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 the <i>SCN1A</i> gene. Brain organoids (BOs) 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 underlying molecular DS pathophysiology affecting neuronal connectivity, revealing an early onset excitatory-inhibitory imbalance in maturing DS organoid circuitry. However, neuronal circuitry modeling in BOs 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 BOs 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 DEEs research and advance toward treatment discovery in a time- and cost-efficient manner.

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Journal
iScience
Published
2025-10-23
DOI
https://doi.org/10.1016/j.isci.2025.113831
Primary Topic
Epilepsy research and treatment
Type
article
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article

Variability vs. phenotype: Multimodal analysis of Dravet syndrome brain organoids powered by deep learning

Daniel Tornero, Sandra Acosta, Alba Ortega-Gascó, Irene Peñuelas‐Haro et al.
iScience
Epilepsy research and treatment
article

Variability vs. phenotype: Multimodal analysis of Dravet syndrome brain organoids powered by deep learning

Daniel Tornero, Sandra Acosta, Alba Ortega-Gascó, Irene Peñuelas‐Haro, Elisa Espinet, Arcadi Navarro, Laura García‐González, Jordi Soriano, Óscar Lao, Andrea Martí-Sarrias, Adriana Modrego, Anna-Christina Haeb, Ruth I. Pareja, Nuria Ruíz, Isabel Turpin-Moreno
article en

Abstract

Dravet syndrome (DS) is a developmental epileptic encephalopathy (DEE) driven by pathogenic variants in the <i>SCN1A</i> gene. Brain organoids (BOs) 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 underlying molecular DS pathophysiology affecting neuronal connectivity, revealing an early onset excitatory-inhibitory imbalance in maturing DS organoid circuitry. However, neuronal circuitry modeling in BOs 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 BOs 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 DEEs research and advance toward treatment discovery in a time- and cost-efficient manner.

iScienceVol. 28(11)
Institució Catalana de Recerca i Estudis Avançats (ES), Universitat Pompeu Fabra (ES), Bellvitge University Hospital (ES), Biomedical Research Networking Center on Neurodegenerative Diseases (ES), Institut d'Investigació Biomédica de Bellvitge (ES), Sant Joan de Déu Research Foundation (ES), Barcelonaβeta Brain Research Center (ES), Barcelona Institute of Science and Technology (ES), Institut de Biologia Evolutiva (ES), Centre for Genomic Regulation (ES), Consorci Institut D'Investigacions Biomediques August Pi I Sunyer (ES), Universitat de Barcelona (ES)
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Epilepsy research and treatment
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