Combined statistical-mechanistic modeling links ion channel genes to physiology of cortical neuron types

Neural cell types have classically been characterized by their anatomy and electrophysiology. More recently, single-cell transcriptomics has enabled an increasingly finer genetically defined taxonomy of cortical cell types but the link between the gene expression of individual cell types and their physiological and anatomical properties remains poorly understood. Here, we develop a hybrid modeling approach to bridge this gap. Our approach combines statistical and mechanistic models to predict cells' electrophysiological activity from their gene expression pattern. To this end, we fit biophysical Hodgkin-Huxley models for a wide variety of cortical cell types using simulation-based inference, while overcoming the challenge posed by the model mismatch between the mathematical model and the data. Using multimodal Patch-seq data, we link the estimated model parameters to gene expression using an interpretable sparse linear regression model. Our approach recovers specific ion channel gene expressions as predictive of Hodgkin-Huxley ion channel densities, directly implicating their mechanistic role in determining neural firing. ### Competing Interest Statement The authors have declared no competing interest.