ALIGN: A modular framework for quantitative analysis of longitudinal development in Caenorhabditis elegans

Abstract

Longitudinal live imaging is a powerful tool for studying growth, development and behaviour. However, reproducible and scalable analysis of the large resulting datasets remains a major bottleneck. Here, we present ALIGN (Automated Longitudinal Image-based Growth aNalyzer), a modular framework that integrates deep learning-based segmentation, automated quality control, computational straightening, feature extraction and interactive annotation within a single configurable pipeline that can be operated end-to-end by users without coding expertise. Applying ALIGN to longitudinal development of C. elegans, we demonstrate accurate segmentation across heterogeneous imaging conditions, detection of larval stage transitions from growth trajectories, and data-driven quality control, ensuring high measurement precision. Computational straightening and alignment enable consistent analysis of motile animals, removing motion artefacts from three-dimensional optical sectioning. Applying ALIGN to measure pharyngeal pumping and growth rates in wild-type and eat-2 mutants uncovered a non-proportional relationship between feeding and growth. ALIGN thus provides generalizable tools and resources for longitudinal, single-animal analysis of development.