HiPSC-derived cardiomyocyte to model Brugada syndrome: both asymptomatic and symptomatic mutation carriers reveal increased arrhythmogenicity.

Brugada syndrome is an inherited cardiac arrhythmia disorder that is mainly associated with mutations of the cardiac voltage-gated sodium channel alpha subunit 5 (SCN5A) gene. The clinical symptoms include ventricular fibrillation and an increased risk of sudden cardiac death. Human-induced pluripotent stem cell (hiPSC) lines were derived from symptomatic and asymptomatic individuals carrying the R1913C mutation in the SCN5A gene. The present work aimed to observe the phenotype-specific differences in hiPSC-derived cardiomyocytes (CMs) obtained from symptomatic and asymptomatic mutation carriers. In this study, CM electrophysiological properties, beating abilities and calcium parameters were measured. Mutant CMs exhibited higher average sodium current densities than healthy CMs, but the differences were not statistically significant. Action potential durations were significantly shorter in CMs from the symptomatic individual, and a spike-and-dome morphology of action potential was exclusively observed in CMs from the symptomatic individual. More arrhythmias occurred in mutant CMs at single cell and cell aggregate levels compared with those observed in wild-type CMs. Moreover, there were no major differences in ionic currents or intracellular calcium dynamics between the CMs of asymptomatic and symptomatic individuals after the administration of adrenaline and flecainide.In conclusion, mutant CMs were more prone to arrhythmia than healthy CMs but did not explain why only one of the mutation carriers was symptomatic.

Fluorescent hiPSC-derived MYH6-mScarlet cardiomyocytes for real-time tracking, imaging, and cardiotoxicity assays.

Human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) hold great potential in the cardiovascular field for human disease modeling, drug development, and regenerative medicine. However, multiple hurdles still exist for the effective utilization of hiPSC-CMs as a human-based experimental platform that can be an alternative to the current animal models. To further expand their potential as a research tool and bridge the translational gap, we have generated a cardiac-specific hiPSC reporter line that differentiates into fluorescent CMs using CRISPR-Cas9 genome editing technology. The CMs illuminated with the mScarlet fluorescence enable their non-invasive continuous tracking and functional cellular phenotyping, offering a real-time 2D/3D imaging platform. Utilizing the reporter CMs, we developed an imaging-based cardiotoxicity screening system that can monitor distinct drug-induced structural toxicity and CM viability in real time. The reporter fluorescence enabled visualization of sarcomeric disarray and displayed a drug dose-dependent decrease in its fluorescence. The study also has demonstrated the reporter CMs as a biomaterial cytocompatibility analysis tool that can monitor dynamic cell behavior and maturity of hiPSC-CMs cultured in various biomaterial scaffolds. This versatile cardiac imaging tool that enables real time tracking and high-resolution imaging of CMs has significant potential in disease modeling, drug screening, and toxicology testing.

Cardiac Ischemia On-a-Chip: Antiarrhythmic Effect of Levosimendan on Ischemic Human-Induced Pluripotent Stem Cell-Derived Cardiomyocytes.

Ischemic heart disease (IHD) is one of the leading causes of mortality worldwide. Preserving functionality and preventing arrhythmias of the heart are key principles in the management of patients with IHD. Levosimendan, a unique calcium (Ca2+) enhancer with inotropic activity, has been introduced into clinical usage for heart failure treatment. Human-induced pluripotent cell-derived cardiomyocytes (hiPSC-CMs) offer an opportunity to better understand the pathophysiological mechanisms of the disease as well as to serve as a platform for drug screening. Here, we developed an in vitro IHD model using hiPSC-CMs in hypoxic conditions and defined the effects of the subsequent hypoxic stress on CMs functionality. Furthermore, the effect of levosimendan on hiPSC-CMs functionality was evaluated during and after hypoxic stress. The morphology, contractile, Ca2+-handling, and gene expression properties of hiPSC-CMs were investigated in response to hypoxia. Hypoxia resulted in significant cardiac arrhythmia and decreased Ca2+ transient amplitude. In addition, disorganization of sarcomere structure was observed after hypoxia induction. Interestingly, levosimendan presented significant antiarrhythmic properties, as the arrhythmia was abolished or markedly reduced with levosimendan treatment either during or after the hypoxic stress. Moreover, levosimendan presented significant protection from the sarcomere alterations induced by hypoxia. In conclusion, this chip model appears to be a suitable preclinical representation of IHD. With this hypoxia platform, detailed knowledge of the disease pathophysiology can be obtained. The antiarrhythmic effect of levosimendan was clearly observed, suggesting a possible new clinical use for the drug.

