Single-cell Multi-omic Profiling of Drug Responses Using Pooled iPSC-CM Differentiation

项目总结 癌症治疗的心血管并发症极大地增加了全球心血管负担 疾病。尽管在了解阿霉素的遗传基础方面取得了显著进展- 诱发性心脏毒性(DIC)，我们无法预测哪些患者会受到DIC的影响或保护处于危险之中的患者 为充分遭受DIC之苦。在这里，我们将使用一种新的多路复用方法来创建一个“细胞村” 通过将多个患者的诱导多能干细胞(IPSC)品系汇集在一个培养皿中，绘制出间质干细胞的遗传基础图。 阿霉素反应的个体差异。在目标1中，我们将在10个不同的细胞中共同培养100个IPSC株 每个“细胞村”包含10个独立的患者特定的IPSC系。接下来，我们将区分 每个“细胞村”分化为IPSC来源的心肌细胞(IPSC-CMS)。最后，我们将执行单细胞多细胞- “细胞村”的组学测序分析以了解遗传变异对心肌细胞的影响 基准线上的基因调控和功能。在目标2中，我们将使用单细胞多组体方法来揭示 验证了反应eQTL在DIC预测中的作用。我们将在每个“细胞村”中用 不同剂量的阿霉素。接下来，我们将进行单细胞多组学分析，以模拟 阿霉素治疗反应变异性的遗传学研究。在目标3中，我们将使用3D工程心脏组织 (EHTS)和CRISPR/Cas9基因组编辑，以全面研究两个候选基因的功能作用 阿霉素反应基因。总而言之，建议的实验将作为使用 “细胞村”模式作为一个高通量的个性化药物筛选平台。

PROJECT SUMMARY Cardiovascular complications of cancer therapy significantly contribute to the global burden of cardiovascular diseases. Although remarkable progress has been made in understanding the genetic basis of doxorubicin- induced cardiotoxicity (DIC), we cannot predict which patients will be affected by DIC or protect patients at risk for suffering from DIC adequately. Here, we will use a novel multiplexing methodology of creating a "cell village" by pooling multiple patients' induced pluripotent stem cell (iPSC) lines in a dish to map the genetic basis of inter- individual differences in response to doxorubicin. In Aim 1, we will co-culture 100 iPSC lines in 10 distinct "cell villages," where each "cell village" contains ten independent patient-specific iPSC lines. Next, we will differentiate each "cell village" into iPSC-derived cardiomyocytes (iPSC-CMs). Finally, we will perform a single-cell multi- omics sequencing analysis of the "cell villages" to understand the impact of genetic variability on cardiomyocyte gene regulation and functions at baseline. In Aim 2, we will employ a single-cell multi-omic approach to uncover and validate the role of response eQTL in DIC prediction. We will treat iPSC-CMs in each "cell village" with doxorubicin at various doses. Next, we will perform single-cell multi-omics profiling to model the contribution of genetics to variability in responses to doxorubicin treatment. In Aim 3, we will employ 3D engineered heart tissues (EHTs) and CRISPR/cas9 genome-editing to comprehensively study the functional role of two candidate doxorubicin response genes. All in all, the proposed experiments will serve as a proof-of- principle in using the "cell village" model as a high throughput personalized drug screening platform.