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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Veröffentlicht in: | STAR protocols 2022-03, Vol.3 (1), p.101097, Article 101097 |
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Sprache: | eng |
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Zusammenfassung: | 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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•Automated pipelines for high-throughput analysis of cardiac imaging data•Focus on image processing and segmentation to derive common functional cardiac measures•Scalable for use in other tissue types
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. |
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ISSN: | 2666-1667 2666-1667 |
DOI: | 10.1016/j.xpro.2021.101097 |