Method Article

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

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DOI:

10.3791/70075

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April 17th, 2026

 ,  ,  , 
1University of California, San Francisco Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco

In This Article

Summary

This research protocol outlines an optimized workflow for metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid. By addressing challenges unique to low-biomass samples, the method enables robust pathogen detection across diverse resource settings, providing a versatile foundation for neuroinfectious disease research applications.

Abstract

Metagenomic next-generation sequencing (mNGS) has emerged as a powerful tool for unbiased pathogen detection and host transcriptional profiling in clinical and research settings. While its utility in diagnosing central nervous system (CNS) infections is increasingly recognized, cerebrospinal fluid (CSF) samples pose unique challenges due to low nucleic acid abundance and susceptibility to degradation. This study presents an optimized research-based protocol for Illumina sequencing platforms tailored to CSF mNGS, spanning sample handling, nucleic acid extraction, library preparation, sequencing, and bioinformatic analysis. Quality control approaches adaptable to both high and low-resource settings are also provided, including alternatives to capillary electrophoresis. This study demonstrates the protocol’s robustness through two representative cohorts: a high-depth, NovaSeq-based workflow and a cost-conscious NextSeq-based workflow. Across these cohorts, pathogens were detected in over 50% of cases, underscoring the method’s diagnostic potential even with resource-constrained adaptations. This protocol facilitates reproducible CSF mNGS, providing a foundation for diverse applications in neuroinfectious disease research and diagnostics.

Introduction

Metagenomic next-generation sequencing (mNGS) is an increasingly utilized molecular technique that allows for the detection of a broad spectrum of nucleic acids within a given environmental or host sample1. By amplifying and sequencing the vast majority of the genetic material within a sample, mNGS does not require a preconceived hypothesis of the specific microorganisms that may be present. mNGS has shown utility as a diagnostic tool in a variety of clinical settings, such as for the detection of uncommon pathogens or atypical presentations of infectious diseases1,2,3. Furthermore, the host transcriptional profile is also captured by the RNA-sequencing data generated by the mNGS assay, allowing researchers to better understand the host immune response to various disease states4. The applicability of mNGS to a broad range of clinical and research contexts, along with the dramatic decrease in cost of generic sequencing in recent decades, has led to a tremendous growth in the use of this technology5. 

In particular, mNGS has shown promise in the detection of central nervous system (CNS) infections, which remain challenging to diagnose2,6,7. Prior to the advent of clinical mNGS testing, the California Encephalitis Project examined causes of encephalitis across the state and found that over half of cases remained undiagnosed, despite utilizing an expanded panel of molecular tests and detailed clinical evaluation8. Now, over two decades later, a clinically validated cerebrospinal fluid (CSF) mNGS assay detected the presence of pathogenic organisms in the CSF of nearly 15% of patients with suspected CNS infections over a seven-year period7. Furthermore, among patients with confirmed infectious diagnoses, one in five were only diagnosed by this clinical CSF mNGS assay, emphasizing the utility of this testing approach7. In addition, studies have also demonstrated the potential of predicting the likelihood of various CNS infections based on the host transcriptome generated by the mNGS assay9,10,11. 

However, the accessibility of this promising technique is limited by challenges unique to CSF sample handling and processing. In particular, the quantity of CSF DNA and RNA is often lower than the minimum quantity specified by library preparation kits12,13. Nucleic acid quality can also be suboptimal, given that CSF samples collected for clinical purposes are often stored at temperatures at which DNAses and RNAses can remain active14. This paper describes a research-based CSF mNGS protocol for Illumina sequencing platforms that has been optimized to maximize the likelihood of generating usable data for pathogen detection (Figure 1). 

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Protocol

This protocol was developed and tested using samples collected as part of studies approved by the UCSF Institutional Review Board.

CSF sequencing workflow: sample collection, DNA/RNA extraction, Illumina sequencing, pathogen ID.
Figure 1: Cerebrospinal fluid (CSF) metagenomic next-generation sequencing (mNGS) sample processing and analytic workflow for pathogen detection. This diagram demonstrates the primary steps in the CSF mNGS wet-lab and bioinformatic pathways. Please click here to view a larger version of this figure.

