Rapid detection and online analysis of microbial changes through flow cytometry

Area of Science:

• Microbiology
• Bioanalytical Chemistry
• Computational Biology

Background:

• Short-read 16S rRNA gene sequencing is the standard for microbial community profiling but is time-consuming.
• Flow cytometry offers a rapid alternative for microbiota analysis, processing samples in minutes.
• Developing efficient analysis tools is crucial for high-throughput flow cytometry data.

Purpose of the Study:

• To introduce FlowSoFine, an open-source tool for analyzing flow cytometry data of microbial communities.
• To validate flow cytometry's ability to differentiate microbial profiles using human skin and stool samples.
• To demonstrate flow cytometry as a viable method for skin microbiota analysis.

Main Methods:

• Development of the open-source analysis tool FlowSoFine.
• Analysis of human skin microbiota from three body sites using flow cytometry and 16S rRNA gene sequencing.
• Analysis of stool samples from patients with and without inflammatory bowel diseases using flow cytometry.

Main Results:

• Flow cytometry successfully distinguished microbial profiles between different human body sites, confirmed by 16S rRNA sequencing.
• Flow cytometry analysis of stool samples discriminated between healthy individuals and those with inflammatory bowel diseases.
• FlowSoFine provides a user-friendly interface for analyzing cytometric fingerprints and generating publication-ready figures.

Conclusions:

• Flow cytometry is a rapid and valid method for analyzing skin microbiota.
• The FlowSoFine tool facilitates the analysis of microbial communities using flow cytometry.
• Cytometric fingerprints generated by flow cytometry can differentiate microbial communities based on host factors like body site and health status.