Validation of an integrated metagenomic pipeline combining optimized wet-lab processing and tiered reporting for CSF pathogen detection

Validated CSF mNGS pipeline combining optimized wet-lab processing with a three-tiered reporting algorithm to distinguish clinically relevant organisms from background contamination

This study validates a metagenomic sequencing (mNGS) pipeline on cerebrospinal fluid (CSF) for diagnosing central nervous system infections, combining optimized wet-lab processing with a three-tiered reporting algorithm. The goal is to reduce interpretation subjectivity and better distinguish clinically relevant organisms from background contamination. Validation, on positive clinical and contrived samples, shows 91.8% overall concordance, 100% sensitivity, and 72.4% specificity. Modified wet-lab processing raises detection of clinically relevant RNA viruses to nearly 100%.

Synthesis written by Geno'X. For the full original abstract, please refer to the source publication.

The main barrier to routine mNGS is not sensitivity but interpretation: a three-tiered reporting scheme that codifies clinical relevance is a pragmatic answer to background noise. The 72.4% specificity remains improvable, but this structured, reproducible approach is exactly what is missing to bring CSF mNGS into routine clinical diagnosis.