4.8 Article

CRISPR-Cas12a powered hybrid nanoparticle for extracellular vesicle aggregation and in-situ microRNA detection

期刊

BIOSENSORS & BIOELECTRONICS
卷 245, 期 -, 页码 -

出版社

ELSEVIER ADVANCED TECHNOLOGY
DOI: 10.1016/j.bios.2023.115856

关键词

CRISPR-Cas12a; DNA catalytic reaction; Extracellular vesicle; Enrichment; Hybrid nanoparticle; MicroRNA detection

向作者/读者索取更多资源

This study reports a technology based on cationic lipid-polymer hybrid nanoparticles for efficient extracellular vesicle (EV) enrichment and in-situ detection of internal microRNAs. The technology demonstrates high EV enrichment efficiency and sensitive internal RNA detection, making it potentially useful for early pancreatic cancer diagnosis.
Efficient extracellular vesicle (EV) enrichment and timely internal RNA detection for cancer diagnostics are highly desirable and remain a challenge. Here, we report a rapid EV aggregation induced in-situ microRNA detection technology based on cationic lipid-polymer hybrid nanoparticles encapsulating cascade system of catalytic hairpin assembly and CRISPR-Cas12a (CLHN-CCC), allowing for EV enrichment in three-dimensional space and in-situ detection of internal microRNAs in one step within 30 min. The enrichment efficiency (>90%) of CLHN-CCC is demonstrated in artificial EVs, cell-secreted EVs and serum EVs, which is 5-fold higher than that of traditional ultracentrifugation. The sensitive detection of artificial EVs and internal miR-1290 was achieved with the limit of detection of 10 particles/mu L and 0.07 amol, respectively. After lyophilization, CLHNCCC shows no obvious loss of performance within 6 months, making it much more robust and user friendly. This technique could sensitively (sensitivity = 92.9%) and selectively (selectivity = 85.7%) identify low amount miR1290 in serum EVs, distinguishing early-stage pancreatic cancer patients from healthy subjects, showing high potential for clinical applications.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据