Machine Learning-enabled Classification of Extracellular Vesicles Using Nanoplasmonic Microfluidics

项目总结/摘要 卵巢癌是一组不同的恶性肿瘤，其在分子生物学、病因学和免疫学方面可能差异很大。 症状的表现。虽然它占所有女性癌症的2.5%，但它导致大约5%的女性癌症。 由于癌症的高死亡率，这是因为75%的患者被诊断为 晚期疾病，主要是由于其相对较晚的症状表现和缺乏可靠的 检测和监测战略。细胞外囊泡（EVs）由所有细胞释放，包括卵巢 它们的货物反映了它们的起源细胞。他们表现出巨大的潜力， 生物标志物，然而，与来自健康细胞的EV相比，它们的丰度低，并且缺乏足够的生物标志物。 敏感的表征工具限制了它们的临床转化。表面增强拉曼光谱 表面增强拉曼光谱（Sers）足够灵敏，甚至可以对单个EV进行生物化学指纹识别，并且信息丰富的光谱 可以将EV Sers中产生的图像馈送到机器学习（ML）算法中，以基于它们的 潜在的光谱特征尽管EV Sers的早期进展，但EV的高度异质性 表明在Sers分析之前将它们分离成不同的亚群可能有助于改善疾病 诊断、分类和监测。微流控装置能够将EV分离成 同时在单个设备中实现Sers光谱采集。在 建议的研究，在指导阶段，ML启用逆向设计将结合高 分辨率纳米纤维技术，以提高信号强度和光谱质量可从EV Sers。初步数据表明，Sers增强高度依赖于基底的 纳米级几何结构，这是特别重要的电动汽车相比，传统的化学分析。 在指导阶段结束时，一旦使用 生物反应器生产的电动汽车，它们将被纳入两个不同的微流体设备和测试 在整个独立阶段使用细胞培养物和患者EV样品。一个装置将捕获 基于比表面积，将不同的EV亚群直接置于微流体Sers基底上， 用于多重表征的抗原，而另一种将通过大小精确分离EV并使它们流动 以产生EV Sers条形码。并行地，ML算法定制 还将开发专门针对这些平台的软件，以处理和分类所获取的光谱。这 该提案是多学科的，利用先进的ML和微纳米纤维技术以及EV 生产、分离和表征策略。这些实验具有创新性和重要意义 因为他们将专门为电动汽车和微流体电动汽车开发ML逆向设计架构， 用于表征和分类卵巢癌的Sers。

PROJECT SUMMARY/ABSTRACT Ovarian cancer is a diverse group of malignancies that can vary greatly in molecular biology, etiology, and presentation of symptoms. While it accounts for 2.5% of all cancers among women, it results in roughly 5% of cancer-related deaths due to its high fatality rate. This is because 75% of patients are diagnosed with advanced disease, largely attributed to its relatively late presentation of symptoms and a lack of reliable detection and monitoring strategies. Extracellular vesicles (EVs) are released by all cells, including ovarian cancer, and their cargo reflects their cells of origin. They have shown immense potential as stable biomarkers, however their low abundance compared to EVs from healthy cells and a lack of sufficiently sensitive characterization tools has limited their clinical translation. Surface-enhanced Raman spectroscopy (SERS) is sensitive enough to biochemically fingerprint even single EVs, and the information-rich spectra produced in EV SERS can be fed into machine learning (ML) algorithms to classify them based on their latent spectral features. Despite early progress in EV SERS, the highly heterogeneous nature of EVs indicates that their separation into distinct subpopulations prior to SERS analysis may help improve disease diagnosis, classification, and monitoring. Microfluidic devices are uniquely capable of separating EV s into subpopulations of interest while simultaneously enabling SERS spectral acquisition in a single device. In the proposed research, during the mentored phase, ML-enabled inverse design will be combined with high resolution nanofabrication techniques to improve the signal strength and spectral quality attainable from EV SERS. Preliminary data indicates that the SERS enhancement is highly dependent on the substrate’s nanoscale geometry, which is particularly important for EVs compared to conventional chemical analysis. Towards the end of the mentored phase, once the improved substrates have been thoroughly tested using bioreactor-produced EVs, they will be incorporated into two distinct microfluidic devices and tested throughout the independent phase using both cell culture and patient EV samples. One device will capture different subpopulations of EVs directly onto the microfluidic SERS substrates based on specific surface- antigens for multiplexed characterization, while the other will separate EVs precisely by size and flow them over the microfluidic SERS substrates to produce EV SERS barcodes. In parallel, ML algorithms tailored specifically to these platforms will also be developed to process and classify the acquired spectra. This proposal is multidisciplinary, utilizing advanced ML and micro-nanofabrication techniques as well as EV production, isolation, and characterization strategies. These experiments are innovative and significant because they will develop ML inverse design architectures specifically for EVs as well as microfluidic EV SERS for characterizing and classifying ovarian cancer.