Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Quantitative microbiome profiling links gut community variation to microbial load

Abstract

Current sequencing-based analyses of faecal microbiota quantify microbial taxa and metabolic pathways as fractions of the sample sequence library generated by each analysis1,2. Although these relative approaches permit detection of disease-associated microbiome variation, they are limited in their ability to reveal the interplay between microbiota and host health3,4. Comparative analyses of relative microbiome data cannot provide information about the extent or directionality of changes in taxa abundance or metabolic potential5. If microbial load varies substantially between samples, relative profiling will hamper attempts to link microbiome features to quantitative data such as physiological parameters or metabolite concentrations5,6. Saliently, relative approaches ignore the possibility that altered overall microbiota abundance itself could be a key identifier of a disease-associated ecosystem configuration7. To enable genuine characterization of host–microbiota interactions, microbiome research must exchange ratios for counts4,8,9. Here we build a workflow for the quantitative microbiome profiling of faecal material, through parallelization of amplicon sequencing and flow cytometric enumeration of microbial cells. We observe up to tenfold differences in the microbial loads of healthy individuals and relate this variation to enterotype differentiation. We show how microbial abundances underpin both microbiota variation between individuals and covariation with host phenotype. Quantitative profiling bypasses compositionality effects in the reconstruction of gut microbiota interaction networks and reveals that the taxonomic trade-off between Bacteroides and Prevotella is an artefact of relative microbiome analyses. Finally, we identify microbial load as a key driver of observed microbiota alterations in a cohort of patients with Crohn’s disease10, here associated with a low-cell-count Bacteroides enterotype (as defined through relative profiling)11,12.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Faecal microbial loads vary across enterotypes.
Figure 2: Relative versus quantitative microbiome profiling.
Figure 3: Relative versus quantitative microbiota network reconstruction.
Figure 4: Quantitative microbiome alterations in Crohn’s disease.

Accession codes

Primary accessions

European Nucleotide Archive

References

  1. 1

    Zhernakova, A. et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565–569 (2016)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  2. 2

    Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016)

    ADS  CAS  PubMed  Google Scholar 

  3. 3

    Valles-Colomer, M. et al. Meta-omics in inflammatory bowel disease research: applications, challenges, and guidelines. J. Crohn’s Colitis 10, 735–746 (2016)

    Google Scholar 

  4. 4

    Satinsky, B. M., Gifford, S. M., Crump, B. C. & Moran, M. A. Use of internal standards for quantitative metatranscriptome and metagenome analysis. Methods Enzymol. 531, 237–250 (2013)

    CAS  PubMed  Google Scholar 

  5. 5

    Morton, J. T. et al. Balance trees reveal microbial niche differentiation. mSystems 2, e00162–16 (2017)

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6

    Gloor, G. B., Wu, J. R., Pawlowsky-Glahn, V. & Egozcue, J. J. It’s all relative: analyzing microbiome data as compositions. Ann. Epidemiol. 26, 322–329 (2016)

    PubMed  Google Scholar 

  7. 7

    Harmsen, H. J. M., Pouwels, S. D., Funke, A., Bos, N. A. & Dijkstra, G. Crohn’s disease patients have more IgG-binding fecal bacteria than controls. Clin. Vaccine Immunol. 19, 515–521 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8

    Props, R. et al. Absolute quantification of microbial taxon abundances. ISME J. 11, 584–587 (2017)

    PubMed  Google Scholar 

  9. 9

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

    PubMed  PubMed Central  Google Scholar 

  10. 10

    Sabino, J. et al. Primary sclerosing cholangitis is characterised by intestinal dysbiosis independent from IBD. Gut 65, 1681–1689 (2016)

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11

    Holmes, I., Harris, K. & Quince, C. Dirichlet multinomial mixtures: generative models for microbial metagenomics. PLoS One 7, e30126 (2012)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  12. 12

    Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Lewis, S. J. & Heaton, K. W. Stool form scale as a useful guide to intestinal transit time. Scand. J. Gastroenterol. 32, 920–924 (1997)

    CAS  PubMed  Google Scholar 

  14. 14

    Vandeputte, D., Falony, G., D’hoe, K., Vieira-Silva, S. & Raes, J. Water activity does not shape the microbiota in the human colon. Gut 66, 1865–1866 (2017)

    PubMed  PubMed Central  Google Scholar 

  15. 15

    Vandeputte, D. et al. Stool consistency is strongly associated with gut microbiota richness and composition, enterotypes and bacterial growth rates. Gut 65, 57–62 (2016)

    CAS  PubMed  Google Scholar 

  16. 16

    Tigchelaar, E. F. et al. Gut microbiota composition associated with stool consistency. Gut 65, 540–542 (2016)

    CAS  PubMed  Google Scholar 

  17. 17

    Fuller, B. J. Cryoprotectants: the essential antifreezes to protect life in the frozen state. Cryo Lett. 25, 375–388 (2004)

    CAS  Google Scholar 

  18. 18

    Hugon, P. et al. Molecular studies neglect apparently gram-negative populations in the human gut microbiota. J. Clin. Microbiol. 51, 3286–3293 (2013)

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19

    Ben-Amor, K. et al. Genetic diversity of viable, injured, and dead fecal bacteria assessed by fluorescence-activated cell sorting and 16S rRNA gene analysis. Appl. Environ. Microbiol. 71, 4679–4689 (2005)

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Roager, H. M. et al. Colonic transit time is related to bacterial metabolism and mucosal turnover in the gut. Nat. Microbiol. 1, 16093 (2016)

    CAS  PubMed  Google Scholar 

  21. 21

    McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLOS Comput. Biol. 10, e1003531 (2014)

    ADS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Goodrich, J. K. et al. Conducting a microbiome study. Cell 158, 250–262 (2014)

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Magurran, A. E. Measuring Biological Diversity (Blackwell, 2004)

  24. 24

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

    CAS  PubMed  Google Scholar 

  25. 25

    Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Pascal, V. et al. A microbial signature for Crohn’s disease. Gut 66, 813–822 (2017)

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    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  PubMed  PubMed Central  Google Scholar 

  28. 28

    Prest, E. I., Hammes, F., Kötzsch, S., van Loosdrecht, M. C. M. & Vrouwenvelder, J. S. Monitoring microbiological changes in drinking water systems using a fast and reproducible flow cytometric method. Water Res. 47, 7131–7142 (2013)

    CAS  PubMed  Google Scholar 

  29. 29

    Ramseier, C. A. et al. Identification of pathogen and host-response markers correlated with periodontal disease. J. Periodontol. 80, 436–446 (2009)

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30

    Tito, R. Y. et al. Dialister as a microbial marker of disease activity in spondyloarthritis. Arthritis Rheumatol. 69, 114–121 (2017)

    CAS  PubMed  Google Scholar 

  31. 31

    Girardot, C., Scholtalbers, J., Sauer, S., Su, S. Y. & Furlong, E. E. Je, a versatile suite to handle multiplexed NGS libraries with unique molecular identifiers. BMC Bioinformatics 17, 419 (2016)

    PubMed  PubMed Central  Google Scholar 

  32. 32

    Magocˇ, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011)

    PubMed  PubMed Central  Google Scholar 

  33. 33

    Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34

    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  PubMed  PubMed Central  Google Scholar 

  35. 35

    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)

    CAS  PubMed  Google Scholar 

  36. 36

    McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    Oksanen, J. et al. vegan: community ecology package. R package version 2.2–1 https://CRAN.R-project.org/package=vegan (2015)

    Google Scholar 

  38. 38

    Ogle, D. H. FSA: fisheries stock analysis. R package version 0.8.13 https://cran.r-project.org/package=FSA (2017)