1. CSF sample handling considerations

NOTE: mNGS results are optimal if CSF library preparation occurs at the time of collection, or if CSF samples are preserved via snap-freezing in liquid nitrogen, followed by long-term storage at -80 °C15. Alternatively, the use of preservative solutions such as DNA/RNA shield at the time of sample collection can allow for storage at room temperature for longer periods of time16,17. This may be preferable in situations when there are concerns about the reliability of the cold chain. In addition, avoidance of unnecessary freeze-thaw cycles can also help preserve nucleic acid integrity, and it may be advisable to pre-aliquot samples at the time of collection if multiple analytic workflows are anticipated for the CSF samples18,19.

2. Extraction of CSF nucleic acid

CAUTION: Perform in an appropriate biosafety level cabinet with gloves and a laboratory coat to reduce the risk of transmission of infectious organisms. Reagents are harmful if swallowed, inhaled, or come into contact with skin. Dispose of used materials as hazardous chemical waste in compliance with local regulations.

  1. The Quick-DNA/RNA Kit generally works well for samples with a low abundance of nucleic acids.
  2. Thaw the CSF samples on ice, then transfer 100 µL–1 mL of each sample to a 1.5 mL tube and centrifuge at 16,000 x g at 4 °C for 10 min.
  3. Carefully pipette off the supernatant, leaving behind the pellet (which is often invisible to the naked eye) and 100 µL of supernatant. 
  4. Add 100 µL of DNA/RNA Shield to the remaining 100 µL of supernatant and pellet, and pipette mix thoroughly.
  5. Then, add 600 µL of DNA/RNA lysis buffer and pipette mix until the solution is homogeneous. 
  6. Follow the remainder of the DNA/RNA extraction protocol as described by the manufacturer in Supplementary Appendices A, with the following important modifications specific to CSF: 
    1. Perform all centrifugation steps for 1 min at max speed (~21,000 x g) except for the final DNA/RNA wash step, which should run for 3 min at max speed. 
    2. Elute the sample by adding 22 µL (rather than 25 µL) of nuclease-free water directly onto the column matrix, then incubate for 3 min at room temperature. The higher volume generally results in a greater total quantity of nucleic acid eluted from the column.
    3. After elution, pipette the flow-through back onto the column matrix and centrifuge again for one minute at max speed. Transfer the sample to a new 1.5 mL storage tube, and either proceed immediately to library preparation or store at -80 °C.

3. CSF RNA library preparation

CAUTION: Perform in an appropriate biosafety cabinet, wearing gloves and a laboratory coat. Reagents are harmful if swallowed, inhaled, or come into contact with skin. Dispose of used materials as hazardous chemical waste in compliance with local regulations.

NOTE: Important considerations prior to beginning: rRNA depletion is preferred over polyadenylation enrichment for isolation of mRNA, given that the rRNA depletion does not require additional bead clean steps (which can reduce the yield of usable mRNA), and some neuroinvasive RNA virus transcripts lack polyadenylation. ERCC (External RNA controls consortium) RNA Spike-In can be added as a quality control measure, though it is not strictly necessary. If the RNA was stored at -80 °C following extraction, thaw on ice prior to beginning.

