Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Milan, Italy.
Center for Neural Science and Medicine, Cedars-Sinai Medical Center, Los Angeles, California, United State of America.
PLoS Comput Biol. 2022 Apr 22;18(4):e1010071. doi: 10.1371/journal.pcbi.1010071. eCollection 2022 Apr.
The transformation of synaptic input into action potential output is a fundamental single-cell computation resulting from the complex interaction of distinct cellular morphology and the unique expression profile of ion channels that define the cellular phenotype. Experimental studies aimed at uncovering the mechanisms of the transfer function have led to important insights, yet are limited in scope by technical feasibility, making biophysical simulations an attractive complementary approach to push the boundaries in our understanding of cellular computation. Here we take a data-driven approach by utilizing high-resolution morphological reconstructions and patch-clamp electrophysiology data together with a multi-objective optimization algorithm to build two populations of biophysically detailed models of murine hippocampal CA3 pyramidal neurons based on the two principal cell types that comprise this region. We evaluated the performance of these models and find that our approach quantitatively matches the cell type-specific firing phenotypes and recapitulate the intrinsic population-level variability in the data. Moreover, we confirm that the conductance values found by the optimization algorithm are consistent with differentially expressed ion channel genes in single-cell transcriptomic data for the two cell types. We then use these models to investigate the cell type-specific biophysical properties involved in the generation of complex-spiking output driven by synaptic input through an information-theoretic treatment of their respective transfer functions. Our simulations identify a host of cell type-specific biophysical mechanisms that define the morpho-functional phenotype to shape the cellular transfer function and place these findings in the context of a role for bursting in CA3 recurrent network synchronization dynamics.
突触输入转化为动作电位输出是一种基本的单细胞计算，它源于不同细胞形态的复杂相互作用，以及定义细胞表型的离子通道的独特表达谱。旨在揭示传递函数机制的实验研究取得了重要的见解，但由于技术可行性的限制，其范围有限，这使得生物物理模拟成为一种有吸引力的补充方法，可以推动我们对细胞计算的理解的边界。在这里，我们通过利用高分辨率形态重建和膜片钳电生理学数据以及多目标优化算法，采用数据驱动的方法，基于构成该区域的两种主要细胞类型，构建了两种基于生物物理细节的小鼠海马 CA3 锥体神经元模型群体。我们评估了这些模型的性能，发现我们的方法在定量上匹配了细胞类型特异性的发放表型，并再现了数据中内在的群体水平变异性。此外，我们证实优化算法发现的电导值与两种细胞类型的单细胞转录组数据中差异表达的离子通道基因一致。然后，我们使用这些模型通过对各自传递函数进行信息论处理，研究了由突触输入驱动的复杂放电输出产生所涉及的细胞类型特异性生物物理特性。我们的模拟确定了许多细胞类型特异性的生物物理机制，这些机制定义了形态功能表型，以塑造细胞的传递函数，并将这些发现置于爆发在 CA3 复发性网络同步动力学中的作用的背景下。
