The adult mammalian heart has limited regenerative capacity and was generally considered to contain no dividing cells. Recently, however, a resident population of progenitor cells has been identified, which could represent a new source of cardiomyocytes. Here, we describe the efficient isolation and propagation of human cardiomyocyte progenitor cells (hCMPCs) from fetal heart and patient biopsies. Establishment of hCMPC cultures was remarkably reproducible, with over 70% of adult atrial biopsies resulting in robustly expanding cell populations. Following the addition of transforming growth factor beta, almost all cells differentiated into spontaneously beating myocytes with characteristic cross striations. hCMPC-derived cardiomyocytes showed gap-junctional communication and action potentials of maturing cardiomyocytes. These are the first cells isolated from human heart that proliferate and form functional cardiomyocytes without requiring coculture with neonatal myocytes. Their scalability and homogeneity are unique and provide an excellent basis for developing physiological, pharmacological, and toxicological assays on human heart cells in vitro.

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. AbstractUnderstanding 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.

Cardiac electrophysiology models have been developed for over 50 years, and now include detailed descriptions of individual ion currents and sub-cellular calcium handling. It is commonly accepted that there are many uncertainties in these systems, with quantities such as ion channel kinetics or expression levels being difficult to measure or variable between samples. Until recently, the original approach of describing model parameters using single values has been retained, and consequently the majority of mathematical models in use today provide point predictions, with no associated uncertainty.In recent years, statistical techniques have been developed and applied in many scientific areas to capture uncertainties in the quantities that determine model behaviour, and to provide a distribution of predictions which accounts for this uncertainty. In this paper we discuss this concept, which is termed uncertainty quantification, and consider how it might be applied to cardiac electrophysiology models.We present two case studies in which probability distributions, instead of individual numbers, are inferred from data to describe quantities such as maximal current densities. Then we show how these probabilistic representations of model parameters enable probabilities to be placed on predicted behaviours. We demonstrate how changes in these probability distributions across data sets offer insight into which currents cause beat-to-beat variability in canine APs. We conclude with a discussion of the challenges that this approach entails, and how it provides opportunities to improve our understanding of electrophysiology.

Preclinical drug screens are not based on human physiology, possibly complicating predictions on cardiotoxicity. Drug screening can be humanised with in vitro assays using human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). However, in contrast to adult ventricular cardiomyocytes, iPSC-CMs beat spontaneously due to presence of the pacemaking current I and reduced densities of the hyperpolarising current I. In adult cardiomyocytes, I finalises repolarisation by stabilising the resting membrane potential while also maintaining excitability. The reduced I density contributes to proarrhythmic traits in iPSC-CMs, which leads to an electrophysiological phenotype that might bias drug responses. The proarrhythmic traits can be suppressed by increasing I in a balanced manner. We systematically evaluated all studies that report strategies to mature iPSC-CMs and found that only few studies report I current densities. Furthermore, these studies did not succeed in establishing sufficient I levels as they either added too little or too much I. We conclude that reduced densities of I remain a major flaw in iPSC-CMs, which hampers their use for in vitro drug screening.

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