A Robust, High-Content NAM for Repeatable and Predictive Developmental and Reproductive Toxicity Assessment in C....

Area of Science:

• Toxicology
• Developmental Biology
• Genetics

Background:

• Developmental and Reproductive Toxicity (DART) assessment is crucial for product safety.
• Current DART methods rely on vertebrate models, which are expensive and time-consuming.
• Caenorhabditis elegans offers a promising New Approach Methodology (NAM) for toxicology but lacks advanced imaging and robust endpoints.

Purpose of the Study:

• To develop a high-resolution, multiparametric imaging-based assay for DART endpoints in C. elegans.
• To enhance DART assessment by incorporating machine learning for embryo classification and reproductive health analysis.
• To establish a robust and repeatable platform for rapid, cost-effective chemical safety evaluation.

Main Methods:

• Utilized a microfluidic device (vivoChip) for high-resolution brightfield imaging of C. elegans.
• Expanded machine learning analysis to quantify embryo number, in utero development, and body dimensions.
• Classified embryos into early- and late-stage to assess reproductive health and sensitivity.

Main Results:

• Achieved high repeatability with coefficients of variation of 1-5% for developmental and 6-17% for reproductive endpoints.
• Demonstrated reproducible, concentration-dependent responses for methylmercury and propiconazole across ~400,000 embryos.
• Identified late-stage embryos as the most sensitive endpoint, preceding other toxicity indicators.

Conclusions:

• The developed assay is sensitive, repeatable, and scalable for DART assessment in C. elegans.
• This platform enables rapid, cost-effective chemical prioritization for safety evaluation.
• The method overcomes limitations of traditional DART models and current C. elegans assays.