A framework for multiplex imaging optimization and reproducible analysis

Multiplex imaging technologies are increasingly used for single-cell phenotyping and spatial characterization of tissues; however, transparent methods are needed for comparing the performance of platforms, protocols and analytical pipelines. We developed a python software, mplexable, for reproducibl...

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Autores principales: Eng, Jennifer, Bucher, Elmar, Hu, Zhi, Zheng, Ting, Gibbs, Summer L., Chin, Koei, Gray, Joe W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9095647/
https://www.ncbi.nlm.nih.gov/pubmed/35545666
http://dx.doi.org/10.1038/s42003-022-03368-y
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author Eng, Jennifer
Bucher, Elmar
Hu, Zhi
Zheng, Ting
Gibbs, Summer L.
Chin, Koei
Gray, Joe W.
author_facet Eng, Jennifer
Bucher, Elmar
Hu, Zhi
Zheng, Ting
Gibbs, Summer L.
Chin, Koei
Gray, Joe W.
author_sort Eng, Jennifer
collection PubMed
description Multiplex imaging technologies are increasingly used for single-cell phenotyping and spatial characterization of tissues; however, transparent methods are needed for comparing the performance of platforms, protocols and analytical pipelines. We developed a python software, mplexable, for reproducible image processing and utilize Jupyter notebooks to share our optimization of signal removal, antibody specificity, background correction and batch normalization of the multiplex imaging with a focus on cyclic immunofluorescence (CyCIF). Our work both improves the CyCIF methodology and provides a framework for multiplexed image analytics that can be easily shared and reproduced.
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spelling pubmed-90956472022-05-13 A framework for multiplex imaging optimization and reproducible analysis Eng, Jennifer Bucher, Elmar Hu, Zhi Zheng, Ting Gibbs, Summer L. Chin, Koei Gray, Joe W. Commun Biol Article Multiplex imaging technologies are increasingly used for single-cell phenotyping and spatial characterization of tissues; however, transparent methods are needed for comparing the performance of platforms, protocols and analytical pipelines. We developed a python software, mplexable, for reproducible image processing and utilize Jupyter notebooks to share our optimization of signal removal, antibody specificity, background correction and batch normalization of the multiplex imaging with a focus on cyclic immunofluorescence (CyCIF). Our work both improves the CyCIF methodology and provides a framework for multiplexed image analytics that can be easily shared and reproduced. Nature Publishing Group UK 2022-05-11 /pmc/articles/PMC9095647/ /pubmed/35545666 http://dx.doi.org/10.1038/s42003-022-03368-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Eng, Jennifer
Bucher, Elmar
Hu, Zhi
Zheng, Ting
Gibbs, Summer L.
Chin, Koei
Gray, Joe W.
A framework for multiplex imaging optimization and reproducible analysis
title A framework for multiplex imaging optimization and reproducible analysis
title_full A framework for multiplex imaging optimization and reproducible analysis
title_fullStr A framework for multiplex imaging optimization and reproducible analysis
title_full_unstemmed A framework for multiplex imaging optimization and reproducible analysis
title_short A framework for multiplex imaging optimization and reproducible analysis
title_sort framework for multiplex imaging optimization and reproducible analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9095647/
https://www.ncbi.nlm.nih.gov/pubmed/35545666
http://dx.doi.org/10.1038/s42003-022-03368-y
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