Probing complexity of microalgae mixtures with novel spectral flow cytometry approach and \"virtual filtering\"

Fluorescence methods are widely applied for the study of the marine and freshwater phytoplankton communities. However, identification of different microalgae populations by autofluorescent pigments remains a challenge because of the very strong signal from chlorophyll. Addressing the issue we developed a novel approach using the flexibility of spectral flow cytometry analysis (SFC) and generated a matrix of virtual filters (VF) capable to of differentiating non-chlorophyll parts of the spectrum. Using this matrix spectral emission regions of algae species were analyzed, and five major algal taxa were discriminated. These results were further applied for tracing particular microalgae taxa in the complex mixtures of laboratory and environmental algal populations. An integrated analysis of single algal events combined with unique spectral emission fingerprints and light scattering parameters of microalgae can be further used to differentiate major microalgal taxa. Our results demonstrate that spectral flow cytometer (SFC-VF) and virtual filtering approach can provide a quantitative assessing of heterogenous phytoplankton communities at single cell level spectra and be helpful in the monitoring of phytoplankton blooms.