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Quantifying bias introduced by sample collection in relative and absolute microbiome measurements

Nature Biotechnology (2023)Cite this article

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Abstract

To gain insight into the accuracy of microbial measurements, it is important to evaluate sources of bias related to sample condition, preservative method and bioinformatic analyses. There is increasing evidence that measurement of the total count and concentration of microbes in the gut, or ‘absolute abundance’, provides a richer source of information than relative abundance and can correct some conclusions drawn from relative abundance data. However, little is known about how preservative choice can affect these measurements. In this study, we investigated how two common preservatives and short-term storage conditions impact relative and absolute microbial measurements. OMNIgene GUT OMR-200 yields lower metagenomic taxonomic variation between different storage temperatures, whereas Zymo DNA/RNA Shield yields lower metatranscriptomic taxonomic variation. Absolute abundance quantification reveals two different causes of variable Bacteroidetes:Firmicutes ratios across preservatives. Based on these results, we recommend OMNIgene GUT OMR-200 preservative for field studies and Zymo DNA/RNA Shield for metatranscriptomics studies, and we strongly encourage absolute quantification for microbial measurements.

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Fig. 1: Overview of study workflow.
Fig. 2: Metagenomic characterization of samples across storage conditions.
Fig. 3: Metatranscriptomic characterization of samples across storage conditions.
Fig. 4: Absolute abundance quantification workflow.
Fig. 5: Absolute abundance quantification of microbiome samples.

Data availability

All sequencing data generated for this study are available on the National Center for Biotechnology Informationʼs Sequence Read Archive under BioProject PRJNA940499 (ref. 48). Source data for figures are available on GitHub at https://github.com/bhattlab/Benchmarking and on Zenodo (https://doi.org/10.5281/zenodo.7738262)49.

Code availability

Workflow for metagenomic and metatranscriptomic preprocessing can be found at https://github.com/bhattlab/bhattlab_workflows. Workflow for metagenomic and metatranscriptomic taxonomic classification can be found at https://github.com/bhattlab/kraken2_classification. Analysis and plotting scripts can be found at https://github.com/bhattlab/Benchmarking and on Zenodo (https://doi.org/10.5281/zenodo.7738262)49. Python code for fitting the GEE models can be found at https://github.com/alex-dahlen/Gut_Microbiome_Measurement_Bias.

References

  1. Song, S. J. et al. Preservation methods differ in fecal microbiome stability, affecting suitability for field studies. mSystems 1, e00021–16 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Lauber, C. L., Zhou, N., Gordon, J. I., Knight, R. & Fierer, N. Effect of storage conditions on the assessment of bacterial community structure in soil and human-associated samples. FEMS Microbiol. Lett. 307, 80–86 (2010).

    Article  CAS  PubMed  Google Scholar 

  3. Carruthers, L. V. et al. The impact of storage conditions on human stool 16S rRNA microbiome composition and diversity. PeerJ 7, e8133 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  4. McLaren, M. R., Willis, A. D. & Callahan, B. J. Consistent and correctable bias in metagenomic sequencing experiments. eLife 8, e46923 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Brooks, J. P. et al. The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies. BMC Microbiol. 15, 66 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Sze, M. A. & Schloss, P. D. The impact of DNA polymerase and number of rounds of amplification in PCR on 16S rRNA gene sequence data. mSphere 4, e00163–19 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Gaulke, C. A. et al. Evaluation of the effects of library preparation procedure and sample characteristics on the accuracy of metagenomic profiles. mSystems 6, e00440–21 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Ye, S. H., Siddle, K. J., Park, D. J. & Sabeti, P. C. Benchmarking metagenomics tools for taxonomic classification. Cell 178, 779–794 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. McIntyre, A. B. R. et al. Comprehensive benchmarking and ensemble approaches for metagenomic classifiers. Genome Biol. 18, 182 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Nearing, J. T., Comeau, A. M. & Langille, M. G. I. Identifying biases and their potential solutions in human microbiome studies. Microbiome 9, 113 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Gibbons, S. M., Duvallet, C. & Alm, E. J. Correcting for batch effects in case-control microbiome studies. PLoS Comput. Biol. 14, e1006102 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Briscoe, L., Balliu, B., Sankararaman, S., Halperin, E. & Garud, N. R. Evaluating supervised and unsupervised background noise correction in human gut microbiome data. PLoS Comput. Biol. 18, e1009838 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Tkacz, A., Hortala, M. & Poole, P. S. Absolute quantitation of microbiota abundance in environmental samples. Microbiome 6, 110 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Vandeputte, D. et al. Quantitative microbiome profiling links gut community variation to microbial load. Nature 551, 507–511 (2017).

