A novel targeted hybrid capture-NGS assay for sensitive detection of multiplex respiratory pathogens

Emerging respiratory infectious diseases represented by COVID-19, along with traditional respiratory infections, pose a serious threat to human health. High-throughput sequencing (NGS), with its high sensitivity and ultra-high throughput, is particularly suitable for the detection of respiratory pathogens (RP) that are extremely diverse in types and frequently involved in mixed infections. In this study, by integrating a Micro-Targets Hybrid Capture (MT-Capture) system, we developed previously with NGS, we developed a novel assay (termed RP-MT-Capture NGS) for the detection of multiple respiratory pathogens (more than 300 species/types). By optimizing probe design and hybridization capture procedures, RP-MT-Capture NGS achieved high detection sensitivity for different types of pathogens. For influenza viruses, this assay could acquire full-length sequences of hemagglutinin (HA) and neuraminidase (NA) genes for samples with CT values < 32, offering a robust tool for viral mutation surveillance and recombination analysis. The results of clinical sample detection showed that RP-MT-Capture NGS exhibited superior accuracy and sensitivity compared to TaqMan array and metagenomic NGS (mNGS) technologies for respiratory pathogen detection. Compared with traditional probe hybridization-based targeted NGS (tNGS), RP-MT-Capture NGS significantly shortens the wet lab experiment time to within 6 h. In summary, the RP-MT-Capture NGS assay developed in this study offers a novel tool for detecting multiple respiratory pathogens, with substantial clinical and public health relevance.

Emerging and traditional respiratory infections pose threats to human health. These diseases are caused by a variety of pathogens, which often lead to co-infections and, thus, make detection difficult. This study combines a novel probe hybridization capture system with high-throughput sequencing to develop a new detection tool (RP-MT-Capture NGS), which can identify over 300 types of respiratory pathogens. For influenza viruses, it can reveal complete details of key viral genes, facilitating the tracking of viral mutations. Compared with existing detection methods, this new tool is more accurate, more sensitive, and has a higher throughput. It provides great value for clinical practice and public health in respiratory pathogen detection.