  39. 39

    Hothorn, T., Hornik, K., van de Wiel, M. A. & Zeileis, A. A Lego system for conditional inference. Am. Stat. 60, 257–263 (2006)

    MathSciNet  Google Scholar 

  40. 40

    Morgan, M. DirichletMultinomial: Dirichlet-multinomial mixture model machine learning for microbiome data. R package version 1.18.0 https://cran.r-project.org/package=dirmult (2017)

  41. 41

    Wolak, M. ICC: facilitating estimation of the intraclass correlation coefficient. R package version 2.3.0 https://cran.r-project.org/package=ICC (2016)

  42. 42

    Delignette-Muller, M. L. & Dutang, C. fitdistrplus: an R package for fitting distributions. J. Stat. Softw. 64, 1–34 (2015)

    Google Scholar 

  43. 43

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995)

    MathSciNet  MATH  Google Scholar 

  44. 44

    Vieira-Silva, S. et al. Species-function relationships shape ecological properties of the human gut microbiome. Nat. Microbiol. 1, 16088 (2016)

    CAS  PubMed  Google Scholar 

  45. 45

    Smith, C. J. & Osborn, A. M. Advantages and limitations of quantitative PCR (Q-PCR)-based approaches in microbial ecology. FEMS Microbiol. Ecol. 67, 6–20 (2009)

    CAS  PubMed  Google Scholar 

  46. 46

    Hammes, F. et al. Flow-cytometric total bacterial cell counts as a descriptive microbiological parameter for drinking water treatment processes. Water Res. 42, 269–277 (2008)

    CAS  PubMed  Google Scholar 

  47. 47

    Habtewold, T., Duchateau, L. & Christophides, G. K. Flow cytometry analysis of the microbiota associated with the midguts of vector mosquitoes. Parasit. Vectors 9, 167 (2016)

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank all study participants, F. Giraldo for enabling sample collection at the PXL Hasselt, L. Rymenans and C. Verspecht for faecal DNA extraction and library preparation, K. Verbeke for facilitating moisture content determinations, and P. Goncalves for advice on simulating microbial data for benchmarking the QMP and RMP approach. The main funding for this study comes from a KU Leuven CREA grant. D.V. is supported by the Agency for Innovation by Science and Technology (IWT). G.K., K.D., M.V.-C., S.V.-S., and J.W. are funded by the Research Foundation Flanders (FWO-Vlaanderen). This work is further supported through funding by VIB, the Rega Institute for Medical Research, KU Leuven, FP7 METACARDIS (HEALTH-F4-2012-305312), and H2020 SYSCID (grant agreement 733100).

Author information

Affiliations

Authors

Contributions

This study was conceived by G.F. Experiments were designed by D.V., S.V., G.F., and J.R. Sampling of cohorts was set up and carried out by D.V., G.K., K.D., S.V.-S., M.V.-C., J.S., J.W., R.Y.T., L.D.C., and G.F. Optimization of sequencing protocols was performed by R.Y.T.; data pre-processing by D.V., M.V.-C., J.S., J.W., and Y.D.; flow cytometry analyses by G.K. and K.D.; statistical analyses by D.V., G.K., K.D., S.V.-S., M.V.-C., J.S., J.W., and G.F.; network analyses by S.V.-S.; and simulation experiments by D.V. and S.V.-S. G.F. developed the QMP protocol. S.V.-S., G.F., and J.R. drafted the manuscript. All authors revised the article and approved the final version for publication.

