Pathogen detection in central nervous system infections: moving metagenomic sequencing closer to clinical practice

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BACKGROUND: Central nervous system infections (CNSI) contribute significantly to global disability and mortality, but the causative agent is often undetected. Metagenomic sequencing offers the potential to enhance diagnostic sensitivity, particularly in cases of unusual or partially treated infections. However, caution is required in interpretation of metagenomics data due to technical artefacts from contamination or non-specific read mapping which can reveal a broad spectrum of biologically plausible but diagnostically unlikely organisms. METHODS: This study compares the performance of metagenomic sequencing with standard clinical microbiology methods using cerebrospinal fluid (CSF) from patients with CNSI and non-infected control samples. To evaluate sensitivity of different laboratory approaches, we sequenced DNA and RNA metagenomic libraries extracted from CSF, using both cell-free and cellular fractions. We then devised a set of simple, easily interpreted yet rigorous filters tailored for clinical metagenomics to generate a framework for result interpretation that can be readily applied by clinical scientists. RESULTS: We demonstrate that composite filtering strategies are essential to reduce misleading signals and support standardised workflows. Additionally, our results suggest that a cell-free sample preparation approach can improve confidence in identifying clinically relevant pathogens, highlighting the impact of sample preparation on results quality. CONCLUSION: In this study we describe a reproducible method that can be incorporated into a practical framework for clinical application of metagenomic sequencing in CNSI diagnostics.

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