Key result
Machine learning classification of Ca2+ transient profiles achieved accuracies of 89% for separating HCM mutations and 100% for separating LQTS mutations.
Why the study?
To evaluate whether and how effectively specific genetic mutations within HCM and LQTS can be separated from each other using machine learning on iPSC-derived cardiomyocyte Ca2+ transient signals.
Machine learning applied to Ca2+ transient profiles from iPSC-derived cardiomyocytes can accurately distinguish between specific genetic mutations causing HCM and LQTS.
May distinguish mutation effects in experimental models; leaves open clinical applicability for HCM and LQTS.
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.
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Joutsijoki et al. (2019) studied Hypertrophic cardiomyopathy (HCM) and long QT syndrome (LQTS) (n=647). Machine learning classification of Ca2+ transient profiles was evaluated on Classification accuracy between specific genetic mutations. Machine learning classification of Ca2+ transient profiles achieved accuracies of 89% for separating HCM mutations and 100% for separating LQTS mutations.
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