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A multi‐laser flow cytometry method to measure single cell and population‐level relative fluorescence action spectra for the targeted study and isolation of phytoplankton in complex assemblages
Author(s) -
Thompson Anne W.,
van den Engh Ger
Publication year - 2016
Publication title -
limnology and oceanography: methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.898
H-Index - 72
ISSN - 1541-5856
DOI - 10.1002/lom3.10068
Subject(s) - phytoplankton , biology , flow cytometry , population , ploidy , ecology , genetics , gene , demography , sociology , nutrient
Discerning the diversity, abundance, and functional role of distinct phytoplankton groups is essential to ecological study of aquatic systems. Flow cytometry is a widely used method to rapidly identify and quantify individual phytoplankton cells. Here, we present a new flow cytometry method that uses up to five excitation colors to determine the relative fluorescence action spectra of phytoplankton within complex assemblages, thus leveraging the precise and high‐throughput capabilities of flow cytometry and the unique combinations of photosynthetic pigments in phylogenetically related groups of phytoplankton. First, we tested the method on cultivated Synechococcus of known pigment composition and genotype then we applied the method to a natural phytoplankton assemblage where we determined the relative fluorescence action spectra of numerous distinct populations. By coupling multi‐laser flow cytometry to cell sorting we demonstrated that natural phytoplankton populations with similar relative fluorescence action spectra belonged to the same taxonomic classes based on 18S rRNA gene phylogeny. This method will be instrumental in studying the ecology of distinct phytoplankton populations within complex microbial communities through spectral analysis of individual cells, identification of unique populations, and cell sorting for downstream applications such as enrichment for isolation, genome assembly, and linkage of chromatic and genetic information in a predictive fashion.