Comparison of methods for fecal microbiome biospecimen collection
© Dominianni et al.; licensee BioMed Central Ltd. 2014
Received: 31 December 2013
Accepted: 14 April 2014
Published: 23 April 2014
Effective means are needed to efficiently collect fecal samples for microbiome analysis in large-scale epidemiological studies. Using twenty-four fecal aliquots prepared from three healthy individuals, we compared the following four fecal sample collection methods for assessment of human gut microbiome: 1) fecal occult blood test cards, held at room temperature for three days, 2) Eppendorf tubes, at room temperature for three days, 3) Eppendorf tubes with RNAlater, at room temperature, and 4) as controls, samples immediately frozen at −80°C. The 24 samples were assayed by 16S rRNA gene sequencing to compare overall microbiome structure and taxon distributions according to collection method.
Storing fecal occult blood test card samples at room temperature for three days did not affect total DNA purity and relative 16S rRNA bacterial gene contents, compared with fresh frozen collection. Overall microbiome structure, based on phylogenetic UniFrac index, differed significantly by subject (p = 0.001), but microbiome structure (p = 0.497) and relative abundance of major microbial taxa (phyla) (p > 0.05) did not differ significantly by collection method.
Our findings suggest that low-cost fecal occult blood test card collection may be a feasible means of sample collection for fecal microbiome assessment in large-scale population-based studies.
The human bowel hosts trillions of gut microbial cells, the gut microbiome. Although case–control investigation points to a potential role of the gut microbiome in colorectal cancer, large-scale prospective study of this association has been impeded by the lack of validated fecal sample collection methods suitable for large-scale studies. Our interest was in development of a fecal sample collection method that is accurate, while also being cost-efficient and easy for the study participant to use. Because fecal collections may take place outside of research clinics, we also wished to develop a fecal collection approach which would not require immediate sample processing.
To address this need, we evaluated the utility of the Beckman Coulter Hemoccult Sensa® card (Beckman Coulter, CA) for gut microbiome characterization because it offers a practical way to collect fecal samples for large-scale study. As freezing at ultralow temperatures stabilizes bacterial samples, we compared results for samples collected by the card method to results for samples immediately stored in Eppendorf tubes at −80°C; we also included storage in Eppendorf tubes at room temperature as part of our evaluation. Finally, we were interested in evaluating the utility of collection in RNAlater, because this RNA-stabilizing agent has been shown to be suitable for samples dedicated for DNA amplification[5, 6]. Our main goal was to assess the effect the different storage conditions have on gut microbiome diversity parameters including overall diversity and specific taxon abundances because different methods might differentially impact bacterial overgrowth or DNA degradation that could lead to biased assessment of these microbial parameters.
Study population and fecal biospecimen collection
Three healthy volunteers (2 females and 1 male) provided fecal biospecimens at NYU Langone Medical Center, New York, NY. Single fecal samples for each subject were aliquotted within 30 minutes of stool production, in duplicate using the four following collection and storage methods. In Method 1 (card) the fecal sample was smeared onto a Beckman Coulter Hemoccult Sensa® card (Beckman Coulter, CA) and kept at room temperature. In Method 2 (room temperature) fecal samples were placed in an Eppendorf tube and left at room temperature. In Method 3 (RNAlater) fecal samples were placed in an Eppendorf tube containing 1 ml RNAlater Solution® (Life technologies, NY) and left at room temperature. In Method 4 (frozen) fecal samples were frozen on collection at −80°C in a 1.5 ml Eppendorf tube. All samples were stored for three days in their respective method. We chose three days to mimic the conditions of samples being collected at home and returned to us by mail.
The study protocol was approved by the NYU Langone Medical Center Institutional Review Board. All study participants provided informed consent.
16S rRNA microbiome assay
After three days of storage for the four methods, genomic DNA was extracted from the 24 fecal aliquots using the PowerLyzer PowerSoil DNA Isolation Kit (Mo Bio Laboratory Inc. CA) following the manufacturer’s protocol. DNA concentration was quantified using the Synergy™ H1M microplate reader (Biotech, VM) and corresponding OD 260/280 ratio was used to check DNA purity. 16S rRNA gene amplicon libraries were generated using primers incorporating FLX Titanium adapters and a sample barcode sequence covering variable region V3 to V4 as we described elsewhere. The amplicon library was sequenced using the 454 Roche FLX Titanium pyrosequencing system following the manufacturer’s instructions.
