Facile PEG-based isolation and classification of cancer extracellular vesicles and particles with label-free surface-enhanced Raman scattering and pattern recognition algorithm

doi:10.1039/d0an02257h

	Facile PEG-based isolation and classification of cancer extracellular vesicles and particles with label-free surface-enhanced Raman scattering and pattern recognition algorithm
	Yin, Pengju 1; Li, Guoqian 1; Zhang, Baoyue 2; Farjana, Haque 2; Zhao, Lei 1; Qin, Hongwei 1; Hu, Bo 1; Ou, Jianzhen 2; Tian, Jie 1,3
发表期刊	ANALYST
ISSN	0003-2654
	2021-03-21
卷号	146 期号:6 页码:1949-1955
通讯作者	Hu, Bo(bohu@xidian.edu.cn) ; Ou, Jianzhen(jianzhen.ou@rmit.edu.au) ; Tian, Jie(jie.tian@ia.ac.cn)
摘要	Extracellular vesicles and particles (EVPs), which contain the same surface proteins as their mother cells, are promising biomarkers for cancer liquid biopsy. However, most of the isolation methods of EVPs are time-consuming and complicated, and hence, sensitive detection and classification methods are required for EVPs. Here, we report a facile polyethylene glycol (PEG)-based method for isolating and classifying EVPs with label-free surface-enhanced Raman scattering (SERS) and pattern recognition algorithm. There are only three steps in the PEG-based isolation method, and it does not require ultracentrifugation, which makes it a low-cost and easy-to-use method. Three types of common male cancer cell lines, namely leukemia (THP-1), prostate cancer (DU-145), and colorectal cancer (COLO-205), and one healthy male blood sample, were utilized to isolate EVPs. To collect the SERS spectra of EVPs, a novel planar nanomaterial, namely amino molybdenum oxide (AMO) nanoflakes, was applied, with the enhancement factor being obtained as 3.2 x 10(2). Based on the principal component analysis and support vector machine (PCA-SVM) algorithm, cancer and normal EVPs were classified with 97.4% accuracy. However, among the cancer EVPs, the accuracy, precision, and sensitivity were found to be 90.0%, 90.9%, and 83.3% for THP-1; 86.7%, 80.0%, and 92.3% for DU-145; 96.7%, 83.3%, and 100% for COLO-205, respectively. Thus, this work will improve the isolation, detection, and classification of EVPs and promote the development of cancer liquid biopsies.
DOI	10.1039/d0an02257h
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[81772011] ; National Natural Science Foundation of China[31800714] ; National Key R&D Program of China[2017YFA0205202] ; Natural Science Basic Research Plan in Shaanxi Province of China[2018JQ3027] ; Fundamental Research Funds for the Central Universities[JC1907]
项目资助者	National Natural Science Foundation of China ; National Key R&D Program of China ; Natural Science Basic Research Plan in Shaanxi Province of China ; Fundamental Research Funds for the Central Universities
WOS研究方向	Chemistry
WOS类目	Chemistry, Analytical
WOS记录号	WOS:000631575100018
出版者	ROYAL SOC CHEMISTRY
七大方向——子方向分类	医学影像处理与分析
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/43989
专题	中国科学院分子影像重点实验室
通讯作者	Hu, Bo; Ou, Jianzhen; Tian, Jie
作者单位	1.Xidian Univ, Sch Life Sci & Technol, Xian 710126, Shaanxi, Peoples R China 2.RMIT Univ, Sch Engn, Melbourne, Vic 3001, Australia 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Yin, Pengju,Li, Guoqian,Zhang, Baoyue,et al. Facile PEG-based isolation and classification of cancer extracellular vesicles and particles with label-free surface-enhanced Raman scattering and pattern recognition algorithm[J]. ANALYST,2021,146(6):1949-1955.
APA	Yin, Pengju.,Li, Guoqian.,Zhang, Baoyue.,Farjana, Haque.,Zhao, Lei.,...&Tian, Jie.(2021).Facile PEG-based isolation and classification of cancer extracellular vesicles and particles with label-free surface-enhanced Raman scattering and pattern recognition algorithm.ANALYST,146(6),1949-1955.
MLA	Yin, Pengju,et al."Facile PEG-based isolation and classification of cancer extracellular vesicles and particles with label-free surface-enhanced Raman scattering and pattern recognition algorithm".ANALYST 146.6(2021):1949-1955.