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Time of sample collection is critical for the replicability of microbiome analyses

Nature Metabolism volume 6, pages 1282–1293 (2024)Cite this article

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Abstract

As the microbiome field moves from descriptive and associative research to mechanistic and interventional studies, being able to account for all confounding variables in the experimental design, which includes the maternal effect1, cage effect2, facility differences3, as well as laboratory and sample handling protocols4, is critical for interpretability of results. Despite significant procedural and bioinformatic improvements, unexplained variability and lack of replicability still occur. One underexplored factor is that the microbiome is dynamic and exhibits diurnal oscillations that can change microbiome composition5,6,7. In this retrospective analysis of 16S amplicon sequencing studies in male mice, we show that sample collection time affects the conclusions drawn from microbiome studies and its effect size is larger than those of a daily experimental intervention or dietary changes. The timing of divergence of the microbiome composition between experimental and control groups is unique to each experiment. Sample collection times as short as only 4 hours apart can lead to vastly different conclusions. Lack of consistency in the time of sample collection may explain poor cross-study replicability in microbiome research. The impact of diurnal rhythms on the outcomes and study design of other fields is unknown but likely significant.

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Fig. 1: Mock circadian data to explain and exemplify new metric BCD.
Fig. 2: Microbiome analysis of Apoe-/- mice exposed to IHC conditions show different outcomes depending on time point of sample collection.
Fig. 3: Gastrointestinal regions have individual time dynamics that are influenced by diet and feeding patterns.
Fig. 4: Longitudinal data are also susceptible to the influence of time.

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Data availability

Literature review data are at https://github.com/knightlab-analyses/dynamics/data/. Figure 1, mock data are at https://github.com/knightlab-analyses/dynamics/data/MockData. Figure 2 (Allaband/Zarrinpar 2021) data are under EBI accession ERP110592. Figure 3 data (longitudinal IHC) are under EBI accession ERP110592 and (longitudinal circadian TRF) EBI accession ERP123226. Figure 4 data (Zarrinpar/Panda 2014) are in the Supplementary Excel file attached to the source paper13; (Leone/Chang 2015) figshare for the 16S amplicon sequence data are at https://doi.org/10.6084/m9.figshare.882928 (ref. 63). Extended Data Fig. 2 data (Caporaso/Knight 2011) are at MG-RAST project mgp93 (IDs mgm4457768.3 and mgm4459735.3). Extended Data Fig. 3 data (Wu/Chen 2018) are under ENA accession PRJEB22049. Extended Data Fig. 4 data (Tuganbaev/Elinav 2021) are under ENA accession PRJEB38869.

Code availability

All relevant code notebooks are on GitHub at https://github.com/knightlab-analyses/dynamics/notebooks.

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Acknowledgements

C.A. was supported by NIH T32 OD017863. S.F.R. is supported by the Soros Foundation. A.L. is supported by the AHA Postdoctoral Fellowship grant. T.K. is supported by NIH T32 GM719876. A.C.D.M. is supported by R01 HL148801-02S1. G.G.H. and A.Z. are supported by NIH R01 HL157445. A.Z. is further supported by the VA Merit BLR&D Award I01 BX005707 and NIH grants R01 AI163483, R01 HL148801, R01 EB030134 and U01 CA265719. All authors receive institutional support from NIH P30 DK120515, P30 DK063491, P30 CA014195, P50 AA011999 and UL1 TR001442.

Author information

Authors and Affiliations

  1. Division of Biomedical Sciences, University of California, San Diego, La Jolla, CA, USA

    Celeste Allaband & Stephany Flores Ramos

  2. Division of Gastroenterology, University of California, San Diego, La Jolla, CA, USA

    Celeste Allaband, Amulya Lingaraju, Stephany Flores Ramos, Haniyeh Javaheri, Maria D. Tiu, Ana Carolina Dantas Machado, R. Alexander Richter & Amir Zarrinpar

  3. Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA

    Celeste Allaband, Stephany Flores Ramos, Gabriel G. Haddad, Pieter C. Dorrestein & Rob Knight

