|Title:||Label-Free Nanosensing Platform for Breast Cancer Exosome Profiling|
Bandarenka, H. V.
|Keywords:||публикации ученых;Bacterial cellulose;silver nanoparticles;SERS;PCA;exosomes;breast cancer|
|Citation:||Label-Free Nanosensing Platform for Breast Cancer Exosome Profiling / Ferreira Nuno [and other] // ACS Sensors. – 2019. – Vol. 4. – P. 2073 – 2083. – DOI: 10.1021/acssensors.9b00760.|
|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 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.|
|Appears in Collections:||Публикации в зарубежных изданиях|
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