Dual-Signal Amplification Strategy Based on Catalytic Hairpin Assembly and APE1-Assisted Amplification for High-Contrast miRNA Imaging in Living Cells

小RNA 化学 滚动圆复制 生物标志物 计算生物学 细胞内 癌症 癌症研究 细胞生物学 基因 生物 生物化学 聚合酶 遗传学
作者
Ya Ping Zhang,Mengxu Sun,Juan Xie,Jing Chen,Ting Huang,Wenjun Duan,Jinxiang Chen,Jun Chen,Zong Dai,Minmin Li
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:96 (2): 910-916
标识
DOI:10.1021/acs.analchem.3c05013
摘要

Early tumor diagnosis is crucial to successful treatment. Earlier studies have shown that microRNA is a biomarker for early tumor diagnosis. The development of highly sensitive miRNA detection methods, especially in living cells, plays an indispensable role for early diagnosis and treatment of tumor. Although the catalytic hairpin assembly (CHA)-based miRNA analysis strategy is commonly used for disease diagnosis, further application of CHA is hindered due to its low amplification efficiency and low tumor recognition contrast. To address these limitations, we propose a dual-signal amplification strategy based on CHA and APE1-assisted amplification, enabling highly sensitive and high-contrast miRNA imaging. The miR-221 was selected as a target model. This dual-signal amplification strategy has exhibited high amplification efficiency, which could analyze miRNA as low as 21 fM. This strategy also exhibited high specificity, which could distinguish target miRNA and nontarget with single-base differences. Moreover, this method showed significant potential for practical application, as it could successfully distinguish the expression difference of miR-221 in the plasma samples of normal people and patients. Most importantly, the expression level of the APE1 enzyme in tumor cells is higher than that in normal cells, allowing this strategy to sensitively and specifically image miRNA within tumor cells. This proposed method has also been successfully used to indicate fluctuations of intracellular miRNA and to distinguish miRNA expression between normal cells and cancer cells with high contrast. We anticipate that this method will provide fresh insights and can be a powerful tool for tumor diagnosis and treatment based on miRNA analysis.
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