W. Waegeman

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

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.

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

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

Cytometric fingerprints of gut microbiota predict Crohn’s disease state

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

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

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.

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

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

Coculturing bacteria leads to reduced phenotypic heterogeneities

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