Quantifying spatiotemporal variability and noise in absolute microbiota abundances using replicate sampling

Abstract

Metagenomic sequencing has enabled detailed investigation of diverse microbial communities, but understanding their spatiotemporal variability remains an important challenge. Here, we present decomposition of variance using replicate sampling (DIVERS), a method based on replicate sampling and spike-in sequencing. The method quantifies the contributions of temporal dynamics, spatial sampling variability, and technical noise to the variances and covariances of absolute bacterial abundances. We applied DIVERS to investigate a high-resolution time series of the human gut microbiome and a spatial survey of a soil bacterial community in Manhattan’s Central Park. Our analysis showed that in the gut, technical noise dominated the abundance variability for nearly half of the detected taxa. DIVERS also revealed substantial spatial heterogeneity of gut microbiota, and high temporal covariances of taxa within the Bacteroidetes phylum. In the soil community, spatial variability primarily contributed to abundance fluctuations at short time scales (weeks), while temporal variability dominated at longer time scales (several months).

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: DIVERS conceptual workflow.
Fig. 2: Variance decomposition of gut bacterial abundance fluctuations using DIVERS.
Fig. 3: Identifying individual bacterial taxa with high temporal or spatial sampling variance.
Fig. 4: Decomposition of temporal and spatial contributions to pairwise OTU abundance correlations in the human gut microbiome.
Fig. 5: Decomposition of factors contributing to the variance of soil bacteria abundances.

Data availability

Sequencing data are available at NCBI SRA under PRJNA541083.

Code availability

MATLAB scripts to perform all variance and covariance decomposition analyses from original OTU abundance tables are available on GitHub at https://github.com/brianwji/DIVERS. Implementation of DIVERS in R is available on GitHub at https://github.com/hym0405/DIVERS.

References

  1. 1.

    Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).

    CAS  Article  Google Scholar 

  2. 2.

    Huttenhower, C. et al. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

    CAS  Article  Google Scholar 

  3. 3.

    Segata, N. et al. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat. Methods 9, 811–814 (2012).

    CAS  Article  Google Scholar 

  4. 4.

    Thompson, L. R. et al. A communal catalogue reveals earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).

    CAS  Article  Google Scholar 

  5. 5.

    Lloyd-Price, J. et al. Strains, functions and dynamics in the expanded human microbiome project. Nature 550, 61–68 (2017).

    CAS  Article  Google Scholar 

  6. 6.

    Hunt, D. E. et al. Resource partitioning and sympatric differentiation among closely related bacterioplankton. Science 320, 1081–1085 (2008).

    CAS  Article  Google Scholar 

  7. 7.

    Faust, K., Lahti, L., Gonze, D., de Vos, W. M. & Raes, J. Metagenomics meets time series analysis: unraveling microbial community dynamics. Curr. Opin. Microbiol. 25, 56–66 (2015).

    Article  Google Scholar 

  8. 8.

    Martin-Platero, A. M. et al. High resolution time series reveals cohesive but short-lived communities in coastal plankton. Nat. Commun. 9, 266 (2018).

    Article  Google Scholar 

  9. 9.

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

    CAS  Article  Google Scholar 

  10. 10.

    Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).

    CAS  Article  Google Scholar 

  11. 11.

    Stammler, F. et al. Adjusting microbiome profiles for differences in microbial load by spike-in bacteria. Microbiome 4, 28 (2016).

    Article  Google Scholar 

  12. 12.

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

    Article  Google Scholar 

  13. 13.

    Zmora, N. et al. Personalized gut mucosal colonization resistance to empiric probiotics is associated with unique host and microbiome features. Cell 174, 1388–1405 e1321 (2018).

    CAS  Article  Google Scholar 

  14. 14.

    Gohl, D. M. et al. Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies. Nat. Biotechnol. 34, 942–949 (2016).

    CAS  Article  Google Scholar 

  15. 15.

    Langille, M. G. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).

    CAS  Article  Google Scholar 

  16. 16.

    Faust, K. & Raes, J. Microbial interactions: from networks to models. Nat. Rev. Microbiol 10, 538–550 (2012).

    CAS  Article  Google Scholar 

  17. 17.

    Sonnenburg, E. D. et al. Diet-induced extinctions in the gut microbiota compound over generations. Nature 529, 212–215 (2016).

    CAS  Article  Google Scholar 

  18. 18.

    Sonnenburg, E. D. et al. Specificity of polysaccharide use in intestinal Bacteroides species determines diet-induced microbiota alterations. Cell 141, 1241–1252 (2010).

    CAS  Article  Google Scholar 

  19. 19.

