One-step quantification of salivary exosomes based on combined aptamer recognition and quantum dot signal amplification

适体 量子点 微泡 信号(编程语言) 化学 小RNA 物理 纳米技术 材料科学 计算机科学 分子生物学 生物 生物化学 基因 程序设计语言
作者
Min Wu,Zhuo-Kun Chen,Qihui Xie,Bo-Lin Xiao,Gang Zhou,Gang Chen,Zhuan Bian
出处
期刊:Biosensors and Bioelectronics [Elsevier BV]
卷期号:171: 112733-112733 被引量:79
标识
DOI:10.1016/j.bios.2020.112733
摘要

As promising fluid biomarkers for non-invasive diagnosis, naturally-occurring exosomes in saliva have attracted a wide interest for their potential application in oral diseases especially oral cancers. However, accurate quantification of salivary exosomes is still challenging due to the current difficulties in simultaneous identification and measurement of these nano-sized vesicles. In this study, we developed a novel fluorescent biosensor for one-step sensitive quantification of salivary exosomes based on magnetic and fluorescent bio-probes (MFBPs). Within the MFBPs, self-assembled DNA concatamers loaded with numerous quantum dots (QDs) were ingeniously tethered to aptamers, which were anchored on the surface of magnetic microspheres (MMs). Efficient recognition and capture of an exosome by the aptamer would simultaneously trigger the release of a DNA concatamer as the detection signal carrier, thereby generating a “one exosome-numerous QDs” amplification effect. As the result, this biosensor allowed one-step quantification with less assay time and achieved a high sensitivity with low limit of detection. Moreover, unique fluorescent properties of QDs and the superparamagnetism of MMs offered a strong anti-interference ability, enabling a robust quantification in complex matrices. Furthermore, this biosensor exhibited a good clinical feasibility with favorable accuracy comparable to nanoscale flow cytometry, and a superiority in label-free analysis and convenient operation. This study provides a novel and general strategy for one-step sensitive quantification of exosomes from body fluids, facilitating the development of exosome-based liquid biopsy for disease diagnosis.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
123发布了新的文献求助30
1秒前
2秒前
ml发布了新的文献求助10
3秒前
Amy关注了科研通微信公众号
4秒前
Yap完成签到,获得积分10
4秒前
4秒前
5秒前
pa发布了新的文献求助10
5秒前
gllllllllll完成签到 ,获得积分10
7秒前
7秒前
Garnieta完成签到,获得积分10
9秒前
科研通AI6.4的应助被噗噗采纳,获得10
9秒前
笑嘻嘻发布了新的文献求助10
11秒前
zk关注了科研通微信公众号
11秒前
11秒前
13秒前
派大星完成签到 ,获得积分10
13秒前
小小发布了新的文献求助20
13秒前
14秒前
希望天下0贩的0的应助被小马采纳,获得10
15秒前
大胆戒指完成签到 ,获得积分10
15秒前
15秒前
16秒前
16秒前
CC发布了新的文献求助10
17秒前
kvence发布了新的文献求助10
17秒前
Yummy完成签到 ,获得积分10
17秒前
19秒前
20秒前
jianwuzhou发布了新的文献求助10
20秒前
21秒前
bigboss完成签到 ,获得积分10
21秒前
尤尤发布了新的文献求助10
21秒前
陆沉完成签到,获得积分10
22秒前
22秒前
爱学习的熊猫完成签到 ,获得积分10
23秒前
科研通AI6.2的应助被魏伯安采纳,获得10
23秒前
科研通AI6.4的应助被Zh采纳,获得10
24秒前
pa完成签到,获得积分10
25秒前
高分求助中
(应助此贴封号)通过应助OA文献获取积分 10000
Rosenblum, Global Change Biology 800
The Student's Guide to Social Neuroscience 600
Computational Chemical Reaction Engineering: Modeling, Simulation, and Design with MATLAB 600
Organizational Behavior 510
Management and the Arts 510
A Will for the Machine: Computerization, Automation, and the Arts in South Africa 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 计算机科学 工程类 纳米技术 内科学 物理 有机化学 化学工程 生物化学 复合材料 光电子学 细胞生物学 心理学 量子力学 催化作用 物理化学 电极
热门帖子
关注 科研通微信公众号,转发送积分 7811282
求助须知:如何正确求助?哪些是违规求助? 9342803
关于积分的说明 20514797
捐赠科研通 7404138
什么是DOI,文献DOI怎么找? 3329662
关于科研通互助平台的介绍 2476410
邀请新用户注册赠送积分活动 2348722