Data analytics for cardiac diseases.

In the present research we tackled the classification of seven genetic cardiac diseases and control subjects by using an extensive set of machine learning algorithms with their variations from simple K-nearest neighbor searching method to support vector machines. The research was based on calcium transient signals measured from induced pluripotent stem cell-derived cardiomyocytes. All in all, 55 different machine learning alternatives were used to model eight classes by applying the principle of 10-fold crossvalidation with the peak data of 1626 signals. The best classification accuracy of approximately 69% was given by random forests, which can be seen high enough here to show machine learning to be potential for the differentiation of the eight disease classes.

On computational classification of genetic cardiac diseases applying iPSC cardiomyocytes.

BACKGROUND: Cardiomyocytes differentiated from human induced pluripotent stem cells (iPSC-CMs) can be used to study genetic cardiac diseases. In patients these diseases are manifested e.g. with impaired contractility and fatal cardiac arrhythmias, and both of these can be due to abnormal calcium transients in cardiomyocytes. Here we classify different genetic cardiac diseases using Ca2+ transient data and different machine learning algorithms. METHODS: By studying calcium cycling of disease-specific iPSC-CMs and by using calcium transients measured from these cells it is possible to classify diseases from each other and also from healthy controls by applying machine learning computation on the basis of peak attributes detected from calcium transient signals. RESULTS: In the current research we extend our previous study having Ca-transient data from four different genetic diseases by adding data from two additional diseases (dilated cardiomyopathy and long QT Syndrome 2). We also study, in the light of the current data, possible differences and relations when machine learning modelling and classification accuracies were computed by using either leave-one-out test or 10-fold cross-validation. CONCLUSIONS: Despite more complex classification tasks compared to our earlier research and having more different genetic cardiac diseases in the analysis, it is still possible to attain good disease classification results. As excepted, leave-one-out test and 10-fold cross-validation achieved virtually equal results.

Analysis of Drug Effects on iPSC Cardiomyocytes with Machine Learning.

Patient-specific induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) offer an attractive experimental platform to investigate cardiac diseases and therapeutic outcome. In this study, iPSC-CMs were utilized to study their calcium transient signals and drug effects by means of machine learning, a central part of artificial intelligence. Drug effects were assessed in six iPSC-lines carrying different mutations causing catecholaminergic polymorphic ventricular tachycardia (CPVT), a highly malignant inherited arrhythmogenic disorder. The antiarrhythmic effect of dantrolene, an inhibitor of sarcoplasmic calcium release, was studied in iPSC-CMs after adrenaline, an adrenergic agonist, stimulation by machine learning analysis of calcium transient signals. First, beats of transient signals were identified with our peak recognition algorithm previously developed. Then 12 peak variables were computed for every identified peak of a signal and by means of this data signals were classified into different classes corresponding to those affected by adrenaline or, thereafter, affected by a drug, dantrolene. The best classification accuracy was approximately 79% indicating that machine learning methods can be utilized in analysis of iPSC-CM drug effects. In the future, data analysis of iPSC-CM drug effects together with machine learning methods can create a very valuable and efficient platform to individualize medication in addition to drug screening and cardiotoxicity studies.

Arrhythmia Mechanisms in Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes.

