纳米探针
脱氧核酶
荧光团
生物物理学
化学
细胞内
荧光
水溶液中的金属离子
金属
纳米技术
DNA
材料科学
生物化学
纳米颗粒
生物
量子力学
物理
有机化学
作者
Xinxin Shi,Hong‐Min Meng,Xin Geng,Lingbo Qu,Zhaohui Li
出处
期刊:ACS Sensors
[American Chemical Society]
日期:2020-09-23
卷期号:5 (10): 3150-3157
被引量:50
标识
DOI:10.1021/acssensors.0c01271
摘要
Monitoring Zn2+ in living cells is critical for fully elucidating the biological process of apoptosis. However, the quantitative intracellular sensing of Zn2+ using DNAzyme remains challenging because of issues related to penetration of the signal through tissue, targeted cellular uptake and activation, and susceptibility toward enzymatic degradation. In this study, we developed a novel phosphate ion-activated DNAzyme–metal–organic frameworks (MOFs) nanoprobe for two-photon imaging of Zn2+ in living cells and tissues. The design of this nanoprobe involved the loading of a Zn2+-specific, RNA-cleaving DNAzyme onto the MOFs through strong coordination between the phosphonate O atoms of the DNAzyme backbone and Zr atoms in the MOFs. This coordination restrained the extracellular activity of DNAzyme; however, after cell entry, the DNAzyme was released from the MOFs through a competitive binding by the phosphate ions present at a high intracellular concentration. Following their release, the two-photon (TP) fluorophore-labeled substrate strands of DNAzyme were cleaved with the aid of Zn2+, which resulted in a strong fluorescence signal. The incorporation of a TP fluorophore into the nanoprobe facilitated near-infrared excitation, which allowed the highly sensitive and specific imaging of Zn2+ in living cells and tissues at greater depths than possible previously. The TP-DNAzyme-MOFs nanoprobe achieved a low detection limit of 3.53 nM, extraordinary selectivity toward Zn2+, and a tissue signal penetration of 120 μm. More importantly, this nanoprobe was successfully used to monitor cell apoptosis, and this application of the DNAzyme-MOFs probe holds great potential for future use in biological studies and medical diagnostics.
科研通智能强力驱动
Strongly Powered by AbleSci AI