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

Kiviaho, Anna; Ahola, Antti; Larsson, Kim; Penttinen, Kirsi; Swan, Heikki; Pekkanen-Mattila, Mari; Venäläinen, Henna; Paavola, Kiti; Hyttinen, Jari; Aalto‐Setälä, Katriina

doi:10.1016/j.ijcha.2015.04.008

Cited by 32 publications

(46 citation statements)

References 34 publications

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“…Human iPSCs were generated as described earlier [42]. The LQT1-specific hiPSCs were derived from patients’ skin fibroblasts carrying G589D missense mutation in KCNQ1 [41, 43]. …”

Section: Methodsmentioning

confidence: 99%

“…All the hiPSC lines (UTA.04602.WT, UTA.00208.LQT1, UTA.00211.LQT1, UTA.00303.LQT1 and UTA.00313.LQT1) and the differentiated CMs from them have been previously characterized elsewhere [41, 43, 44]. …”

Section: Methodsmentioning

confidence: 99%

“…In Finland, the prevalence of gene mutations associated with LQTS is high (0.4 % of Finnish population), which has been explained to be caused by four founder mutations with one of them being C-terminal G589D missense mutation in KCNQ1 gene [31, 32]. The hiPSCs represent excellent research tool to study the pathophysiology of inherited cardiac diseases [33–41]. Until now, the fractal dynamics of hiPSC-LQT-CMs have not been investigated.…”

Section: Introductionmentioning

confidence: 99%

“…Here, we utilize patient-specific LQTS disease model [41] to study the fractal dynamics in the symptomatic and asymptomatic LQTS type 1 (LQT1)-specific CMs carrying Finnish founder mutation G589D. The aim of this study was to investigate the fractal dynamics of LQT-CMs in unmedicated and medicated conditions as compared to healthy control and under the effect of various compounds affecting cardiac action potential.…”

Section: Introductionmentioning

confidence: 99%

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The Effects of Pharmacological Compounds on Beat Rate Variations in Human Long QT-Syndrome Cardiomyocytes

Kuusela

Kim

Räsänen

et al. 2016

Stem Cell Rev and Rep

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Healthy human heart rate fluctuates overtime showing long-range fractal correlations. In contrast, various cardiac diseases and normal aging show the breakdown of fractal complexity. Recently, it was shown that human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) intrinsically exhibit fractal behavior as in humans. Here, we investigated the fractal complexity of hiPSC-derived long QT-cardiomyocytes (LQT-CMs). We recorded extracellular field potentials from hiPSC-CMs at baseline and under the effect of various compounds including β-blocker bisoprolol, ML277, a specific and potent IKs current activator, as well as JNJ303, a specific IKs blocker. From the peak-to-peak-intervals, we determined the long-range fractal correlations by using detrended fluctuation analysis. Electrophysiologically, the baseline corrected field potential durations (cFPDs) were more prolonged in LQT-CMs than in wildtype (WT)-CMs. Bisoprolol did not have significant effects to the cFPD in any CMs. ML277 shortened cFPD in a dose-dependent fashion by 11 % and 5–11 % in WT- and LQT-CMs, respectively. JNJ303 prolonged cFPD in a dose-dependent fashion by 22 % and 7–13 % in WT- and LQT-CMs, respectively. At baseline, all CMs showed fractal correlations as determined by short-term scaling exponent α. However, in all CMs, the α was increased when pharmacological compounds were applied indicating of breakdown of fractal complexity. These findings suggest that the intrinsic mechanisms contributing to the fractal complexity are not altered in LQT-CMs. The modulation of IKs channel and β1-adrenoreceptors by pharmacological compounds may affect the fractal complexity of the hiPSC-CMs.Electronic supplementary materialThe online version of this article (doi:10.1007/s12015-016-9686-0) contains supplementary material, which is available to authorized users.

