Abstract
Metagenomic next-generation sequencing (mNGS) experiments involving small amounts of nucleic acid input are highly susceptible to erroneous conclusions resulting from unintentional sequencing of occult contaminants, especially those derived from molecular biology reagents. Recent work suggests that, for any given microbe detected by mNGS, an inverse linear relationship between microbial sequencing reads and sample mass implicates that microbe as a contaminant. By associating sequencing read output with the mass of a spike-in control, we demonstrate that contaminant nucleic acid can be quantified in order to identify the mass contributions of each constituent. In an experiment using a high-resolution (n = 96) dilution series of HeLa RNA spanning 3-logs of RNA mass input, we identified a complex set of contaminants totaling 9.1 ± 2.0 attograms. Given the competition between contamination and the true microbiome in ultra-low biomass samples such as respiratory fluid, quantification of the contamination within a given batch of biological samples can be used to determine a minimum mass input below which sequencing results may be distorted. Rather than completely censoring contaminant taxa from downstream analyses, we propose here a statistical approach that allows separation of the true microbial components from the actual contribution due to contamination. We demonstrate this approach using a batch of n = 97 human serum samples and note that despite E. coli contamination throughout the dataset, we are able to identify a patient sample with significantly more E. coli than expected from contamination alone. Importantly, our method assumes no prior understanding of possible contaminants, does not rely on any prior collection of environmental or reagent-only sequencing samples, and does not censor potentially clinically relevant taxa, thus making it a generalized approach to any kind of metagenomic sequencing, for any purpose, clinical or otherwise.
Highlights
Metagenomic next-generation sequencing is a highly sensitive tool capable of detecting even single fragments of nucleic acid
Their approach relies on two core principles: first, that contaminant sequences are inversely correlated with total sequencing reads, and second, that contaminant sequences are present in more controls than samples
We added 25 pg of a stock of 92 standardized RNA transcripts present in varying concentrations ranging from 1.4 × 10−2 to 3.0 × 10−22 mol/L (External RNA Controls Consortium, ERCC, Thermo Fisher Cat #4456740), which we have previously demonstrated can facilitate quantitation of ultra-low biomass samples [25]
Summary
Metagenomic next-generation sequencing (mNGS) is a highly sensitive tool capable of detecting even single fragments of nucleic acid. * Correspondence: joe@derisilab.ucsf.edu 5Department of Biochemistry and Biophysics, University of California, San Francisco School of Medicine, San Francisco, CA, USA 6Chan Zuckerberg Biohub, San Francisco, CA, USA Full list of author information is available at the end of the article during the collection of the sample, the extraction of nucleic acid, or the preparation of libraries [1–6].
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