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Shipped and shifted: modeling collection-induced bias in microbiome multi-omics using a tractable fermentation system

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Abstract

Large-scale, decentralized microbiome sampling surveys and citizen science initiatives often require periods of storage at ambient temperature, potentially altering sample composition during collection and transport. We developed a generalizable framework to quantify and model these biases using sourdough as a tractable fermentation system, with samples subjected to controlled storage conditions (4 °C, 17 °C, 30 °C, regularly sampled up to 28 days). Machine-learning models paired with multi-omics profiling—including microbiome, targeted and untargeted metabolome profiling, and cultivation—revealed temperature-dependent shifts in bacterial community structure and metabolic profiles, while fungal communities remained stable. Storage induced ecological restructuring, marked by reduced network modularity and increased centrality of dominant taxa at higher temperatures. Notably, storage duration and temperature were strongly encoded in the multi-omics data, with temperature exerting a more pronounced influence than time. 24 of the top 25 predictors of storage condition were metabolites, underscoring functional layers as both sensitive to and informative of environmental exposure. These findings demonstrate that even short-term ambient storage (<2 days) can substantially reshape microbiome, metabolome, and biochemical profiles, posing risks to data comparability in decentralized studies and emphasizing the need to recognize and address such biases. Critically, the high predictability of storage history offers a path toward bias detection and correction— particularly when standardized collection protocols are infeasible, as is common in decentralized sampling contexts. Our approach enables robust quantification and modeling of such storage effects across multi-omics datasets, unlocking more accurate interpretation of large-scale microbiome surveys.

Data availability

All sequence data have been deposited on EBI-ENA under accession number PRJEB94514 (16S) and PRJEB94515 (ITS). Source data (metadata) along with processed HPLC and FIA-MS data have been deposited together with all code notebooks in github (see code availability).

Code availability

All code notebooks for bioinformatic processing, statistical analyses, and machine-learning models have been deposited and are openly accessible in github https://github.com/bokulich-publications/shipped-and-shifted.

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Acknowledgements

The authors acknowledge financial support from the project HealthFerm, which is funded by the European Union under the Horizon Europe grant agreement No. 101060247 and by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract No. 22.00210. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union nor European Research Executive Agency (REA). Neither the European Union nor REA can be held responsible for them.The authors thank Luisa Ferreira for support in data collection and the Genomic Diversity Center of ETH Zürich for their support with amplicon library preparation. The microbiome amplicon sequencing was performed at the Functional Genomics Center Zurich of University of Zurich and ETH Zurich.

Funding

Open access funding provided by Swiss Federal Institute of Technology Zurich.

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Authors and Affiliations

  1. Laboratory of Food Systems Biotechnology, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland

    Annina R. Meyer, Mihnea Paul Mihaila, Michelle Neugebauer & Nicholas A. Bokulich

  2. Laboratory of Food Biochemistry, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland

    Jan Patrick Tan & Laura Nyström

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  1. Annina R. Meyer
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Contributions

Annina R. Meyer, Conceptualization, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing; Jan P. Tan, Formal analysis, Investigation, Methodology, Writing – review and editing; Mihnea P. Mihaila, Michelle Neugebauer, Investigation, Writing – review and editing; Laura Nyström, Resources, Supervision, Funding acquisition, Writing – review and editing; Nicholas A. Bokulich, Conceptualization, Resources, Supervision, Funding acquisition, Writing – original draft, Writing – review and editing.

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Nicholas A. Bokulich.

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Meyer, A.R., Tan, J.P., Mihaila, M.P. et al. Shipped and shifted: modeling collection-induced bias in microbiome multi-omics using a tractable fermentation system.
npj Biofilms Microbiomes (2026). https://doi.org/10.1038/s41522-025-00909-1

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