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Updated: Jun 23, 2026

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

PhenoGMM: Gaussian Mixture Modeling of Cytometry Data Quantifies Changes in Microbial Community Structure.

Peter Rubbens^1,2, Ruben Props³, Frederiek-Maarten Kerckhof³

¹KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium peter.rubbens@vliz.be.

|February 4, 2021

Summary

This summary is machine-generated.

We developed PhenoGMM, an automated method using Gaussian mixture models to analyze microbial flow cytometry data. This approach quantifies microbial community structure and dynamics rapidly and quantitatively.

Keywords:

diversity fingerprint flow cytometry machine learning microbial communities mixture model

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Area of Science:

Microbiology
Computational Biology
Ecology

Background:

Microbial communities are crucial in ecosystems.
Traditional 16S rRNA gene sequencing is time-consuming.
Flow cytometry offers rapid single-cell analysis of microbial communities.

Purpose of the Study:

To develop an automated fingerprinting method for microbial flow cytometry data.
To address limitations of manual annotation and multivariate data handling in existing methods.
To enable rapid and quantitative characterization of microbial community structure and dynamics.

Main Methods:

Development of PhenoGMM, a model-based fingerprinting approach using Gaussian mixture models.
Application of PhenoGMM to analyze flow cytometry data from synthetic and natural ecosystems.
Comparison of PhenoGMM performance against a generic binning fingerprinting approach.

Main Results:

PhenoGMM successfully quantifies changes in microbial community structure.
The method expresses community changes in terms of cytometric diversity.
PhenoGMM demonstrates general applicability across diverse ecosystem data.

Conclusions:

PhenoGMM provides a rapid and quantitative tool for screening microbial community structure.
The automated approach overcomes limitations of traditional cytometric fingerprinting methods.
PhenoGMM facilitates efficient analysis of microbial dynamics using flow cytometry.