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bioRxiv · 10.1101/2025.11.22.689965

WormSpot: a machine learning-powered viability scoring platform in C. elegans for Candida pathogenicity studies

Lee, J. G. W.·Ong, V. E. Y.·Zhang, D.
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Abstract

Invasive Candida infections pose a critical health challenge, exacerbated by emerging antifungal resistance. Caenorhabditis elegans (C. elegans) offers a genetically tractable and scalable model for studying Candida pathogenicity, yet conventional viability assays remain labor-intensive, limiting high-throughput applications. In this study, we developed a machine learning-driven worm viability scoring platform, WormSpot, using representative images of Candida-infected worm and the robust YOLOv8-based framework. By analyzing static morphological features, our model accurately classifies worm viability post-infection, achieving strong concordance with manual scoring while requiring minimal image input. Validation with well-characterized Candida albicans mutants and antifungal agents confirms the platforms robustness in predicting worm survival trends. Notably, WormSpot performs efficiently with diverse image formats and is compatible with standard multi-well microscopy workflows, enabling automated data analysis and scalable application in various experimental setups. In conclusion, WormSpot provides a data-efficient, reproducible tool for assessing Candida virulence using C. elegans, supporting both basic pathogenicity study and antifungal discovery in high-throughput settings.

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Lee, J. G. W., Ong, V. E. Y., Zhang, D.. 2025-11-25. WormSpot: a machine learning-powered viability scoring platform in C. elegans for Candida pathogenicity studies. https://doi.org/10.1101/2025.11.22.689965

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