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

Longitudinal live imaging offers valuable insights into growth, development, and behavior. However, analyzing the massive datasets produced by this technique can be challenging due to the need for reproducible and scalable methods. Researchers have developed ALIGN (Automated Longitudinal Image-based Growth aNalyzer), a modular framework that streamlines the analysis process by integrating deep learning-based segmentation, automated quality control, computational straightening, feature extraction, and interactive annotation. This comprehensive pipeline allows users without coding expertise to operate ALIGN end-to-end.

When applied to the longitudinal development of Caenorhabditis elegans, ALIGN demonstrated accurate segmentation even across diverse imaging conditions. The framework successfully detected larval stage transitions from growth trajectories, ensuring precise measurement of biological processes. To further enhance the analysis, ALIGN incorporated computational straightening and alignment, which effectively removes motion artifacts from three-dimensional optical sectioning, resulting in consistent analysis of motile animals.

Using ALIGN, researchers were able to measure pharyngeal pumping and growth rates in both wild-type and eat-2 mutants. The study revealed a fascinating non-proportional relationship between feeding and growth, shedding new light on the underlying biological mechanisms. By providing generalizable tools and resources for longitudinal, single-animal analysis of development, ALIGN significantly contributes to the field of quantitative analysis in Caenorhabditis elegans research.