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CRISPR-Cas-mediated Multianalyte Synthetic Urine Biomarker Test for Portable Diagnostics
Published on: December 8, 2023
Proximity-confined DNA walking-nanozyme cascades for multiplexed urinary extracellular vesicle phenotyping and
Feng Lin1, Wanglong Li1, Xinhuang Hou2
1Department of Vascular Surgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China; Department of Vascular Surgery, National Regional Medical Center, Binhai Campus of The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China.
Abstract:
Urinary extracellular vesicles (uEVs) provide noninvasive biomarkers for liquid biopsy owing to their ability to reflect disease-associated molecular alterations. However, the accurate analysis of low-abundance uEV surface proteins in complex biological matrices remains a major analytical challenge. Here, we report a proximity-activated dual-cascade (PADC) biosensing platform that integrates a target-responsive 3D DNA machine with a nanozyme-mediated ratiometric transduction circuit for high-fidelity multiplexed phenotyping of uEVs. Upon dual-recognition of target uEVs, the DNA machine undergoes proximity-induced spatial confinement on individual magnetic particles, creating a localized catalytic microenvironment that markedly enhances DNA walking kinetics and signal amplification efficiency. Coupled with an enzyme-nanozyme redox cascade, this confined catalytic process generates a robust ratiometric fluorescence output for sensitive and reliable molecular profiling. Benefiting from the spatially confined amplification architecture, the proposed platform enables ultrasensitive multiplexed analysis of CD63, EGFR, and PAK6 with detection limits down to 4.3 × 104, 2.2 × 104, and 1.7 × 103 particles/mL, respectively. Importantly, the target-uEV response was approximately six-fold higher than those generated by common urinary interferents or their mixture, demonstrating high analytical selectivity in complex urine matrices. To further evaluate its universality for multi-disease screening, multidimensional molecular profiling combined with machine learning algorithms was performed on clinical uEV samples collected from healthy controls, Diabetes Mellitus (DM) patients, and prostate cancer (PCa) patients. The optimal classification model achieved an overall multiclass diagnostic accuracy of 91% for distinguishing healthy, DM, and PCa groups. This work establishes a proximity-confined catalytic biosensing strategy for multiplexed uEV analysis and demonstrates its potential for uEV-based liquid biopsy and proof-of-concept disease stratification.
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