文献类型：期刊文献

英文题名：Amperometric Identification of Single Exosomes and Their Dopamine Contents Secreted by Living Cells

作者：Lv, Jian[1];Wang, Xiao-Yuan[1];Chang, Shuai[1];Xi, Cheng-Ye[1];Wu, Xue[1];Chen, Bin-Bin[2];Guo, Zhi-Qian[1];Li, Da-Wei[1];Qian, Ruo-Can[1]

机构：[1]East China Univ Sci & Technol, Feringa Nobel Prize Scientist Joint Res Ctr, Frontiers Sci Ctr Materiobiol & Dynam Chem, Sch Chem & Mol Engn,Key Lab Adv Mat,Joint Int Lab, Shanghai 200237, Peoples R China;[2]Chinese Univ Hong Kong, Shenzhen Inst Aggregate Sci & Technol, Sch Sci & Engn, Shenzhen 518172, Peoples R China

年份：2023

卷号：95

期号：30

起止页码：11273

外文期刊名：ANALYTICAL CHEMISTRY

收录：;EI(收录号：20233214484919);WOS:【SCI-EXPANDED(收录号:WOS:001033848700001)】；

基金：This research was supported by the National Natural Science Foundation of China (21977031, 21974046, and 22176058), Shanghai Science and Technology Committee (19520744000, 22ZR1416800, and 23ZR1416100), the China Postdoctoral Science Foundation (2021M701195), the Program of Introducing Talents of Discipline to Universities (B16017), and the Fundamental Research Funds for the Central Universities (222201717003). The authors thank the Research Center of Analysis and Test of East China University of Science and Technology for the help on the characterization.

语种：英文

外文关键词：Cells - Cytology - Lanthanum compounds - Neurophysiology

摘要：Dopamine (DA) is an important neurotransmitter,which not onlyparticipates in the regulation of neural processes but also playscritical roles in tumor progression and immunity. However, directidentification of DA-containing exosomes, as well as quantificationof DA in single vesicles, is still challenging. Here, we report ananopipette-assisted method to detect single exosomes and their dopaminecontents via amperometric measurement. The resistive-pulse currentmeasured can simultaneously provide accurate information of vesicletranslocation and DA contents in single exosomes. Accordingly, DA-containingexosomes secreted from HeLa and PC12 cells under different treatmentmodes successfully detected the DA encapsulation efficiency and theamount of exosome secretion that distinguish between cell types. Furthermore,a custom machine learning model was constructed to classify the exosomesignals from different sources, with an accuracy of more than 99%.Our strategy offers a useful tool for investigating single exosomesand their DA contents, which facilitates the analysis of DA-containingexosomes derived from other untreated or stimulated cells and mayopen up a new insight to the research of DA biology.