Rapid clinical validation of an RNA/DNA hybrid tagmentation-based metagenomic workflow for respiratory RNA virus detection.

Background: In the post-pandemic era, co-circulation of multiple respiratory RNA viruses has increased the need for timely diagnosis and reliable recognition of mixed infections. Although reverse transcription quantitative polymerase chain reaction (RT-qPCR) remains the clinical standard for respiratory virus detection, its target-restricted design limits the detection of unexpected or coinfecting pathogens. Conventional metagenomic next-generation sequencing (mNGS) provides hypothesis-free pathogen detection, but routine clinical use is still limited by long turnaround times and complex library preparation. Therefore, a sequencing-based strategy that preserves broad, unbiased detection while offering a simplified workflow and clinically acceptable turnaround time is needed. Methods: We optimized and clinically validated CATCH, a rapid RNA/DNA hybrid tagmentation-based mNGS workflow, for respiratory RNA virus detection. Analytical performance was assessed using standardized reference materials, including SARS-CoV-2 and influenza A virus, with evaluations of sensitivity, reproducibility, short-term stability, and host-background interference. Clinical validation was performed in retrospective and prospective respiratory infection cohorts, and assay performance was benchmarked against RT-qPCR and multiplex PCR. The same sequencing data were further examined for semiquantitative viral assessment, coinfection detection, and exploratory respiratory microbial profiling. Results: The optimized CATCH workflow shortened library preparation to approximately 3 h, with about 35 min of hands-on time, enabling same-day sequencing-based diagnostics. Broad detection was achieved across seven clinically relevant respiratory RNA viruses. Sequencing-derived viral abundance showed a significant overall correlation with viral input concentration, supporting semiquantitative interpretation, although virus- and subtype-specific variability highlighted biological constraints on absolute quantification. Using SARS-CoV-2 and influenza A virus as representative targets, CATCH achieved clinically actionable limits of detection with high reproducibility and stability. In clinical cohorts, CATCH showed high concordance with routine molecular assays and identified mixed respiratory infections missed by targeted testing. Exploratory analyses also demonstrated the feasibility of respiratory microbial community profiling from the same sequencing dataset. Conclusion: CATCH is a rapid and clinically deployable RNA virus mNGS workflow that helps bridge targeted molecular diagnostics and conventional metagenomic sequencing. By combining broad pathogen detection, coinfection identification, and semiquantitative assessment within a streamlined workflow, CATCH provides a practical framework for comprehensive respiratory RNA virus diagnosis and syndromic surveillance.