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

项目摘要 细胞外囊泡（EV）是一组异质的磷脂双层封闭的颗粒， 生物分子含量反映了其亲本细胞的生物分子含量。由于电动汽车目前在流通中， 疾病的早期阶段，并持续在所有疾病阶段，纯化和表征肿瘤源性 电动汽车有望为早期癌症诊断提供机会。肝细胞癌（HCC）， 全球癌症相关死亡的第四大常见原因，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））差 区分早期HCC（有可能治愈的治疗方法）和高危肝硬化 （如需进行HCC监测）。此外，HCC术后复发的敏感生物标志物 （及时的挽救治疗干预可以抑制疾病进展） 切除术和肝移植仍然是早期HCC的重大挑战。因此，利用 HCC EV和EV货物分析对HCC早期检测和术后复发的诊断潜力 有很大的希望，以显着提高目前的诊断模式的能力。 用于分离EV的常规方法，例如超离心、过滤和沉淀，不能用于分离EV。 区分肿瘤来源的EV和非肿瘤来源的EV的能力。为了满足这一未满足的需求，我们的团队 开发了用于HCC EV纯化的“EV Click Chips”。我们设备的创新包括i）共价键 点击化学介导的EV捕获和二硫键裂解- 驱动EV释放，ii）靶向HCC相关表面标志物的优化的多标志物混合物， 用于克服HCC EV的异质性; iii）结合密集堆积的硅纳米线 - SiNWS显著增加了用于与EV接触/相互作用的器件表面积;以及 iv）微流体混沌混合器促进SiNWS和流通物之间的重复物理接触， 电动汽车，进一步提高电动汽车的净化性能。纯化的HCC EV可以通过以下来表征： 通过结合液滴数字PCR定量一组10种充分验证的HCC特异性mRNA标记物 （ddPCR）技术。拟议的研究将进行：i）探索性开发和优化 用于HCC EV纯化的EV点击芯片，和ii）用于HCC早期检测的EV点击芯片的临床验证 和术后复发的研究。我们的长期目标是开发新的HCC EV 纯化系统（即，电动汽车点击芯片）通过协同整合四个非常强大的方法，包括 共价化学介导的EV捕获/释放，多标记抗体混合物，纳米结构基底， 和微流体混沌混合器。纯化的HCC EV将容易地允许定量货物分析， 增强当前HCC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.