ABSTRACT: Despite major efforts by clinicians and researchers, cardiac arrhythmia remains a leading cause of morbidity and mortality in the world. Experimental work has relied on combining high-throughput strategies with standard molecular and electrophysiological studies, which are, to a great extent, based on the use of animal models. Because this poses major challenges for translation, the progress in the development of novel antiarrhythmic agents and clinical care has been mostly disappointing. Recently, the advent of human induced pluripotent stem cell-derived cardiomyocytes has opened new avenues for both basic cardiac research and drug discovery; now, there is an unlimited source of cardiomyocytes of human origin, both from healthy individuals and patients with cardiac diseases. Understanding arrhythmic mechanisms is one of the main use cases of human induced pluripotent stem cell-derived cardiomyocytes, in addition to pharmacological cardiotoxicity and efficacy testing, in vitro disease modeling, developing patient-specific models and personalized drugs, and regenerative medicine. Here, we review the advances that the human induced pluripotent stem cell-derived-based modeling systems have brought so far regarding the understanding of both arrhythmogenic triggers and substrates, while also briefly speculating about the possibilities in the future.

hiPSC-Derived Cardiomyocyte Model of LQT2 Syndrome Derived from Asymptomatic and Symptomatic Mutation Carriers Reproduces Clinical Differences in Aggregates but Not in Single Cells.

Mutations in the HERG gene encoding the potassium ion channel HERG, represent one of the most frequent causes of long QT syndrome type-2 (LQT2). The same genetic mutation frequently presents different clinical phenotypes in the family. Our study aimed to model LQT2 and study functional differences between the mutation carriers of variable clinical phenotypes. We derived human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) from asymptomatic and symptomatic HERG mutation carriers from the same family. When comparing asymptomatic and symptomatic single LQT2 hiPSC-CMs, results from allelic imbalance, potassium current density, and arrhythmicity on adrenaline exposure were similar, but a difference in Ca2+ transients was observed. The major differences were, however, observed at aggregate level with increased susceptibility to arrhythmias on exposure to adrenaline or potassium channel blockers on CM aggregates derived from the symptomatic individual. The effect of this mutation was modeled in-silico which indicated the reactivation of an inward calcium current as one of the main causes of arrhythmia. Our in-vitro hiPSC-CM model recapitulated major phenotype characteristics observed in LQT2 mutation carriers and strong phenotype differences between LQT2 asymptomatic vs. symptomatic were revealed at CM-aggregate level.

Separation of HCM and LQT Cardiac Diseases with Machine Learning of Ca2+ Transient Profiles.

BACKGROUND: Modeling human cardiac diseases with induced pluripotent stem cells not only enables to study disease pathophysiology and develop therapies but also, as we have previously showed, it can offer a tool for disease diagnostics. We previously observed that a few genetic cardiac diseases can be separated from each other and healthy controls by applying machine learning to Ca2+ transient signals measured from iPSC-derived cardiomyocytes (CMs). OBJECTIVES: For the current research, 419 hypertrophic cardiomyopathy (HCM) transient signals and 228 long QT syndrome (LQTS) transient signals were measured. HCM signals included data recorded from iPSC-CMs carrying either α-tropomyosin, i.e., TPM1 (HCMT) or MYBPC3 or myosin-binding protein C (HCMM) mutation and LQTS signals included data recorded from iPSC-CMs carrying potassium voltage-gated channel subfamily Q member 1 (KCNQ1) mutation (long QT syndrome 1 [LQT1]) or KCNH2 mutation (long QT syndrome 2 [LQT2]). The main objective was to study whether and how effectively HCMM and HCMT can be separated from each other as well as LQT1 from LQT2. METHODS: After preprocessing those Ca2+ signals where we computed peak waveforms we then classified the two mutations of both disease pairs by using several different machine learning methods. RESULTS: We obtained excellent classification accuracies of 89% for HCM and even 100% for LQT at their best. CONCLUSION: The results indicate that the methods applied would be efficient for the identification of these genetic cardiac diseases.

Automatic Optimization of an in Silico Model of Human iPSC Derived Cardiomyocytes Recapitulating Calcium Handling Abnormalities.

The growing importance of human induced pluripotent stem cell-derived cardiomyoyctes (hiPSC-CMs), as patient-specific and disease-specific models for studying cellular cardiac electrophysiology or for preliminary cardiotoxicity tests, generated better understanding of hiPSC-CM biophysical mechanisms and great amount of action potential and calcium transient data. In this paper, we propose a new hiPSC-CM in silico model, with particular attention to Ca2+ handling. We used (i) the hiPSC-CM Paci2013 model as starting point, (ii) a new dataset of Ca2+ transient measurements to tune the parameters of the inward and outward Ca2+ fluxes of sarcoplasmic reticulum, and (iii) an automatic parameter optimization to fit action potentials and Ca2+ transients. The Paci2018 model simulates, together with the typical hiPSC-CM spontaneous action potentials, more refined Ca2+ transients and delayed afterdepolarizations-like abnormalities, which the old Paci2013 was not able to predict due to its mathematical formulation. The Paci2018 model was validated against (i) the same current blocking experiments used to validate the Paci2013 model, and (ii) recently published data about effects of different extracellular ionic concentrations. In conclusion, we present a new and more versatile in silico model, which will provide a platform for modeling the effects of drugs or mutations that affect Ca2+ handling in hiPSC-CMs.

