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

文献类型：期刊论文

作者Yin, Pengju1; Li, Guoqian1; Zhang, Baoyue3; Farjana, Haque3; Zhao, Lei1; Qin, Hongwei1; Hu, Bo1; Ou, Jianzhen3; Tian, Jie1,2
刊名ANALYST
出版日期2021-03-21
卷号146期号:6页码:1949-1955
ISSN号0003-2654
DOI10.1039/d0an02257h
通讯作者Hu, Bo(bohu@xidian.edu.cn) ; Ou, Jianzhen(jianzhen.ou@rmit.edu.au) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要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.
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]
WOS研究方向Chemistry
语种英语
WOS记录号WOS:000631575100018
出版者ROYAL SOC CHEMISTRY
资助机构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
源URL[http://ir.ia.ac.cn/handle/173211/43988]  
专题自动化研究所_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.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
3.RMIT Univ, Sch Engn, Melbourne, Vic 3001, Australia
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.

入库方式： OAI收割

来源：自动化研究所

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