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

ABSTRACT: 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).

SUBMITTER: Ward AO

PROVIDER: S-EPMC9076969 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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

Ward Alexander O AO Janbandhu Vaibhao V Chapman Gavin G Dunwoodie Sally L SL Harvey Richard P RP

STAR protocols 20220117 1

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 cel ...[more]

PMID: 35535162

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Project description:BackgroundHistological assessment of skeletal muscle sections is important for the research of muscle physiology and diseases. Quantifiable measures of skeletal muscle often include mean fiber diameter, fiber size distribution, and centrally nucleated muscle fibers. These parameters offer insights into the dynamic adaptation of skeletal muscle cells during repeated cycles of degeneration and regeneration associated with many muscle diseases and injuries. Computational programs designed to obtain these parameters would greatly facilitate such efforts and offer significant advantage over manual image analysis, which is very labor-intensive and often subjective. Here, we describe a customized pipeline termed MuscleAnalyzer for muscle histology analysis based upon CellProfiler, a free, open-source software for measuring and analyzing cell images.ResultsThe MuscleAnalyzer pipeline consists of loading, adjusting, and running a series of image-processing modules provided by CellProfiler. This pipeline was evaluated using wild-type and mdx muscle sections co-stained with laminin (to demarcate the muscle fiber boundaries) and 4',6-diamidino-2-phenylindole (DAPI, to label the nuclei). The immunofluorescence images analyzed using the MuscleAnalyzer pipeline or manually yielded similar results in the number of muscle fibers per image (p = 0.42) and central nucleated fiber (CNF) percentage (p = 0.29) in mdx mice. However, for a total of 67 images, CellProfiler completed the analysis in ~ 10 min on a regular PC while it took an investigator ~ 3 h using the manual approach in order to quantify the number of muscle fibers and CNF. Moreover, the MuscleAnalyzer pipeline also provided the measurement of the cross-sectional area (CSA) and minimal Feret's diameter (MFD) of muscle fibers, and thus fiber size distribution can be plotted.ConclusionsOur data indicate that the MuscleAnalyzer pipeline can efficiently and accurately analyze laminin and DAPI co-stained muscle images in a batch format and provide quantitative measurements for muscle histological properties such as muscle fiber diameters, fiber size distribution, and CNF percentage.

Dataset Information

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

Publications

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

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