纳米技术
核酸
溶解
细胞外小泡
化学
计算生物学
劈开
小RNA
DNA
电化学
胞外囊泡
纳米颗粒
计算机科学
工作流程
生物传感器
蛋白质组学
多路复用
滚动圆复制
生物物理学
微流控
材料科学
适体
微泡
裂解缓冲液
蛋白质检测
分子诊断学
仿形(计算机编程)
蛋白质组
靶蛋白
作者
Rui Fan,Yihang Tong,Shihua Luo,Yide He,Chao Yang,W S Li,Jieyan Liu,Jiezhen Pan,Yiping Zhu,Xiaohe Zhang,Junfang Zhu,Yitong Zhu,Yuhang Guo,Ling Li,Bo Situ,Xiaohui Yan,Wen Ma,Lingqian Chang,Ye Zhang
出处
期刊:Small
[Wiley]
日期:2026-01-05
卷期号:22 (13): e13331-e13331
标识
DOI:10.1002/smll.202513331
摘要
ABSTRACT Proteins and miRNAs in extracellular vesicles (EVs) have emerged as crucial biomarkers for tumor diagnosis. While CRISPR/Cas12a‐based platforms have shown great promise in nucleic acid and protein detection, their susceptibility to off‐target activation and structural instability remains a significant limitation. Here, we have developed an electroporation‐lysis electrochemical platform integrated with DNA cube‐cage‐locked CRISPR/Cas12a (DC‐Cas12a), termed EL‐DC‐Cas12a. This platform utilizes an electric field to rapidly lyse EVs, releasing their internal proteins and miRNAs. These released molecules then activate the DC‐Cas12a system, thereby triggering the displacement of two distinct crRNA/Cas12a complexes that correspond to EV proteins and miRNAs, respectively. These complexes then specifically recognize and cleave electrochemical probes, generating quantifiable electrochemical signals that enable synchronous and accurate analysis of the two biomarkers. The integrated workflow for EV lysis and detection can be completed within 40 min, greatly simplifying the overall operation. The detection limits (LOD) of this platform for EV PD‐L1 protein and miR‐1246 were 5.44 × 10 4 particles/mL and 3.59 × 10 3 particles/mL, respectively. Moreover, by applying machine learning algorithms to analyze the EV‐associated proteins and miRNAs profiling, the platform demonstrated a diagnostic accuracy of 98.3% in distinguishing healthy donors from early‐stage GC patients, and 99% in differentiating early‐stage from advanced‐stage GC patients in a clinical gastric cancer cohort. Therefore, the proposed platform offers a promising strategy for multiplexed detection of EV biomarkers and precise discrimination of GC.
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