  1. RNA fragmentation and priming: Pipette mix the following in a polymerase chain reaction (PCR) tube: 3.5 µL RNA sample, 0.5 µL 1:2500 diluted ERCC spike-in, 4 µL first-strand reaction buffer (5x), 1 µL random primers, and 1 µL 1:100 diluted rRNA (Table of Materials). In a thermocycler, set the heated lid to 105 °C and incubate the samples at 75 °C for 2 min, 70 °C for 2 min, 65 °C for 2 min, 60 °C for 2 min, 55 °C for 2 min, 37 °C for 5 min, and 25 °C for 5 min. 
  2. First-strand cDNA synthesis: Pipette mix the fragmented and primed RNA (10 µL) with 8 µL nuclease-free water and 2 µL first-strand synthesis enzyme mix. In a thermocycler, set the heated lid to 105 °C and incubate the samples at 25 °C for 10 min, 42 °C for 15 min, and 70 °C for 15 min. 
  3. Second-strand cDNA synthesis: Pipette mix the first-strand synthesized DNA (20 µL) with 8 µL second-strand synthesis reaction buffer, 4 µL second-strand synthesis enzyme mix, and 48 µL nuclease-free water. In a thermocycler, incubate the samples for 1 h at 16 °C, with the heated lid turned off.
  4. Perform a magnetic bead purification (Supplementary Appendices B) using a 1.8x ratio of SPRI magnetic beads (144 µL). Elute into 53 µL nuclease-free water and transfer 50 µL of the final supernatant (containing purified double-strand cDNA) to a clean nuclease-free PCR tube. At this point, the samples can be safely frozen at -20 °C overnight if needed. 
  5. cDNA library end preparation: If the sample was stored at -20 °C overnight, thaw on ice prior to restarting. Pipette mix the purified ds-cDNA (50 µL) with 7 µL of end-prep reaction buffer and 3 µL of end-prep enzyme mix.
  6. In a thermocycler, incubate the samples at 20 °C for 30 min, and 65 °C for 30 min, with the heated lid turned off.
  7. Adapter ligation (perform this step on ice): Create a 1:100 dilution of adapter in nuclease-free water. Pipette the end-preparation reaction mixture (60 µL) into a tube containing 30 µL ligation master mix, 1 µL ligation enhancer, and 2.5 µL 1:100 diluted adapter. Ensure the adapter is added separately (e.g., not premixed with the ligation master mix or ligation enhancer) to avoid adapter dimer formation.
  8. In a thermocycler, incubate the samples at 20 °C for 15 min, with the heated lid turned off.
  9. Proceed immediately to magnetic bead purification (Supplementary Appendix 2) using a 0.9x ratio of SPRI magnetic beads (87 µL).
  10. Elute into 17 µL nuclease-free water, and transfer 15 µL of the final supernatant to a clean nuclease-free PCR tube
  11. Barcoding PCR: Pipette the purified, adapter-ligated cDNA (15 µL) into a tube with 3 µL USER enzyme, 25 µL Q5 Master Mix, and 10 µL unique barcoded primers. In a thermocycler, set the heated lid to 105 °C, incubate the samples at 37 °C for 15 min, then 98 °C for 30 s, and then perform 19 cycles of 98 °C for 10 s and 65 °C for 75 s. Finish the PCR by incubating at 65 °C for 5 min.
  12. Perform a final magnetic bead purification using a 0.8x ratio of magnetic beads (43 µL). Elute into 23 µL nuclease-free water, and following the final bead separation step, transfer 20 µL to a clean nuclease-free PCR tube. At this point, the samples can be safely frozen at -20 °C overnight if needed. 

4. CSF DNA library preparation

CAUTION: Perform in an appropriate biosafety level cabinet with gloves and a laboratory coat. Reagents are harmful if swallowed, inhaled, or come into contact with skin. Dispose of used materials as hazardous chemical waste in compliance with local regulations.

  1. If the extracted DNA was stored at -20 °C overnight, thaw on ice prior to restarting.
  2. DNA fragmentation and end-preparation: Mix the first-strand reaction buffer thoroughly by vortexing and pipette mixing the solution to resuspend any precipitate. Pipette mix 3.5 µL of the extracted DNA, 22.5 µL nuclease-free water, 7 µL first-strand reaction buffer, and 2 µL first-strand enzyme mix.
  3. In a thermocycler, set the heated lid to 105 °C and incubate the samples at 37 °C for 5 min, then 65 °C for 30 min.
  4. The adapter ligation and barcoding PCR process is identical for the DNA and RNA libraries. Repeat sections 7–10 of the “CSF RNA Library Preparation” with the fragmented and end-prepped DNA to complete the DNA library preparation.

