Kirsi Penttinen

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 Ca2+ release, has been shown to rescue this abnormal Ca2+ 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 Ca2+ imaging. In iPSC derived CMs with RyR2 mutations in the N-terminal or central region, dantrolene suppressed the Ca2+ 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

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.

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.

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 Ca2+ 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.

Classification of iPSC colony images using hierarchical strategies with support vector machines

In this preliminary research we examine the suitability of hierarchical strategies of multi-class support vector machines for classification of induced pluripotent stem cell (iPSC) colony images. The iPSC technology gives incredible possibilities for safe and patient specific drug therapy without any ethical problems. However, growing of iPSCs is a sensitive process and abnormalities may occur during the growing process. These abnormalities need to be recognized and the problem returns to image classification. We have a collection of 80 iPSC colony images where each one of the images is prelabeled by an expert to class bad, good or semigood. We use intensity histograms as features for classification and we evaluate histograms from the whole image and the colony area only having two datasets. We perform two feature reduction procedures for both datasets. In classification we examine how different hierarchical constructions effect the classification. We perform thorough evaluation and the best accuracy was around 54% obtained with the linear kernel function. Between different hierarchical structures, in many cases there are no significant changes in results. As a result, intensity histograms are a good baseline for the classification of iPSC colony images but more sophisticated feature extraction and reduction methods together with other classification methods need to be researched in 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.

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.

Interactive Biosignal Analysis and Classification

We often encounter experimental results that are difficult to quantify although the visual recognition of a phenomenon is apparent. We are able to detect patterns and features with our vision that are complex to specify algorithmically. This is because our brain has evolved into a powerful pattern matching machine. One important class of measurement results in medical research are signals that describe a cycling pattern of some phenomenon. In this work, we developed a software tool, Anomaly Explorer, to assist in classification of Ca2+ signals emitted by cardiomyocytes. Anomaly Explorer was designed and implemented to analyze and classify Ca2+ signals with criteria similar to the criteria used by laboratory personnel. The software tool is based on interactive visualization with a direct manipulation user interface. Our Anomaly Explorer has proven to be a valuable tool as it makes the analysis faster, more accurate, and person-independent. The definition of signal anomalies in terms of visual properties is more flexible than numerical or analytical specification. Our approach is not limited to the Ca2+ signal analysis. The anomaly types are domain-specific, but the interaction and visualization ideas can be applied in any signal analysis problem.

Antiarrhythmic Effects of Carvedilol and Flecainide in Cardiomyocytes Derived from Catecholaminergic Polymorphic Ventricular Tachycardia Patients

Mutations in the cardiac ryanodine receptor (RYR2) are the leading cause for catecholaminergic polymorphic ventricular tachycardia (CPVT). In this study, we evaluated antiarrhythmic efficacy of carvedilol and flecainide in CPVT patient-specific induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) carrying different mutations in RYR2. iPSC-CMs were generated from skin biopsies of CPVT patients carrying exon 3 deletion and L4115 or V4653F mutation in RYR2 and of a healthy individual. Ca2+ kinetics and drug effects were studied with Fluo-4 AM indicator. Carvedilol abolished Ca2+ abnormalities in 31% of L4115F, 36% of V4653F, and 46% of exon 3 deletion carrying CPVT cardiomyocytes and flecainide 33%, 30%, and 52%, respectively. Both drugs lowered the intracellular Ca2+ level and beating rate of the cardiomyocytes significantly. Moreover, flecainide caused abnormal Ca2+ transients in 61% of controls compared to 26% of those with carvedilol. Carvedilol and flecainide were equally effective in CPVT iPSC-CMs. However, flecainide induced arrhythmias in 61% of control cells. CPVT cardiomyocytes carrying the exon 3 deletion had the most severe Ca2+ abnormalities, but they had the best response to drug therapies. According to this study, the arrhythmia-abolishing effect of neither of the drugs is optimal. iPSC-CMs provide a unique platform for testing drugs for CPVT.

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.