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Updated: Jun 13, 2026

Metagenomic Next-Generation Sequencing of Cerebrospinal Fluid for the Detection of Central Nervous System Pathogens
10:08

Metagenomic Next-Generation Sequencing of Cerebrospinal Fluid for the Detection of Central Nervous System Pathogens

Published on: April 17, 2026

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

Nicola Cumley1, Josh Quick1, Thomas Brier1

  • 1Institute of Microbiology and Infection, University of Birmingham, Edgbaston, Birmingham, Birmingham, B15 2TT, UK.

BMC Infectious Diseases
|June 12, 2026
PubMed
Abstract

Insights

Metagenomic sequencing can improve diagnosis of central nervous system infections (CNSI). A new filtering method and cell-free sample preparation enhance accuracy and reduce misleading results for clinical application.

Area of Science:

  • Neuroscience
  • Genomics
  • Infectious Diseases

Background:

  • Central nervous system infections (CNSI) are a major cause of disability and death globally, often with undetected causative agents.
  • Metagenomic sequencing shows promise for improving diagnostic sensitivity in complex or partially treated CNSI cases.
  • Interpreting metagenomic data requires caution due to potential artifacts like contamination or non-specific read mapping.

Purpose of the Study:

  • To compare metagenomic sequencing with standard clinical microbiology for CNSI diagnosis.
  • To develop and evaluate robust filtering strategies for clinical metagenomics.
  • To assess the impact of sample preparation (cell-free vs. cellular) on diagnostic accuracy.

Main Methods:

  • Cerebrospinal fluid (CSF) from CNSI patients and controls were analyzed using both DNA and RNA metagenomic sequencing.
  • Cell-free and cellular fractions of CSF samples were processed.
  • A framework with simple, rigorous filters was developed for interpreting clinical metagenomics results.

Main Results:

  • Composite filtering strategies are crucial for minimizing misleading signals and standardizing workflows.
  • Cell-free sample preparation enhances confidence in identifying clinically relevant pathogens.
  • The study highlights the significant impact of sample preparation techniques on data quality and diagnostic outcomes.

Conclusions:

  • A reproducible method for clinical metagenomic sequencing in CNSI diagnostics has been established.
  • This method can be integrated into a practical framework for routine clinical use.
  • The findings support the adoption of metagenomic sequencing as a valuable tool for diagnosing central nervous system infections.
Keywords:
BioinformaticsCell-freeCentral nervous system infections (CNSI)Cerebrospinal fluid (CSF)DiagnosticsMetagenomicsPathogen detection

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