Abstract
BackgroundCareful consideration of experimental artefacts is required in order to successfully apply high-throughput 16S ribosomal ribonucleic acid (rRNA) gene sequencing technology. Here we introduce experimental design, quality control and “denoising” approaches for sequencing low biomass specimens.ResultsWe found that bacterial biomass is a key driver of 16S rRNA gene sequencing profiles generated from bacterial mock communities and that the use of different deoxyribonucleic acid (DNA) extraction methods [DSP Virus/Pathogen Mini Kit® (Kit-QS) and ZymoBIOMICS DNA Miniprep Kit (Kit-ZB)] and storage buffers [PrimeStore® Molecular Transport medium (Primestore) and Skim-milk, Tryptone, Glucose and Glycerol (STGG)] further influence these profiles. Kit-QS better represented hard-to-lyse bacteria from bacterial mock communities compared to Kit-ZB. Primestore storage buffer yielded lower levels of background operational taxonomic units (OTUs) from low biomass bacterial mock community controls compared to STGG. In addition to bacterial mock community controls, we used technical repeats (nasopharyngeal and induced sputum processed in duplicate, triplicate or quadruplicate) to further evaluate the effect of specimen biomass and participant age at specimen collection on resultant sequencing profiles. We observed a positive correlation (r = 0.16) between specimen biomass and participant age at specimen collection: low biomass technical repeats (represented by < 500 16S rRNA gene copies/μl) were primarily collected at < 14 days of age. We found that low biomass technical repeats also produced higher alpha diversities (r = − 0.28); 16S rRNA gene profiles similar to no template controls (Primestore); and reduced sequencing reproducibility. Finally, we show that the use of statistical tools for in silico contaminant identification, as implemented through the decontam package in R, provides better representations of indigenous bacteria following decontamination.ConclusionsWe provide insight into experimental design, quality control steps and “denoising” approaches for 16S rRNA gene high-throughput sequencing of low biomass specimens. We highlight the need for careful assessment of DNA extraction methods and storage buffers; sequence quality and reproducibility; and in silico identification of contaminant profiles in order to avoid spurious results.
Highlights
Careful consideration of experimental artefacts is required in order to successfully apply highthroughput 16S ribosomal ribonucleic acid gene sequencing technology
For each of the four bacterial mock communities, we present sequencing profiles generated from triplicate deoxyribonucleic acid (DNA) extracts using two DNA extraction kits (Kit-QS and ZymoBIOMICS DNA Miniprep Kit (Kit-ZB)) (Table 2)
We detected 11 additional genera (6 of which were unclassifiable at genus-level) at low abundances from BEI-DNA controls processed in our laboratory
Summary
Careful consideration of experimental artefacts is required in order to successfully apply highthroughput 16S ribosomal ribonucleic acid (rRNA) gene sequencing technology. High-throughput 16S ribosomal ribonucleic acid (rRNA) gene sequencing has the potential to provide detailed characterization of microbial communities from a range of ecological niches in humans [1,2,3] These range from the densely colonized gastro-intestinal tract [4, 5] to low biomass sites including the lower respiratory tract [6,7,8] and womb [9, 10] which have previously been considered sterile. DNA and amplicon “spill-over” from high biomass to low biomass specimens ( referred to as wellto-well contamination) adds to exogenous biological profiles in neighbouring low biomass specimens [20] (Fig. 1b) This physical exchange of DNA/amplicon between biological specimens, and between biological specimens and NTCs (Fig. 1b), calls for a rigorous in silico approach to identify and remove contamination. The only way to efficiently deal with contaminants is to include controls tailored to each experiment and to use optimal decontamination approaches
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