Protocol for automated analysis of oil red O-stained Caenorhabditis elegans using Redwormpix, a Python-based...

Area of Science:

• Biomedical research
• Histology
• Computational biology

Background:

• Oil red O (ORO) staining is crucial for visualizing neutral lipids in *Caenorhabditis elegans*.
• Current quantitative image analysis of ORO-stained worms is manual, laborious, and error-prone.

Purpose of the Study:

• To present a protocol for automating ORO staining quantification in *C. elegans*.
• To introduce Redwormpix, an open-source Python application for this purpose.

Main Methods:

• Detailed protocol for installing Redwormpix software.
• Image transfer and application execution procedures.
• Methods for manual validation and results export.

Main Results:

• Successful automation of ORO staining quantification.
• Streamlined workflow for analyzing lipid content in *C. elegans*.
• Reduced time and human error in histological image analysis.

Conclusions:

• Redwormpix offers an efficient, automated solution for ORO staining quantification.
• The protocol facilitates accurate and reproducible lipid analysis in *C. elegans* research.
• This tool enhances high-throughput histological image processing.