Fast quantification of gut bacterial species in cocultures using flow cytometry and supervised classification

Area of Science:

• Microbiology
• Bioinformatics
• Analytical Chemistry

Background:

• High-throughput microbial community analysis is crucial for research.
• Current methods like 16S rRNA gene sequencing can be time-consuming for large sample sets.
• Accurate and rapid quantification of individual bacterial taxa is needed.

Purpose of the Study:

• To develop and validate a novel, high-throughput method for enumerating human gut bacteria.
• To combine flow cytometry with supervised classification for species identification and quantification.
• To assess the performance of this method against traditional 16S rRNA gene sequencing.

Main Methods:

• Utilized flow cytometry to capture multivariate data from bacterial species.
• Employed supervised classification algorithms for species identification.
• Tested the method in silico with a 5-species community and in vitro with 2- and 4-species cocultures.
• Compared results with 16S rRNA gene sequencing data.

Main Results:

• Achieved 71% F1 score for species identification in a 5-species in silico community.
• Demonstrated comparable or superior performance to 16S rRNA gene sequencing in vitro.
• Confirmed agreement with sequencing data for abundant species in a 4-species community.
• Identified that species-specific variability in flow cytometry data necessitates multivariate analysis.

Conclusions:

• Flow cytometry combined with supervised classification offers a faster alternative to 16S rRNA gene sequencing for gut bacteria enumeration.
• Method performance is species-dependent but accurate enough for certain community compositions.
• Exploiting multivariate flow cytometry data is essential for distinguishing bacterial species effectively.