Label-Free Nanosensing Platform for Breast Cancer Exosome Profiling

外体 表面增强拉曼光谱 乳腺癌 拉曼光谱 微泡 分析物 纳米技术 生物医学工程 化学 材料科学 癌症 医学 色谱法 内科学 生物化学 拉曼散射 基因 光学 物理 小RNA
作者
Nuno Ferreira,Ana C. Marques,Hugo Águas,Hanna Bandarenka,Rodrigo Martins,Cristian Bodo,Bruno Costa‐Silva,Elvira Fortunato
出处
期刊:ACS Sensors [American Chemical Society]
卷期号:4 (8): 2073-2083 被引量:68
标识
DOI:10.1021/acssensors.9b00760
摘要

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 nata de coco 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 104 to 105 were obtained, detecting Rhodamine 6G (R6G) concentrations as low as 10–11 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.
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