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Updated: Oct 4, 2025
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Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
Published on: July 12, 2018
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Recent advances in microbial community analysis from machine learning of multiparametric flow cytometry data
Birge D Özel Duygan1, Jan R van der Meer2
1Department of Fundamental Microbiology, University of Lausanne, Lausanne, 1015, Switzerland; Institute of Microbiology, CHUV, Lausanne, 1011, Switzerland.
Current Opinion in Biotechnology
|February 5, 2022
Summary
Quantitative multiparametric flow cytometry (FCM) offers a real-time complement to sequencing for analyzing microbial communities. Machine learning applied to FCM data enables rapid, quantitative deconvolution of community dynamics and physiological states.
Area of Science:
- Microbiology
- Bioinformatics
- Systems Biology
Background:
- Dynamic analysis of microbial composition is vital for understanding community function and identifying dysbiosis.
- Current methods like sequencing provide compositional data but can be complemented by real-time techniques.
- Multiparametric flow cytometry (FCM) offers quantitative, real-time insights into microbial communities.
Purpose of the Study:
- To explore the utility of quantitative community multiparametric flow cytometry (FCM) as a complement to sequencing for microbial analysis.
- To demonstrate the application of machine learning techniques for analyzing FCM data from microbial communities.
- To enable rapid, quantitative assessment of microbial community changes in response to stimuli.
Main Methods:
- Utilizing real-time quantitative community multiparametric flow cytometry (FCM) for data acquisition.
- Applying unsupervised machine learning to distinguish patterns and clusters in FCM data.
- Employing supervised machine learning, trained on preselected strain phenotypes, to differentiate cell types.
- Optimizing procedures for recurrent microbiome samples to simultaneously assess physiological and compositional states.
Main Results:
- Machine learning effectively deconvolutes complex FCM community data sets.
- Both unsupervised and supervised machine learning approaches can quantitatively analyze FCM data.
- Rapid analysis of global community changes in response to treatments is achievable.
- FCM, coupled with machine learning, allows simultaneous quantification of physiological and compositional states in microbiome samples.
Conclusions:
- Quantitative multiparametric flow cytometry (FCM) provides a valuable, real-time complement to traditional sequencing methods for microbial ecology.
- Machine learning algorithms are powerful tools for deconvoluting and interpreting complex FCM data from microbial communities.
- This approach enables rapid, quantitative monitoring of microbial community dynamics and host-microbe interactions.
