Integrated Electroporated‐Lysis Electrochemical Platform Enables Sensitive and Rapid EV Protein and miRNA Profiling Based on Multiplex‐Responsive CRISPR/Cas12a

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]
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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