适体
癌细胞
荧光
细胞生物学
肿瘤微环境
化学
核酸
内生
癌症
小RNA
检出限
生物物理学
生物化学
计算生物学
体外
生物
信号(编程语言)
细胞
酶
荧光寿命成像显微镜
纳米技术
细胞培养
癌症研究
三磷酸腺苷
HEK 293细胞
分子生物学
细胞生长
肿瘤细胞
作者
Dimin Tong,Lingjie Ma,Jie Chen,Wen-Xue Zhou,Liang Xu,Zhifa Shen,Wei Shen
出处
期刊:ACS Sensors
[American Chemical Society]
日期:2026-04-06
卷期号:11 (4): 3397-3406
被引量:1
标识
DOI:10.1021/acssensors.5c04929
摘要
The tumor microenvironment is characterized by abnormal endogenous signals, including elevated ATP levels. Developing probes that specifically respond to such signals is essential for accurate tumor detection. While catalytic hairpin assembly (CHA) provides efficient, enzyme-free signal amplification, conventional systems suffer from single target design and background signal leakage. To address this, we designed a nucleic acid hybrid probe, termed ATMiR-LOCK ( A denosine T riphosphate and MiR NA Lo gic C ontrolled C ircuit K ey), which integrates a dual-ATP aptamer with two CHA hairpins. This system enables synchronous response to ATP and microRNA-21 (miR-21), an oncogenic miRNA overexpressed in various malignant tumors. In this target-dependent mechanism, ATP binding triggers initiator release to partially open H1, enabling subsequent miR-21 binding to fully activate the CHA circuit with H2 for strong fluorescence signal amplification. The ATMiR-LOCK system functions as an “AND” logic gate, and it requires the presence of both ATP and miR-21 inputs to generate an output. This dual-input design greatly improves detection specificity and effectively minimizes the background signal. We validated the probe through in vitro experiments, fluorescence imaging in HepG2 liver cancer cells, and tests across multiple cell lines. The results demonstrated high sensitivity, with a picomolar detection limit for miR-21, and an effective response to millimolar levels of cellular ATP in cancer cells, both with excellent specificity and reliability. The ATMiR-LOCK probe represents a promising strategy for developing highly specific diagnostic tools for early tumor detection.
科研通智能强力驱动
Strongly Powered by AbleSci AI