Automated, image-based quantification of peroxisome characteristics with perox-per-cell.
basic_science · Level V
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- Record sourced from PubMed, PMID 39001800.
- Also identified by DOI 10.1093/bioinformatics/btae442 and PMC identifier 11269463.
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Abstract
perox-per-cell automates cumbersome, image-based data collection tasks often encountered in peroxisome research. The software processes microscopy images to quantify peroxisome features in yeast cells. It uses off-the-shelf image processing tools to automatically segment cells and peroxisomes and then outputs quantitative metrics including peroxisome counts per cell and spatial areas. In validation tests, we found that perox-per-cell output agrees well with manually quantified peroxisomal counts and cell instances, thereby enabling high-throughput quantification of peroxisomal characteristics. The software is coded in Python. Compiled executables and source code are available at https://github.com/AitchisonLab/perox-per-cell. Supplementary data are available at Bioinformatics online.