Analytica chimica acta · 2026
A purification-free one-pot CRISPR/Cas13a assay for detection of porcine epidemic diarrhea virus.
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Zhang Xuan, Pan Zhuhao, Wen Jiafeng, Wang Chen, Tang Chao, Tang Zhonglin
Аннотация
Porcine epidemic diarrhea virus (PEDV) causes up to 80-100% mortality in neonatal piglets, yet field surveillance remains constrained by RT-qPCR's dependence on column-based nucleic acid purification and centralized laboratory infrastructure-a bottleneck especially severe in the inhibitor-rich matrices typical of swine clinical samples. Here we report an integrated sample-to-answer methodology coupling a purification-free thermal lysis step with one-pot RT-RPA-CRISPR/Cas13a chemistry, delivering PEDV detection within 35 min. Reverse transcription, recombinase polymerase amplification, T7 in vitro transcription, and Cas13a collateral cleavage are confined to a single sealed tube, eliminating open-tube transfer and aerosol contamination. Optimized thermal lysis (80°C, 7 min) liberates amplifiable viral RNA directly from crude anal swabs and feces, obviating column purification. The assay attained 101 copies/μL analytical sensitivity with high analytical specificity against six non-target porcine pathogens under the tested conditions. Critically, matrix-tolerance profiling showed the CRISPR-based workflow suppressed inhibition to 7.16% (anal swabs) and 11.89% (feces), versus 63.65% and 69.39% for RT-qPCR (P < 0.01), demonstrating markedly superior robustness under authentic matrices. In a double-blind evaluation of 297 clinical specimens, the platform reached 98.65% concordance with national reference standards (Cohen's κ = 0.941), correctly identifying all 37 RT-qPCR-confirmed positives and additionally resolving four low-titer infections missed by RT-qPCR. By converting an inhibitor-sensitive, infrastructure-bound assay into a purification-free, contamination-resistant workflow with validated clinical reliability, this work provides a robust sample-to-answer analytical strategy for PEDV detection in complex clinical matrices.