Deconvolution model to resolve cytometric microbial community patterns in flowing waters.

Flow cytometry is suitable to discriminate and quantify aquatic microbial cells within a spectrum of fluorescence and light scatter signals. Using fixed gating and operational settings, we developed a finite distribution mixture model, followed by the Voronoi tessellation, to resolve bivariate cytometric profiles into cohesive subgroups of events. This procedure was applied to outline recurrent patterns and quantitative changes of the aquatic microbial community along a river hydrologic continuum. We found five major subgroups within each of the commonly retrieved populations of cells with Low and High content of Nucleic Acids (namely, LNA, and HNA cells). Moreover, the advanced analysis allowed assessing changes of community patterns perturbed by a wastewater feed. Our approach for cytometric data deconvolution confirmed that flow cytometry could represent a prime candidate technology for assessing microbial community patterns in flowing waters.