    Article  CAS  PubMed  Google Scholar 

  15. Rao, C. et al. Multi-kingdom ecological drivers of microbiota assembly in preterm infants. Nature 591, 633–638 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Sender, R., Fuchs, S. & Milo, R. Revised estimates for the number of human and bacteria cells in the body. PLoS Biol. 14, e1002533 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Wang, X., Howe, S., Deng, F. & Zhao, J. Current applications of absolute bacterial quantification in microbiome studies and decision-making regarding different biological questions. Microorganisms 9, 1797 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Sinha, R., Abnet, C. C., White, O., Knight, R. & Huttenhower, C. The microbiome quality control project: baseline study design and future directions. Genome Biol. 16, 276 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Choo, J. M., Leong, L. E. & Rogers, G. B. Sample storage conditions significantly influence faecal microbiome profiles. Sci. Rep. 5, 16350 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Doukhanine, E., Bouevitch, A., Pozza, L. & Merino, C. OMNIgene®•GUT enables reliable collection of high quality fecal samples for gut microbiome studies. https://dnagenotek.com/ROW/pdf/PD-WP-00040.pdf (2014).

  21. Doukhanine, E. et al. OMNIgene®•GUT stabilizes the microbiome profile at ambient temperature for 60 days and during transport. https://www.dnagenotek.com/US/pdf/PD-WP-00042.pdf (2016).

  22. Anderson, E. L. et al. A robust ambient temperature collection and stabilization strategy: enabling worldwide functional studies of the human microbiome. Sci. Rep. 6, 31731 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kazantseva, J., Malv, E., Kaleda, A., Kallastu, A. & Meikas, A. Optimisation of sample storage and DNA extraction for human gut microbiota studies. BMC Microbiol. 21, 158 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Bartolomaeus, T. U. P. et al. Quantifying technical confounders in microbiome studies. Cardiovasc. Res. 117, 863–875 (2021).

    Article  CAS  PubMed  Google Scholar 

  25. Hill, C. J. et al. Effect of room temperature transport vials on DNA quality and phylogenetic composition of faecal microbiota of elderly adults and infants. Microbiome 4, 19 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Han, M. et al. A novel affordable reagent for room temperature storage and transport of fecal samples for metagenomic analyses. Microbiome 6, 43 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Sinha, R. et al. Collecting fecal samples for microbiome analyses in epidemiology studies. Cancer Epidemiol. Prev. Biomark. 25, 407–416 (2016).

    Article  Google Scholar 

  28. Carroll, I. M., Ringel-Kulka, T., Siddle, J. P., Klaenhammer, T. R. & Ringel, Y. Characterization of the fecal microbiota using high-throughput sequencing reveals a stable microbial community during storage. PLoS ONE 7, e46953 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Ott, S. J. et al. In vitro alterations of intestinal bacterial microbiota in fecal samples during storage. Diagn. Microbiol. Infect. Dis. 50, 237–245 (2004).

    Article  CAS  PubMed  Google Scholar 

  30. Manor, O. et al. Health and disease markers correlate with gut microbiome composition across thousands of people. Nat. Commun. 11, 5206 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Verberk, J. D. M. et al. Third national biobank for population-based seroprevalence studies in the Netherlands, including the Caribbean Netherlands. BMC Infect. Dis. 19, 470 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Tso, L., Bonham, K. S., Fishbein, A., Rowland, S. & Klepac-Ceraj, V. Targeted high-resolution taxonomic identification of Bifidobacterium longum subsp. infantis using human milk oligosaccharide metabolizing genes. Nutrients 13, 2833 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Magne, F. et al. The Firmicutes/Bacteroidetes ratio: a relevant marker of gut dysbiosis in obese patients? Nutrients 12, 1474 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Cani, P. D. Human gut microbiome: hopes, threats and promises. Gut 67, 1716–1725 (2018).

    Article  CAS  PubMed  Google Scholar 

  35. Baruch, E. N. et al. Fecal microbiota transplant promotes response in immunotherapy-refractory melanoma patients. Science 371, 602–609 (2021).

    Article  CAS  PubMed  Google Scholar 

  36. Davar, D. et al. Fecal microbiota transplant overcomes resistance to anti–PD-1 therapy in melanoma patients. Science 371, 595–602 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Nadkarni, M. A., Martin, F. E., Jacques, N. A. & Hunter, N. Determination of bacterial load by real-time PCR using a broad-range (universal) probe and primers set. Microbiology (Reading) 148, 257–266 (2002).

