Single Particle Automated Raman Trapping Analysis of Breast Cancer Cell-Derived Extracellular Vesicles as Cancer Biomarkers.
Single Particle Automated Raman Trapping Analysis of Breast Cancer Cell-Derived Extracellular Vesicles as Cancer Biomarkers.
复制标题
作为癌症生物标志物的乳腺癌细胞衍生的细胞外囊泡的单颗粒自动拉曼捕获分析。
DOI：
10.1021/acsnano.1c07075
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发表时间：
2021-11-23
期刊：
影响因子：
17.1
通讯作者：
Stevens MM
中科院分区：
文献类型：
--
作者：
Penders J;Nagelkerke A;Cunnane EM;Pedersen SV;Pence IJ;Coombes RC;Stevens MM
癌细胞分泌的细胞外囊泡（EV）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的无标记细胞外囊泡分析方法，用于研究基础EV生物学，以微创方式诊断和监测癌症，并消除解释偏差。我们提出了我们的下一代单粒子自动拉曼捕获分析-SPARTA-系统通过开发一个专用的独立设备优化单粒子分析的电动汽车。我们的可视化方法，被称为降维分析（ESTA），提出了一个方便和全面的方法比较多个EV光谱。我们证明，专用斯巴达系统可以区分癌症和非癌症EV，具有高度的灵敏度和特异性（两者均>95%）。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种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，进一步支持我们基于SPARTA的方法用于详细EV分析的实用性。
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.
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影响因子：
9.5
作者：
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通讯作者：
Garland, M
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通讯作者：
Ahn, Y. H.