ArticlePatterns (New York, N.Y.)2025
Combined statistical-biophysical modeling links ion channel genes to physiology of cortical neuron types.
Article in Patterns (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Patch-Clamp Single-Cell Proteomics in Acute Brain Slices: A Framework for Recording, Retrieval, and Interpretation.ACS chemical neuroscience · 2026Article
- JAXLEY: differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics.Nature methods · 2025Article
- Linking ion channel gene expression to neuronal firing patterns through a statistical-biophysical model.Patterns (New York, N.Y.) · 2025Article
- Biophysical modelling of intrinsic cardiac nervous system neuronal electrophysiology based on single-cell transcriptomics.The Journal of physiology · 2025Article
- Transcriptomic Correlates of State Modulation in GABAergic Interneurons: A Cross-Species Analysis.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Review
- Controlling morpho-electrophysiological variability of neurons with detailed biophysical models.iScience · 2023Article
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Authors and funding
10 authors.
Funding
Abstract
Neurons have classically been characterized by their anatomy, electrophysiology, and molecular markers. More recently, single-cell transcriptomics has enabled an increasingly fine 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 gene expression patterns. To this end, we fit Hodgkin-Huxley-based models for a wide variety of cortical cell types by using simulation-based inference while overcoming the mismatch between model and data. Using multimodal Patch-seq data, we link the estimated model parameters to gene expression using an interpretable linear sparse regression model. Our approach identifies the expression of specific ion channel genes as predictive of biophysical model parameters including ion channel densities, implicating their mechanistic role in determining neural firing properties.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.