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Variability vs Phenotype: multimodal analysis of Dravet Syndrome Brain Organoids powered by Deep Learning

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE256142 | GEO | 2025/09/25

REPOSITORIES: GEO

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Project description:Individuals with Down syndrome (DS) exhibit neurological deficits throughout the lifespan culminating in Alzheimer’s disease (AD) pathology and cognitive impairment during early mid-life. At the cellular level, dysregulation in neuronal gene expression has been shown in the human brain and mouse models of DS. However, RNA- sequencing (RNA-Seq) analysis of genomic neuronal expression in the hippocampus have been limited, without separation of neuronal populations based on circuitry inputs. We postulate spatially characterized hippocampal excitatory neurons will present with unique gene expression patterns due in part to unique circuitry inputs in the DS mouse model. Here, we combined laser capture microdissection (LCM) to isolate individual neuron populations with low input RNA-seq analysis to determine gene expression analysis of three excitatory neuron populations from the rostral hippocampus. We utilized the Ts65Dn mouse model of DS at the start of degeneration (6MO) to query gene expression profiles of CA1 and CA3 pyramidal neurons, along with dentate gyrus (DG) granule cells. The hippocampal structure is critical for learning and memory, with significant impairments of both behavior and synaptic activity in the DS mouse model which undergo degeneration during aging. Each population of excitatory hippocampal neurons exhibited unique gene expression alterations in the DS mice. Bioinformatic queries revealed unique vulnerabilities and mechanistic differences which coincide with onset of degeneration in the Ts65Dn model of DS/AD. These unique vulnerabilities may underlie the degenerative phenotype in DS, suggesting precision medicine targeting individual populations of neurons may be required for therapeutic advancement. We further analyzed maternal choline supplementation in each neuronal population, treating the dams during gestation through weaning with choline normal diet (1.1g/kc choline chloride) or choline suppmentation (5.0g/kg choline chloride) in the normal (AIN-76A) mouse chow from Dyets. At weaning (postnatal day 21), pups were aged on a choline normal diet until 6 months old.

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Variability vs Phenotype: multimodal analysis of Dravet Syndrome Brain Organoids powered by Deep Learning

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