Variability vs. phenotype: Multimodal analysis of Dravet syndrome brain organoids powered by deep learning
Dravet Syndrome (DS) is a developmental epileptic encephalopathy (DEE) driven by pathogenic variants in SCN1A gene. Brain organoids (BO) have emerged as reliable models for neurodevelopmental genetic disorders, reproducing human brain developmental milestones and rising as a promising drug testing tool. Here, we determined the underlaying molecular DS pathophysiology affecting neuronal connectivity, revealing an early onset excitatory-inhibitory imbalance in maturing DS organoids circuitry. However, neuronal circuitry modeling in BO remains hampered by the notorious inter- and intra-organoid variability. Thus, leveraging deep learning (DL), we developed ImPheNet, a predictive tool grounded in BO live imaging datasets, to overcome the limitations of the intrinsic BO variability. ImPheNet accurately classified healthy and DS phenotypes at early onset stages, revealing differences between genotypes and upon antiseizure drug exposure. Altogether, our DL-predictive live imaging strategy, ImPheNet, emerges as a powerful tool to accelerate DEE research and advancing towards treatment discovery in a time- and cost-efficient manner. Overall design: RNA sequencing data