Corresponding author

Correspondence to Jeroen Raes.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks W. M. de Vos and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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

Extended data figures and tables

Extended Data Figure 1 Quantification of microbial loads of frozen faecal samples.

a, Microbial cell counts in fresh and frozen faecal aliquots strongly correlate with one another (study cohort, n = 39; Pearson’s r = 0.91, two-sided P = 4.9 × 10−16). Data points represent mean values. Error bars represent the s.d. of duplicate (fresh) and triplicate (frozen) cell counts. b, Comparison between flow cytometric assessment of microbial loads and estimation of bacterial abundances on the basis of qPCR (study cohort, n = 40). Data points represent mean values. Error bars represent the s.d. of triplicate values (qPCR and cell counts). Although comparing cell-based and molecular enumeration methods is not recommended18, the measurements were correlated (Pearson’s r = −0.53, two-sided P = 4.7 × 10−4). In a complex ecosystem, enumerating bacteria on the basis of qPCR would introduce biases through the extraction, purification, and amplification of DNA, 16S rRNA gene copy number variation, and community replication rate (shown to differ between enterotypes44), among others. Moreover, qPCR has been reported to be only sensitive enough to detect twofold changes in gene concentration or microbial load45. Flow cytometry is less specific and results might be affected by formation of aggregates8. For QMP analyses, we opted for a flow cytometry approach given its technical straightforwardness (limited number of technical manipulations; see Methods), reproducibility46,47, and throughput. Source data

Extended Data Figure 2 Intra-individual versus inter-individual microbial cell count variation in human faeces.

Twenty healthy individuals sampled daily over the course of a week (maximum of one sample per day; longitudinal cohort). Healthy individuals have significantly higher cell counts than patients with Crohn’s disease. Patient group disease cohort, n = 29 (CD; purple) versus overall daily samples, n = 132 (Ind. All; grey) or the average cell count of individuals (Ind. av; orange). Two-sided Wilcoxon rank-sum test, ***P < 0.001. The body of the box plot represents the first and third quartiles of the distribution and the median line. The whiskers extend from the quartiles to the last data point within 1.5× interquartile range, with outliers beyond. Source data

Extended Data Figure 3 Faecal microbial loads correlate with sample moisture content and genus richness.

a, Microbial cell counts and moisture content were negatively correlated in the longitudinal and the validation cohort, though not in the study cohort (study cohort, n = 37, Spearman’s ρ = −0.12, two-sided FDR = 0.56; longitudinal cohort, n = 132, Spearman’s ρ = −0.52, two-sided FDR = 1.6 × 10−9; validation cohort, n = 54, Spearman’s ρ = −0.56, two-sided FDR = 9.1 × 10−5). b, Microbial cell counts and observed richness correlated mildly (study cohort, n = 40; Spearman’s ρ = 0.36, two-sided P = 2.3 × 10−2). Source data

Extended Data Figure 4 Faecal microbial loads vary across enterotypes.

Microbial load differences between the four enterotypes in the disease cohort (n = 95). Box plot representation of microbial load (cells per gram of faeces) distribution across the four enterotypes. The body of the box plot represents the first and third quartiles of the distribution and the median line. The whiskers extend from the quartiles to the last data point within 1.5× interquartile range, with outliers beyond. Two-sided Dunn’s adjusted test, **P < 0.01, ***P < 0.001. Source data

Extended Data Figure 5 Microbial loads are not associated with sequencing depth.

Sequencing depth did not reflect the total microbial load of a sample (Spearman’s ρ = 0.17, two-sided P = 0.28). Samples are ranked according to decreasing cell counts. Source data

Extended Data Figure 6 Illustration of differences between RMP and QMP methodology.

