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Author Spotlight: Development of Simplified CRISPR-Based Tests for Rapid Detection of Infectious Diseases
Published on: August 16, 2024
Integrated Electroporated-Lysis Electrochemical Platform Enables Sensitive and Rapid EV Protein and miRNA Profiling
Rui Fan1,2, Yihang Tong3,4, Shihua Luo5,6
1Department of Laboratory Medicine, Guangdong Provincial Key Laboratory of Precision Medical Diagnostics, Guangdong Engineering and Technology Research Center for Rapid Diagnostic Biosensors, Guangdong Provincial Key Laboratory of Single-cell and Extracellular Vesicles, Nanfang Hospital, Southern Medical University, Guangzhou, P. R. China.
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 × 104 particles/mL and 3.59 × 103 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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