BACKGROUND Cardiotoxicity, characterized by severe cardiac dysfunction, is a major problem in patients treated with different classes of anticancer drugs. Development of predictable human-based models and assays for drug screening are crucial for preventing potential drug-induced adverse effects. Current animal in vivo models and cell lines are not always adequate to represent human biology. Alternatively, human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) show great potential for disease modelling and drug-induced toxicity screenings. Fully automated high-throughput screening of drug toxicity on hiPSC-CMs by fluorescence image analysis is, however, very challenging, due to clustered cell growth patterns and strong intracellular and intercellular variation in the expression of fluorescent markers. RESULTS In this paper, we report on the development of a fully automated image analysis system for quantification of cardiotoxic phenotypes from hiPSC-CMs that are treated with various concentrations of anticancer drugs doxorubicin or crizotinib. This high-throughput system relies on single-cell segmentation by nuclear signal extraction, fuzzy C-mean clustering of cardiac α-actinin signal, and finally nuclear signal propagation. When compared to manual segmentation, it generates precision and recall scores of 0.81 and 0.93, respectively. CONCLUSIONS Our results show that our fully automated image analysis system can reliably segment cardiomyocytes even with heterogeneous α-actinin signals.

中文翻译：

---

自动图像分析系统，用于研究人多能干细胞衍生的心肌细胞的心脏毒性。

背景技术以严重的心脏功能障碍为特征的心脏毒性是用不同类型的抗癌药治疗的患者中的主要问题。开发可预测的基于人的药物筛选模型和测定方法对于预防潜在的药物诱导的不良反应至关重要。当前的动物体内模型和细胞系并不总是足以代表人类生物学。或者，人类诱导的多能干细胞衍生的心肌细胞（hiPSC-CM）在疾病建模和药物诱导的毒性筛选中显示出巨大的潜力。通过荧光图像分析对hiPSC-CM进行药物毒性的全自动高通量筛选是非常具有挑战性的，这是由于成簇的细胞生长模式以及荧光标记表达的强烈的细胞内和细胞间变化。结果在本文中，我们报道了开发了一种全自动图像分析系统的开发，该系统可用于定量定量hiPSC-CMs的心脏毒性表型，所述hiPSC-CMs用不同浓度的抗癌药阿霉素或克唑替尼治疗。这种高通量系统依赖于通过核信号提取进行单细胞分割，心脏α-肌动蛋白信号的模糊C均值聚类以及最终的核信号传播。与手动细分相比，它的精确度和召回率分别为0.81和0.93。结论我们的结果表明，即使存在异质α-肌动蛋白信号，我们的全自动图像分析系统也可以可靠地分割心肌细胞。