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An image analysis protocol using CellProfiler for automated quantification of post-ischemic cardiac parameters

Quantitative assessment of post-ischemic cardiac remodeling is often hampered by tissue complexity and structural heterogeneity of the scar. Automated quantification of microscopy images offers an unbiased approach to reduce inter-observer variability. Here, we present a CellProfiler-based analytica...

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Detalles Bibliográficos
Autores principales: Ward, Alexander O., Janbandhu, Vaibhao, Chapman, Gavin, Dunwoodie, Sally L., Harvey, Richard P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9076969/
https://www.ncbi.nlm.nih.gov/pubmed/35535162
http://dx.doi.org/10.1016/j.xpro.2021.101097
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author Ward, Alexander O.
Janbandhu, Vaibhao
Chapman, Gavin
Dunwoodie, Sally L.
Harvey, Richard P.
author_facet Ward, Alexander O.
Janbandhu, Vaibhao
Chapman, Gavin
Dunwoodie, Sally L.
Harvey, Richard P.
author_sort Ward, Alexander O.
collection PubMed
description Quantitative assessment of post-ischemic cardiac remodeling is often hampered by tissue complexity and structural heterogeneity of the scar. Automated quantification of microscopy images offers an unbiased approach to reduce inter-observer variability. Here, we present a CellProfiler-based analytical pipeline for the high-throughput analysis of confocal images to quantify post-ischemic cardiac parameters. We describe image preprocessing and the quantification of capillary rarefaction, immune cell infiltration, cell death, and proliferating fibroblasts. This protocol can be adapted to other tissue types. For complete details on the use and execution of this profile, please refer to Janbandhu et al. (2021).
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spelling pubmed-90769692022-05-08 An image analysis protocol using CellProfiler for automated quantification of post-ischemic cardiac parameters Ward, Alexander O. Janbandhu, Vaibhao Chapman, Gavin Dunwoodie, Sally L. Harvey, Richard P. STAR Protoc Protocol Quantitative assessment of post-ischemic cardiac remodeling is often hampered by tissue complexity and structural heterogeneity of the scar. Automated quantification of microscopy images offers an unbiased approach to reduce inter-observer variability. Here, we present a CellProfiler-based analytical pipeline for the high-throughput analysis of confocal images to quantify post-ischemic cardiac parameters. We describe image preprocessing and the quantification of capillary rarefaction, immune cell infiltration, cell death, and proliferating fibroblasts. This protocol can be adapted to other tissue types. For complete details on the use and execution of this profile, please refer to Janbandhu et al. (2021). Elsevier 2022-01-17 /pmc/articles/PMC9076969/ /pubmed/35535162 http://dx.doi.org/10.1016/j.xpro.2021.101097 Text en Crown Copyright © 2021. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Ward, Alexander O.
Janbandhu, Vaibhao
Chapman, Gavin
Dunwoodie, Sally L.
Harvey, Richard P.
An image analysis protocol using CellProfiler for automated quantification of post-ischemic cardiac parameters
title An image analysis protocol using CellProfiler for automated quantification of post-ischemic cardiac parameters
title_full An image analysis protocol using CellProfiler for automated quantification of post-ischemic cardiac parameters
title_fullStr An image analysis protocol using CellProfiler for automated quantification of post-ischemic cardiac parameters
title_full_unstemmed An image analysis protocol using CellProfiler for automated quantification of post-ischemic cardiac parameters
title_short An image analysis protocol using CellProfiler for automated quantification of post-ischemic cardiac parameters
title_sort image analysis protocol using cellprofiler for automated quantification of post-ischemic cardiac parameters
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9076969/
https://www.ncbi.nlm.nih.gov/pubmed/35535162
http://dx.doi.org/10.1016/j.xpro.2021.101097
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