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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.

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.

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.

Ischemic heart disease (IHD) is one of the leading causes of mortality worldwide. Preserv-ing 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 pathophysio-logical 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, contrac-tile, 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 ampli-tude. 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 ob-tained. The antiarrhythmic effect of levosimendan was clearly observed, suggesting a possible new clinical use for the drug. publishedVersion

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.

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.

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. β-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 . β-Agonists slow depolarization in RyR2-mutant cells and in CPVT patients. These findings may constitute a marker of arrhythmogenicity.

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.

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.

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. Public Library of Science open access