Author

P. Rubbens

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Microbiology· 5 Bioengineering· 1 Ecology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 5 Assay:flow cytometry· 5 Approach:experimental assay· 4 Assay:metagenomics· 4 Research Focus:differential expression· 3 Approach:deep learning· 2 Organism:bacteria (other)· 1 Research Focus:benchmarking· 1

Frequent Co-Authors

N. Boon· 7 shared R. Props· 7 shared W. Waegeman· 6 shared J. Heyse· 4 shared A. Skirtach· 2 shared F.-M. Kerckhof· 2 shared B. A. Biddanda· 1 shared B. Buysschaert· 1 shared

Activity by Year

2018-2022 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 32 mutual co-author links

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7 shared papersMicrobiology

N. Boon

Mutual co-authors in cluster

Author profile →

7 papers

Recent·Most cited

Real-Time Flow Cytometry to assess qualitative and quantitative responses of oral pathobionts during exposure to antiseptics

I. Chatzigiannidou, J. Heyse, R. Props, P. Rubbens, W. Teughels, T. van de Wiele, N. Boon

This study uses real-time flow cytometry to rapidly assess the quantitative and qualitative responses of oral bacteria to antiseptics and identifies treatment-specific phenotypic subpopulations.

bioRxiv · Feb 28, 2022Microbiology

Predicting the presence and abundance of bacterial taxa in environmental communities through flow cytometric fingerprinting

J. Heyse, F. Schattenberg, P. Rubbens, S. Müller, W. Waegeman, N. Boon, R. Props

The paper develops a computational pipeline using flow cytometry and 16S rRNA gene amplicon sequencing to predict bacterial taxa presence and abundance in environmental communities.

bioRxiv · May 5, 2021Microbiology

PhenoGMM: Gaussian mixture modelling of microbial cytometry data enables efficient predictions of biodiversity

P. Rubbens, R. Props, F.-M. Kerckhof, N. Boon, W. Waegeman

PhenoGMM uses Gaussian Mixture Models to predict microbial diversity from flow cytometry data, integrating with 16S rRNA sequencing for improved accuracy.

bioRxiv · Jun 19, 2020 · PreprintMicrobiology

Cytometric fingerprints of gut microbiota predict Crohn’s disease state

P. Rubbens, R. Props, F. - M. Kerckhof, N. Boon, W. Waegeman

The paper shows that flow cytometry can classify Crohn’s disease samples based on gut microbiota changes, matching 16S rRNA gene analysis results.

bioRxiv · May 24, 2019Microbiology

Discriminating bacterial phenotypes at the population and single-cell level: a comparison of flow cytometry and Raman spectroscopy fingerprinting

C. Garcia-Timermans, P. Rubbens, J. Heyse, R. Props, F.-M. Kerckhof, A. Skirtach, W. Waegeman, N. Boon

This paper compares flow cytometry and Raman spectroscopy for analyzing bacterial phenotypic heterogeneity, showing flow cytometry's population-level resolution and Raman's single-cell biochemical detail.

bioRxiv · May 24, 2019 · PreprintBioengineering

Randomized lasso associates freshwater lake-system specific bacterial taxa with heterotrophic production through flow cytometry

P. Rubbens, M. L. Schmidt, R. Props, B. A. Biddanda, N. Boon, W. Waegeman, V. J. Denef

The paper uses machine learning to link bacterial taxa with heterotrophic production in freshwater lakes using flow cytometry and 16S rRNA data.

bioRxiv · Feb 16, 2019 · PreprintEcology

Coculturing bacteria leads to reduced phenotypic heterogeneities

J. Heyse, B. Buysschaert, R. Props, P. Rubbens, A. Skirtach, W. Waegeman, N. Boon

This study shows that coculturing bacteria reduces their individual phenotypic heterogeneity, suggesting that community composition influences taxon-specific diversity.

bioRxiv · Sep 23, 2018Microbiology