  4. Medical Scientist Training Program, University of California San Diego, La Jolla, CA, USA

    Tanya Kumar

  5. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA

    Emmanuel Elijah & Pieter C. Dorrestein

  6. Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA

    Emmanuel Elijah, Pieter C. Dorrestein, Rob Knight & Amir Zarrinpar

  7. Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA

    Gabriel G. Haddad

  8. Rady Children’s Hospital, San Diego, CA, USA

    Gabriel G. Haddad

  9. Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, USA

    Vanessa A. Leone

  10. Center for Computational Mass Spectrometry, University of California, San Diego, La Jolla, CA, USA

    Pieter C. Dorrestein

  11. Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA

    Rob Knight

  12. Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA

    Rob Knight

  13. Shu Chien-Gene Lay Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA

    Rob Knight & Amir Zarrinpar

  14. Division of Gastroenterology, Jennifer Moreno Department of Veterans Affairs Medical Center, La Jolla, CA, USA

    Amir Zarrinpar

  15. Institute of Diabetes and Metabolic Health, University of California, San Diego, La Jolla, CA, USA

    Amir Zarrinpar

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  1. Celeste Allaband
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Contributions

C.A. and A.Z. conceptualized the work. C.A., E.E., P.C.D., R.K. and A.Z. determined the methodology. C.A., A.L., S.F.R., T.K., H.J., M.D.T., A.C.D.M. and R.A.R. were involved in data investigation. C.A., S.F.R., T.K., H.J., M.D.T., A.C.D.M. and R.A.R. created visualizations. A.Z. acquired funding and was the project administrator. R.K. and A.Z. supervised the work. G.G.H. and V.A.L. provided resources. C.A., A.L., S.F.R., T.K., H.J., M.D.T. and A.Z. wrote the first draft. All authors contributed to the review and editing of the manuscript.

Corresponding author

Correspondence to Amir Zarrinpar.

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Competing interests

A.Z. is a co-founder and a chief medical officer, and holds equity in Endure Biotherapeutics. P.C.D. is an advisor to Cybele and co-founder and advisor to Ometa and Enveda with previous approval from the University of California, San Diego. All other authors declare no competing interests.

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Nature Metabolism thanks Robin Voigt-Zuwala, Jacqueline M. Kimmey, John R. Kirby and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Yanina-Yasmin Pesch, in collaboration with the Nature Metabolism team.

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Extended data

Extended Data Fig. 1 Microbiome Literature Review.

A) 2019 Literature Review Summary. Of the 586 articles containing microbiome (16 S or metagenomic) data, found as described in the methods section, the percentage of microbiome articles from each of the publication groups. B) The percentage of microbiome articles belonging to each individual journal in 2019. Because the numerous individual journals from Science represented low percentages individually, they were grouped together. C) The percentage articles where collection time was explicitly stated (yes: 8 AM, ZT4, etc.), implicitly stated (relative: ‘before surgery’, ‘in the morning’, etc.), or unstated (not provided: ‘daily’, ‘once a week’, etc.). D) Meta-Analysis Inclusion Criteria Flow Chart. Literature review resulting in the five previously published datasets for meta-analysis11,13,28,29,30.

Extended Data Fig. 2 Single Time Point (Non-Circadian) Example.

A) Weighted UniFrac PCoA Plot - modified example from Moving Pictures Qiime2 tutorial data [https://docs.qiime2.org/2022.11/tutorials/moving-pictures/]. Each point is a sample. Points were coloured by body site of origin. There are 8 gut, 8 left palm, 9 right palm, and 9 tongue samples. B) Within-Condition Distances (WCD) boxplot/stripplot for each body site (n = 8–9 mouse per group per time point). C) Between Condition Distances (BCD) boxplot/stripplot for each unique body site comparison (n = 8–9 mouse per group per time point). D) All pairwise grouping comparisons, both WCD and BCD, are shown in the boxplots/stripplots (n = 8–9 mouse per group per time point). Only WCD to BCD statistical differences are shown. Boxplot centre line indicates median, edges of boxes are quartiles, error bars are min and max values. Significance was determined using a paired Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction. Notation: ns (not significant) = p > 0.05, * = p < 0.05; ** = p < 0.01; *** = p < 0.001, **** = p < 0.00001.

