Cytometric fingerprints of gut microbiota predict Crohn’s disease state


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.

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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


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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”.

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Correspondence to Peter Rubbens or Willem Waegeman.

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Rubbens, P., Props, R., Kerckhof, F. et al. Cytometric fingerprints of gut microbiota predict Crohn’s disease state. ISME J (2020).

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