Harnessing C. elegans as a Biosensor: Integrating Microfluidics, Image Analysis, and Machine Learning for Environmental Sensing

Abstract

Environmental contamination is becoming an increasingly evident risk to human health worldwide. The small, free-living nematode Caenorhabditis elegans (C. elegans) has become a compelling model organism for environmental toxicity studies in recent years, owing to its numerous advantages, including its transparent body, small size, well-characterized biology, genetic tractability, short lifespan, and ease of culture. Several assays have been developed using C. elegans to enable a better understanding of toxicant effects, from whole-animal to single-cell levels. While these methods can be extremely useful, they can be time-consuming and cumbersome to perform on a large scale. Recent advances in microfluidics have adapted many of these assays to enable high-throughput analysis of C. elegans, greatly reducing time and resource consumption while increasing efficiency and scalability. Further integration of these microfluidic platforms with machine learning expands their analytical capabilities and accuracy, revolutionizing what can be achieved with this model organism. This article will review the physiological basis of C. elegans as a model organism for environmental toxicity studies, and recent advances in integrating microfluidics and machine learning which could lead to using C. elegans as a promising living biosensor for environmental sensing.