Search

Temporal lobe epilepsy is the fourth most common neurological disorder, with about 40% of patients not responding to pharmacological treatment. Increased cellular loss is linked to disease severity and pathological phenotypes such as heightened seizure propensity. While the hippocampus is the target of therapeutic interventions, the impact of the disease at the cellular level remains unclear. Here, we show that hippocampal granule cells change with disease progression as measured in living, resected hippocampal tissue excised from patients with epilepsy. We show that granule cells increase excitability and shorten response latency while also enlarging in cellular volume and spine density. Single-nucleus RNA sequencing combined with simulations ascribes the changes to three conductances: BK, Cav2.2, and Kir2.1. In a network model, we show that these changes related to disease progression bring the circuit into a more excitable state, while reversing them produces a less excitable, "early-disease-like" state. [Display omitted] • Transcriptomics in human granule cells separate early vs. late disease state • Phenotypic changes in human granule cells occur across modalities • Changes in granule cell excitability are linked to three ionic conductances The cellular changes brought about by temporal lobe epilepsy in humans remain unknown. Buchin et al. generate a multimodal dataset from human hippocampal surgical tissue to map cellular changes linked to disease progression. They find three ion conductances that explain changes across modalities. [ABSTRACT FROM AUTHOR]

Which cell types constitute brain circuits is a fundamental question, but establishing the correspondence across cellular data modalities is challenging. Bio-realistic models allow probing cause-and-effect and linking seemingly disparate modalities. Here, we introduce a computational optimization workflow to generate 9,200 single-neuron models with active conductances. These models are based on 230 in vitro electrophysiological experiments followed by morphological reconstruction from the mouse visual cortex. We show that, in contrast to current belief, the generated models are robust representations of individual experiments and cortical cell types as defined via cellular electrophysiology or transcriptomics. Next, we show that differences in specific conductances predicted from the models reflect differences in gene expression supported by single-cell transcriptomics. The differences in model conductances, in turn, explain electrophysiological differences observed between the cortical subclasses. Our computational effort reconciles single-cell modalities that define cell types and enables causal relationships to be examined. [Display omitted] • 9,200 models are generated that capture multimodal single-cell datasets • Models preserve cortical cell class relationships reproducing class differences • Model-based conductance predictions correlate with gene expression differences • Cell class differences in Ih translate to synaptic integration differences Integrating and reconciling multimodal cellular datasets has been challenging. Nandi et al. generate models that are robust representations of cellular experiments and cortical cell types defined by electrophysiology, morphology, or transcriptomics. These models causally link between seemingly disparate modalities offering mechanistic hypotheses about cellular phenotypes. [ABSTRACT FROM AUTHOR]