TY - JOUR
T1 - Integrated Electroporated-Lysis Electrochemical Platform Enables Sensitive and Rapid EV Protein and miRNA Profiling Based on Multiplex-Responsive CRISPR/Cas12a
AU - Fan, Rui
AU - Tong, Yihang
AU - Luo, Shihua
AU - He, Yangfen
AU - Yang, Chao
AU - Li, Wenbin
AU - Liu, Jieyan
AU - Pan, Jiezhen
AU - Zhu, Yiping
AU - Zhang, Xiaohe
AU - Zhu, Junfang
AU - Zhu, Yitong
AU - Guo, Yuhang
AU - Li, Ling
AU - Situ, Bo
AU - Yan, Xiaohui
AU - Ma, Wen
AU - Chang, Lingqian
AU - Zhang, Ye
N1 - Publisher Copyright:
© 2026 Wiley-VCH GmbH.
PY - 2026/3/3
Y1 - 2026/3/3
N2 - 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.
AB - 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.
KW - CRISPR/Cas12a
KW - electroporated-lysis
KW - extracellular vesicles
KW - gastric cancer diagnosis
KW - machine learning
UR - https://www.scopus.com/pages/publications/105026612683
U2 - 10.1002/smll.202513331
DO - 10.1002/smll.202513331
M3 - 文章
AN - SCOPUS:105026612683
SN - 1613-6810
VL - 22
JO - Small
JF - Small
IS - 13
M1 - e13331
ER -