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Sinusoidal voltage protocols for rapid characterisation of ion channel kinetics
KEY POINTS: 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 tradition...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5978315/ https://www.ncbi.nlm.nih.gov/pubmed/29573276 http://dx.doi.org/10.1113/JP275733 |
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author | Beattie, Kylie A. Hill, Adam P. Bardenet, Rémi Cui, Yi Vandenberg, Jamie I. Gavaghan, David J. de Boer, Teun P. Mirams, Gary R. |
author_facet | Beattie, Kylie A. Hill, Adam P. Bardenet, Rémi Cui, Yi Vandenberg, Jamie I. Gavaghan, David J. de Boer, Teun P. Mirams, Gary R. |
author_sort | Beattie, Kylie A. |
collection | PubMed |
description | KEY POINTS: 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. 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 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. |
format | Online Article Text |
id | pubmed-5978315 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-59783152018-06-06 Sinusoidal voltage protocols for rapid characterisation of ion channel kinetics Beattie, Kylie A. Hill, Adam P. Bardenet, Rémi Cui, Yi Vandenberg, Jamie I. Gavaghan, David J. de Boer, Teun P. Mirams, Gary R. J Physiol Techniques for Physiology KEY POINTS: 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. 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 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. John Wiley and Sons Inc. 2018-04-17 2018-05-15 /pmc/articles/PMC5978315/ /pubmed/29573276 http://dx.doi.org/10.1113/JP275733 Text en © 2018 The Authors. The Journal of Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Techniques for Physiology Beattie, Kylie A. Hill, Adam P. Bardenet, Rémi Cui, Yi Vandenberg, Jamie I. Gavaghan, David J. de Boer, Teun P. Mirams, Gary R. Sinusoidal voltage protocols for rapid characterisation of ion channel kinetics |
title | Sinusoidal voltage protocols for rapid characterisation of ion channel kinetics |
title_full | Sinusoidal voltage protocols for rapid characterisation of ion channel kinetics |
title_fullStr | Sinusoidal voltage protocols for rapid characterisation of ion channel kinetics |
title_full_unstemmed | Sinusoidal voltage protocols for rapid characterisation of ion channel kinetics |
title_short | Sinusoidal voltage protocols for rapid characterisation of ion channel kinetics |
title_sort | sinusoidal voltage protocols for rapid characterisation of ion channel kinetics |
topic | Techniques for Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5978315/ https://www.ncbi.nlm.nih.gov/pubmed/29573276 http://dx.doi.org/10.1113/JP275733 |
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