Reproducibility, stability, and accuracy of microbial profiles by fecal sample collection method in three distinct populations

Autoři: Doratha A. Byrd aff001;  Jun Chen aff002;  Emily Vogtmann aff001;  Autumn Hullings aff001;  Se Jin Song aff004;  Amnon Amir aff004;  Muhammad G. Kibriya aff005;  Habibul Ahsan aff005;  Yu Chen aff006;  Heidi Nelson aff002;  Rob Knight aff004;  Jianxin Shi aff009;  Nicholas Chia aff002;  Rashmi Sinha aff001
Působiště autorů: Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America aff001;  Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America aff002;  Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America aff003;  Department of Pediatrics, University of California San Diego, La Jolla, California, United States of America aff004;  Department of Public Health Sciences, University of Chicago, Chicago, Illinois, United States of America aff005;  New York School of Medicine, New York, New York, United States of America aff006;  Department of Surgery, Mayo Clinic, Rochester, Minnesota, United States of America aff007;  Department of Computer Science & Engineering, University of California San Diego, La Jolla, California, United States of America aff008;  Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America aff009;  Biomedical Engineering and Physiology, Mayo College, Rochester, Minnesota, United States of America aff010
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224757


The gut microbiome likely plays a role in the etiology of multiple health conditions, especially those affecting the gastrointestinal tract. Little consensus exists as to the best, standard methods to collect fecal samples for future microbiome analysis. We evaluated three distinct populations (N = 132 participants) using 16S rRNA gene amplicon sequencing data to investigate the reproducibility, stability, and accuracy of microbial profiles in fecal samples collected and stored via fecal occult blood test (FOBT) or Flinders Technology Associates (FTA) cards, fecal immunochemical tests (FIT) tubes, 70% and 95% ethanol, RNAlater, or with no solution. For each collection method, based on relative abundance of select phyla and genera, two alpha diversity metrics, and four beta diversity metrics, we calculated intraclass correlation coefficients (ICCs) to estimate reproducibility and stability, and Spearman correlation coefficients (SCCs) to estimate accuracy of the fecal microbial profile. Comparing duplicate samples, reproducibility ICCs for all collection methods were excellent (ICCs ≥75%). After 4–7 days at ambient temperature, ICCs for microbial profile stability were excellent (≥75%) for most collection methods, except those collected via no-solution and 70% ethanol. SCCs comparing each collection method to immediately-frozen no-solution samples ranged from fair to excellent for most methods; however, accuracy of genus-level relative abundances differed by collection method. Our findings, taken together with previous studies and feasibility considerations, indicated that FOBT/FTA cards, FIT tubes, 95% ethanol, and RNAlater are excellent choices for fecal sample collection methods in future microbiome studies. Furthermore, establishing standard collection methods across studies is highly desirable.

Klíčová slova:

Bangladesh – Blood – DNA extraction – Ethanol – Microbiome – Shannon index – Species diversity


1. Wang X, Yang Y, Huycke MM. Microbiome-driven carcinogenesis in colorectal cancer: Models and mechanisms. Free Radic Biol Med. 2017;105:3–15. doi: 10.1016/j.freeradbiomed.2016.10.504 27810411

2. Kostic AD, Xavier RJ, Gevers D. The microbiome in inflammatory bowel disease: current status and the future ahead. Gastroenterology. 2014;146(6):1489–99. doi: 10.1053/j.gastro.2014.02.009 24560869

3. Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 2012;490(7418):55–60. doi: 10.1038/nature11450 23023125

4. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444(7122):1027–31. doi: 10.1038/nature05414 17183312

5. Choo JM, Leong LE, Rogers GB. Sample storage conditions significantly influence faecal microbiome profiles. Scientific Reports. 2015;5:16350. doi: 10.1038/srep16350 26572876

