Label-free characterization of exosome via surface enhanced Raman spectroscopy for the early detection of pancreatic cancer

被引:114
|
作者
Carmicheal, Joseph [1 ]
Hayashi, Chihiro [1 ]
Huang, Xi [2 ]
Liu, Lei [2 ]
Lu, Yao [2 ]
Krasnoslobodtsev, Alexey [3 ,4 ]
Lushnikov, Alexander [4 ]
Kshirsagar, Prakash G. [1 ]
Patel, Asish [1 ]
Jain, Maneesh [1 ]
Lyubchenko, Yuri L. [4 ]
Lu, Yongfeng [2 ]
Batra, Surinder K. [1 ]
Kaur, Sukhwinder [1 ]
机构
[1] Univ Nebraska Med Ctr, Dept Biochem & Mol Biol, Omaha, NE USA
[2] Univ Nebraska Lincoln, Dept Elect & Comp Engn, Lincoln, NE 68588 USA
[3] Univ Nebraska Omaha, Dept Phys, 6001 Dodge St, Omaha, NE USA
[4] Univ Nebraska Med Ctr, Coll Pharm, Nanoimaging Core Facil, Omaha, NE USA
关键词
Pancreatic cancer; Liquid biopsy; Exosome; Surface enhanced Raman spectroscopy; Label-free; SINGLE-MOLECULE SERS; AMERICAN SOCIETY; VESICLES; BIOPSIES; PROTEIN;
D O I
10.1016/j.nano.2018.11.008
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
Pancreatic cancer is a highly lethal malignancy. Lack of early diagnostic markers makes timely detection of pancreatic cancer a highly challenging endeavor. Exosomes have emerged as information-rich cancer specific biomarkers. However, characterization of tumor-specific exosomes has been challenging. This study investigated. the proof of principle that exosomes could be used for the detection of pancreatic cancer. Label-free analysis of exosomes purified from normal and pancreatic cancer cell lines was performed using surface enhanced Raman Spectroscopy (SERS) and principal component differential function analysis (PC-DFA), to identify tumor-specific spectral signatures. This method differentiated exosomes originating from pancreatic cancer or normal pancreatic epithelial cell lines with 90% accuracy. The cell line trained PC-DFA algorithm was next applied to SERS spectra of serum-purified exosomes. This method exhibited up to 87% and 90% predictive accuracy for HC and EPC individual samples, respectively. Overall, our study identified utility of SERS spectral signature for deciphering exosomal surface signature. (C) 2018 Elsevier Inc. All rights reserved.
引用
收藏
页码:88 / 96
页数:9
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