Cytometric fingerprints of gut microbiota predict Crohn’s disease state

Abstract

Variations in the gut microbiome have been associated with changes in health state such as Crohn’s disease (CD). Most surveys characterize the microbiome through analysis of the 16S rRNA gene. An alternative technology that can be used is flow cytometry. In this report, we reanalyzed a disease cohort that has been characterized by both technologies. Changes in microbial community structure are reflected in both types of data. We demonstrate that cytometric fingerprints can be used as a diagnostic tool in order to classify samples according to CD state. These results highlight the potential of flow cytometry to perform rapid diagnostics of microbiome-associated diseases.

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: Summary of Random Forest classification of CD vs. HC test samples for ten runs of the procedure.
Fig. 2: Microbial diversity estimations and cytometric structure for CD (n = 29) vs. HC (n = 66).

Data availability

The genus table can be accessed as supporting information to the original publication [15]. Denoised raw flow cytometry data can be accessed via FlowRepository (ID:FR-FCM-ZYVH). Code and data to reproduce the analysis supporting the paper can be accessed via https://github.com/prubbens/PhenoGMM_CD.

References

  1. 1.

    Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, et al. A core gut microbiome in obese and lean twins. Nature. 2009;457:480–5.

    CAS  Article  Google Scholar 

  2. 2.

    Gevers D, Kugathasan S, Denson LA, Vázquez-Baeza Y, Van Treuren W, Ren B, et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe. 2014;15:382–92.

    CAS  Article  Google Scholar 

  3. 3.

    Larsen N, Vogensen FK, Van Den Berg FWJ, Nielsen DS, Andreasen AS, Pedersen BK, et al. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLOS ONE. 2010;5:e9085.

  4. 4.

    Kuntz TM, Gilbert JA. Introducing the microbiome into precision medicine. Trends Pharmacol Sci. 2017;38:81–91.

    CAS  Article  Google Scholar 

  5. 5.

    Sims D, Sudbery I, Ilott NE, Heger A, Ponting CP. Sequencing depth and coverage: key considerations in genomic analyses. Nat Rev Genet. 2014;15:121–32.

    CAS  Article  Google Scholar 

  6. 6.

    van Dorst J, Bissett A, Palmer AS, Brown M, Snape I, Stark JS, et al. Community fingerprinting in a sequencing world. FEMS Microbiol Ecol. 2014;89:316–30.

    Article  Google Scholar 

  7. 7.

    Müller S, Nebe-Von-Caron G. Functional single-cell analyses: flow cytometry and cell sorting of microbial populations and communities. FEMS Microbiol Rev. 2010;34:554–87.

    Article  Google Scholar 

  8. 8.

    Young VB. The role of the microbiome in human health and disease: an introduction for clinicians. BMJ. 2017;356:j831.

  9. 9.

    Gilbert JA, Blaser MJ, Caporaso JG, Jansson JK, Lynch SV, Knight R. Current understanding of the human microbiome. Nat Med. 2018;24:392–400.

    CAS  Article  Google Scholar 

  10. 10.

    Koch C, Müller S. Personalized microbiome dynamics—cytometric fingerprints for routine diagnostics. Mol Aspects Med. 2018;59:123–34.

    CAS  Article  Google Scholar 

  11. 11.

    Zimmermann J, Hübschmann T, Schattenberg F, Schumann J, Durek P, Riedel R, et al. High-resolution microbiota flow cytometry reveals dynamic colitis-associated changes in fecal bacterial composition. Eur J Immunol. 2016;46:1300–3.

    CAS  Article  Google Scholar 

  12. 12.

    Li WKW. Cytometric diversity in marine ultraphytoplankton. Limnol Oceanogr. 1997;42:874–80.

    CAS  Article  Google Scholar 

  13. 13.

    García FC, Alonso-Sáez L, Morán XAG, López-Urrutia Á. Seasonality in molecular and cytometric diversity of marine bacterioplankton: the re-shuffling of bacterial taxa by vertical mixing. Environ Microbiol. 2015;17:4133–42.

    Article  Google Scholar 

  14. 14.

    Props R, Monsieurs P, Mysara M, Clement L, Boon N. Measuring the biodiversity of microbial communities by flow cytometry. Methods Ecol Evol. 2016;7:1376–85.

    Article  Google Scholar 

  15. 15.

    Vandeputte D, Kathagen G, D’Hoe K, Vieira-Silva S, Valles-Colomer M, Sabino J, et al. Quantitative microbiome profiling links gut community variation to microbial load. Nature. 2017;551:507–11.

    CAS  Article  Google Scholar 

  16. 16.

    Rubbens P, Props R, Kerckhof F-M, Boon N, Waegeman W. PhenoGMM: Gaussian mixture modelling of cytometry data enables efficient predictions of microbial biodiversity. biorXiv. 2019:641464. https://doi.org/10.1101/641464.

  17. 17.

    Gorzelak MA, Gill SK, Tasnim N, Ahmadi-Vand Z, Jay M, Gibson D. Methods for improving human gut microbiome data by reducing variability through sample processing and storage of stool. PLOS ONE. 2015;10:e0134802.

    Article  Google Scholar 

  18. 18.