5. Library quality control and pooling

  1. Quantify the concentration of the sample libraries via the DNA quantification kit and corresponding fluorometer. Water controls should have substantially less (or unquantifiable) concentrations compared to the rest of the samples.
  2. Assess the sample library sizes by running the samples on an automated capillary electrophoresis machine.
  3. Alternatively, if an automated capillary electrophoresis machine is not available, further amplify 1-2 µL of the sample by performing a PCR reaction with Illumina universal primers, and then perform electrophoresis of 10 µL of the final product on a 2% agarose gel. Dispose of used materials as hazardous chemical waste in compliance with local regulations.
    NOTE: The optimal library length is approximately 400–500 base pairs, so future fragmentation incubations should be adjusted accordingly if the library sizes are larger or shorter than desired. In addition, it is critical to assess for the presence of adapter dimers, which are approximately 150 base pairs in length and occur when two adapter molecules inadvertently ligate together without an insert sequence. Even if present in low quantities, adapter dimers tend to cluster and sequence efficiently on the flow cell, reducing the proportion of usable reads during a sequencing run. As such, if adapter dimers are present, perform an additional magnetic bead purification with a 0.8x ratio of SPRI magnetic beads. (Of note, if >10% of the sample is adapter dimer, it may take 2-3 rounds of magnetic bead purification to remove most of the adapter dimers).
  4. To ensure a similar depth of sequencing across samples within the same sequencing run, it is necessary to pool equimolar quantities of each sample. If the library sizes are comparable across all samples, simply pool equivalent masses of each RNA library and repeat separately for each DNA library.

6. Sequencing considerations

  1. The pooled samples are now ready for sequencing on Illumina sequencing devices. If sequencing in-house (rather than via a specialized sequencing core facility), carefully denature and dilute the pooled libraries according to the sequencing device instructions prior to loading the pool onto the reagent cartridge (typical concentration is 4 nM for most Illumina sequencing platforms). 
  2. Calculate the estimated sequencing depth by dividing the expected data yield for the Illumina flow cell by the number of multiplexed samples in the run.
    NOTE: Determining the desired sequencing depth depends on several factors, including cost, the sequencing device used, and the objectives of the experiment. Two examples of different sequencing depths are presented below in the Representative Results section.

7. Data analysis for pathogen detection

  1. Utilize the open-source web-based platform Chan Zuckerberg ID (CZID, https://www.czid.org, Illumina mNGS Pipeline v8.3) to perform the metagenomic analysis for pathogen detection. CZID also includes options to upload samples via command line interface and direct transfer from an Illumina BaseSpace account to CZID. A detailed overview of CZID mNGS data analysis is available at https://chanzuckerberg.zendesk.com/hc/en-us/articles/13770737266196-Guide-to-mNGS-Data-Analysis
  2. Log into CZID, click “upload,” select “metagenomics” for analysis type, and select the input fastq files from the sequencing run. Input the necessary information for each sample, including sample name, sample type (CSF), and nucleotide (RNA or DNA).
    NOTE: The uploaded files undergo automated processing via the CZID pipeline, which incorporates several computational tools such as STAR, Bowtie2, Trimmomatic, PriceSeq, and GSNAP, among others, to remove low-quality reads and human read sequences. The pipeline then aligns and assembles the filtered sequences utilizing the National Center for Biotechnology Information (NCBI) nucleotide (NT) and protein (NR) databases via the Minimap2 and DIAMOND software programs, respectively. The final CZID output includes NT/NR taxon counts and contig counts (contiguous, overlapping segments generated during assembly) for each sample. 
  3. Once processed, click the project folder containing the samples and navigate to the summary dashboard.
  4. Scroll down to examine the number of reads per sample (and compare to the expected number of reads based on the Illumina flow cell used and the number of samples multiplexed in the sequencing run), the percentage of reads passing quality control filtering, and the duplicate compression ratio to assess for biased PCR overamplification.
  5. The presence of contamination by environmental nucleic acid fragments - either acquired during the CSF sample collection process, or during extraction/library preparation - is a common occurrence20. To determine whether bacterial, fungal, and parasitic reads are true positives, potential methods utilizing the normalized read counts (reads per million, or rPM) can be considered:
    1. Create a background model (e.g., using water samples that have been extracted, prepared, and sequenced) directly within CZID to filter out any taxons with rPMs that are similar to or lower in abundance relative to the background model (and therefore, likely to represent contamination). Within the project folder, check each of the water control samples and then click “background model” to create. While analyzing each sample, select the background model and click “threshold filters” to apply an “NTZ Score” > 1 (or higher) to filter out likely contaminants.
    2. Utilize pre-specified rPM thresholds, such as rpMsample > 10*(average rPMcontrols), or log10(rPMsample) at least 1 log greater than log 10(average rPMentire cohort). First, download the taxon-specific rPM for each sample by checking each sample on the project page, clicking download, and selecting “Combined Sample Taxon Results.” The resulting .csv file can be analyzed using statistical software such as R or Stata as desired.
      NOTE: Given the lower relative abundance of viral pathogens, any virus with known neuroinvasive potential and at least one read aligning to the viral genome should be considered a positive result (and ideally, confirmed via repeat mNGS or pathogen-specific clinical testing). In addition, the analytic pipeline requires each read in the fastq file to be computationally aligned to the most likely reference genome in the NCBI database. As a result, some reads may be relatively non-specific to a particular pathogen and could theoretically have aligned to a broad range of organisms rather than the specific taxon assigned in CZID. Given this risk, manual confirmation of positive calls should be performed by confirming the alignment via the web-based NCBI BLAST tool (the specific reads for a taxon can be directly sent from CZID to NCBI BLAST by clicking the blast icon next to the relevant taxon). 

