Literature DB >> 8678818

Ataxonomic assessment of phytoplankton integrity by means of flow cytometry.

C E Steinberg1, H Schäfer, M Siedler, W Beisker.   

Abstract

Flow cytometry, a method well established in medicine and biotechnology, can also make an important contribution to (applied) limnological as well as ecotoxicological studies on phytoplankton. Flow cytometry can, for instance, contribute to the ataxonomic structural and functional assessment of phytoplankton. This approach may serve as a supplement to the well-established taxonomic evaluation by means of various microscope techniques. We present some examples for such ataxonomic phytoplankton evaluation. These examples include phytoplankton of eutrophicated and acidified water bodies as well as slowly flowing rivers. Phytoplankters may be differentiated by their pigment contents into carotinoid-rich ones (such as Chrysophyceae, Bacillariophyceae, and Dinophyceae) and carotinoid-poor ones (such as Euglenophyceae and Chlorophyceae). As a useful biomass parameter of phytoplankton algae we tested successfully protein staining by fluorescein isothiocyanate. We discuss the advantage of this approach as compared with results obtained by Coulter counter or by biomass calculations from microscope analyses. Up to now, evaluation of the biological quality of pelagic water bodies is still laborious and time consuming because of the microscopical examination of planktic communities usually practiced. As a possible improvement we present a structural ataxonomic approach for assessing the integrity of individual phytoplankters (on the basis of physiological parameters) as well as of the phytoplankton communities that is based on annual means of biomass spectra. Flow cytometry can provide considerable relief.

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Year:  1996        PMID: 8678818     DOI: 10.1007/978-3-642-61105-6_39

Source DB:  PubMed          Journal:  Arch Toxicol Suppl        ISSN: 0171-9750


  1 in total

1.  Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering.

Authors:  Mridul K Thomas; Simone Fontana; Marta Reyes; Francesco Pomati
Journal:  PLoS One       Date:  2018-05-10       Impact factor: 3.240

  1 in total

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