    Johnson JS, Spakowicz DJ, Hong BY, Petersen LM, Demkowicz P, Chen L, et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat Commun. 2019;10:1–11.

    CAS  Article  Google Scholar 

  19. 19.

    Byrd DA, Chen J, Vogtmann E, Hullings A, Song SJ, Amir A, et al. Reproducibility, stability, and accuracy of microbial profiles by fecal sample collection method in three distinct populations. PLOS ONE. 2019;14:e0224757.

    CAS  Article  Google Scholar 

  20. 20.

    Liang Y, Dong T, Chen M, He L, Wang T, Liu X, et al. Systematic analysis of impact of sampling regions and storage methods on fecal gut microbiome and metabolome profiles. mSphere. 2020;5:1–13.

    Article  Google Scholar 

  21. 21.

    Vandeputte D, Falony G, Vieira-Silva S, Tito RY, Joossens M, Raes J. Stool consistency is strongly associated with gut microbiota richness and composition, enterotypes and bacterial growth rates. Gut. 2016;65:57–62.

    CAS  Article  Google Scholar 

  22. 22.

    Robinson JP, Roederer M. Flow cytometry strikes gold. Science. 2015;350:739–40.

    CAS  Article  Google Scholar 

  23. 23.

    Gryp T, Paepe KD, Vanholder R, Kerckhof F-M, Van Biesen W, Van de Wiele T, et al. Gut microbiota generation of protein-bound uremic toxins and related metabolites is not altered at different stages of chronic kidney disease. Kidney Int. 2020;97:1230–42.

  24. 24.

    Schäpe SS, Krause JL, Engelmann B, Fritz-Wallace K, Schattenberg F, Liu Z, et al. The Simplified Human Intestinal Microbiota (SIHUMIx) shows high structural and functional resistance against changing transit times in in vitro bioreactors. Microorganisms. 2019;7:641.

    Article  Google Scholar 

  25. 25.

    Van Nevel S, Koetzsch S, Proctor CR, Besmer MD, Prest EI, Vrouwenvelder JS, et al. Flow cytometric bacterial cell counts challenge conventional heterotrophic plate counts for routine microbiological drinking water monitoring. Water Res. 2017;113:191–206.

    Article  Google Scholar 

  26. 26.

    Sabino J, Vieira-Silva S, Machiels K, Joossens M, Falony G, Ballet V, et al. Primary sclerosing cholangitis is characterised by intestinal dysbiosis independent from IBD. Gut. 2016;65:1681–9.

    CAS  Article  Google Scholar 

  27. 27.

    Falony G, Joossens M, Vieira-Silva S, Wang J, Darzi Y, Faust K, et al. Population-level analysis of gut microbiome variation. Science. 2016;352:560–4.

    CAS  Article  Google Scholar 

  28. 28.

    Prest EI, Hammes F, Kötzsch S, van Loosdrecht MCM, Vrouwenvelder JS. Monitoring microbiological changes in drinking water systems using a fast and reproducible flow cytometric method. Water Res. 2013;47:7131–42.

    CAS  Article  Google Scholar 

  29. 29.

    Monaco G, Chen H, Poidinger M, Chen J, Magalhães JPD, Larbi A. FlowAI: automatic and interactive anomaly discerning tools for flow cytometry data. Bioinformatics. 2016;32:2473–80.

    CAS  Article  Google Scholar 

  30. 30.

    Airola A, Pahikkala T, Waegeman W, Baets BD, Salakoski T. An experimental comparison of cross-validation techniques for estimating the area under the ROC curve. Comput Stat Data Anal. 2011;55:1828–44.

    Article  Google Scholar 

  31. 31.

    Breiman L. Random forests. Mach Learn. 2001;45:5–32.

    Article  Google Scholar 

  32. 32.

    Bergstra J, Bengio Y. Random search for hyper-parameter optimization. J Mach Learn Res. 2012;13:281–305.

    Google Scholar 

  33. 33.

    Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.

    Google Scholar 

  34. 34.

    Hill MO. Diversity and evenness: a unifying notation and its consequences. Ecology. 1973;54:427–32.

    Article  Google Scholar 

  35. 35.

    Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0–fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17:261–72.

    CAS  Article  Google Scholar 

  36. 36.

    Seabold S, Perktold J. Statsmodels: econometric and statistical modeling with Python. In: 9th Python in science conference. Austin, Texas, USA, 2010. https://conference.scipy.org/proceedings/scipy2010/pdfs/proceedings.pdf.

Download references

Acknowledgements

We thank Gunther Kathagen and Jeroen Raes for sharing the raw flow cytometry data. Part of the computational resources (Stevin Supercomputer Infrastructure) and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by Ghent University, the Hercules Foundation, and the Flemish Government department EWI. P.R. was supported by Ghent University (BOFSTA2015000501). W.W. received funding from the Flemish Government under the “Onderzoeksprogramma Artificielë Intelligentie (AI) Vlaanderen”.

Author information

Affiliations

Authors

Corresponding authors

Correspondence to Peter Rubbens or Willem Waegeman.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

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

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rubbens, P., Props, R., Kerckhof, F. et al. Cytometric fingerprints of gut microbiota predict Crohn’s disease state. ISME J (2020). https://doi.org/10.1038/s41396-020-00762-4

Download citation

Search