Exosome Classification by Pattern Analysis of Surface-Enhanced Raman Spectroscopy Data for Lung Cancer Diagnosis

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Owing to the role of exosome as a cargo for intercellular communication, especially in cancer metastasis, the evidence has been consistently accumulated that exosomes can be used as a noninvasive indicator of cancer. Consequently, several studies applying exosome have been proposed for cancer diagnostic methods such as ELISA assay. However, it has been still challenging to get reliable results due to the requirement of a labeling process and high concentration of exosome. Here, we demonstrate a label-free and highly sensitive classification method of exosome by combining-enhanced (SERS) and statistical surface Raman, scattering pattern analysis. Unlike the conventional method to read different peak positions and amplitudes of a spectrum, whole SERS spectra of exosomes were analyzed by principal component analysis (PCA). By employing this pattern analysis, lung cancer cell derived exosomes were clearly distinguished from normal cell derived exosomes by 95.3% sensitivity and 97.3% specificity. Moreover, by analyzing the PCA result, we could suggest that this difference was induced by 11 different points in SERS signals from lung cancer cell derived exosomes. This result paved the way for new real-time diagnosis and classification of lung cancer by using exosome as a cancer marker.
Publisher
AMER CHEMICAL SOC
Issue Date
2017-06
Language
English
Article Type
Article
Keywords

PRINCIPAL COMPONENT ANALYSIS; CELLS; SCATTERING; SERS; VESICLES; IDENTIFICATION; MICROSCOPY; MOLECULES; TISSUES; PROTEIN

Citation

ANALYTICAL CHEMISTRY, v.89, no.12, pp.6695 - 6701

ISSN
0003-2700
DOI
10.1021/acs.analchem.7b00911
URI
http://hdl.handle.net/10203/224874
Appears in Collection
BiS-Journal Papers(저널논문)
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