    Article  CAS  PubMed  Google Scholar 

  38. Jian, C., Luukkonen, P., Yki-Järvinen, H., Salonen, A. & Korpela, K. Quantitative PCR provides a simple and accessible method for quantitative microbiota profiling. PLoS ONE 15, e0227285 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Stoddard, S. F., Smith, B. J., Hein, R., Roller, B. R. K. & Schmidt, T. M. rrnDB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. Nucleic Acids Res. 43, D593–D598 (2015).

    Article  CAS  PubMed  Google Scholar 

  40. Mallick, H. et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput. Biol. 17, e1009442 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019).

    Article  Google Scholar 

  42. Wickham, H. Reshaping data with the reshape package. J. Stat. Softw. 21, 1–20 (2007).

    Article  Google Scholar 

  43. Ahlmann-Eltze, C. & Patil, I. ggsignif: R package for displaying significance brackets for ‘ggplot2’. Preprint at PsyArXiv https://psyarxiv.com/7awm6/ (2021).

  44. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

  45. Wilke, C. O. cowplot: streamlined plot theme and plot annotations for ‘ggplot2’. https://cran.r-project.org/web/packages/cowplot/index.html (2020).

  46. Campitelli, E. ggnewscale: multiple fill and colour scales in ‘ggplot2’. https://cran.r-project.org/web/packages/ggnewscale/readme/README.html (2022).

  47. Hvitfeldt, E. paletteer: comprehensive collection of color palettes. https://cran.r-project.org/web/packages/paletteer/citation.html (2021).

  48. Quantifying bias introduced by sample collection in relative and absolute microbiome measurements. National Center for Biotechnology Information. https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA940499 (2023).

  49. Maghini, D. & Dvorak, M. Benchmarking. GitHub. https://zenodo.org/record/7738262#.ZCxyk-zMJqs (2023).

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Acknowledgements

We thank the study participants for their participation in this study. We thank S. Hazelhurst, O. Oduaran and G. Schroth for their thoughtful recommendations for this project. We thank E. Brooks, M. Chakraborty, A. Han, A. Natarajan, R. Park, S. Vance and S. Zlitni for their technical assistance and support. This work was supported, in part, by National Institutes of Health (NIH) grant P30 CA124435, which supports the Stanford Cancer Institute Genetics Bioinformatics Service Center. This work used supercomputing resources provided by the Stanford Genetics Bioinformatics Service Center, supported by NIH S10 Instrumentation Grant S10OD023452. This work was supported, in part, by NIH R01AI148623 and R01AI143757, a Stand Up 2 Cancer grant, the Chan Zuckerberg Initiative, a Sloan Foundation Fellowship and the Allen Distinguished Investigator Award (to A.S.B.). We thank D. Solow-Cordero and S. Sim for assistance in using the Stanford Functional Genomics Facility and High-Throughput Bioscience Center, which is supported by NIH Shared Instrumentation Grants S10RR019513, S10RR026338, S10OD025004 and S10OD026899 and by an anonymous donation. D.M. is supported by the Stanford Graduate Fellowships in Science and Engineering program and the Stanford Gerald J. Lieberman Fellowship. M.D. is supported by NIH Cellular and Molecular Biology Training Program Training Grant T32GM007276.

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Author notes

  1. These authors contributed equally: Dylan Maghini, Mai Dvorak.

Authors and Affiliations

  1. Department of Genetics, Stanford University, Stanford, CA, USA

    Dylan G. Maghini & Ami S. Bhatt

  2. Department of Biology, Stanford University, Stanford, CA, USA

    Mai Dvorak

  3. Quantitative Sciences Unit, Stanford University, Stanford, CA, USA

    Alex Dahlen

  4. Illumina, Inc., Madison, WI, USA

    Morgan Roos & Scott Kuersten

  5. Department of Medicine (Hematology, Blood and Marrow Transplantation), Stanford University, Stanford, CA, USA

    Ami S. Bhatt

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Contributions

A.S.B., S.K., D.M., M.D. and A.D. conceptualized the study. D.M. and M.D. designed the study, enrolled participants, collected samples and performed extraction and qPCR on all samples. S.K. and M.R. performed ribosomal RNA depletion, library preparation and sequencing on all metatranscriptomic samples. D.M., M.D. and A.D. carried out all analysis and generated figures. D.M., M.D., A.D. and A.S.B. wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ami S. Bhatt.

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Competing interests

S.K. and M.R. are employees of Illumina, Inc. The remaining authors declare no competing interests.

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Supplementary Figs. 1–8

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Supplementary Tables 1–4

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Supplementary Data 1–14

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Maghini, D.G., Dvorak, M., Dahlen, A. et al. Quantifying bias introduced by sample collection in relative and absolute microbiome measurements. Nat Biotechnol (2023). https://doi.org/10.1038/s41587-023-01754-3

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