Abstract
High throughput sequencing has emerged as one of the most important techniques for characterizing microbial dynamics and revealing bacteria and host interactions. However, data interpretation using this technique is mainly based on relative abundance and ignores total bacteria load. In certain cases, absolute abundance is more important than compositional relative data, and interpretation of microbiota data based solely on relative abundance can be misleading. The available approaches for absolute quantification are highly diverse and challenging, especially for quantification in differing biological situations, such as distinguishing between live and dead cells, quantification of specific taxa, enumeration of low biomass samples, large sample size feasibility, and the detection of various other cellular features. In this review, we first illustrate the importance of integrating absolute abundance into microbiome data interpretation. Second, we briefly discuss the most widely used cell-based and molecular-based bacterial load quantification methods, including fluorescence spectroscopy, flow cytometry, 16S qPCR, 16S qRT-PCR, ddPCR, and reference spike-in. Last, we present a specific decision-making scheme for absolute quantification methods based on different biological questions and some of the latest quantitative methods and procedure modifications.
Highlights
Subtle alterations in the host environment may induce significant shifts in microbial communities and the corresponding interplays between the host and microbiota [1–4].The changes in abundances of certain taxa inhabiting the gut, skin, respiratory system, blood vessels, and other organs can play beneficial roles in human health or cause serious disease [5,6]
DdPCR can accurately quantify samples with a low abundance of the target gene and reduce background noise in negative control samples. This is demonstrated by Droplet Digital PCR (ddPCR) significantly lower coefficients of variation and its ability to detect lower numbers of 16S rRNA gene copies compared to quantitative polymerase chain reaction (qPCR), while the sample 16S rRNA gene copies were the same [54]
Absolute quantification of specific taxa can be achieved with a simple calculation: multiplying the relative abundance of the taxa generated by 16S rRNA amplicon sequencing with total cell counts
Summary
Subtle alterations in the host environment may induce significant shifts in microbial communities and the corresponding interplays between the host and microbiota [1–4]. Most data analyses resulting from the use of this technique are based on relative quantification, with the absolute bacterial abundance being discounted. Inappropriate interpretation from relative quantification may completely change the results of some studies [7]. When two types of bacteria start with the same initial cell number, a treatment that doubles the cell number of bacteria A (while bacteria B remains unaffected) results in the same relative abundance between bacteria A and B (67% and 33%) as a treatment that halves bacteria B (while bacteria A remains unaffected). Compositional data using relative abundance is not appropriate for addressing certain biological problems, such as community interactions. We first discuss the concerns and limitations of using relative abundances under different biological conditions. We briefly describe several recent cell- and molecular-based absolute bacterial quantification techniques. We provide decision-making directions for method selection regarding different biological questions and challenges for future microbial studies
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