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

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.

FIG 1

Overview of the method comparison study. Each CSF sample had four matched negative controls (A to D) and nine CSF subsamples (E to M). BB, bead beating; WGA, whole-genome amplification; WTA, whole-transcriptome amplification; DNA, DNASeq (no WGA); DNAamp, DNASeq with WGA; cDNA, RNASeq with WTA; NEC, negative extraction control; NA, nucleic acid; not spun down, neat CSF.

FIG 2

Unique kmer and alignment analysis methods for CSF sample 3 positive for Pseudomonas aeruginosa. (A) Comparison of the numbers of unique kmers and kmer duplicity assigned to the P. aeruginosa reads among sample 3 cDNA subsamples (F, I, and L) with those of the negative extraction control (subsample A-NEC) by KrakenHLL. (B and C) Demonstration of the reads aligned to the P. aeruginosa genome found in the NEC (B) compared to those in the neat CSF sample without bead beating (BB) using cDNA methods (subsample L with 7.4% genome coverage) (C). An asterisk indicates a positive cutoff for these samples of 4,855 (10 times the RPM of the NEC).

FIG 3

Heat map summarizing the results comparing processing, bead-beading, and sequencing methods for the expected positive samples. BB, the samples were processed with a bead-beating step on the FastPrep instrument; DNAamp, a whole-genome amplification step was performed prior to DNASeq; n/a, not applicable.