Department of Materials, Imperial College London, London SW7 2AZ, United Kingdom.
Department of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom.
ACS Nano. 2021 Nov 23;15(11):18192-18205. doi: 10.1021/acsnano.1c07075. Epub 2021 Nov 4.
Extracellular vesicles (EVs) secreted by cancer cells provide an important insight into cancer biology and could be leveraged to enhance diagnostics and disease monitoring. This paper details a high-throughput label-free extracellular vesicle analysis approach to study fundamental EV biology, toward diagnosis and monitoring of cancer in a minimally invasive manner and with the elimination of interpreter bias. We present the next generation of our single particle automated Raman trapping analysis─SPARTA─system through the development of a dedicated standalone device optimized for single particle analysis of EVs. Our visualization approach, dubbed dimensional reduction analysis (DRA), presents a convenient and comprehensive method of comparing multiple EV spectra. We demonstrate that the dedicated SPARTA system can differentiate between cancer and noncancer EVs with a high degree of sensitivity and specificity (>95% for both). We further show that the predictive ability of our approach is consistent across multiple EV isolations from the same cell types. Detailed modeling reveals accurate classification between EVs derived from various closely related breast cancer subtypes, further supporting the utility of our SPARTA-based approach for detailed EV profiling.
细胞外囊泡（EVs）由癌细胞分泌，为癌症生物学提供了重要的见解，并可用于增强诊断和疾病监测。本文详细介绍了一种高通量无标记细胞外囊泡分析方法，用于以微创方式和消除解释偏差的方式研究基本的 EV 生物学，以用于癌症的诊断和监测。我们通过开发专用于 EV 单颗粒分析的专用独立设备，展示了我们的下一代单颗粒自动拉曼捕获分析（SPARTA）系统。我们的可视化方法称为维度降低分析（DRA），提供了一种方便且全面的方法来比较多个 EV 光谱。我们证明，专用 SPARTA 系统可以高度敏感和特异性地（两者均大于 95%）区分癌症和非癌症 EV。我们进一步表明，我们的方法的预测能力在来自相同细胞类型的多个 EV 分离中是一致的。详细的建模揭示了源自各种密切相关的乳腺癌亚型的 EV 之间的准确分类，进一步支持了我们基于 SPARTA 的方法用于详细的 EV 分析的实用性。
