纳米器件
脱氧核酶
体内
级联
辅因子
细胞生物学
化学
生物
纳米技术
DNA
生物化学
材料科学
酶
遗传学
色谱法
作者
Xiaohong Zhong,Yifang He,Ming Shi,Yong Huang,Kexin Liang,Beilei Wang,Jing Hua,Liangliang Zhang,Shulin Zhao,Hong Liang
标识
DOI:10.1016/j.snb.2024.135495
摘要
In vivo imaging of tumor-related mRNA has prosperous prospects for tumor diagnosis. Given the low expression levels of tumor-related mRNA at early stages of cancer, its in vivo amplified imaging still requires more efforts. Herein, we report a novel cofactor self-supplying and self-feedback DNAzyme (Dz) nanodevice (SFDND) for imaging of tumor-related mRNA in vitro and in vivo with cascade signal amplification ability. The SFDND is built by assembling a hairpin DNA recognition probe with locked Dz and a DNA trigger-incorporated fluorogenic hairpin substrate probe on ZnO@polydopamine nanoparticles (ZnO@PDA NPs). After entering cells, ZnO@PDA NPs are decomposed in acidic lysosomes to release nucleic acids and DNAzyme cofactor Zn2+. Intracellular hybridization of target mRNA with the released hairpin DNA recognition probe activates Dz, which can cyclically cleave hairpin substrate probes to liberate many fluorophores and substrate segments containing DNA triggers, thereby generating amplified fluorescence signal. Moreover, each DNA trigger within the released substrate segments can also bind with the hairpin DNA recognition probe to activate Dz, which induces further signal amplification via a feedback mechanism. This SFDND achieves ~103-fold improvement in detection sensitivity over conventional non-feedback Dz nanodevice. It could accurately discriminate tumor cells from normal cells and selectively diagnose tumors in vivo during different stages of tumor growth by means of mRNA imaging. The proposed SFDND may be extended for amplified sensing and imaging of other low-abundance targets (e.g., microRNAs and aptamer substrates) by simply switching corresponding nucleic acid sequences, thus possesses promising application in basic biomedical research and disease diagnosis.
科研通智能强力驱动
Strongly Powered by AbleSci AI