Amplification-Free CRISPR-Cas System Integrated Centrifugal Digital Microfluidic Platform Developed for Multiplexed Respiratory Pathogen Nucleic Acid Analysis

In response to the urgent demand for highly sensitive and rapid multiplex detection technologies in the prevention and control of respiratory infectious diseases, this study presents the development of an integrated CRISPR-Cas9/Cas13a detection platform based on a centrifugal digital microfluidic chip. It aims to overcome the reliance of traditional real-time fluorescence quantitative PCR on specialized equipment and trained personnel. Additionally, it addresses the issue of false positives commonly associated with existing isothermal amplification technologies, while also meeting the requirement for preamplification in sensitive CRISPR-based detection methods. In this study, Methicillin-resistant Staphylococcus aureus (MRSA) and influenza A virus subtype H1N1 were selected as model pathogens. The off-chip CRISPR-Cas9/Cas13a dual nucleic acid detection system was initially developed and optimized to enable highly specific detection of MRSA-mecA DNA at a concentration of 173 pM and H1N1-HA RNA at a concentration of 117 pM. Subsequently, the optimal centrifugal digital chip structure was designed and screened to achieve a droplet filling rate of 99.6%. The optimized CRISPR system was finally integrated into the digital chip, resulting in significantly improved sensitivity, reaching 0.7 copies/μL for MRSA DNA and 1.2 copies/μL for H1N1 RNA within a 20 min reaction time at 37 °C. Furthermore, both the negative and positive detection rates achieved 100% accuracy across all 20 simulated clinical samples. The platform integrates centrifugal digital droplet segmentation technology with the CRISPR-Cas system in an innovative manner, enabling subcopy sensitivity detection without the need for nucleic acid preamplification. Therefore, this convenient, cost-effective, and contamination-resistant method provides a reliable solution for the rapid detection of respiratory pathogens in resource-constrained scenarios.