A quantitative sequencing framework for absolute abundance measurements of mucosal and lumenal microbial communities

Abstract

A fundamental goal in microbiome studies is determining which microbes affect host physiology. Standard methods for determining changes in microbial taxa measure relative, rather than absolute abundances. Moreover, studies often analyze only stool, despite microbial diversity differing substantially among gastrointestinal (GI) locations. Here, we develop a quantitative framework to measure absolute abundances of individual bacterial taxa by combining the precision of digital PCR with the high-throughput nature of 16S rRNA gene amplicon sequencing. In a murine ketogenic-diet study, we compare microbial loads in lumenal and mucosal samples along the GI tract. Quantitative measurements of absolute (but not relative) abundances reveal decreases in total microbial loads on the ketogenic diet and enable us to determine the differential effects of diet on each taxon in stool and smallintestine mucosa samples. This rigorous quantitative microbial analysis framework, appropriate for diverse GI locations enables mapping microbial biogeography of the mammalian GI tract and more accurate analyses of changes in microbial taxa in microbiome studies.

The quest for absolute abundance: The use of internal standards for DNA‐based community ecology

“…Droplet digital PCR (ddPCR; Hindson et al, 2011) is a promising tool for this approach because it provides heightened accuracy and throughput compared to conventional real-time qPCR; most importantly, it estimates abundances directly and does not rely on comparison to a quantitative standard (Baker, 2012;Hindson et al, 2011;Kim, Jeong, & Cho, 2015;Morella, Yang, Hernandez, & Koskella, 2018). Barlow, Bogatyrev, and Ismagilov (2020)…”

“…Droplet digital PCR (ddPCR; Hindson et al., 2011) is a promising tool for this approach because it provides heightened accuracy and throughput compared to conventional real‐time qPCR; most importantly, it estimates abundances directly and does not rely on comparison to a quantitative standard (Baker, 2012; Hindson et al., 2011; Kim, Jeong, & Cho, 2015; Morella, Yang, Hernandez, & Koskella, 2018). Barlow, Bogatyrev, and Ismagilov (2020) recently used such an approach to demonstrate that absolute abundances of gut bacteria shifted in mice eating a ketogenic diet and that relative abundances of particular taxa gave misleading results compared to absolute abundances. At the time of writing, ddPCR is currently more expensive than qPCR and also operates over a smaller dynamic range.…”

The human respiratory tract microbial community structures in healthy and cystic fibrosis infants

“…The human DNA was exploited as natural spike-in control to normalise the bacterial reads to human reads, and obtain insights into the absolute abundance patterns of bacterial airway inhabitants 19 . The absolute abundance estimations enabled us to approach a broad range of statistical tools for comparative microbial community analyses, including ordination, clustering and network analysis [24][25][26][27][28][29][30] . We probed the maximum number of bacterial genome positions by generating single-end instead of paired-end reads and consequently, were able to cover the rare species of the airway habitat in our metagenome investigation.…”

Host-associated microbe PCR (hamPCR): accessing new biology through convenient measurement of both microbial load and community composition

“…Critically, such microbial sequence counts lack a denominator accounting for the amount of the habitat sampled, and thus, sparsely-colonized and densely-colonized samples become indistinguishable, despite most study systems being open systems in which the total number of microbial cells can vary over many orders of magnitude. Another limitation of such compositional data is that because the sum of all microbes is constrained, an increase in the abundance of one microbe reduces the relative abundance of all other microbes, creating misleading interpretations in the absence of appropriate statistical methods [1][2][3][4] . Experimental determination of the microbial load, for example by relating microbial abundance to sample volume, mass, or surface area, has led to important insights in microbiome research that otherwise would have been missed with relative abundance data [5][6][7][8][9][10][11][12] .…”

“…Most commonly, researchers combine amplicon sequencing with an additional orthogonal method. These include supplementary shallow WMS 12 , quantitative PCR (qPCR) or digital PCR of host and/or microbial genes 3,16,19,[23][24][25][26] , adding sequenceable "spike-ins" calibrated based on sample volume 27 , mass 10 , or qPCR-determined host DNA content 25 , counting colony forming units (CFU) 11,28 , and flow cytometry 5,7 . The multitude of methods and publications hints at the enduring nature of this problem.…”