Deconvolution model to resolve cytometric microbial community patterns in flowing waters

Area of Science:

• Environmental microbiology
• Analytical chemistry
• Biotechnology

Background:

• Flow cytometry is a powerful tool for analyzing microbial populations.
• Understanding aquatic microbial community dynamics is crucial for environmental monitoring.
• Existing methods may lack the resolution to fully characterize complex microbial communities.

Purpose of the Study:

• To develop and apply an advanced computational method for deconvoluting flow cytometry data of aquatic microbial communities.
• To identify distinct microbial subgroups based on cellular characteristics.
• To assess the impact of environmental factors, such as wastewater, on microbial community structure.

Main Methods:

• Utilized flow cytometry to measure fluorescence and light scatter signals from aquatic microbial cells.
• Developed a finite distribution mixture model combined with Voronoi tessellation for data analysis.
• Applied the method to microbial samples collected along a river continuum and near a wastewater outfall.

Main Results:

• Identified five major subgroups within both Low Nucleic Acid (LNA) and High Nucleic Acid (HNA) cell populations.
• Quantified recurrent patterns and changes in microbial community structure.
• Detected alterations in community patterns influenced by wastewater input.

Conclusions:

• The developed finite mixture model and Voronoi tessellation approach effectively deconvolutes complex flow cytometry data.
• Flow cytometry, with advanced analytical techniques, is a suitable technology for assessing microbial community patterns in aquatic environments.
• This method provides a robust framework for monitoring environmental changes and microbial responses.