Automating Aggregate Quantification in Caenorhabditis elegans.

Area of Science:

• Neurobiology
• Genetics
• Biotechnology

Background:

• Neurodegenerative protein conformational diseases (PCDs) are increasingly prevalent, necessitating improved animal models.
• Murine models are valuable but costly and low-throughput for studying PCDs.
• Caenorhabditis elegans (C. elegans) offers a cost-effective, rapid, and high-throughput model due to genomic conservation and ease of maintenance.

Purpose of the Study:

• To develop and standardize a high-throughput protocol for aggregate quantification in C. elegans models of PCDs.
• To overcome the limitations of manual aggregate counting, including time consumption and subjectivity.
• To facilitate large-scale screening of genetic and drug libraries for therapeutic targets.

Main Methods:

• Standardized a protocol for C. elegans culturing, image acquisition, and data processing.
• Utilized CellProfiler software for automated image analysis of intestine-specific polyglutamine (polyQ) expressing nematodes.
• Developed a pipeline to separate individual worms and enumerate polyQ aggregates within each worm.

Main Results:

• Successfully automated the quantification of protein aggregates in C. elegans.
• The automated pipeline effectively separates and identifies individual worms and their respective aggregates.
• This method significantly increases throughput and reduces bias compared to manual quantification.

Conclusions:

• The standardized, automated protocol provides a reproducible and efficient method for studying PCDs in C. elegans.
• This approach can drastically simplify and accelerate the screening of large libraries for potential therapeutic interventions.
• The C. elegans model, coupled with automated analysis, is a powerful tool for neurodegenerative disease research.