Abstract
High-throughput sequencing of 16S rRNA amplicon has been extensively employed to perform microbiome characterization worldwide. As a culture-independent methodology, it has allowed high-level profiling of sample bacterial composition directly from samples. However, most studies are limited to information regarding relative bacterial abundances (sample proportions), ignoring scenarios in which sample microbe biomass can vary widely. Here, we use an equivolumetric protocol for 16S rRNA amplicon library preparation capable of generating Illumina sequencing data responsive to input DNA, recovering proportionality between observed read counts and absolute bacterial abundances within each sample. Under specified conditions, we show that the estimation of colony-forming units (CFU), the most common unit of bacterial abundance in classical microbiology, is challenged mostly by resolution and taxon-to-taxon variation. We propose Bayesian cumulative probability models to address such issues. Our results indicate that predictive errors vary consistently below one order of magnitude for total microbial load and abundance of observed bacteria. We also demonstrate our approach has the potential to generalize to previously unseen bacteria, but predictive performance is hampered by specific taxa of uncommon profile. Finally, it remains clear that high-throughput sequencing data are not inherently restricted to sample proportions only, and such technologies bear the potential to meet the working scales of traditional microbiology.
Highlights
The application of high-throughput sequencing (HTS) methodologies allows large-scale identification of microorganisms, revealing colonization and dispersion patterns throughout studied sites such as hospitals, indoor or outdoor natural environments (Lax et al, 2017; LloydPrice et al, 2017; Thompson et al, 2017; Christoff et al, 2019; Gasparrini et al, 2019; Ribeiro et al, 2019)
We developed a customized laboratory protocol for 16S rRNA amplicon library preparation to recover absolute microbial abundances in each sample after Illumina short read sequencing (Figure 1)
Our results indicate that the equivolumetric protocol does recover proportionality between sequencing data and microbial load in terms of both library sizes (Figure 2C) and bacteria-specific counts (Figure 2D)
Summary
The application of high-throughput sequencing (HTS) methodologies allows large-scale identification of microorganisms, revealing colonization and dispersion patterns throughout studied sites such as hospitals, indoor or outdoor natural environments (Lax et al, 2017; LloydPrice et al, 2017; Thompson et al, 2017; Christoff et al, 2019; Gasparrini et al, 2019; Ribeiro et al, 2019). Despite various detailed microbiome characterization studies, most efforts address solely relative bacterial abundances within each sample, i.e., do not account for major variations of total microbial load (Vandeputte et al, 2017; Morton et al, 2019; Zemb et al, 2020). Recent studies claim that the total number of reads in HTSderived samples (library size) is an arbitrary sum, without biological relevance, yielding microbiome data as necessarily compositional in nature (Gloor et al, 2017; Jiang et al, 2019; Morton et al, 2019). The possibility of estimating absolute microbial abundance from HTS data has major impacts for research, government agencies, and industry, allowing researchers and policy makers to address microbiological issues in common scales, such as colony-forming units (CFU), without giving up the advantages of high-throughput technology. Bacterial percentages within a sample are hardly informative in terms of surface contamination levels or even risk of microbial environmental dispersion
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