Two samples, each containing four genera, are analysed (numbers are illustrative). Genus abundance distributions in sample A and B are markedly distinct, with the microbial load in sample B more than double that of the load in sample A. Genus ‘purple’ carries two copies of the 16S rRNA gene. (1) DNA extraction and library preparation. Neither RMP nor QMP correct for biases introduced by DNA extraction, primer specificity, PCR amplification, or other library preparation steps. The resulting sequencing depth is independent of microbial load. (2) By rarefying to an even number of reads per sample, RMP assumes similar genus abundance distributions in samples A and B: sample A is therefore sequenced far more intensively than sample B. The resulting profiles therefore poorly reflect the genus distribution in the original samples. Given the multiple copies of the 16S rRNA gene, the relative abundance of ‘purple’ is overestimated. (3) The first step of QMP corrects for 16S rRNA copy number variation. In the resulting copy-corrected profile (CCP), each read corresponds with a single bacterium sequenced. (4) By dividing the CCP reads total (R) by the microbial loads (X), sampling depth is estimated for each sample. For sample A and B, sampling depth is [R]A divided by [X]A and [R]B divided by [X]B, respectively. The sampling depth for B is the lowest (3.33%) of the two; sample A is rarefied to the same level. This implies that ‘orange’ is no longer detected. As ‘orange’ was equally abundant in A and B, the fact that it is included in sample A RMP can be considered an artefact of uneven sampling intensity. The resulting rarefied genus abundances are proportional with sample microbial loads and can be extrapolated to generate QMPs expressed as number of cells per gram.

Extended Data Figure 7 Distribution of RMP and QMP abundances of Bacteroides and Prevotella in healthy controls.

Samples (n = 66) are ranked according to decreasing Bacteroides abundance in both the RMP and QMP panel (stacked bars). The trade-off between Bacteroides and Prevotella (RMP; Spearman’s ρ = −0.59, two-sided FDR = 2 10−4) was no longer significant after correction for microbial load (QMP; Spearman’s ρ = −0.33, two-sided FDR = 1). Source data

Extended Data Figure 8 False discovery rate and sensitivity in network reconstruction based on QMP and RMP simulated data.

QMP resulted in increased sensitivity and decreased FDR compared to RMP (two-sided t-test, P < 10−15). For a two-, four- and eightfold maximum difference in microbial load, QMP FDRs were 11%, 15%, and 22% lower, respectively, than for RMP; and QMP true positive discovery rate (sensitivity) was increased by 10%, 12%, and 15%. Data points depict repetitions (n = 50). The body of the box plot represents the first and third quartiles of the distribution and the median line. The whiskers extend from the quartiles to the last simulated data point within 1.5× interquartile range. Source data

Extended Data Figure 9 RMP underestimates the decrease in microbiome richness associated with Crohn’s disease.

Observed richness in healthy controls (n = 66) and patients with Crohn’s disease (n = 29). The decrease in richness associated with Crohn’s disease is more pronounced in QMP. The body of the box plot represents the first and third quartiles of the distribution and the median line. The whiskers extend from the quartiles to the last data point within 1.5× interquartile range. Two-sided Wilcoxon test, ***P < 0.001, **P < 0.01. Source data

Extended Data Figure 10 Flow cytometry gating strategy.

A fixed gating and staining approach was applied28. Both blank and sample solutions were stained with SYBR Green I. a, The FL1-A/FL3-A acquisition plot of a blank sample (0.85% w/v physiological solution) with gate boundaries indicated. A threshold value of 2,000 was applied on the FL1 channel. b, Secondary gating was performed on the FSC-A/SSC-A channels to further discriminate between debris or background and microbial events. c, d, FL1-A/FL3-A count acquisition of a faecal sample (c) with secondary gating on FSC-A/SSC-A channels on the basis of blank analyses (d). Total counts were defined as events registered in the FL1-A/FL3-A gating area, excluding debris or background events observed in the FSC-A/SSC-A R1 gate. The flow rate was set at 14 μl per minute and the acquisition rate did not exceed 10,000 events per second. Each panel reflects events registered over the course of a 30-s acquisition period.

Supplementary information

Life Sciences Reporting Summary (PDF 78 kb)

Supplementary Table

This file contains Supplementary Tables 1-11. (XLSX 90 kb)

PowerPoint slides

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Vandeputte, D., Kathagen, G., D’hoe, K. et al. Quantitative microbiome profiling links gut community variation to microbial load. Nature 551, 507–511 (2017). https://doi.org/10.1038/nature24460

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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