An award is made to the University of Arizona to develop and disseminate an advanced high-content imaging platform for comprehensive, long-term study of the roundworm Caenorhabditis elegans. C. elegans, a widely used experimental system in biological research, is favored for its short lifespan, easy and cost-effective lab cultivation, and the availability of powerful molecular tools. Even with these advantages, standard methods to assess physiological and molecular characteristics are often labor intensive and are typically limited to observing only one or a few traits. This project aims to transform this process, integrating recent advancements in automated image acquisition, machine learning, and specialized data analysis to concurrently capture and interpret numerous physiological and molecular traits, significantly enhancing data collection efficiency. The platform will further allow for continuous monitoring of the same animals throughout their lifespan, offering a unique opportunity to observe dynamic changes in molecular processes over time, as well as stochastic variation among individuals within a population. The platform will be compatible with hundreds of existing transgenic fluorescent biomarker strains and made accessible to the wider scientific community, fostering collaboration and promoting innovative research across diverse biological fields, including aging, development, metabolism, stress response, toxicology, inflammation, and immunity. This project also promotes experiential education by providing students with real-world training in robotics, imaging technology, machine learning, database systems, and genetic engineering. The core of this research project is the development and validation of a robotic imaging system for primary data collection, supported by a complementary database and analysis suite for efficient data processing, storage, and analysis. The project will further generate a panel of validated transgenic C. elegans strains, each expressing multiple fluorescent biomarkers designed to report on different key molecular processes optimized for use with the imaging platform and supporting researchers in various subdisciplines. This platform will enable researchers to observe dynamic interactions between physiological outcomes (e.g., survival, body size, activity) and underlying molecular systems (e.g., activation of core molecular signaling or stress response pathways) by enhancing experimental efficiency, scope, and throughput while improving reproducibility by limiting human bias in measurement and analysis. By collecting data on the same individual animals over time, the project will allow researchers to delve into dynamic interactions between molecular and physiological signatures within a population. In summary, this project will produce an innovative imaging platform that will enhance our ability to study fundamental biological processes and accelerate discovery across diverse disciplines of biological sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.