Wavelet-based protocols for ion channel electrophysiology

Fast functional mapping of ligand-gated ion channels

Ligand-gated ion channels are formed by three to five subunits that control the opening of the pore in a cooperative fashion. We developed a microfluidic chip-based technique for studying ion currents and fluorescence signals in either excised membrane patches or whole cells to measure activation and deactivation kinetics of the channels as well as ligand binding and unbinding when using confocal patch-clamp fluorometry. We show how this approach produces in a few seconds either unidirectional concentration-activation relationships at or near equilibrium and, moreover, respective time courses of activation and deactivation for a large number of freely designed steps of the ligand concentration. The short measuring period strongly minimizes the contribution of disturbing superimposing effects such as run-down phenomena and desensitization effects. To validate gating mechanisms, complex kinetic schemes are quantified without the requirement to have data at equilibrium. The new method has potential for functionally analyzing any ligand-gated ion channel and, beyond, also for other receptors.

Calibration of ionic and cellular cardiac electrophysiology models

Cardiac electrophysiology models are among the most mature and well-studied mathematical models of biological systems. This maturity is bringing new challenges as models are being used increasingly to make quantitative rather than qualitative predictions. As such, calibrating the parameters within ion current and action potential (AP) models to experimental data sets is a crucial step in constructing a predictive model. This review highlights some of the fundamental concepts in cardiac model calibration and is intended to be readily understood by computational and mathematical modelers working in other fields of biology. We discuss the classic and latest approaches to calibration in the electrophysiology field, at both the ion channel and cellular AP scales. We end with a discussion of the many challenges that work to date has raised and the need for reproducible descriptions of the calibration process to enable models to be recalibrated to new data sets and built upon for new studies. This article is categorized under: Analytical and Computational Methods > Computational Methods Physiology > Mammalian Physiology in Health and Disease Models of Systems Properties and Processes > Cellular Models.

Illumination/Darkness-Induced Changes in Leaf Surface Potential Linked With Kinetics of Ion Fluxes

A highly reproducible plant electrical signal-light-induced bioelectrogenesis (LIB) was obtained by means of periodic illumination/darkness stimulation of broad bean (Vicia faba L.) leaves. By stimulating the same position of the same leaf with different concentrations of NaCl, we observed that the amplitude and waveform of the LIB was correlated with the intensity of stimulation. This method allowed us to link dynamic ion fluxes induced by periodic illumination/darkness to salt stress. The self-referencing ion electrode technique was used to explore the ionic mechanisms of the LIB. Fluxes of H+, Ca2+, K+, and Cl- showed periodic changes under periodic illumination/darkness before and after 50 mM NaCl stimulation. Gray relational analysis was used to analyze correlations between each of these ions and LIB. The results showed that different ions are involved in surface potential changes at different stages under periodic illumination/darkness. The gray relational grade reflected the contribution of each ion to the change in surface potential at a certain time period. The ion fluxes data obtained under periodic illumination/darkness stimulation will contribute to the future development of a dynamic model for interpretation of electrophysiological events in plant cells.

Ribosome elongating footprints denoised by wavelet transform comprehensively characterize dynamic cellular translation events

Translation is dynamically regulated during cell development and stress response. In order to detect actively translated open reading frames (ORFs) and dynamic cellular translation events, we have developed a computational method, RiboWave, to process ribosome profiling data. RiboWave utilizes wavelet transform to denoise the original signal by extracting 3-nt periodicity of ribosomes and precisely locate their footprint denoted as Periodic Footprint P-site (PF P-site). Such high-resolution footprint is found to capture the full track of actively elongating ribosomes, from which translational landscape can be explicitly characterized. We compare RiboWave with several published methods, like RiboTaper, ORFscore and RibORF, and found that RiboWave outperforms them in both accuracy and usage when defining actively translated ORFs. Moreover, we show that PF P-site derived by RiboWave shows superior performance in characterizing the dynamics and complexity of cellular translatome by accurately estimating the abundance of protein levels, assessing differential translation and identifying dynamic translation frameshift.

Four Ways to Fit an Ion Channel Model

Mathematical models of ionic currents are used to study the electrophysiology of the heart, brain, gut, and several other organs. Increasingly, these models are being used predictively in the clinic, for example, to predict the risks and results of genetic mutations, pharmacological treatments, or surgical procedures. These safety-critical applications depend on accurate characterization of the underlying ionic currents. Four different methods can be found in the literature to fit voltage-sensitive ion channel models to whole-cell current measurements: method 1, fitting model equations directly to time-constant, steady-state, and I-V summary curves; method 2, fitting by comparing simulated versions of these summary curves to their experimental counterparts; method 3, fitting to the current traces themselves from a range of protocols; and method 4, fitting to a single current trace from a short and rapidly fluctuating voltage-clamp protocol. We compare these methods using a set of experiments in which hERG1a current was measured in nine Chinese hamster ovary cells. In each cell, the same sequence of fitting protocols was applied, as well as an independent validation protocol. We show that methods 3 and 4 provide the best predictions on the independent validation set and that short, rapidly fluctuating protocols like that used in method 4 can replace much longer conventional protocols without loss of predictive ability. Although data for method 2 are most readily available from the literature, we find it performs poorly compared to methods 3 and 4 both in accuracy of predictions and computational efficiency. Our results demonstrate how novel experimental and computational approaches can improve the quality of model predictions in safety-critical applications.

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 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.

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.