文献类型：期刊文献

英文题名：Live-cell profiling of membrane sialic acids by fluorescence imaging combined with SERS labelling

作者：Lv, Jian[1];Chang, Shuai[1];Wang, Xiaoyuan[1];Zhou, Zerui[1];Chen, Binbin[1];Qian, Ruocan[1];Li, Dawei[1]

机构：[1]East China Univ Sci & Technol, Sch Chem & Mol Engn, Key Lab Adv Mat, Shanghai 200237, Peoples R China

年份：2022

卷号：351

外文期刊名：SENSORS AND ACTUATORS B-CHEMICAL

收录：;EI(收录号：20214311082417);WOS:【SCI-EXPANDED(收录号:WOS:000714412400002)】；

基金：This research was supported by the National Natural Science Foundation of China (21977031, 21777041, 21974046, 22176058), Shanghai Science and Technology Committee (19ZR1472300, 19391901700, 19520744000), and Shanghai Municipal Science and Technology Major Project (2018SHZDZX03). The authors thank Dr Yan Kang at Research Center of Analysis and Test of East China University of Science and Technology for the help on Laser Micro-Raman Spectrometer analysis.

语种：英文

外文关键词：Sialic acid; Proteins; Fluorescence imaging; SERS labelling

摘要：Detecting cell-surface sialic acids (SAs) is essential for cancer research, as accumulating evidence indicates that SA overexpression is closely related to unusual biopathways, such as tumorigenesis. However, it remains challenging to detect and classify membrane SAs. Here, a highly efficient and convenient two-step tagging strategy is described for live-cell profiling of membrane sialic acids with fluorescence imaging and SERS labelling. The fluorescence of SA-linked dyes indicates the SA distribution, whereas the Raman signals recognize the SA-linked proteins marked by aptamer modified SERS probes. Using the tumor markers MUC-1 and TNC as the model proteins, SAs can be partitioned into distinct space domains via Flu/SERS information on living cell membrane. Importantly, we find that SAs are highly expressed near MUC-1 and TNC on the surface of cancer cells, with subtle differences existing among various carcinoma cell lines, enabling classification of various types of cells. Together, our strategy provides a robust and versatile platform for highly detailed analysis of cell surface saccharide profiles and that it has great potential for optical biosensing and molecular diagnosis.