中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Dynamic Liquid Surface Enhanced Raman Scattering Platform Based on Soft Tubular Microfluidics for Label-Free Cell Detection

文献类型:期刊论文

作者Xu, Xiaoding1; Zhao, Lei1; Xue, Qilu1; Fan, Jinkun1; Hu, Qingqing1; Tang, Chu1; Shi, Hongyan1,3; Hu, Bo1; Tian, Jie1,2
刊名ANALYTICAL CHEMISTRY
出版日期2019-07-02
卷号91期号:13页码:7973-7979
ISSN号0003-2700
DOI10.1021/acs.analchem.9b01111
通讯作者Shi, Hongyan(hyshi@xidian.edu.cn) ; Hu, Bo(bohu@xidian.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要Cell detection is of great significance for biomedical research. Surface enhanced Raman scattering (SERS) has been widely applied to the detection of cells. However, there is still a lack of a general, low-cost, rapid, and sensitive SERS method for cell detection. Herein, a dynamic liquid SERS platform, which combines label-free SERS technique with soft tubular microfluidics for cell detection, is proposed. Compared with common static solid and static liquid measurement, the dynamic liquid SERS platform can present dynamical mixing, precise control of the mixing time, and continuous spectra collection. By characterizing the model molecules, the proposed dynamic liquid SERS platform has successfully demonstrated good stability and repeatability with 1.90% and 4.98% relative standard deviation (RSD), respectively. Three cell lines including one normal breast cell line (MCF-10A) and two breast cancer cell lines (MCF-7 and MDA-MB-231) were investigated in this platform. 270 cell spectra were selected as the training set for the classification of the models based on the K-Nearest Neighbor (K-NN) algorithm. In three independent experiments, three types of cells were identified by a test set containing 180 cell spectra with sensitivities above 83.3% and specificities above 91.6%. The accuracy was 94.1 +/- 1.14% among three independent cell identifications. The dynamic liquid SERS platform has shown higher signal intensity, better repeatability, less pretreatment, and obtainment of more spectra with less time consumption. It will be a powerful detection tool in the area of cell research, clinical diagnosis, and food safety.
WOS关键词SERS ; SPECTROSCOPY ; DEVICES
资助项目National Key Research and Development Program of China[2016YFC0102000] ; National Key Research and Development Program of China[2017YFA0205202] ; National Natural Science Foundation of China[81772011] ; National Natural Science Foundation of China[31800714] ; National Natural Science Foundation of China[81601548] ; 100 Talents Project of Shaanxi Province, China[SXBR9181] ; Natural Science Basic Research Plan in Shaanxi Province of China[2018JQ3027] ; China Postdoctoral Science Foundation[2018M633458] ; China Postdoctoral Science Foundation[2018M633475]
WOS研究方向Chemistry
语种英语
WOS记录号WOS:000474477900002
出版者AMER CHEMICAL SOC
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; 100 Talents Project of Shaanxi Province, China ; Natural Science Basic Research Plan in Shaanxi Province of China ; China Postdoctoral Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/26858]  
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Shi, Hongyan; Hu, Bo; 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.Kunpad Commun Pty Ltd, Kunshan 215300, Jiangsu, Peoples R China
推荐引用方式
GB/T 7714
Xu, Xiaoding,Zhao, Lei,Xue, Qilu,et al. Dynamic Liquid Surface Enhanced Raman Scattering Platform Based on Soft Tubular Microfluidics for Label-Free Cell Detection[J]. ANALYTICAL CHEMISTRY,2019,91(13):7973-7979.
APA Xu, Xiaoding.,Zhao, Lei.,Xue, Qilu.,Fan, Jinkun.,Hu, Qingqing.,...&Tian, Jie.(2019).Dynamic Liquid Surface Enhanced Raman Scattering Platform Based on Soft Tubular Microfluidics for Label-Free Cell Detection.ANALYTICAL CHEMISTRY,91(13),7973-7979.
MLA Xu, Xiaoding,et al."Dynamic Liquid Surface Enhanced Raman Scattering Platform Based on Soft Tubular Microfluidics for Label-Free Cell Detection".ANALYTICAL CHEMISTRY 91.13(2019):7973-7979.

入库方式: OAI收割

来源:自动化研究所

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