Machine Learning-enabled Classification of Extracellular Vesicles Using Nanoplasmonic Microfluidics

使用纳米等离子微流体对细胞外囊泡进行机器学习分类

• 批准号: 10571515
• 负责人: Colin Lee Hisey
• 金额: $ 10万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10571515
• 关键词: Architecture; Bar Codes; Biochemical; Biological Assay; Biological Markers; Bioreactors; Body Fluids; Cancer Patient; Cell Culture Techniques; Cells; Cessation of life; Chemicals; Classification; Clinical; Column Chromatography; Communicable Diseases; Complementary Feeding; Data; Data Analytics; Data Set; Dependence; Detection; Development; Devices; Diagnosis; Diagnostic; Diagnostic Neoplasm Staging; Disease; Engineering; Etiology; Fatality rate; Fingerprint; Geometry; Goals; Gold; Health; Heterogeneity; Human; Image; Intervention; Label; Laboratories; Lateral; Lipids; Machine Learning; Malignant Neoplasms; Malignant neoplasm of ovary; Mentors; Methods; Microfluidic Microchips; Microfluidics; Molecular Biology; Molecular Sieve Chromatography; Monitor; Nature; Optics; Patient-Focused Outcomes; Patients; Pattern; Phase; Plasma; Population; Positioning Attribute; Process; Production; Public Health; Raman Spectrum Analysis; Reproducibility; Research; Resolution; Sampling; Serous; Signal Transduction; Source; Spectrum Analysis; Staging; Sulfhydryl Compounds; Surface; Surface Antigens; Survival Rate; Symptoms; Techniques; Testing; Training; Translating; Translations; Woman; advanced disease; algorithm training; analytical tool; aptamer; cancer subtypes; classification algorithm; clinical application; clinical translation; data acquisition; design; disease classification; disease diagnosis; disease diagnostic; disorder subtype; electron beam lithography; experimental study; extracellular vesicles; generative adversarial network; improved; innovation; interest; machine learning algorithm; machine learning classification; machine learning classifier; mortality risk; multidisciplinary; nanofabrication; nanoparticle; nanoplasmonic; nanoscale; new technology; particle; prevent; programs; rapid growth; reproductive system disorder; sensor; simulation; skills; tool; treatment strategy; vesicular release

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.

项目摘要/摘要 卵巢癌是一组不同的恶性肿瘤，在分子生物学、病因和 出现症状。虽然它占女性所有癌症的2.5%，但它导致的结果是大约5% 由于癌症的高死亡率，与癌症相关的死亡人数最多。这是因为75%的患者被诊断为 晚期疾病，主要归因于其相对较晚的症状和缺乏可靠的 检测和监测战略。细胞外小泡(EV)由包括卵巢在内的所有细胞释放 癌症，它们的货物反映了它们的起源细胞。他们表现出了巨大的潜力，作为稳定的 然而，与来自健康细胞的EV相比，它们的丰度较低，而且缺乏足够的 敏感的表征工具限制了它们的临床翻译。表面增强拉曼光谱 (SERS)足够灵敏，甚至可以对单个电动汽车进行生化指纹识别，并且信息丰富的光谱 在电动汽车中产生的SERS可以被馈送到机器学习(ML)算法中，以基于其 潜在的光谱特征。尽管电动汽车SERS研究取得了早期进展，但电动汽车的高度异质性 表明在SERS分析之前，它们被分成不同的亚群可能有助于改善疾病 诊断、分类和监测。微流控设备是唯一能够将EV S分离成 在单个设备中实现SERS光谱采集的同时，还可以获得感兴趣的子群。在 建议的研究，在指导阶段，启用ML的反向设计将与高 提高电动汽车信号强度和光谱质量的分辨率纳米制造技术 SERS。初步数据表明，SERS增强高度依赖于衬底的 纳米级几何，与传统的化学分析相比，这对电动汽车特别重要。 在指导阶段接近尾声时，一旦使用以下方法彻底测试了改进的底物 生物反应器生产的电动汽车将被整合到两个不同的微流控设备中并进行测试 在整个独立阶段，使用细胞培养和患者EV样本。一台设备将捕获 不同亚群的电动汽车直接在微流控SERS衬底上基于比表面- 抗原用于多重鉴定，而另一种将根据大小和流动精确地分离EV 在微流控SERS衬底上生成EV SERS条形码。同时，ML算法量身定做 具体到这些平台，还将开发对获取的光谱进行处理和分类。这 建议是多学科的，利用先进的ML和微纳米制造技术以及电动汽车 制作、隔离和表征策略。这些实验具有创新性和重要意义。 因为他们将开发专门用于电动汽车以及微流控电动汽车的ML逆向设计架构 SERS用于卵巢癌的定性和分类。