Joutsijoki Henry, Penttinen Kirsi, Juhola Martti, Aalto-Setälä Katriina
Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.
Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
Methods Inf Med. 2019 Nov;58(4-05):167-178. doi: 10.1055/s-0040-1701484. Epub 2020 Feb 20.
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 Ca transient signals measured from iPSC-derived cardiomyocytes (CMs).
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.
After preprocessing those Ca signals where we computed peak waveforms we then classified the two mutations of both disease pairs by using several different machine learning methods.
We obtained excellent classification accuracies of 89% for HCM and even 100% for LQT at their best.
The results indicate that the methods applied would be efficient for the identification of these genetic cardiac diseases.
利用诱导多能干细胞对人类心脏疾病进行建模，不仅能够研究疾病病理生理学并开发治疗方法，而且正如我们之前所展示的，它还可为疾病诊断提供一种工具。我们之前观察到，通过将机器学习应用于从诱导多能干细胞衍生的心肌细胞（CMs）测量的钙瞬变信号，可以将一些遗传性心脏疾病彼此区分开来，并与健康对照区分开。
在当前研究中，测量了419个肥厚型心肌病（HCM）瞬变信号和228个长QT综合征（LQTS）瞬变信号。HCM信号包括从携带α-原肌球蛋白（即TPM1，HCMT）或肌球蛋白结合蛋白C（MYBPC3，HCMM）突变的诱导多能干细胞-心肌细胞记录的数据，LQTS信号包括从携带钾电压门控通道亚家族Q成员1（KCNQ1，长QT综合征1 [LQT1]）或KCNH2突变（长QT综合征2 [LQT2]）的诱导多能干细胞-心肌细胞记录的数据。主要目的是研究HCMM和HCMT彼此之间以及LQT1和LQT2之间是否以及能在多大程度上有效区分。
在对那些计算了峰值波形的钙信号进行预处理之后，我们使用几种不同的机器学习方法对这两种疾病对的两种突变进行分类。
我们获得了出色的分类准确率，HCM最高可达89%，LQTS甚至可达100%。
结果表明所应用的方法对于识别这些遗传性心脏疾病将是有效的。