6. Vandeputte D, Tito RY, Vanleeuwen R, Falony G, Raes J. Practical considerations for large-scale gut microbiome studies. FEMS Microbiol Rev. 2017;41(Supp_1):S154–S67. doi: 10.1093/femsre/fux027 28830090

7. Dominianni C, Wu J, Hayes RB, Ahn J. Comparison of methods for fecal microbiome biospecimen collection. BMC Microbiology. 2014;14:103. doi: 10.1186/1471-2180-14-103 24758293

8. Bundgaard-Nielsen C, Hagstrøm S, Sørensen S. Interpersonal Variations in Gut Microbiota Profiles Supersedes the Effects of Differing Fecal Storage Conditions. Scientific Reports. 2018(June):1–9. doi: 10.1038/s41598-017-17765-5

9. Roesch LF, Casella G, Simell O, Krischer J, Wasserfall CH, Schatz D, et al. Influence of fecal sample storage on bacterial community diversity. Open Microbiol J. 2009;3:40–6. doi: 10.2174/1874285800903010040 19440250

10. Fu BC, Randolph TW, Lim U, Monroe KR, Cheng I, Wilkens LR, et al. Characterization of the gut microbiome in epidemiologic studies: the multiethnic cohort experience. Annals of Epidemiology. 2016;26(5):373–9. doi: 10.1016/j.annepidem.2016.02.009 27039047

11. McInnes P, Cutting M. Manual of Procedures for Human Microbiome Project, V 12.0. 2010.

12. Sinha R, Chen J, Amir A, Vogtmann E, Shi J, Inman KS, et al. Collecting fecal samples for microbiome analyses in epidemiology studies. Cancer Epidemiology Biomarkers & Prevention. 2016;25(2):407–16.

13. Song SJ, Amir A, Metcalf JL, Amato KR, Xu ZZ, Humphrey G, et al. Preservation Methods Differ in Fecal Microbiome Stability, Affecting Suitability for Field Studies. mSystems. 2016;1(3):e00021–16. doi: 10.1128/mSystems.00021-16 27822526

14. Vogtmann E, Chen J, Amir A, Shi J, Abnet CC, Nelson H, et al. Comparison of Collection Methods for Fecal Samples in Microbiome Studies. Am J Epidemiol. 2017;185(2):115–23. doi: 10.1093/aje/kww177 27986704

15. Vogtmann E, Chen J, Kibriya MG, Chen Y, Islam T, Eunes M, et al. Comparison of Fecal Collection Methods for Microbiota Studies in Bangladesh. Appl Environ Microbiol. 2017;83(10).

16. Sinha R, Chen J, Amir A, Vogtmann E, Shi J, Inman KS, et al. Collecting Fecal Samples for Microbiome Analyses in Epidemiology Studies. Cancer Epidemiol Biomarkers Prev. 2016;25(2):407–16. doi: 10.1158/1055-9965.EPI-15-0951 26604270

17. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. The ISME Journal. 2012;6(8):1621–4. doi: 10.1038/ismej.2012.8 22402401

18. Walters WA, Caporaso JG, Lauber CL, Berg-Lyons D, Fierer N, Knight R. PrimerProspector: de novo design and taxonomic analysis of barcoded polymerase chain reaction primers. Bioinformatics. 2011;27(8):1159–61. doi: 10.1093/bioinformatics/btr087 21349862

19. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nature Methods. 2010;7(5):335–6. doi: 10.1038/nmeth.f.303 20383131

20. Amir A, McDonald D, Navas-Molina JA, Kopylova E, Morton JT, Zech Xu Z, et al. Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns. mSystems. 2017;2(2):e00191–16. doi: 10.1128/mSystems.00191-16 28289731

21. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PloS One. 2013;8(4):e61217. doi: 10.1371/journal.pone.0061217 23630581