The QIIME pipeline was used to process and filter multiplexed sequence reads. The UCLUST method was used to cluster the filtered sequences with ≥97% similarity into Operational Taxonomic Unit (OTUs). Chimeric sequences were identified by ChimeraSlayer and removed. Representative sequences from each OTU were assigned taxonomy using the Ribosomal Database Project classifier method and the IMG/GG GreenGenes database of microbial genomes. A phylogenetic tree was constructed by applying the FastTree method to the representative sequences.
Rarefactions of 10 to 8,414 [minimum-maximum sequence depth] randomly selected sequences from each sample were used to calculate the Shannon index, a measure of within sample diversity, and to generate rarefaction plots. Pairwise comparisons of Shannon indices by subject and storage condition were obtained by Monte Carlo permutation. All p-values were adjusted by Bonferroni correction. To measure the diversity among subjects or storage conditions, a single rarefaction was performed at a sequencing depth of 4000 so that all samples were included in analyses. Distance matrices containing all pairwise comparisons were created for unweighted (presence/absence) dissimilarity values using the UniFrac phylogenetic method. Principal coordinates were computed for the unweighted distance matrices and used to generate Principal Coordinate Analysis plots (PCoA). The non-parametric method, adonis, was used to identify significant differences in phylogenetic distance variation by subjects and by storage condition. The Unweighted Pair Group Method with Arithmetic Mean (UPGMA) for clustering of samples was also carried out on the unweighted distance matrices. A two-sample t-test was used to test for differences between the within and between group variances, with p-values adjusted by Bonferroni correction. Relative abundances of the three major phyla (Bacteroidetes, Firmicutes, Actinobacteria) were compared for the four methods, using the Mann–Whitney-Wilcoxon test, and compared by subject, using the Kruskal-Wallis test (SAS, version 9.3, SAS Institute, Cary, NC).
DNA purity and 16 s rRNA sequence reads by fecal collection method
OD 260/280 (Mean ± SD)b
Filtered sequence reads (Mean ± SD)d
Method 1: Card
1.86 ± 0.05
12,448 ± 8,761
Method 2: Room Temperature
1.81 ± 0.07
16,451 ± 12,004
Method 3: RNAlater
1.66 ± 0.14c
13,393 ± 5,909
Method 4: Frozen
1.80 ± 0.05
14,467 ± 10,030
We found no evidence of significant differences in gut microbial community composition and taxon distributions for storage at room temperature on a fecal occult blood test card or in an Eppendorf tube compared to immediately frozen samples. Not surprisingly, overall microbial diversity varied by subject. We found a decrease in DNA purity for samples collected with RNAlater.
Although the effect of collection container has not been previously assessed, our general observation that inter-individual differences in bacterial composition were greater than the differences by collection method is consistent with findings from previous studies. Multiple studies have tested storage durations (up to six months) and storage temperatures ranging from 20°C to −80°C; most studies[4, 15, 16], though not all[17, 18], have found that these fecal collection methods did not significantly influence the gut microbiome diversity and taxon distribution. Two other studies reported that storage at −20°C for up to 53 days influenced specific taxa, including Bacteroidetes abundance and the Firmicutes to Bacteroidetes ratio, however, we did not observe these trends in our study.
Samples collected with RNAlater had significantly lower DNA purity and tended to show lower microbial diversity. RNAlater is used to stabilize and protect RNA from degradation in tissue during long term storage and has been shown to also be suitable for DNA preservation. However, we observed that fecal samples were very hard to disperse evenly in RNAlater during processing and that DNA purity was lower. Low-quality DNA can interfere with downstream applications including PCR amplification, a possible reason for the trend toward reduced Shannon indices. Two studies showed that storage in RNAlater is suitable for PCR amplification of bacterial DNA[5, 6]. While the first study showed that total DNA yields from RNAlater samples were higher compared to refrigeration storage and liquid nitrogen freezing, the impact on Shannon indices was not described. Using gorilla fecal samples, the second study reported that DNA purity and Shannon indices were not significantly different between RNAlater samples and samples stored at −30°C. RNAlater storage increases the potential utility of stored fecal samples, so further study is warranted to determine the conditions of collection for which this reagent is suitable.
