No abstract

Flow cytometry is suitable to discriminate and quantify aquatic microbial cells within a spectrum of fluorescence and light scatter signals. Using fixed operational and gating settings, a mixture model, coupled to Laplacian operator and Nelder-Mead optimization algorithm, allowed deconvolving bivariate cytometric profiles into single cell subgroups. 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 persistent subgroups within each of the commonly retrieved populations of cells with Low and High content of Nucleic Acids (namely, LNA and HNA cells). Moreover, we assessed changes of the cytometric community profile over-imposed by water inputs from a wastewater treatment plant. Our approach for multiparametric data deconvolution confirmed that flow cytometry could represent a prime candidate technology for assessing microbial community patterns in flowing waters.