22. Dixon P. VEGAN, a package of R functions for community ecology. Journal of Vegetation Science. 2003;14(6):927–30.

23. Chen J. Package 'GUniFrac'. 2018.

24. Rosner B. Fundamentals of biostatistics. 8th edition. ed. Boston, MA: Cengage Learning; 2016. xix, 927.

25. Gloor GB, Wu JR, Pawlowsky-Glahn V, Egozcue JJ. It's all relative: analyzing microbiome data as compositions. Ann Epidemiol. 2016;26(5):322–9. doi: 10.1016/j.annepidem.2016.03.003 27143475

26. Kostic AD, Chun E, Robertson L, Glickman JN, Gallini CA, Michaud M, et al. Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment. Cell Host Microbe. 2013;14(2):207–15. doi: 10.1016/j.chom.2013.07.007 23954159

27. Harrison XA. Using observation-level random effects to model overdispersion in count data in ecology and evolution. PeerJ. 2014;2:e616. doi: 10.7717/peerj.616 25320683

28. DerSimonian R, Kacker R. Random-effects model for meta-analysis of clinical trials: an update. Contemp Clin Trials. 2007;28(2):105–14. doi: 10.1016/j.cct.2006.04.004 16807131

29. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–58. doi: 10.1002/sim.1186 12111919

30. 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):80–6. doi: 10.1111/j.1574-6968.2010.01965.x 20412303

31. 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. doi: 10.1371/journal.pone.0046953 23071673

32. Shaw AG, Sim K, Powell E, Cornwell E, Cramer T, McClure ZE, et al. Latitude in sample handling and storage for infant faecal microbiota studies: the elephant in the room? Microbiome. 2016: 4(1):40. doi: 10.1186/s40168-016-0186-x 27473284

33. Stearns JC, Lynch MDJ, Senadheera DB, Tenenbaum HC, Goldberg MB, Cvitkovitch DG, et al. Bacterial biogeography of the human digestive tract. Scientific Reports. 2011;1:170. doi: 10.1038/srep00170 22355685

34. 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):237–45. doi: 10.1016/j.diagmicrobio.2004.08.012 15582296

35. Cardona S, Eck A, Cassellas M, Gallart M, Alastrue C, Dore J, et al. Storage conditions of intestinal microbiota matter in metagenomic analysis. BMC Microbiol. 2012;12:158. doi: 10.1186/1471-2180-12-158 22846661

36. Amir A, McDonald D, Navas-Molina JA, Debelius J, Morton JT, Hyde E, et al. Correcting for Microbial Blooms in Fecal Samples during Room-Temperature Shipping. mSystems. 2017;2(2): e00199–16. doi: 10.1128/mSystems.00199-16 28289733

37. Baxter NT, Koumpouras CC, Rogers MA, Ruffin MTt, Schloss PD. DNA from fecal immunochemical test can replace stool for detection of colonic lesions using a microbiota-based model. Microbiome. 2016;4(1):59. doi: 10.1186/s40168-016-0205-y 27842559

38. Gudra D, Shoaie S, Fridmanis D, Klovins J, Wefer H, Silamikelis I, et al. A widely used sampling device in colorectal cancer screening programmes allows for large-scale microbiome studies. Gut. 2018;68(9):1723–25 doi: 10.1136/gutjnl-2018-316225 30242040

39. Rounge TB, Meisal R, Nordby JI, Ambur OH, de Lange T, Hoff G. Evaluating gut microbiota profiles from archived fecal samples. BMC Gastroenterol. 2018;18(1):171. doi: 10.1186/s12876-018-0896-6 30409123

40. Nechvatal JM, Ram JL, Basson MD, Namprachan P, Niec SR, Badsha KZ, et al. Fecal collection, ambient preservation, and DNA extraction for PCR amplification of bacterial and human markers from human feces. J Microbiol Methods. 2008;72(2):124–32. doi: 10.1016/j.mimet.2007.11.007 18162191

41. Taylor MW. Examining the potential use and long-term stability of guaiac faecal occult blood test cards for microbial DNA 16S rRNA sequencing. J Clin Pathol. 2017;70(7):600–6. doi: 10.1136/jclinpath-2016-204165 28011577

