Behavioral age-detection in individuals reconstructs minute-scale developmental transcriptomics.

Area of Science:

• Developmental Biology
• Genomics
• Neuroscience

Background:

• Understanding gene expression dynamics during development is essential for deciphering complex biological processes.
• Existing methods often lack the temporal resolution to capture rapid molecular changes.
• Precise age determination is critical for accurate temporal reconstruction of gene expression.

Purpose of the Study:

• To introduce BehaveSeq, a novel method for high-resolution temporal reconstruction of gene expression during development.
• To integrate longitudinal behavioral monitoring with genome-wide transcriptomic profiling in single individuals.
• To reveal rapid gene expression changes and their functional associations during development.

Main Methods:

• Developed BehaveSeq, integrating behavioral monitoring for age detection with single-individual transcriptomic profiling.
• Densely sampled individuals (~3-minute intervals) during C. elegans development.
• Utilized neural networks to predict developmental age from molecular signatures.

Main Results:

• Uncovered thousands of genes with rapid, temporally structured expression trajectories during development.
• Identified distinct dynamical gene expression patterns linked to specific developmental functions.
• Revealed time-specific neuromodulatory effects on developmental expression programs in serotonin-deficient mutants.
• Successfully trained a neural network to predict developmental age from gene expression data.

Conclusions:

• BehaveSeq provides a powerful framework for high-temporal-precision analysis of dynamic gene regulation.
• The method enables detailed reconstruction of developmental gene expression patterns.
• Revealed novel insights into developmental timing, gene regulation, and neuromodulation.