Engineered Human Heart Slice for Testing Drug-Induced Arrhythmia

这项名为“用于检测药物引起的心律失常的人造人体心脏切片”的项目旨在 药物性心脏病体外预测药理实验平台的研制 人工诱导多能干细胞植入工程化心脏切片治疗心律失常 细胞来源的心肌细胞(HiPSC-CMS)。目标1侧重于改进量化方法 控制HiPSC-CMS的成熟度是实施和解释的关键 这些模型中的。这里提出的工作将通过扩展扩展来补充父赠款的目标1 超越健康野生型HiPSC-CMS了解携带遗传性心脏的HiPSC的成熟度 疾病，特别是致心律失常的右室心肌病(ARVC)。的第一个目标 本工作旨在建立病态HiPSC-CMS的成熟轨迹，并与 通配单元格。这将通过在几个时间点评估结构特征来实现 (即肌节组织、肌钙蛋白亚型表达)、CD36阳性(代谢标志物 成熟度)，以及转录熵评分(一种基于RNA测序的新方法)。这个 第二个目的是评估靶向代谢成熟对疾病和健康的影响 HiPSC-CMS。我们将测量用标准分化程序处理的细胞的成熟度。 和添加了一种新的营养配方的细胞培养液，显示出可以促进 野生型HiPSC-CMS。通过提供对健康和疾病成熟差异的洞察 细胞，并潜在地允许我们解释这个令人困惑的变量，这项工作将有助于 为药物反应测试和治疗开发创建强大的疾病模型。

The parent grant, “Engineered Human Heart Slice for Testing Drug-Induced Arrhythmia”, aims to develop a pharmacologic testing platform for in vitro prediction of drug-induced cardiac arrhythmia using engineered heart slices (EHS) embedded with human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). Aim 1 focuses on improving methods of quantifying and controlling the maturity of hiPSC-CMs, which is key to the implementation and interpretation of these models. The work proposed here will supplement Aim 1 of the parent grant by expanding beyond healthy wildtype hiPSC-CMs to understand maturity in hiPSCs harboring genetic cardiac disorders, specifically arrhythmogenic right ventricular cardiomyopathy (ARVC). The first aim of this work is to establish the maturation trajectory of diseased hiPSC-CMs in comparison to wildtype cells. This will be accomplished by assessing, at several time points, structural features (i.e. sarcomere organization, troponin isoform expression), CD36 positivity (marker of metabolic maturity), and transcriptomic entropy scoring (a novel RNA-sequencing based method). The second aim is to evaluate the effects of targeted metabolic maturation on diseased vs healthy hiPSC-CMs. We will measure maturity in cells treated with standard differentiation procedures and media against cells supplemented with a novel nutrient formula shown to boost maturity in wildtype hiPSC-CMs. By providing insight into the maturational differences of healthy and disease cells and potentially allowing us to account for that confounding variable, this work will facilitate the creation of robust disease models for drug response testing and therapeutic development.