Construction of a Programmable Feedback Network with Continuously Activatable Molecular Beacon Fluorescence for One-Step Quantification of Long Noncoding RNAs in Clinical Breast Tissues

化学 分子信标 寡核苷酸 马拉特1 计算生物学 底漆(化妆品) 荧光 分子生物学 DNA 核糖核酸 长非编码RNA 生物化学 生物 基因 物理 有机化学 量子力学
作者
Wenjing Liu,Lingfei Zhang,Chun‐yang Zhang
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:95 (44): 16343-16351 被引量:14
标识
DOI:10.1021/acs.analchem.3c03575
摘要

Long noncoding RNAs (lncRNAs) are key regulators in numerous pathological and physiological processes, and their aberrant expression is implicated in many diseases. Herein, we develop a programmable feedback network with continuously activatable molecular beacon (MB) fluorescence for one-step quantification of mammalian-metastasis-associated lung adenocarcinoma transcript 1 (lncRNA MALAT1) in clinical breast tissues. We introduce a functional MB with three domains, including a substrate for lncRNA MALAT1 recognition, a template for strand displacement amplification (SDA), and a reporter for signal output with FAM fluorescence being quenched by BHQ1. When MALAT1 is present, it recognizes and unfolds the MB, leading to the recovery of FAM fluorescence. Once the MB is opened, multiple rounds of SDA reaction are automatically initiated by recruiting primer, KF DNA polymerase, and Nt.BbvCI nicking enzyme, inducing the opening of more MBs and the dissociation of more FAM/BHQ1 pairs. Consequently, a feedback network is constructed through multicycle cascade SDA, achieving the exponential accumulation of fluorescence signals for accurate quantification of MALAT1. In this assay, only two oligonucleotides (i.e., MB and primer) are involved for the establishment of a feedback amplification network, greatly simplifying the design of the reaction system. Moreover, this assay requires only one step to realize the isothermal exponential amplification for real-time monitoring of MALAT1 with attomolar sensitivity. This assay displays single-base mismatch selectivity with high anti-interference capability, and it can further quantify endogenous MALAT1 at the single-cell level and differentiate MALAT1 expression between breast cancer patient tissues and healthy person tissues.
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
青木聪聪完成签到,获得积分10
刚刚
爆米花应助大脑袋媛媛采纳,获得10
刚刚
1秒前
1秒前
Jasper应助阿符采纳,获得10
3秒前
烟花应助文艺小蕊采纳,获得10
4秒前
单纯的雁芙完成签到,获得积分10
4秒前
4秒前
xuan发布了新的文献求助10
4秒前
7秒前
称心不尤发布了新的文献求助10
7秒前
7秒前
仓颉完成签到 ,获得积分20
8秒前
8秒前
9秒前
9秒前
thirty发布了新的文献求助10
10秒前
MQL发布了新的文献求助10
11秒前
Honcy完成签到,获得积分20
12秒前
will_li完成签到,获得积分10
12秒前
CN发布了新的文献求助10
13秒前
13秒前
14秒前
14秒前
xuan发布了新的文献求助10
14秒前
15秒前
molihuakai应助Honcy采纳,获得10
16秒前
16秒前
全球发布了新的文献求助10
18秒前
颂歌998发布了新的文献求助10
18秒前
cdercder应助kk采纳,获得10
19秒前
科目三应助草上飞采纳,获得10
20秒前
lucky发布了新的文献求助30
20秒前
mlzmlz发布了新的文献求助10
20秒前
22秒前
xuan发布了新的文献求助10
23秒前
顾矜应助成功的院士采纳,获得10
24秒前
25秒前
全球完成签到,获得积分10
25秒前
lailai发布了新的文献求助10
29秒前
高分求助中
Markov Chain Monte Carlo 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
How to Use Machine Learning in Chemistry: An Introduction 1000
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7583572
求助须知:如何正确求助?哪些是违规求助? 9162318
关于积分的说明 19606672
捐赠科研通 7165624
什么是DOI,文献DOI怎么找? 3266302
关于科研通互助平台的介绍 2431200
邀请新用户注册赠送积分活动 2257778