ArticleMicrobiology spectrum2026
Validation of an integrated metagenomic pipeline combining optimized wet-lab processing and tiered reporting for CSF pathogen detection.
Article in Microbiology spectrum, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
Metagenomic next-generation sequencing (mNGS) in the infectious disease diagnostic space has been gaining traction and is popular for aiding in the diagnosis of central nervous system infections. However, many challenges and obstacles remain in making this technology a gold standard for infectious disease diagnostic testing. One major challenge is being able to distinguish between the clinically relevant organisms from background contamination. We performed a validation study for mNGS on cerebrospinal fluid (CSF) that utilized positive clinical samples and contrived samples that incorporated a bioinformatics pipeline that can better distinguish between background contamination and clinically relevant organisms and used a three-tiered reporting algorithm meant to decrease the inherent subjectivity that comes with interpreting and reporting data from clinical metagenomic sequencing. The validation of this assay and category-based reporting pipeline revealed an overall concordance of 91.8%, with a sensitivity of 100% and a specificity of 72.4%. In addition, we improved the detection of clinically relevant RNA viruses to almost 100% in the CSF by modifying the wet lab processing of the sample. This bioinformatics pipeline with a category-based reporting algorithm will provide more confidence in reporting microorganisms detected with this technology, mNGS, and improving patient care. IMPORTANCE: Metagenomic next-generation sequencing (mNGS) can offer a broad, unbiased approach for the detection of infectious pathogens and has shown promise in diagnosing central nervous system infections. Despite its potential, clinical implementation remains limited by challenges in distinguishing clinically relevant organisms from background contamination. This study validated an mNGS assay for cerebrospinal fluid that incorporates an optimized bioinformatics pipeline with a three-tiered reporting algorithm designed to reduce subjectivity and enhance diagnostic confidence. The assay also has improved detection of clinically relevant RNA viruses through modified wet-lab processing. These findings support the clinical utility of a structured, category-based reporting approach for mNGS, advancing its reliability as a diagnostic tool in infectious disease testing.
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