42. Tap J, Cools-Portier S, Pavan S, Druesne A, Öhman L, Törnblom H, et al. Effects of the long-term storage of human fecal microbiota samples collected in RNAlater. Scientific Reports. 2019;9(1):1–9. doi: 10.1038/s41598-018-37186-2

43. Wang Z, Zolnik CP, Qiu Y, Usyk M, Wang T, Strickler HD, et al. Comparison of Fecal Collection Methods for Microbiome and Metabolomics Studies. Frontiers in Cellular and Infection Microbiology. 2018;8(August):1–10. doi: 10.3389/fcimb.2018.00001

44. Hale VL, Tan CL, Knight R, Amato KR. Effect of preservation method on spider monkey (Ateles geoffroyi) fecal microbiota over 8 weeks. J Microbiol Methods. 2015;113:16–26. doi: 10.1016/j.mimet.2015.03.021 25819008

45. 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. doi: 10.1016/j.mimet.2012.07.015 22828127

46. Kilpatrick CW. Noncryogenic preservation of mammalian tissues for DNA extraction: an assessment of storage methods. Biochem Genet. 2002;40(1–2):53–62. doi: 10.1023/a:1014541222816 11989787

47. Franzosa EA, Morgan XC, Segata N, Waldron L, Reyes J, Earl AM, et al. Relating the metatranscriptome and metagenome of the human gut. Proc Natl Acad Sci U S A. 2014;111(22):E2329–38. doi: 10.1073/pnas.1319284111 24843156

48. Gorzelak MA, Gill SK, Tasnim N, Ahmadi-Vand Z, Jay M, Gibson DL. Methods for Improving Human Gut Microbiome Data by Reducing Variability through Sample Processing and Storage of Stool. PLoS One. 2015;10(8):e0134802. doi: 10.1371/journal.pone.0134802 26252519

49. Voigt AY, Costea PI, Kultima JR, Li SS, Zeller G, Sunagawa S, et al. Temporal and technical variability of human gut metagenomes. Genome Biol. 2015;16:73. doi: 10.1186/s13059-015-0639-8 25888008

50. Chen Z, Hui PC, Hui M, Yeoh YK, Wong PY, Chan MCW, et al. Impact of Preservation Method and 16S rRNA Hypervariable Region on Gut Microbiota Profiling. mSystems. 2019;4(1):1–15.

51. Flores R, Shi J, Yu G, Ma B, Ravel J, Goedert JJ, et al. Collection media and delayed freezing effects on microbial composition of human stool. Microbiome. 2015;3:33. doi: 10.1186/s40168-015-0092-7 26269741

52. Shah MS, DeSantis TZ, Weinmaier T, McMurdie PJ, Cope JL, Altrichter A, et al. Leveraging sequence-based faecal microbial community survey data to identify a composite biomarker for colorectal cancer. Gut. 2018;67(5):882–91. doi: 10.1136/gutjnl-2016-313189 28341746

53. Drew DA, Lochhead P, Abu-Ali G, Chan AT, Huttenhower C, Izard J. Fecal microbiome in epidemiologic studies—letter. Cancer Epidemiology Biomarkers & Prevention. 2016: 25(5):869.

54. Sinha R, Vogtmann E, Chen J, Amir A, Shi J, Sampson J, et al. Fecal Microbiome in Epidemiologic Studies—Response. Cancer Epidemiology Biomarkers & Prevention. 2016;25(5):870–1.

55. Loftfield E, Vogtmann E, Sampson JN, Moore SC, Nelson H, Knight R, et al. Comparison of collection methods for fecal samples for discovery metabolomics in epidemiological studies. Cancer Epidemiology Biomarkers & Prevention. 2016;25(11):1483–90.

Článek vyšel v časopise


2019 Číslo 11