Detection of genetic cardiac diseases by Ca2+ transient profiles using machine learning methods.

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have revolutionized cardiovascular research. Abnormalities in Ca2+ transients have been evident in many cardiac disease models. We have shown earlier that, by exploiting computational machine learning methods, normal Ca2+ transients corresponding to healthy CMs can be distinguished from diseased CMs with abnormal transients. Here our aim was to study whether it is possible to separate different genetic cardiac diseases (CPVT, LQT, HCM) on the basis of Ca2+ transients using machine learning methods. Classification accuracies of up to 87% were obtained for these three diseases, indicating that Ca2+ transients are disease-specific. By including healthy controls in the classifications, the best classification accuracy obtained was still high: approximately 79%. In conclusion, we demonstrate as the proof of principle that the computational machine learning methodology appears to be a powerful means to accurately categorize iPSC-CMs and could provide effective methods for diagnostic purposes in the future.

Maturation of human pluripotent stem cell derived cardiomyocytes is improved in cardiovascular construct.

In order to translate preclinical data into the clinical studies, relevant in vitro models with structure and key functional properties similar to native human tissue should be used. In vitro cardiac models with vascular structures mimic the highly vascularized myocardium and provide interactions between endothelial cells, stromal cells and cardiomyocytes. Currently, human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) have been shown to present immature morphology and fetal-like electrophysiological properties that may limit their use as physiological test platform. The aim of this study was to develop multicellular in vitro cardiovascular construct modeling human heart tissue. In the cardiovascular construct, hPSC-CMs were cultured with a vascular-like network formed by human foreskin fibroblasts and human umbilical vein endothelial cells that served as a platform in the construct. Cardiomyocyte orientation, maturation, electrophysiological properties and drug responses of the cardiovascular construct were characterized and compared to CM monoculture. hPSC-CMs in cardiovascular construct showed elongated morphology and aligned with the vascular-like network. Electrophysiological properties and calcium metabolism of hPSC-CMs as well as response to E-4031 and adrenaline demonstrated normal physiological behavior. Increased expression of cardiac structural proteins and ion channels in cardiovascular construct compared to CM monoculture were detected. In conclusion, vascular-like network supports the structural and functional maturation of hPSC-CMs. Our results suggest that cardiovascular construct presents more mature in vitro cardiac model compared to CM monoculture and could therefore serve as an advanced test system for cardiac safety and efficacy assessment as well as a model system for biomedical research.

Slowed depolarization and irregular repolarization in catecholaminergic polymorphic ventricular tachycardia: a study from cellular Ca2+ transients and action potentials to clinical monophasic action potentials and electrocardiography.

AIMS: Spontaneous Ca2+ release leads to afterdepolarizations and triggered arrhythmia in catecholaminergic polymorphic ventricular tachycardia (CPVT). Irregular Ca2+ release is hypothesized to manifest as slowed depolarization and irregular repolarization. Our goal was to study depolarization and repolarization abnormalities in CPVT, as they remain largely uninvestigated. METHODS AND RESULTS: We studied intracellular Ca2+ handling and action potentials (APs) in an induced pluripotent stem cell (iPSC) model of CPVT. Induced pluripotent stem cell cardiomyocytes from a RyR2-P2328S patient showed increased non-alternating variability of Ca2+ transients in response to isoproterenol. ß-Agonists decreased AP upslope velocity in CPVT cells and in monophasic AP recordings of CPVT patients. We compared 24 h electrocardiograms (ECGs) of 19 CPVT patients carrying RyR2 mutations and 19 healthy controls. Short-term variability (STV) of the QT interval was 6.9 ± 0.5 ms in CPVT patients vs. 5.5 ± 0.4 ms in controls (P < 0.05) and associated with a history of arrhythmic events. Mean T-wave alternans (TWA) was 25 ± 1.4 µV in CPVT patients vs. 31 ± 2.0 µV in controls (P < 0.05). Older CPVT patients showed lower maximal upslope velocity of the ECG R-spike than control patients. CONCLUSION: Catecholaminergic polymorphic ventricular tachycardia patients show higher STV of repolarization but lower TWA on the 24 h ECG than control patients, which is likely to reflect increased non-alternating variability of Ca2+ release by mutant RyR2s as observed in vitro. ß-Agonists slow depolarization in RyR2-mutant cells and in CPVT patients. These findings may constitute a marker of arrhythmogenicity.