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“…Human iPSCs were generated as described earlier [42]. The LQT1-specific hiPSCs were derived from patients’ skin fibroblasts carrying G589D missense mutation in KCNQ1 [41, 43]. …”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Effects of Pharmacological Compounds on Beat Rate Variations in Human Long QT-Syndrome Cardiomyocytes

Kuusela

Kim

Räsänen

et al. 2016

Stem Cell Rev and Rep

Self Cite

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“…The LQT1-specific hiPSCs are derived from LQTS patients' skin fibroblasts carrying G589D missense mutation in KCNQ1 [7,8]. The skin biopsies are obtained from a 55-year-old female healthy individual, a symptomatic 41-year-old female LQTS patient, and an asymptomatic 28-year-old LQT-mutation carrier.…”

Section: Hipsc-cm Preparationmentioning

confidence: 99%

Short- and Long-Range Correlations in Beat Rate Variability of Human Pluripotent-Stem-Cell-Derived Cardiomyocytes

Kim

Kuusela

Aalto‐Setälä

et al. 2017

Computing in Cardiology Conference (CinC)

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A healthy heart exhibits fractal, i.e., long-range correlated fluctuations in heart rate variability (HRV). It is recently shown that fractal dynamics is also an intrinsic feature of human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). In this study, we investigate short-and long-range correlations in beat rate variability (BRV) of hiPSC-CMs, obtained from a healthy subject and symptomatic and asymptomatic long QT syndrome patients. It is shown that it is important to distinguish correlation properties in short and long time scales, as the scaling exponents are significantly different and also behave differently in the acute exposure to pharmacological compounds that modulate β1-adrenoreceptors and cardiac ion channel generating delayed, outwardly rectifying K + current (I Ks ). While long-range scaling is sensitive to the drug exposure, short-range scaling is barely affected.

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A method to measure data complexity of a complicated medical data set

Juhola

Joutsijoki

Penttinen

et al. 2022

Int J Imaging Syst Tech

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In this article, we consider data complexity in the context of calcium transient signal data collected from induced pluripotent stem cell-derived cardiomyocytes. We present a novel way to measure data complexity based on the nearest neighbour searching method. Data complexity here is seen as overlapping and mixed data classes in addition to a relatively great number of data cases. Complexity affects classification results, which were run with nearest neighbour searching, feedforward artificial neural networks and random forests for seven genetic cardiological disease classes and healthy controls. The data are obtained from individuals carrying mutations for genetic cardiac diseases with induced pluripotent stem cell (iPSC) technology and the diseases include hypertrophic cardiomyopathy with two different founder gene mutations, dilated cardiomyopathy, long QT syndrome type 1 and 2, Brugada syndrome, a severe genetic ventricular arrhythmia (CPVT) and healthy controls.The data are from calcium transients from spontaneously beating iPSC-derived cardiomyocytes cultured in a biotechnology laboratory. When the genotype of the iPSC-derived cardiomyocytes is the same as the donor of the tissue sample and based on the characteristics of the calcium transients, it was possible to classify the seven diseases and healthy controls with machine learning. Peak data first detected before actual pre-processing from calcium transient signals corresponded to beats (repeating excitation-contraction coupling) of induced stem cell-derived cardiomyocytes and formed the basis of classification. During pre-processing of the calcium transient signals, we found that such techniques among others as even strong outlier cleaning or class size balancing by generating artificial cases improved only slightly or not at all classification accuracies. Therefore, the current data set was sufficiently complicated for our data complexity study. Random forests produced the best classification accuracies, 68% for all eight classes.

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

Cited by 32 publications

References 34 publications

The Effects of Pharmacological Compounds on Beat Rate Variations in Human Long QT-Syndrome Cardiomyocytes

The Effects of Pharmacological Compounds on Beat Rate Variations in Human Long QT-Syndrome Cardiomyocytes

Short- and Long-Range Correlations in Beat Rate Variability of Human Pluripotent-Stem-Cell-Derived Cardiomyocytes

A method to measure data complexity of a complicated medical data set

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