    Rakoff-Nahoum, S., Foster, K. R. & Comstock, L. E. The evolution of cooperation within the gut microbiota. Nature 533, 255–259 (2016).

    CAS  Article  Google Scholar 

  20. 20.

    Rakoff-Nahoum, S., Coyne, M. J. & Comstock, L. E. An ecological network of polysaccharide utilization among human intestinal symbionts. Curr. Biol. 24, 40–49 (2014).

    CAS  Article  Google Scholar 

  21. 21.

    Ramirez, K. S. et al. Biogeographic patterns in below-ground diversity in New York City’s Central Park are similar to those observed globally. Proc. Biol. Sci. 281, 20141988 (2014).

    Article  Google Scholar 

  22. 22.

    O’Brien, S. L. et al. Spatial scale drives patterns in soil bacterial diversity. Environ. Microbiol. 18, 2039–2051 (2016).

    Article  Google Scholar 

  23. 23.

    Carini, P. et al. Unraveling the effects of spatial variability and relic DNA on the temporal dynamics of soil microbial communities. Preprint at https://www.biorxiv.org/content/10.1101/402438v1 (2018).

  24. 24.

    Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl Acad. Sci. USA 103, 626–631 (2006).

    CAS  Article  Google Scholar 

  25. 25.

    Contijoch, E. J. et al. Gut microbiota density influences host physiology and is shaped by host and microbial factors. eLife 8, e40553 (2019).

  26. 26.

    Wargo, J. A., Reddy, S. M., Reuben, A. & Sharma, P. Monitoring immune responses in the tumor microenvironment. Curr. Opin. Immunol. 41, 23–31 (2016).

    CAS  Article  Google Scholar 

  27. 27.

    Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    CAS  Article  Google Scholar 

  28. 28.

    Wood, D. E. & Salzberg, S. L. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 15, R46 (2014).

  29. 29.

    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

    CAS  Article  Google Scholar 

  30. 30.

    Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).

    CAS  Article  Google Scholar 

  31. 31.

    Baym, M. et al. Inexpensive multiplexed library preparation for megabase-sized genomes. PLoS ONE 10, e0128036 (2015).

    Article  Google Scholar 

  32. 32.

    Kilpatrick, A. M. & Ives, A. R. Species interactions can explain Taylor’s power law for ecological time series. Nature 422, 65–68 (2003).

    CAS  Article  Google Scholar 

  33. 33.

    Grun, D., Kester, L. & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637–640 (2014).

    Article  Google Scholar 

  34. 34.

    Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M. & Gilad, Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509–1517 (2008).

    CAS  Article  Google Scholar 

  35. 35.

    Williams, C. R. CE Gaussian Processes for Machine Learning (MIT Press, 2006).

  36. 36.

    Sala, C. et al. Stochastic neutral modelling of the gut microbiota’s relative species abundance from next generation sequencing data. BMC Bioinforma. 17, S16 (2016).

    Article  Google Scholar 

Download references

Acknowledgements

H.H.W. acknowledges funding from the NIH (grant nos. R01AI132403, R01DK118044), Burroughs Wellcome Fund (no. PATH 1016691), Bill & Melinda Gates Foundation (no. INV-000609), and the Schaefer Research Scholars Program for this work. R.U.S. is supported by a Fannie and John Hertz Foundation Fellowship and a NSF Graduate Research Fellowship (no. DGE-1644869). B.W.J. is supported in part by the NIH under Ruth L. Kirschstein National Research Service Award (NRSA) Institutional Research Training Grant (no. T32GM007367) and by the MD-PhD program at Columbia University. D.V. acknowledges funding from the NIH (grant nos. R01GM079759, R01DK118044).

Author information

Affiliations

Authors

Contributions

B.W.J. and R.U.S. conceived the study, designed the data collection workflow and performed all data analysis. B.W.J. and P.D.D. developed the variance and covariance decomposition models. R.U.S. performed all experiments with assistance from A.K. Y.H. assisted with data analysis and code implementation in R. H.H.W. and D.V. oversaw the project, and guided experiments and data analysis. All authors wrote the manuscript.

Corresponding authors

Correspondence to Harris H. Wang or Dennis Vitkup.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information: Nicole Rusk was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–16, Supplementary Tables 1–3 and Supplementary Note

Reporting Summary

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ji, B.W., Sheth, R.U., Dixit, P.D. et al. Quantifying spatiotemporal variability and noise in absolute microbiota abundances using replicate sampling. Nat Methods 16, 731–736 (2019). https://doi.org/10.1038/s41592-019-0467-y

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing