueden, Curtis T.
Grislis, Aivar R.
Hiner, Mark C.
Eliceiri, Kevin W.
author_sort Gao, Dasong
collection PubMed
description In the field of fluorescence microscopy, there is continued demand for dynamic technologies that can exploit the complete information from every pixel of an image. One imaging technique with proven ability for yielding additional information from fluorescence imaging is Fluorescence Lifetime Imaging Microscopy (FLIM). FLIM allows for the measurement of how long a fluorophore stays in an excited energy state, and this measurement is affected by changes in its chemical microenvironment, such as proximity to other fluorophores, pH, and hydrophobic regions. This ability to provide information about the microenvironment has made FLIM a powerful tool for cellular imaging studies ranging from metabolic measurement to measuring distances between proteins. The increased use of FLIM has necessitated the development of computational tools for integrating FLIM analysis with image and data processing. To address this need, we have created FLIMJ, an ImageJ plugin and toolkit that allows for easy use and development of extensible image analysis workflows with FLIM data. Built on the FLIMLib decay curve fitting library and the ImageJ Ops framework, FLIMJ offers FLIM fitting routines with seamless integration with many other ImageJ components, and the ability to be extended to create complex FLIM analysis workflows. Building on ImageJ Ops also enables FLIMJ’s routines to be used with Jupyter notebooks and integrate naturally with science-friendly programming in, e.g., Python and Groovy. We show the extensibility of FLIMJ in two analysis scenarios: lifetime-based image segmentation and image colocalization. We also validate the fitting routines by comparing them against industry FLIM analysis standards.
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spelling pubmed-77732312021-01-07 FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis Gao, Dasong Barber, Paul R. Chacko, Jenu V. Kader Sagar, Md. Abdul Rueden, Curtis T. Grislis, Aivar R. Hiner, Mark C. Eliceiri, Kevin W. PLoS One Research Article In the field of fluorescence microscopy, there is continued demand for dynamic technologies that can exploit the complete information from every pixel of an image. One imaging technique with proven ability for yielding additional information from fluorescence imaging is Fluorescence Lifetime Imaging Microscopy (FLIM). FLIM allows for the measurement of how long a fluorophore stays in an excited energy state, and this measurement is affected by changes in its chemical microenvironment, such as proximity to other fluorophores, pH, and hydrophobic regions. This ability to provide information about the microenvironment has made FLIM a powerful tool for cellular imaging studies ranging from metabolic measurement to measuring distances between proteins. The increased use of FLIM has necessitated the development of computational tools for integrating FLIM analysis with image and data processing. To address this need, we have created FLIMJ, an ImageJ plugin and toolkit that allows for easy use and development of extensible image analysis workflows with FLIM data. Built on the FLIMLib decay curve fitting library and the ImageJ Ops framework, FLIMJ offers FLIM fitting routines with seamless integration with many other ImageJ components, and the ability to be extended to create complex FLIM analysis workflows. Building on ImageJ Ops also enables FLIMJ’s routines to be used with Jupyter notebooks and integrate naturally with science-friendly programming in, e.g., Python and Groovy. We show the extensibility of FLIMJ in two analysis scenarios: lifetime-based image segmentation and image colocalization. We also validate the fitting routines by comparing them against industry FLIM analysis standards. Public Library of Science 2020-12-30 /pmc/articles/PMC7773231/ /pubmed/33378370 http://dx.doi.org/10.1371/journal.pone.0238327 Text en © 2020 Gao et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Gao, Dasong
Barber, Paul R.
Chacko, Jenu V.
Kader Sagar, Md. Abdul
Rueden, Curtis T.
Grislis, Aivar R.
Hiner, Mark C.
Eliceiri, Kevin W.
FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis
title FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis
title_full FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis
title_fullStr FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis
title_full_unstemmed FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis
title_short FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis
title_sort flimj: an open-source imagej toolkit for fluorescence lifetime image data analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773231/
https://www.ncbi.nlm.nih.gov/pubmed/33378370
http://dx.doi.org/10.1371/journal.pone.0238327
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