Computational Biology, Department of Computer Science, University of Oxford, Oxford, OX1 3QD, UK.
Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA.
J Physiol. 2018 May 15;596(10):1813-1828. doi: 10.1113/JP275733. Epub 2018 Apr 17.
Ion current kinetics are commonly represented by current-voltage relationships, time constant-voltage relationships and subsequently mathematical models fitted to these. These experiments take substantial time, which means they are rarely performed in the same cell. Rather than traditional square-wave voltage clamps, we fitted a model to the current evoked by a novel sum-of-sinusoids voltage clamp that was only 8 s long. Short protocols that can be performed multiple times within a single cell will offer many new opportunities to measure how ion current kinetics are affected by changing conditions. The new model predicts the current under traditional square-wave protocols well, with better predictions of underlying currents than literature models. The current under a novel physiologically relevant series of action potential clamps is predicted extremely well. The short sinusoidal protocols allow a model to be fully fitted to individual cells, allowing us to examine cell-cell variability in current kinetics for the first time.
Understanding the roles of ion currents is crucial to predict the action of pharmaceuticals and mutations in different scenarios, and thereby to guide clinical interventions in the heart, brain and other electrophysiological systems. Our ability to predict how ion currents contribute to cellular electrophysiology is in turn critically dependent on our characterisation of ion channel kinetics - the voltage-dependent rates of transition between open, closed and inactivated channel states. We present a new method for rapidly exploring and characterising ion channel kinetics, applying it to the hERG potassium channel as an example, with the aim of generating a quantitatively predictive representation of the ion current. We fitted a mathematical model to currents evoked by a novel 8 second sinusoidal voltage clamp in CHO cells overexpressing hERG1a. The model was then used to predict over 5 minutes of recordings in the same cell in response to further protocols: a series of traditional square step voltage clamps, and also a novel voltage clamp comprising a collection of physiologically relevant action potentials. We demonstrate that we can make predictive cell-specific models that outperform the use of averaged data from a number of different cells, and thereby examine which changes in gating are responsible for cell-cell variability in current kinetics. Our technique allows rapid collection of consistent and high quality data, from single cells, and produces more predictive mathematical ion channel models than traditional approaches.
离子电流动力学通常通过电流-电压关系、时间常数-电压关系以及随后拟合这些关系的数学模型来表示。这些实验需要大量时间，这意味着它们很少在同一个细胞中进行。我们拟合了一个模型来表示由一种新的正弦和电压钳产生的电流，该电压钳仅持续 8 秒。可以在单个细胞内多次执行的短协议将提供许多新的机会来测量离子电流动力学如何受到变化条件的影响。新模型很好地预测了传统方波电压钳下的电流，比文献模型更好地预测了基础电流。新型生理相关动作电位钳下的电流预测非常准确。短正弦协议允许对单个细胞进行完整的模型拟合，使我们能够首次检查电流动力学中的细胞间变异性。
了解离子电流的作用对于预测不同情况下药物和突变的作用至关重要，从而指导心脏、大脑和其他电生理系统的临床干预。我们预测离子电流如何对细胞电生理做出贡献的能力反过来又严重依赖于我们对离子通道动力学的描述-开放、关闭和失活通道状态之间的电压依赖性转换速率。我们提出了一种快速探索和描述离子通道动力学的新方法，并用 hERG 钾通道作为示例应用该方法，目的是生成离子电流的定量预测表示。我们拟合了一个数学模型，用于拟合 CHO 细胞中超表达 hERG1a 时由新型 8 秒正弦电压钳产生的电流。然后，该模型用于预测同一细胞中对进一步方案的超过 5 分钟的记录：一系列传统的方波阶跃电压钳，以及由一系列生理相关动作电位组成的新型电压钳。我们证明，我们可以做出具有预测性的细胞特异性模型，这些模型优于使用来自多个不同细胞的平均数据，从而检查门控变化是如何导致电流动力学中的细胞间变异性的。我们的技术允许从单个细胞快速收集一致且高质量的数据，并产生比传统方法更具预测性的数学离子通道模型。
