Advances in CRISPR-Cas12a/13a-Based Nucleic Acid Detection for Porcine Viral Diseases: A Comprehensive Review

The global swine industry suffers persistent economic losses and health challenges due to major viral pathogens such as African swine fever virus (ASFV), porcine reproductive and respiratory syndrome virus (PRRSV), classical swine fever virus (CSFV), and porcine circovirus (PCV). Traditional diagnostic methods, including virus isolation, serology, and quantitative PCR (qPCR), are limited by time, equipment requirements, and field applicability. Recent advances in CRISPR-based diagnostics, particularly those leveraging the collateral cleavage activity of Cas12a and Cas13a, have enabled rapid, sensitive, and field-deployable nucleic acid detection. This review outlines the principles of CRISPR-Cas12a/13a systems, their integration with isothermal amplification techniques, and their application in detecting major swine viruses. Cas12a-based platforms (e.g., DETECTR) and Cas13a-based systems (e.g., SHERLOCK) achieve detection limits as low as single-copy/μL within 25–60 min at 37 °C, offering high specificity and compatibility with visual readouts. Applications include ASFV, PRRSV, CSFV, PCV, foot-and-mouth disease virus (FMDV), porcine rotavirus (PoRV), and porcine parvovirus 7 (PPV7). Despite significant advances, challenges remain, notably the reliance on nucleic acid extraction and the need for fully integrated “sample-in, result-out” systems. Ongoing innovations in extraction-free methods, lyophilized reagents, and multiplex detection will strengthen the role of CRISPR diagnostics in swine disease surveillance and control. From an application standpoint, the technology offers a low-capital, field-adaptable alternative to qPCR, with its value proposition rooted in early outbreak containment and loss prevention. Its adoption pathway is expected to vary across production systems—serving as a sentinel tool in intensive settings, a leapfrogging solution in rapidly intensifying regions, and through shared-service models in resource-limited contexts. However, translation to routine use still requires overcoming standardization hurdles, regulatory validation, and workflow integration.