Covalent Chemistry on Nanosubstrates Enables Molecular Analysis of Purified Extracellular Vesicles in Hepatocellular Carcinoma

项目总结 细胞外小泡(EVS)是一组由磷脂双层包裹的异质颗粒 生物分子含量反映了它们亲代细胞的含量。由于电动汽车在流通中的地位相对较低 疾病的早期阶段，并持续贯穿所有疾病阶段，提纯和表征肿瘤来源 电动汽车有望为癌症的早期诊断提供机会。肝细胞癌(HCC)， 全球癌症相关死亡的第四大常见原因是迫切需要诊断和预后 生物标志物。目前的临床放射学系统和血清生物标志物(例如甲胎蛋白(AFP))很差 区分早期肝细胞癌(有可能治愈的治疗方法)和高危肝硬变 (在有肝细胞癌监测的情况下)。此外，肝细胞癌术后复发的敏感生物标志物 (其中及时的抢救治疗干预可以抑制疾病进展)治疗意向肝后 切除和肝移植仍然是早期肝细胞癌的一个重大挑战。因此，利用 肝细胞癌EVS和EV货物轮廓对肝癌早期发现和术后复发的诊断价值 极有希望显著增强当前诊断模式的能力。 传统的分离肠道病毒的方法，如超速离心法、过滤法和沉淀法是不能实现的。 区分肿瘤来源的电动汽车和非肿瘤来源的电动汽车。为了解决这一未得到满足的需求，我们的团队 开发了用于肝癌EV纯化的“EV点击芯片”。我们器件的创新包括：1)共价 化学介导的EV捕获/释放对点击化学介导的EV捕获和二硫化物裂解- 驱动EV释放，II)针对肝细胞癌相关表面标记的优化多标记鸡尾酒 采用以克服肝细胞癌EVS的异质性；iii)结合密集堆积的硅纳米线 衬底(SiNW)显著增加了与电动汽车接触/相互作用的设备表面积；以及 四)微流控混沌混合器促进了SiNWS和通流之间的反复物理接触 EVS，进一步提高了EV净化性能。纯化的肝细胞癌EVS可以通过 应用Droplet Digital聚合酶链式反应对一组10个有效的肝细胞癌特异性mRNA标记物进行定量 (DdPCR)技术。拟议的研究将进行：i)探索性开发和优化 EV Click芯片用于肝癌EV纯化，以及II)EV Click芯片用于肝癌早期检测的临床验证 以及术后复发患者的血液样本。我们的长期目标是开发一种新的肝细胞癌电动汽车 通过协同集成四种非常强大的方法来净化系统(即电动汽车点击芯片)，包括 共价化学介导的EV捕获/释放，多标记抗体鸡尾酒，纳米结构底物， 和微流控混沌混合器。纯化的肝细胞癌电动汽车将容易地允许定量货物概况，以 增强现有的肝癌诊断算法。

PROJECT SUMMARY Extracellular vesicles (EVs) are a heterogeneous group of phospholipid bilayer-enclosed particles with the biomolecular contents mirroring those of their parental cells. Since EVs are present in circulation at a relatively early stage of disease and persist across all disease stages, purification and characterization of tumor-derived EVs are expected to offer an opportunity for early cancer diagnosis. Hepatocellular carcinoma (HCC), the fourth most common cause of cancer-related deaths worldwide, is in dire need of diagnostic and prognostic biomarkers. Current clinical radiographic system and serum biomarkers (e.g., alpha-fetoprotein (AFP)) poorly discriminate early-stage HCC (where potentially curative therapies are available) from at-risk liver cirrhosis (where HCC surveillance is indicated). Moreover, sensitive biomarkers for HCC postoperative recurrence (where timely salvage treatment interventions can suppress disease progression) after curative-intent liver resection and liver transplantation remain a significant challenge for early-stage HCC. Therefore, exploiting the diagnostic potential of HCC EVs and EV cargo profiling for HCC early detection and postoperative recurrence holds great promise to significantly augment the ability of current diagnostic modalities. Conventional methods for isolating EVs, such as ultracentrifugation, filtration, and precipitation, are incapable of discriminating tumor-derived EVs from non-tumor-derived EVs. To address this unmet need, our team developed “EV Click Chips” for HCC EV purification. The innovation of our devices includes i) the covalent chemistry-mediated EV capture/release couples click chemistry-mediated EV capture and disulfide cleavage- driven EV release, ii) an optimized multi-marker cocktail targeting HCC-associated surface markers was adopted to overcome the heterogeneity of HCC EVs; iii) the incorporation of densely packed silicon nanowire substrates (SiNWS) dramatically increases the device surface areas for contacting/interacting with EVs; and iv) the microfluidic chaotic mixer facilitates repeated physical contact between SiNWS and the flow-through EVs, further enhancing the performance of EV purification. The purified HCC EVs can be characterized by quantifying a panel of 10 well-validated HCC-specific mRNA markers by incorporating Droplet Digital PCR (ddPCR) technology. The proposed research will conduct: i) an exploratory development and optimization of EV Click Chips for HCC EV purification, and ii) clinical validations of EV Click Chips for HCC early detection and postoperative recurrence using patient blood samples. Our long-term goal is to develop a new HCC EV purification system (i.e., EV Click Chips) by synergistically integrating four very powerful approaches, including covalent chemistry-mediated EV capture/release, multimarker antibody cocktails, nanostructured substrates, and microfluidic chaotic mixers. The purified HCC EVs will readily allow for quantitative cargo profiling to augment current HCC diagnostic algorithms.