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无标记纳米传感平台用于乳腺癌外泌体分析。

Label-Free Nanosensing Platform for Breast Cancer Exosome Profiling.

机构信息

i3N|CENIMAT, Departamento de Ciência dos Materiais, Faculdade de Ciências e Tecnologia , Universidade NOVA de Lisboa , Campus de Caparica , 2829-516 Caparica , Portugal.

Laboratory of Applied Plasmonics , Belarusian State University of Informatics and Radioelectronics , 220013 Minsk , Belarus.

出版信息

ACS Sens. 2019 Aug 23;4(8):2073-2083. doi: 10.1021/acssensors.9b00760. Epub 2019 Aug 1.

Abstract

Breast cancer accounts for 11.6% of all cancer cases in both genders. Even though several diagnostic techniques have been developed, the mostly used are invasive, complex, time-consuming, and cannot guarantee an early diagnosis, significantly constraining the tumor treatment success rate. Exosomes are extracellular vesicles that carry biomolecules from tissues to the peripheral circulation, representing an emerging noninvasive source of markers for early cancer diagnosis. Current techniques for exosomes analysis are frequently complex, time-consuming, and expensive. Raman spectroscopy interest has risen lately, because of its nondestructive analysis and little to no sample preparation, while having very low analyte concentration/volume, because of surface enhancement signal (SERS) possibility. However, active SERS substrates are needed, and commercially available substrates come with a high cost and low shelf life. In this work, composites of commercial to produce bacterial nanocellulose and in-situ-synthesized silver nanoparticles are tested as SERS substrates, with a low cost and green approach. Enhancement factors from 10 to 10 were obtained, detecting Rhodamine 6G (R6G) concentrations as low as 10 M. Exosome samples coming from MCF-10A (nontumorigenic breast epithelium) and MDA-MB-231 (breast cancer) cell cultures were tested on the synthesized substrates, and the obtained Raman spectra were subjected to statistical principal component analysis (PCA). Combining PCA with Raman intravariability and intervariability in exosomal samples, data grouping with 95% confidence was possible, serving as a low-cost, green, and label-free diagnosis method, with promising applicability in clinical settings.

摘要

乳腺癌在男女所有癌症病例中占 11.6%。尽管已经开发了几种诊断技术,但最常用的技术是侵入性的、复杂的、耗时的,并且不能保证早期诊断,这极大地限制了肿瘤治疗的成功率。外泌体是携带组织中生物分子到外周循环的细胞外囊泡,代表了一种新兴的非侵入性早期癌症诊断标志物来源。目前的外泌体分析技术通常复杂、耗时且昂贵。拉曼光谱技术由于其无损分析和几乎不需要样品制备,同时具有非常低的分析物浓度/体积,因为表面增强信号(SERS)的可能性,最近受到了关注。然而,需要活性 SERS 基底,并且商业上可用的基底具有高成本和低保质期。在这项工作中,测试了商业上可获得的 to 与原位合成的银纳米粒子的复合材料作为 SERS 基底,具有低成本和绿色方法。获得了 10 到 10 的增强因子,检测到 10 M 的 Rhodamine 6G(R6G)浓度。从 MCF-10A(非致瘤性乳腺上皮)和 MDA-MB-231(乳腺癌)细胞培养物中测试了合成基底上的外泌体样品,并且对获得的拉曼光谱进行了统计主成分分析(PCA)。将 PCA 与拉曼内变异和外泌体样品中的间变异相结合,以 95%置信度进行数据分组是可能的,这是一种低成本、绿色和无标记的诊断方法,在临床环境中具有广阔的应用前景。

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