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. 2016 Feb;25(2):407-16.
doi: 10.1158/1055-9965.EPI-15-0951. Epub 2015 Nov 24.

Collecting Fecal Samples for Microbiome Analyses in Epidemiology Studies

Rashmi Sinha  1 , Jun Chen  2 , Amnon Amir  3 , Emily Vogtmann  4 , Jianxin Shi  5 , Kristin S Inman  6 , Roberto Flores  7 , Joshua Sampson  5 , Rob Knight  8 , Nicholas Chia  9
Affiliations

Collecting Fecal Samples for Microbiome Analyses in Epidemiology Studies

Rashmi Sinha et al. Cancer Epidemiol Biomarkers Prev. 2016 Feb.

Abstract

Background: The need to develop valid methods for sampling and analyzing fecal specimens for microbiome studies is increasingly important, especially for large population studies.

Methods: Some of the most important attributes of any sampling method are reproducibility, stability, and accuracy. We compared seven fecal sampling methods [no additive, RNAlater, 70% ethanol, EDTA, dry swab, and pre/post development fecal occult blood test (FOBT)] using 16S rRNA microbiome profiling in two laboratories. We evaluated nine commonly used microbiome metrics: abundance of three phyla, two alpha-diversities, and four beta-diversities. We determined the technical reproducibility, stability at ambient temperature, and accuracy.

Results: Although microbiome profiles showed systematic biases according to sample method and time at ambient temperature, the highest source of variation was between individuals. All collection methods showed high reproducibility. FOBT and RNAlater resulted in the highest stability without freezing for 4 days. In comparison with no-additive samples, swab, FOBT, and 70% ethanol exhibited the greatest accuracy when immediately frozen.

Conclusions: Overall, optimal stability and reproducibility were achieved using FOBT, making this a reasonable sample collection method for 16S analysis.

Impact: Having standardized method of collecting and storing stable fecal samples will allow future investigations into the role of gut microbiota in chronic disease etiology in large population studies.

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Figures

Figure 1
Figure 1
Sources of microbiome variability. Principal coordinate plot based on unweighted Unifrac of the microbial community profiles from all samples analyzed in the Knight laboratory (A) and the Mayo Microbiome laboratory (B). A distance-based coefficient of determination R2 (unweighted Unifrac, generalized Unifrac, weighted UniFrac, and Bray-Curtis (BC) distance) was used to quantify the percentage of microbiota variability in the Knight laboratory (C) and the Mayo Microbiome laboratory (D).
Figure 2
Figure 2
Evaluation of technical reproducibility. Intraclass correlation coefficients for microbiome metrics including the abundance of three phyla, two alpha-diversity metrics (number of observed OTUs and Shannon index) and four beta-diversity metrics (top PCoA component for unweighted Unifrac, generalized Unifrac, weighted UniFrac, and Bray-Curtis distance) analyzed at day 0 in the Knight laboratory (A) and the Mayo Microbiome laboratory (B).
Figure 3
Figure 3
Evaluation of microbiome stability. Intraclass correlation coefficients for microbiome metrics including the abundance of three phyla, two alpha-diversity metrics (number of observed OTUs and Shannon index) and four beta-diversity metrics (top PCoA component for unweighted Unifrac, generalized Unifrac, weighted UniFrac, and Bray-Curtis distance) in the Knight laboratory (A) and the Mayo Microbiome laboratory (B).
Figure 4
Figure 4
Evaluation of accuracy. Spearman correlation (all samples sampled by six different methods at time zero were compared to those sampled with no additive at time zero) of microbiome metrics including the abundance of three phyla, two alpha-diversity metrics (number of observed operational taxonomic units and Shannon index) and four beta-diversity metrics (top PCoA component for unweighted Unifrac, generalized Unifrac, weighted UniFrac, and Bray-Curtis distance) in the Knight laboratory (A) and the Mayo Microbiome laboratory (B).
Figure 5
Figure 5
OTU correlation between FOBT pre- and post-peroxide treatment after 4 days at ambient temperature as analyzed in the Knight laboratory (A) and the Mayo Microbiome laboratory (B). Different colors represent different participants.
Figure 6
Figure 6
Preservation of key biomarkers. Histogram of fold change in frequency for each OTU (compared to day 0 fresh frozen samples) after incubation for 4 days at ambient temperature in specimens collected using no-additive sampling (A, B) or FOBT cards (C, D) as determined by the Knight laboratory (A, C) and the Mayo Microbiome laboratory (B, D).
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Comment in

  • Fecal Microbiome in Epidemiologic Studies-Letter.
    Drew DA, Lochhead P, Abu-Ali G, Chan AT, Huttenhower C, Izard J. Drew DA, et al. Cancer Epidemiol Biomarkers Prev. 2016 May;25(5):869. doi: 10.1158/1055-9965.EPI-16-0063. Epub 2016 Mar 9. Cancer Epidemiol Biomarkers Prev. 2016. PMID: 26961995 Free PMC article. No abstract available.

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References

    1. Gough EK, Stephens DA, Moodie EE, Prendergast AJ, Stoltzfus RJ, Humphrey JH, et al. Linear growth faltering in infants is associated with Acidaminococcus sp. and community-level changes in the gut microbiota. Microbiome. 2015;3:24. - PMC - PubMed
    1. Raman M, Ahmed I, Gillevet PM, Probert CS, Ratcliffe NM, Smith S, et al. Fecal microbiome and volatile organic compound metabolome in obese humans with nonalcoholic fatty liver disease. Clin Gastroenterol Hepatol. 2013;11:868–875. e1–e3. - PubMed
    1. Nelson AM, Walk ST, Taube S, Taniuchi M, Houpt ER, Wobus CE, et al. Disruption of the human gut microbiota following Norovirus infection. PloS one. 2012;7:e48224. - PMC - PubMed
    1. Estrada-Velasco BI, Cruz M, Garcia-Mena J, Valladares Salgado A, Peralta Romero J, Guna Serrano Mde L, et al. Childhood obesity is associated to the interaction between firmicutes and high energy food consumption. Nutr Hosp. 2014;31:1074–1081. - PubMed
    1. Engsbro AL, Stensvold CR, Vedel Nielsen H, Bytzer P. Prevalence, incidence, and risk factors of intestinal parasites in Danish primary care patients with irritable bowel syndrome. Scand J Infect Dis. 2014;46:204–209. - PubMed

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