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Results

Library quality control
Following DNA/RNA extraction, the measured concentration of the extracted DNA is generally 1–10 ng/µL. However, the concentration of the extracted RNA is often on the order of pg/µL, and thus it is frequently undetectable by spectrophotometers. Following library preparation, the purified and amplified cDNA and DNA libraries are generally at a concentration of 0.5–10 ng/µL and 10–30 ng/µL, respectively. The capillary electrophoresis and electropherogram data in

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Discussion

There are important considerations that may require alterations to this protocol, depending on the desired sequencing depth, the sequencing platform used, and the availability of equipment for quality control. Nonetheless, the two representative cohorts presented here demonstrate that robust pathogen detection is possible across a broad range of experimental conditions. Specifically, Cohort B demonstrated that, despite several cost-saving measures – shallow-depth sequencing, the exclusion of ERCC controls, miniatur...

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Disclosures

M.R.W. receives unrelated research grant funding from Roche/Genentech, Novartis, and Kyverna Therapeutics and is a founder and board member of Delve Bio Inc. He has provided consulting services to Pfizer, Vertex Pharmaceuticals, Ouro Medicines, and Indapta Therapeutics.

Acknowledgements

This study was supported by funding from the Fogarty International Center of the National Institutes of Health under Award Number D43TW009343. We would like to thank the many researchers at the Wilson Lab, UCSF, and CZI who have contributed to the development and optimization of mNGS protocols for diverse research applications over the past several years.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
0.2mL PCR tube stripEppendorf30124359
1.5 mL DNA LoBind TubesEppendorf30108051
1.5 mL Safe Lock tubesEppendorf30120191
ERCC RNA Spike-In MixThermo Fisher4456740
High Sensitivity D1000 ReagentsAgilent5067-5585
Invitrogen Collibri Library Amplification Master MixThermo FisherA38539050
Mastercycler x50Thermo Fisher6311000010
NEBNext Multiplex Oligos for IlluminaNew England Biolabs7335S/L
NEBNext Ultra II DNA Library Prep KitNew England BiolabsE7645
NEBNext Ultra II RNA Library Prep Kit for IlluminaNew England BiolabsE7770
NextSeq 500/550 High Output Kit v2.5 (150 Cycles)Illumina20024907
NextSeq 550IlluminaN/a
QIAseq FastSelect -rRNA HMR KitQuiagen334386
Qubit 1X dsDNA High Sensitivity Assay KitQubitQ33230
Qubit 4 FluorometerThermo Fishern/a
Quick-DNA/RNA Microprep Plus Kit ZymoD7005
TapeStation 4150AgilentG2992AA

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Tags

Metagenomic SequencingPathogen DetectionNucleic Acid ExtractionLibrary PreparationMagnetic Bead PurificationCapillary ElectrophoresisBioinformatic AnalysisHost Transcriptomics

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