Tracking defined microbial communities by multicolor flow cytometry reveals tradeoffs between productivity and...

Area of Science:

• Microbial Ecology
• Systems Biology
• Synthetic Biology

Background:

• Microbial cross-feeding is common but its effects on community diversity and productivity are not fully understood.
• Quantifying multiple microbial species in high-throughput requires rapid, affordable methods.

Purpose of the Study:

• To develop and validate a multicolor flow cytometry approach for quantifying microbial species in synthetic communities.
• To detect metabolic exchange and analyze community dynamics in a four-member *Bacteroides* species community.

Main Methods:

• Utilized multicolor flow cytometry to measure species abundances in fluorescently labeled *Bacteroides* species.
• Employed a machine learning classifier for rapid and accurate quantification of microbial populations.
• Monitored community structure and metabolite exchange over time in co-cultures.

Main Results:

• Successfully quantified species abundances in synthetic microbial communities.
• Detected evidence of cooperation in polysaccharide processing and competition for monosaccharides.
• Observed reduced microbial productivity on simple sugars, with less productive communities showing higher diversity.

Conclusions:

• Multicolor flow cytometry with machine learning offers an economical and effective model system for microbial ecology.
• The approach can be expanded to study more complex communities and responses to environmental disturbances.
• This method facilitates the study of microbial interactions and their ecosystem-level consequences.