Novel Analysis Software for Detecting and Classifying Ca2+ Transient Abnormalities in Stem Cell-Derived Cardiomyocytes.

Comprehensive functioning of Ca2+ cycling is crucial for excitation-contraction coupling of cardiomyocytes (CMs). Abnormal Ca2+ cycling is linked to arrhythmogenesis, which is associated with cardiac disorders and heart failure. Accordingly, we have generated spontaneously beating CMs from induced pluripotent stem cells (iPSC) derived from patients with catecholaminergic polymorphic ventricular tachycardia (CPVT), which is an inherited and severe cardiac disease. Ca2+ cycling studies have revealed substantial abnormalities in these CMs. Ca2+ transient analysis performed manually lacks accepted analysis criteria, and has both low throughput and high variability. To overcome these issues, we have developed a software tool, AnomalyExplorer based on interactive visualization, to assist in the classification of Ca2+ transient patterns detected in CMs. Here, we demonstrate the usability and capability of the software, and we also compare the analysis efficiency to manual analysis. We show that AnomalyExplorer is suitable for detecting normal and abnormal Ca2+ transients; furthermore, this method provides more defined and consistent information regarding the Ca2+ abnormality patterns and cell line specific differences when compared to manual analysis. This tool will facilitate and speed up the analysis of CM Ca2+ transients, making it both more accurate and user-independent. AnomalyExplorer can be exploited in Ca2+ cycling analysis to study basic disease pathology and the effects of different drugs.

Antiarrhythmic Effects of Dantrolene in Patients with Catecholaminergic Polymorphic Ventricular Tachycardia and Replication of the Responses Using iPSC Models.

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a highly malignant inherited arrhythmogenic disorder. Type 1 CPVT (CPVT1) is caused by cardiac ryanodine receptor (RyR2) gene mutations resulting in abnormal calcium release from sarcoplasmic reticulum. Dantrolene, an inhibitor of sarcoplasmic Ca(2+) release, has been shown to rescue this abnormal Ca(2+) release in vitro. We assessed the antiarrhythmic efficacy of dantrolene in six patients carrying various RyR2 mutations causing CPVT. The patients underwent exercise stress test before and after dantrolene infusion. Dantrolene reduced the number of premature ventricular complexes (PVCs) on average by 74% (range 33-97) in four patients with N-terminal or central mutations in the cytosolic region of the RyR2 protein, while dantrolene had no effect in two patients with mutations in or near the transmembrane domain. Induced pluripotent stem cells (iPSCs) were generated from all the patients and differentiated into spontaneously beating cardiomyocytes (CMs). The antiarrhythmic effect of dantrolene was studied in CMs after adrenaline stimulation by Ca(2+) imaging. In iPSC derived CMs with RyR2 mutations in the N-terminal or central region, dantrolene suppressed the Ca(2+) cycling abnormalities in 80% (range 65-97) of cells while with mutations in or near the transmembrane domain only in 23 or 32% of cells. In conclusion, we demonstrate that dantrolene given intravenously shows antiarrhythmic effects in a portion of CPVT1 patients and that iPSC derived CM models replicate these individual drug responses. These findings illustrate the potential of iPSC models to individualize drug therapy of inherited diseases.Trial Registration: EudraCT Clinical Trial Registry 2012-005292-14.

Signal analysis and classification methods for the calcium transient data of stem cell-derived cardiomyocytes.

