BehaveSeq: behavioral age-detection in single individuals for reconstruction of high-resolution transcriptional dynamics during development

Capturing rapid changes in gene expression states across development time is crucial for revealing underlying molecular pathways that organize dynamic developmental processes. Here, we present 'BehaveSeq', a framework that integrates behavioral age detection with transcriptomic profiling in the same individuals, to reconstruct minute-resolution developmental profiles of gene expression. By densely sampling isolated C. elegans individuals every ~3 minutes during the L4 stage, we identified thousands of genes showing fast and temporally-structured expression patterns during development. These genes were organized into distinct clusters associated with specific developmental and cellular functions. Further applying the new framework to serotonin-deficient individuals revealed both conserved and altered expression patterns across conditions. Interestingly, using the same single-animal dataset, we were able to train a neural network model to accurately predict the developmental age of each individual solely based on its individual-specific transcriptional signature. Overall, our approach offers a new, generalizable framework for revealing dynamic processes of gene regulation with high temporal precision across the developmental trajectory. Overall design: Single individual C. elegans RNA seq using SMARTseq2 in multipul time points