Automated, image-based quantification of peroxisome characteristics with perox-per-cell

Area of Science:

• Cell Biology
• Biophysics
• Computational Biology

Background:

• Peroxisome research often involves laborious manual quantification of organelle features from microscopy images.
• Automated tools are needed to increase throughput and reproducibility in studying peroxisome dynamics and function.

Approach:

• The perox-per-cell software utilizes standard image processing algorithms to segment yeast cells and their peroxisomes.
• It automatically quantifies peroxisome number per cell and spatial distribution using microscopy image data.

Key Points:

• Perox-per-cell successfully automates the quantification of peroxisomal features in yeast.
• Software output demonstrates strong agreement with manual quantification for peroxisome counts and cell instances.
• Enables high-throughput analysis of peroxisomal characteristics.

Conclusions:

• Perox-per-cell provides an efficient and accurate solution for image-based peroxisome research.
• The software facilitates large-scale studies of peroxisome biology and function.
• Open-source availability promotes wider adoption and further development in the research community.