Although our study showed no differences in microbiome composition between card collection with room temperature storage and collection in Eppendorf tube with immediate freezing, we recognize that a larger series of samples may have identified some differences not found here. Also, our subjects were healthy and the collected samples may not have captured the full range of stool conditions that might be expected if subjects were ill. These considerations may be important in carrying out stool collection in different study settings.
Our findings support the use of fecal occult blood test card collections for microbiome assessment of fecal samples. These cards are commercially available and inexpensive. The small size and flat shape also makes the card easier to include in packages to be sent to participants, compared to bulkier Eppendorf tubes. Study subjects can easily collect samples on the cards. Because the cards are widely used in colorectal cancer screening, potential participants might also be more accepting of collecting samples in this way. A possible drawback of the Hemoccult Sensa® card is that it contains a chemical reagent used to detect blood in the stool which could possibly affect gut microbiome. However, we found no evidence of a significant difference in gut microbiome in fecal samples collected by this method. Findings that results were unaffected by three-day storage at room temperature of the collection cards or Eppendorf tubes suggests that participant home-collection and mailing of these samples is suitable for epidemiological studies.
Our findings suggest that fecal collection on a fecal occult blood test card or in an Eppendorf tube and storage for three days at room temperature does not substantially influence the assessment of gut microbiome. Because of the low-cost and simplicity of use, fecal occult blood test card collection may be a feasible method for large-scale population-based studies.
This work was supported by the R01 CA159036 NCI award and R03 CA159414, R21 CA183887, AACR/PanCan career development award.
- Savage DC: Microbial ecology of the gastrointestinal tract. Annu Rev Microbiol. 1977, 31: 107-133. 10.1146/annurev.mi.31.100177.000543.View ArticlePubMedGoogle Scholar
- Ahn J, Sinha R, Pei Z, Dominianni C, Wu J, Shi J, Goedert JJ, Hayes RB, Yang L: Human gut microbiome and risk for colorectal cancer. J Natl Cancer Inst. 2013, 105 (24): 1907-1911. 10.1093/jnci/djt300.PubMed CentralView ArticlePubMedGoogle Scholar
- A guide to bacteria preservation: refrigeration, freezing and freeze drying. OPS Diagnositics. Accessed 10 March 2014, http://www.opsdiagnostics.com/notes/ranpri/aguidetobacteriapreservation.htmGoogle Scholar
- Wu GD, Lewis JD, Hoffmann C, Chen YY, Knight R, Bittinger K, Hwang J, Chen J, Berkowsky R, Li NLH, Bushman FD: Sampling and pyrosequencing methods for characterizing bacterial communities in the human gut using 16S sequence tags. BMC Microbiol. 2010, 10: 206-10.1186/1471-2180-10-206.PubMed CentralView ArticlePubMedGoogle Scholar
- Nechvatal JM, Ram JL, Basson MD, Namprachan P, Niec SR, Badsha KZ, Matherly LH, Majumdar AP, Kato I: Fecal collection, ambient preservation, and DNA extraction for PCR amplification of bacterial and human markers from human feces. J Microbiol Methods. 2008, 72 (2): 124-132. 10.1016/j.mimet.2007.11.007.View ArticlePubMedGoogle Scholar
- Vlckova K, Mrazek J, Kopecny J, Petrzelkova KJ: Evaluation of different storage methods to characterize the fecal bacterial communities of captive western lowland gorillas (Gorilla gorilla gorilla). J Microbiol Methods. 2012, 91 (1): 45-51. 10.1016/j.mimet.2012.07.015.View ArticlePubMedGoogle Scholar
- Wu J, Lin I, Hayes RB, Ahn J: Comparison of DNA extraction methods for human oral microbiome research. Brit J Med & Med Res. 2014, 4 (10): 1980-1991.View ArticleGoogle Scholar
- Kuczynski J, Stombaugh J, Walters WA, Gonzalez A, Caporaso JG, Knight R: Using QIIME to analyze 16S rRNA gene sequences from microbial communities. Curr Protoc Bioinformatics. 2011, Chapter 10: Unit 10 17-editoral board, Andreas D Baxevanis [et al]Google Scholar
