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

Nature Biotechnology volume 42, pages 328–338 (2024)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.

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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.

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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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Maghini, D.G., Dvorak, M., Dahlen, A. et al. Quantifying bias introduced by sample collection in relative and absolute microbiome measurements. Nat Biotechnol 42, 328–338 (2024). https://doi.org/10.1038/s41587-023-01754-3

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