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Detection of Circulating Tumor Cells Using Membrane-Based SERS Platform: A New Diagnostic Approach for ‘Liquid Biopsy’

机译:使用基于膜的SERS平台检测循环肿瘤细胞:液体活检的新诊断方法

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摘要

The detection and monitoring of circulating tumor cells (CTCs) in blood is an important strategy for early cancer evidence, analysis, monitoring of therapeutic response, and optimization of cancer therapy treatments. In this work, tailor-made membranes (MBSP) for surface-enhanced Raman spectroscopy (SERS)-based analysis, which permitted the separation and enrichment of CTCs from blood samples, were developed. A thin layer of SERS-active metals deposited on polymer mat enhanced the Raman signals of CTCs and provided further insight into CTCs molecular and biochemical composition. The SERS spectra of all studied cells—prostate cancer (PC3), cervical carcinoma (HeLa), and leucocytes as an example of healthy (normal) cell—revealed significant differences in both the band positions and/or their relative intensities. The multivariate statistical technique based on principal component analysis (PCA) was applied to identify the most significant differences (marker bands) in SERS data among the analyzed cells and to perform quantitative analysis of SERS data. Based on a developed PCA algorithm, the studied cell types were classified with an accuracy of 95% in 2D PCA to 98% in 3D PCA. These results clearly indicate the diagnostic efficiency for the discrimination between cancer and normal cells. In our approach, we exploited the one-step technology that exceeds most of the multi-stage CTCs analysis methods used and enables simultaneous filtration, enrichment, and identification of the tumor cells from blood specimens.
机译:血液中循环肿瘤细胞(CTC)的检测和监测是早期癌症证据,分析,监测治疗反应以及优化癌症治疗方法的重要策略。在这项工作中,开发了用于基于表面增强拉曼光谱(SERS)的分析的定制膜(MBSP),该膜可以从血液样本中分离和富集四氯化碳。沉积在聚合物垫上的SERS活性金属薄层增强了CTC的拉曼信号,并提供了对CTC分子和生化成分的进一步了解。所有研究细胞(前列腺癌(PC3),子宫颈癌(HeLa)和白细胞,作为健康(正常)细胞的一个例子)的SERS光谱揭示了谱带位置和/或相对强度的显着差异。应用基于主成分分析(PCA)的多元统计技术来确定所分析细胞之间SERS数据中最显着的差异(标志带),并对SERS数据进行定量分析。基于已开发的PCA算法,将所研究的细胞类型以2D PCA中的95%到3D PCA中的98%的准确度进行分类。这些结果清楚地表明了鉴别癌症和正常细胞的诊断效率。在我们的方法中,我们利用了一步式技术,该技术超越了所使用的大多数多阶段CTC分析方法,并且能够同时过滤,富集和鉴定血液样本中的肿瘤细胞。

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