Beattie, Kylie A and Hill, Adam P and Bardenet, Rémi and Cui, Yi and Vandenberg, Jamie I and Gavaghan, David J and de Boer, Teun P and Mirams, Gary R (2018) Sinusoidal voltage protocols for rapid characterisation of ion channel kinetics. Journal of Physiology, ePub. ISSN 00223751 (Not OA)
Beattie, Kylie A and Hill, Adam P and Bardenet, Rémi and Cui, Yi and Vandenberg, Jamie I and Gavaghan, David J and de Boer, Teun P and Mirams, Gary R (2018) Sinusoidal voltage protocols for rapid characterisation of ion channel kinetics. Journal of Physiology, ePub. ISSN 00223751 (Not OA)
Beattie, Kylie A and Hill, Adam P and Bardenet, Rémi and Cui, Yi and Vandenberg, Jamie I and Gavaghan, David J and de Boer, Teun P and Mirams, Gary R (2018) Sinusoidal voltage protocols for rapid characterisation of ion channel kinetics. Journal of Physiology, ePub. ISSN 00223751 (Not OA)
Abstract
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 fit a mathematical model to currents evoked by a novel 8 s sinusoidal voltage clamp in CHO cells over-expressing hERG1a. The model is then used to predict over 5 min 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 comprised of 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. This article is protected by copyright. All rights reserved.
Metadata
Subjects: | R Medicine > R Medicine (General) |
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Depositing User: | Repository Administrator |
Date Deposited: | 27 Mar 2018 20:57 |
Last Modified: | 27 Mar 2018 20:57 |