Probing complexity of microalgae mixtures with novel spectral flow cytometry approach and “virtual filtering”

Abstract

170 27 MS words: 2712 28 29 Abstract 30Fluorescence methods are widely applied for the study of the marine and freshwater 31 phytoplankton communities. However, identification of different microalgae populations by 32 autofluorescent pigments remains a challenge because of the very strong signal from chlorophyll. 33Addressing the issue we developed a novel approach using the flexibility of spectral flow 34 cytometry analysis (SFC) and generated a matrix of virtual filters (VF) capable to of 35 differentiating non-chlorophyll parts of the spectrum. Using this matrix spectral emission regions 36 of algae species were analyzed, and five major algal taxa were discriminated. These results were 37 further applied for tracing particular microalgae taxa in the complex mixtures of laboratory and 38 environmental algal populations. An integrated analysis of single algal events combined with 39 unique spectral emission fingerprints and light scattering parameters of microalgae can be further 40 used to differentiate major microalgal taxa. Our results demonstrate that spectral flow cytometer 41 (SFC-VF) and virtual filtering approach can provide a quantitative assessing of heterogenous 42 phytoplankton communities at single cell level spectra and be helpful in the monitoring of 43 phytoplankton blooms.44 45

Spectral algal fingerprinting and long sequencing in synthetic algal-microbial communities

“…This study introduces full-spectrum cytometry as a powerful tool for analyzing synthetic algalmicrobial communities, facilitating rapid, high-throughput analysis from species identification to enumeration without extensive sample preparation. By capturing the complete emission spectrum of each cell, full-spectrum cytometry provides valuable insights into phytoplankton community composition, structure, and dynamics, as well as their responses to environmental stressors that influence community structure (Barteneva et al, 2019(Barteneva et al, , 2023Jeppesen et al, 2005). Algal cells, distinguished by autofluorescent spectral signatures, are separated taxonomically, while light scattering parameters assess cell size and complexity, distinguishing target events from debris.…”

“…Algal cells, distinguished by autofluorescent spectral signatures, are separated taxonomically, while light scattering parameters assess cell size and complexity, distinguishing target events from debris. Spectral cytometry identifies highly autofluorescent subpopulations using virtual filtering (Barteneva et al, 2019) or only autofluorescence finder algorithm (Wanner et al, 2022). Characterizing microbiota associated with algae from in-house cultures or natural consortia is crucial as microalgae growth phases correlate with shifts in microbial phylotypes (Geng et al, 2016), although microbial dynamics in synthetic systems remain understudied.…”