Extended Data Fig. 3 Additional Analysis of Apoe-/- Mice Exposed to IHC Conditions.

A) Weighted UniFrac PCoA stacked view (same as Fig. 2b but different orientation). Good for assessing overall similarity not broken down by time point. Significance determined by PERMANOVA (p = 0.005). B) Weighted UniFrac PCoA of only axis 1 over time. C) Boxplot/scatterplot of within-group weighted UniFrac distance values for the control group (Air, n = 3–4 samples per time point). Unique non-zero values in the matrix were kept. Dotted line indicates the mean of all values presented. No significant differences (p > 0.05) found. D) Boxplot/scatterplot of within-group weighted UniFrac distance values for the experimental group (IHC, n = 3–4 samples per time point)). Unique non-zero values in the matrix were kept. Dotted line indicates the mean of all values presented. No significant differences (p > 0.05) found. E) Boxplot/scatterplot of within-group weighted UniFrac distance values for both control (Air) and experimental (IHC) groups [n = 3–4 samples per group per time point]. Mann-Whitney-Wilcoxon test with Bonferroni correction used to determine significant differences between groups. Boxplot centre line indicates median, edges of boxes are quartiles, error bars are min and max values. Notation: ns = not significant, p > 0.05; * = p < 0.05; ** = p < 0.01; *** = p < 0.001.

Extended Data Fig. 4 Irregular differences in diurnal rhythm patterns leads to generally minor shifts in BCD when comparing LD vs DD mice.

A) Experimental design. Balb/c mice were fed NCD ad libitum under 0:24 L:D (24 hr darkness, DD) experimental conditions and compared to 12:12 L:D (LD) control conditions. After 2 weeks, mice from each group were euthanized every 4 hours for 24 hours (N = 4–5 mice/condition) and samples were collected from the proximal small intestine (‘jejunum’) and distal small intestine (‘ileum’) contents. B) BCD for luminal contents of proximal small intestine samples comparing LD to DD mice (N = 4–5 mice/condition). Dotted line is the average of all shown weighted UniFrac distances. Significance was determined using a paired Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction; notation: **** = p < 0.00001. C) BCD for luminal contents of distal small intestine samples comparing LD to DD mice (N = 4–5 mice/condition). Dotted line is the average of all shown weighted UniFrac distances. Boxplot centre line indicates median, edges of boxes are quartiles, error bars are min and max values.

Extended Data Fig. 5 Localized changes in BCD between luminal and mucosal contents.

A) Experimental design and sample collection for a local site study. Small intestinal samples were collected every 4 hours for 24 hours (N = 4–5 mice/condition, skipping ZT8). Mice were fed ad libitum on the same diet (NCD) for 4 weeks before samples were taken. B) BCD for luminal vs mucosal conditions (N = 4–5 mice/condition). The dotted line is the average of all shown weighted UniFrac distances. Significance is determined using the Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction. C) Heatmap of mean BCD distances comparing luminal and mucosal by time point (N = 4–5 mice/condition). Highest value highlighted in navy, lowest value highlighted in gold. Boxplot centre line indicates median, edges of boxes are quartiles, error bars are min and max values. Significance was determined using a paired Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction. Notation: * = p < 0.05; ** = p < 0.01; *** = p < 0.001, **** = p < 0.00001. D) Experimentally relevant log ratio, highlighting the changes seen at ZT20 (N = 4–5 mice/condition). Boxplot center line indicates median, edges of boxes are quartiles, error bars are min and max values. Significance was determined using a paired Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction. Notation: * = p < 0.05; ** = p < 0.01; *** = p < 0.001, **** = p < 0.00001.

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Allaband, C., Lingaraju, A., Flores Ramos, S. et al. Time of sample collection is critical for the replicability of microbiome analyses. Nat Metab 6, 1282–1293 (2024). https://doi.org/10.1038/s42255-024-01064-1

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