In the present study, channelrhodopsin 2 (ChR2) was specifically introduced into murine cells expressing the Phenylethanolamine n-methyltransferase (Pnmt) gene, which encodes for the enzyme responsible for conversion of noradrenaline to adrenaline. The new murine model enabled the identification of a distinctive class of Pnmt-expressing neuroendocrine cells and their descendants (i.e. Pnmt+ cell derived cells) within the heart. Here, we show that Pnmt+ cells predominantly localized to the left side of the adult heart. Remarkably, many of the Pnmt+ cells in the left atrium and ventricle appeared to be working cardiomyocytes based on their morphological appearance and functional properties. These Pnmt+ cell derived cardiomyocytes (PdCMs) are similar to conventional myocytes in morphological, electrical and contractile properties. By stimulating PdCMs selectively with blue light, we were able to control cardiac rhythm in the whole heart, isolated tissue preparations and single cardiomyocytes. Our new murine model effectively demonstrates functional dissection of cardiomyocyte subpopulations using optogenetics, and opens new frontiers of exploration into their physiological roles in normal heart function as well as their potential application for selective cardiac repair and regeneration strategies.

In cardiac electrophysiology, there exist many sources of inter- and intra-personal variability. These include variability in conditions and environment, and genotypic and molecular diversity, including differences in expression and behavior of ion channels and transporters, which lead to phenotypic diversity (e.g., variable integrated responses at the cell, tissue, and organ levels). These variabilities play an important role in progression of heart disease and arrhythmia syndromes and outcomes of therapeutic interventions. Yet, the traditional in silico framework for investigating cardiac arrhythmias is built upon a parameter/property-averaging approach that typically overlooks the physiological diversity. Inspired by work done in genetics and neuroscience, new modeling frameworks of cardiac electrophysiology have been recently developed that take advantage of modern computational capabilities and approaches, and account for the variance in the biological data they are intended to illuminate. In this review, we outline the recent advances in statistical and computational techniques that take into account physiological variability, and move beyond the traditional cardiac model-building scheme that involves averaging over samples from many individuals in the construction of a highly tuned composite model. We discuss how these advanced methods have harnessed the power of big (simulated) data to study the mechanisms of cardiac arrhythmias, with a special emphasis on atrial fibrillation, and improve the assessment of proarrhythmic risk and drug response. The challenges of using in silico approaches with variability are also addressed and future directions are proposed.

Background and Purpose Pharmacotherapy of atrial fibrillation (AF), the most common cardiac arrhythmia, remains unsatisfactory due to low efficacy and safety concerns. New therapeutic strategies target atrial‐predominant ion‐channels and involve multichannel block (poly)therapy. As AF is characterized by rapid and irregular atrial activations, compounds displaying potent antiarrhythmic effects at fast and minimal effects at slow rates are desirable. We present a novel systems pharmacology framework to quantitatively evaluate synergistic anti‐AF effects of combined block of multiple atrial‐predominant K+ currents (ultra‐rapid delayed rectifier K+ current, IKur, small conductance Ca2+‐activated K+ current, IKCa, K2P3.1 2‐pore‐domain K+ current, IK2P) in AF. Experimental Approach We constructed experimentally calibrated populations of virtual atrial myocyte models in normal sinus rhythm and AF‐remodelled conditions using two distinct, well‐established atrial models. Sensitivity analyses on our atrial populations was used to investigate the rate dependence of action potential duration (APD) changes due to blocking IKur, IK2P or IKCa and interactions caused by blocking of these currents in modulating APD. Block was simulated in both single myocytes and one‐dimensional tissue strands to confirm insights from the sensitivity analyses and examine anti‐arrhythmic effects of multi‐atrial‐predominant K+ current block in single cells and coupled tissue. Key Results In both virtual atrial myocytes and tissues, multiple atrial‐predominant K+‐current block promoted favourable positive rate‐dependent APD prolongation and displayed positive rate‐dependent synergy, that is, increasing synergistic antiarrhythmic effects at fast pacing versus slow rates. Conclusion and Implications Simultaneous block of multiple atrial‐predominant K+ currents may be a valuable antiarrhythmic pharmacotherapeutic strategy for AF.

Models of electrical activation and recovery in cardiac cells and tissue have become valuable research tools, and are beginning to be used in safety-critical applications including guidance for clinical procedures and for drug safety assessment. As a consequence, there is an urgent need for a more detailed and quantitative understanding of the ways that uncertainty and variability influence model predictions. In this paper, we review the sources of uncertainty in these models at different spatial scales, discuss how uncertainties are communicated across scales, and begin to assess their relative importance. We conclude by highlighting important challenges that continue to face the cardiac modelling community, identifying open questions, and making recommendations for future studies. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.

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