A Real-Time Automated Deep Learning Workflow for Non-invasive High-Magnification Imaging of C. elegans

Abstract

Caenorhabditis elegans is a premier model organism for aging and neurobiology research, valued for its short lifespan, optical transparency, genetic tractability, and well-mapped nervous system. Non-invasive automated recording of biomarkers is a fundamental goal in modern biology because it preserves natural physiology and eliminates confounds from anesthesia, restraint, or repeated handling in C. elegans. Yet high-magnification imaging of freely moving worms remains a persistent challenge: as magnification increases, the narrowing field of view compounds target loss, motion blur, and focal drift, pushing researchers toward immobilization strategies that compromise physiology, suppress natural behavior, and preclude the continuous longitudinal observation essential for aging and neurobiological studies. Here, we present a real-time tracking workflow for imaging individual worms in a microfluidic platform under controlled culture conditions. The system integrates deep learning head detection, image-based autofocus, and rapid motorized-stage feedback to support stable imaging across multiple magnifications, including neuronal-scale imaging. Hundreds of individually housed worms in separate incubation chambers enable repeated daily imaging of the same animals throughout their lifespan. Built entirely on a commercially available inverted microscope without additional custom hardware, the platform features a modular, user-configurable interface adaptable to diverse microscope setups, specimens, and experimental goals. Fluorescence images from freely moving worms were visually comparable to those from immobilized animals, supporting longitudinal phenotyping in aging and neurobiology studies.