Calcium cycling is crucial in the excitation-contraction coupling of cardiomyocytes, and therefore has a key role in cardiac functionality. Cardiac disorders and different drugs alter the calcium transients of cardiomyocytes and can cause serious dysfunction of the heart. New insights into this biochemical phenomena can be achieved by studying and analyzing calcium transients. Calcium transients of spontaneously beating human induced pluripotent stem cell-derived cardiomyocytes were recorded for a data set of 280 signals. Our objective was to develop and program procedures: (1) to automatically detect cycling peaks from signals and to classify the peaks of signals as either normal or abnormal, and (2) on the basis of the preceding peak detection results, to classify the entire signals into either a normal class or an abnormal class. We obtained a classification accuracy of approximately 80% compared to class decisions made separately by an experienced researcher, which is promising for the further development of an automatic classification approach. Automated classification software would be beneficial in the future for analyzing cardiomyocyte functionality on a large scale when screening for the adverse cardiac effects of new potential compounds, and also in future clinical applications.

Distinct electrophysiological and mechanical beating phenotypes of long QT syndrome type 1-specific cardiomyocytes carrying different mutations.

BACKGROUND: Long QT syndrome (LQTS) is associated with increased risk of ventricular arrhythmias and cardiac arrest. LQTS type 1 (LQT1), the most prevalent subtype of LQTS, is caused by defects of slow delayed rectifier potassium current (IKs) that lead to abnormal cardiac repolarization. Here we used pluripotent stem cell (iPSC)-technology to investigate both the electrophysiological and also for the first time the mechanical beating behavior of genetically defined, LQT1 specific cardiomyocytes (CMs) carrying different mutations. METHODS: We established in vitro models for LQT1 caused by two mutations (G589D or ivs7-2A>G). LQT1 specific CMs were derived from patient specific iPSCs and characterized for their electrophysiology using a current clamp and Ca2 +-imaging. Their mechanical beating characteristics were analyzed with video-image analysis method. RESULTS AND CONCLUSIONS: Both LQT1-CM-types showed prolonged repolarization, but only those with G589D presented early after-depolarizations at baseline. Increased amounts of abnormal Ca2 + transients were detected in both types of LQT1-CMs. Surprisingly, also the mechanical beating behavior demonstrated clear abnormalities and additionally the abnormalities were different with the two mutations: prolonged contraction was seen in G589D-CMs while impaired relaxation was observed in ivs7-2A>G-CMs. The CMs carrying two different LQT1 specific mutations (G589D or ivs7-2A>G) presented clear differences in their electrical properties as well as in their mechanical beating behavior. Results from different methods correlated well with each other suggesting that simply mechanical beating behavior of CMs could be used for screening of diseased CMs and possibly for diagnostic purposes in the future.

[Beating heart cells--targeted diagnostics, individual optimization of therapy and tailored cell therapies]. / Kantasoluista sykkiviä sydänsoluja.

Myocardial infarction causes scarring and loss of functional capacity of the heart, because the heart is itself unable to repair the damaged area. While the development of new forms of treatment for the repair of myocardial destruction has actually been investigated by introducing into the heart various stem cells present in an adult human, the efficacy of the treatments conducted in the studies has so far unfortunately been low. Embryonic stem cells and iPS cells are a highly significant research subject. Cardiomyocytes differentiated from stem cells are being studied also in drug testing, and they are expected to revolutionize drug development and safety tests of novel drugs as well as enable personalized medication in the future.

On computation of calcium cycling anomalies in cardiomyocytes data.

Induced pluripotent stem cell (iPSC) lines derived from skin fibroblasts of patients suffering from cardiac disorders were differentiated to cardiomyocytes and used to generate a data set of Ca(2+) transients of 136 recordings. The objective was to separate normal signals for later medical research from abnormal signals. We constructed a signal analysis procedure to detect peaks representing calcium cycling in signals and another procedure to classify them into either normal or abnormal peaks. Using machine learning methods we classified signals into normal or abnormal signals on the basis of peak findings in them. We compared classification results obtained to those made visually by an expert biotechnologist who assessed the signals independent of the computer method. Classification accuracies of around 85% indicated high congruence between two modes denoting the high capability and usefulness of computer based processing for the present data.