- Edgar RC: Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010, 26 (19): 2460-2461. 10.1093/bioinformatics/btq461.View ArticlePubMedGoogle Scholar
- Haas BJ, Gevers D, Earl AM, Feldgarden M, Ward DV, Giannoukos G, Ciulla D, Tabbaa D, Highlander SK, Sodergren E, Methe B, DeSantis TZ, Petrosino JF, Knight R, Birren BW: Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res. 2011, 21 (3): 494-504. 10.1101/gr.112730.110.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang Q, Garrity GM, Tiedje JM, Cole JR: Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007, 73 (16): 5261-5267. 10.1128/AEM.00062-07.PubMed CentralView ArticlePubMedGoogle Scholar
- Price MN, Dehal PS, Arkin AP: FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol. 2009, 26 (7): 1641-1650. 10.1093/molbev/msp077.PubMed CentralView ArticlePubMedGoogle Scholar
- Lozupone C, Hamady M, Knight R: UniFrac–an online tool for comparing microbial community diversity in a phylogenetic context. BMC Bioinforma. 2006, 7: 371-10.1186/1471-2105-7-371.View ArticleGoogle Scholar
- Anderson BHMMJ: Fitting multivariate models to community data: a comment on distance based redundancy analysis. Ecology. 2001, 82 (1): 290-297. 10.1890/0012-9658(2001)082[0290:FMMTCD]2.0.CO;2.View ArticleGoogle Scholar
- Lauber CL, Zhou N, Gordon JI, Knight R, Fierer N: Effect of storage conditions on the assessment of bacterial community structure in soil and human-associated samples. FEMS Microbiol Lett. 2010, 307 (1): 80-86. 10.1111/j.1574-6968.2010.01965.x.PubMed CentralView ArticlePubMedGoogle Scholar
- Carroll IM, Ringel-Kulka T, Siddle JP, Klaenhammer TR, Ringel Y: Characterization of the fecal microbiota using high-throughput sequencing reveals a stable microbial community during storage. PLoS One. 2012, 7 (10): e46953-10.1371/journal.pone.0046953.PubMed CentralView ArticlePubMedGoogle Scholar
- Cardona S, Eck A, Cassellas M, Gallart M, Alastrue C, Dore J, Azpiroz F, Roca J, Guarner F, Manichanh C: Storage conditions of intestinal microbiota matter in metagenomic analysis. BMC Microbiol. 2012, 12: 158-10.1186/1471-2180-12-158.PubMed CentralView ArticlePubMedGoogle Scholar
- Ott SJ, Musfeldt M, Timmis KN, Hampe J, Wenderoth DF, Schreiber S: In vitro alterations of intestinal bacterial microbiota in fecal samples during storage. Diagn Microbiol Infect Dis. 2004, 50 (4): 237-245. 10.1016/j.diagmicrobio.2004.08.012.View ArticlePubMedGoogle Scholar
- Maukonen J, Simoes C, Saarela M: The currently used commercial DNA-extraction methods give different results of clostridial and actinobacterial populations derived from human fecal samples. FEMS Microbiol Ecol. 2012, 79 (3): 697-708. 10.1111/j.1574-6941.2011.01257.x.View ArticlePubMedGoogle Scholar
- Bahl MI, Bergstrom A, Licht TR: Freezing fecal samples prior to DNA extraction affects the Firmicutes to Bacteroidetes ratio determined by downstream quantitative PCR analysis. FEMS Microbiol Lett. 2012, 329 (2): 193-197. 10.1111/j.1574-6968.2012.02523.x.View ArticlePubMedGoogle Scholar
- RNAlater®: Preserving RNA Before Isolation TechNotes. 11 (4): Life Technologies. Accessed 14 November 2013, http://www.lifetechnologies.com/us/en/home/references/ambion-tech-support/rna-isolation/tech-notes/rnalater-faqs.htmlGoogle Scholar
- Boesenberg-Smith KA, Pessarakli MM, Wolk DM: Assessment of DNA yield and purity: an overlooked detail of PCR troubleshooting. Clin Microbiol Newsl. 2012, 34 (1): 1-6.View ArticleGoogle Scholar
- U.S. Preventive Services Task Force: AHRQ Publication No. 08-05122. Guide to Clinical Preventive Services, 2008: recommendations of the U.S. Services Task Force. 2008, Rockville, MD: Agency for Healthcare Research and QualityGoogle Scholar
- Allison JE: Review article: faecal occult blood testing for colorectal cancer. Aliment Pharmacol Ther. 1998, 12 (1): 1-10. 10.1046/j.1365-2036.1998.00231.x.View ArticlePubMedGoogle Scholar
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