Bernaerts Yves, Deistler Michael, Gonçalves Pedro J, Beck Jonas, Stimberg Marcel, Scala Federico, Tolias Andreas S, Macke Jakob, Kobak Dmitry, Berens Philipp
Hertie Institute for AI in Brain Health, University of Tübingen, 72076 Tübingen, Germany.
Tübingen AI Center, 72076 Tübingen, Germany.
bioRxiv. 2025 Jan 2:2023.03.02.530774. doi: 10.1101/2023.03.02.530774.
Neural cell types have classically been characterized by their anatomy and electrophysiology. 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 their gene expression pattern. To this end, we fit biophysical Hodgkin-Huxley-based models for a wide variety of cortical cell types using simulation-based inference, while overcoming the challenge posed by the 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 biophysical model parameters including ion channel densities, directly implicating their mechanistic role in determining neural firing.
神经细胞类型传统上是通过其解剖结构和电生理学来表征的。最近，单细胞转录组学使得对皮质细胞类型进行越来越精细的基因定义分类成为可能，但个体细胞类型的基因表达与其生理和解剖特性之间的联系仍知之甚少。在此，我们开发了一种混合建模方法来弥合这一差距。我们的方法结合了统计模型和机制模型，以根据细胞的基因表达模式预测其电生理活动。为此，我们使用基于模拟的推理为多种皮质细胞类型拟合基于霍奇金-赫胥黎生物物理模型，同时克服数学模型与数据不匹配带来的挑战。使用多模态膜片钳测序（Patch-seq）数据，我们使用可解释的稀疏线性回归模型将估计的模型参数与基因表达联系起来。我们的方法恢复了特定离子通道基因表达，这些表达可预测包括离子通道密度在内的生物物理模型参数，直接表明它们在决定神经放电中的机制作用。
