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J Clin Microbiol. 2018 Aug 27;56(9). pii: e00472-18. doi: 10.1128/JCM.00472-18. Print 2018 Sep.

Development and Optimization of Metagenomic Next-Generation Sequencing Methods for Cerebrospinal Fluid Diagnostics.

Author information

1
Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA psimner1@jhmi.edu.
2
Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
3
Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.
4
Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
5
Departments of Biomedical Engineering, Computer Science, and Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA.
6
Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
7
Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Abstract

The purpose of this study was to develop and optimize different processing, extraction, amplification, and sequencing methods for metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF) specimens. We applied mNGS to 10 CSF samples with known standard-of-care testing (SoC) results (8 positive and 2 negative). Each sample was subjected to nine different methods by varying the sample processing protocols (supernatant, pellet, neat CSF), sample pretreatment (with or without bead beating), and the requirement of nucleic acid amplification steps using DNA sequencing (DNASeq) (with or without whole-genome amplification [WGA]) and RNA sequencing (RNASeq) methods. Negative extraction controls (NECs) were used for each method variation (4/CSF sample). Host depletion (HD) was performed on a subset of samples. We correctly determined the pathogen in 7 of 8 positive samples by mNGS compared to SoC. The two negative samples were correctly interpreted as negative. The processing protocol applied to neat CSF specimens was found to be the most successful technique for all pathogen types. While bead beating introduced bias, we found it increased the detection yield of certain organism groups. WGA prior to DNASeq was beneficial for defining pathogens at the positive threshold, and a combined DNA and RNA approach yielded results with a higher confidence when detected by both methods. HD was required for detection of a low-level-positive enterovirus sample. We demonstrate that NECs are required for interpretation of these complex results and that it is important to understand the common contaminants introduced during mNGS. Optimizing mNGS requires the use of a combination of techniques to achieve the most sensitive, agnostic approach that nonetheless may be less sensitive than SoC tools.

KEYWORDS:

CSF; metagenomics; next-generation sequencing

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