An integrated human organ-on-chip ultrasensitive miRNA detection platform for novel biomarker discovery

摘要 循环中的miRNAs被认为是疾病状态的特异性生物标志物，包括一些最重要的生物标志物。 常见的疾病，如心血管疾病和癌症。然而，使用miRNAs作为生物标志物是非常困难的。 尽管最近在高通量miRNA谱方面取得了进展，但这仍具有挑战性。各种检测技术， 方案、连接和提取/纯化方法导致细胞的不同miRNA谱分析结果， 不同条件下的生物流体。最重要的是，所有这些都需要长达数天的样品到答案的测定时间，因此， 排除它们用于检测和监测紧急的、危及生命的状况， （密歇根州）。一种快速实时、无PCR的miRNA谱分析设备在精确度方面将是非常有价值的， 在未来的几年里，个性化医疗。然而，即使是开发这样一个平台，开始也非常困难 因为测试模型的局限性。动物模型通常无法预测人类的反应;研究 不容易精确控制疾病事件或时间相关性， 疾病状态和生物标志物表达动态。为了应对这一挑战，在本研究中，我们将开发一种 具有集成阿托摩尔（aM）水平miRNA传感能力的器官芯片设备，我们将用于 优化多种miRNA波动的实时监测，以发现新型生物标志物。作为 立即应用，我们将从作为临床相关模型的人心肌芯片（MoC）开始， 模仿心脏病发作的过程我们假设使用MoC和超灵敏的miRNA检测， 将发现一个独特的签名，表明在MI治疗期间再灌注损伤的发作。最后我们将 使用临床血液样品测试传感器装置和miRNA签名。我们的微流体器官芯片 该平台将由四个基本组成部分组成：1）来自人诱导的组织工程化人MoC， 多能干细胞（hiPSC），2）外泌体裂解单位，3）裂解RNA的浓缩单位和4） miRNA的检测单元。在目标1中，我们将把这些组件耦合到一个完全集成的微流体系统中， 平台首先，我们将通过与人体组织和血液进行比较来验证MoC模型的临床相关性 样品然后，我们将表征和优化一种新的miRNA检测生物传感器的性能， MoC并将其与已建立的miRNA分析技术进行基准测试。在目标2中，我们将重点关注多路复用 用于实时检测一组miRNA的传感方法，以及1）使用MoC来发现miRNA 特征用作捕获RI发作的新型生物标志物，以及2）优化多重 更快的临床翻译传感器。在目标3中，我们将确定的诊断和预后能力， 我们在目标1和2中使用MoC模型开发了新型生物传感器和miRNA生物标志物签名， 来自MI患者的临床样品。我们的长期目标是利用这个综合平台来研究外泌体， 它们的RNA含量，以促进目前对它们在人类健康中的作用的理解，并确定它们的潜力。 作为疾病状态的生物标志物。

Abstract Circulating miRNAs have proposed as specific biomarkers of disease states, including some of the most prevailing ones such as cardiovascular diseases and cancer. However using miRNAs as biomarkers is very challenging despite recent advances in high-throughput miRNA profiling. Various detection technologies, protocols, ligation and extraction/purification methods have led to varying miRNA profiling results of cells and biofluids under different conditions. Most importantly, all require days long sample-to-answer assay times, thus ruling them out for detection and monitoring of urgent, life threatening conditions such as myocardial infarction (MI). A rapid real-time, PCR-free miRNA-profiling device would be exceedingly valuable for precision, personalized medicine in years to come. However, it is very difficult to start even developing such a platform because of the limitations in testing models. Animal models often fail to predict responses in humans; and studies of human subjects do not readily allow for precise control over the disease events or temporal correlation of the disease state and biomarker expression dynamics. To address this challenge, in this study, we will develop an organ-on-a-chip device with an integrated attomolar (aM)-level miRNA sensing capability, which we will use for optimizing real-time monitoring of fluctuations in multiple miRNAs for novel biomarker discovery. As an immediate application, we will start with a human myocardium-on-chip (MoC) as a clinically relevant model and imitate the course of a heart attack. We hypothesize that using the MoC with ultrasensitive miRNA detection, we will discover a unique signature that indicates the onset of reperfusion injury during MI treatment. Finally, we will test the sensor device and the miRNA signature using clinical blood samples. Our microfluidic organ-on-a-chip platform will consist of four basic components: 1) the tissue engineered human MoC from human induced pluripotent stem cells (hiPSCs), 2) the exosome lysing unit, 3) the concentration unit for the lysed RNAs and 4) the detection unit for the miRNAs. In Aim 1, we will couple these components into a fully integrated microfluidic platform. First we will validate the clinical relevance of the MoC model by comparing with human tissue and blood samples. Then we will characterize and optimize the performance of a novel miRNA detection biosensor using MoC and benchmark it against established miRNA analysis techniques. In Aim 2 we will focus on multiplexing the sensing approach for the real-time detection of a panel of miRNAs, and 1) use the MoC to discover a miRNA signature to be used as a novel biomarker that captures the RI onset, as well as 2) to optimize the multiplexed sensor for faster clinical translation. In Aim 3 we will determine the diagnostic and prognostic capabilities of the novel biosensor and miRNA biomarker signature we developed in Aims 1 and 2 using the MoC model, with clinical samples from MI patients. Our long-term goal is to utilize this integrated platform to study exosomes and their RNA content to advance current understanding of their role in human health and to determine their potential as biomarkers for disease states.