Ion Channel Metadata.

Unmyelinated C-fibers constitute the vast majority of axons in peripheral nerves and play key roles in homeostasis and signaling pain. However, little is known about their ion channel expression, which controls their firing properties. Also, because of their small diameters (~ 1 μm), it has not been possible to characterize their membrane properties using voltage clamp. We developed a novel library of isoform-specific ion channel models to serve as the basis functions of our C-fiber models. We then developed a particle swarm optimization (PSO) framework that used the isoform-specific ion channel models to reverse engineer C-fiber membrane properties from measured autonomic and cutaneous C-fiber conduction responses. Our C-fiber models reproduced experimental conduction velocity, chronaxie, action potential duration, intracellular threshold, and paired pulse recovery cycle. The models also matched experimental activity-dependent slowing, a property not included in model optimization. We found that simple conduction responses, characterizing the action potential, were controlled by similar membrane properties in both the autonomic and cutaneous C-fiber models, but complicated conduction response, characterizing the afterpotenials, were controlled by differential membrane properties. The unmyelinated C-fiber models constitute important tools to study autonomic signaling, assess the mechanisms of pain, and design bioelectronic devices. Additionally, the novel reverse engineering approach can be applied to generate models of other neurons where voltage clamp data are not available.无髓鞘C类神经纤维（Unmyelinated C-fibers）在外周神经的轴突中占据绝大多数，在机体稳态维持与疼痛信号传导过程中发挥关键作用。目前学界对其调控自身放电特性的离子通道表达特征仍知之甚少。此外，由于这类神经纤维直径极小（约1 μm），此前无法通过电压钳（voltage clamp）技术对其膜特性进行表征。我们构建了一套全新的亚型特异性离子通道模型库，作为本研究中C类纤维模型的基函数。随后，我们开发了一套粒子群优化（particle swarm optimization, PSO）框架，借助该亚型特异性离子通道模型，从实测的自主神经与皮肤C类纤维传导响应数据中逆向解析C类纤维的膜特性。我们构建的C类纤维模型成功复现了实验测得的传导速度、时值、动作电位时程、胞内阈值以及成对脉冲恢复周期。该模型同时匹配了实验观测到的活动依赖性减慢这一未纳入模型优化环节的特性。研究发现，在自主神经与皮肤C类纤维模型中，表征动作电位的简单传导响应由相似的膜特性调控，而表征后电位的复杂传导响应则由差异化的膜特性所控制。这套无髓鞘C类纤维模型可为自主神经信号传导研究、疼痛机制解析以及生物电子装置的设计提供重要工具。此外，该新型逆向工程方法可推广应用于其他无法获取电压钳数据的神经元模型构建。