Ataxonomic Assessment of Phytoplankton Integrity by Means of Flow Cytometry

Steinberg, Christian; Schäfer, Hendrik; Siedler, Michael; Beisker, Wolfgang

doi:10.1007/978-3-642-61105-6_39

Cited by 8 publications

(6 citation statements)

References 28 publications

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“…For protein content analysis and CFR examinations, we used chlorophyte, cryptophyte, chrysophyte, euglenophyte, bacillariophyte, and picoplanktic cyanobacteria algal species. The species list is given elsewhere (Steinberg et al 1996). A ciliate, Tetrahymena pyriformis , was used in addition to the algae for protein content analysis.…”

Section: Protein Staining and Photometrical Quantification Of Cellulamentioning

confidence: 99%

“…The range of mean cell volume of the tested organisms determined by image analysis spread over five orders of magnitude from 0.8 m 3 ( Synechococcus ) to about 10,000 m 3 ( Tetrahymena ). Based on the calibration given by Steinberg et al (1996), we estimated the protein content of single algal cells and thus established size classes as protein in pg per cell. Biomass spectra was established by counting the numbers of cells per size (protein) class.…”

Section: Protein Staining and Photometrical Quantification Of Cellulamentioning

confidence: 99%

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Deriving Restoration Goals for Acidified Lakes from Ataxonomic Phytoplankton Studies

Steinberg

Schäfer

Beisker

et al. 1998

Restoration Ecology

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The ataxonomic phytoplankton composition and abundance (biomass classes) in lakes of differing acidity were examined by flow cytometry. The ataxonomic parameters applied here were photosynthesis pigments and cellular protein content. Up to 1000 cells per second can be assessed by this method. Consequently, enough cells to create biomass spectra can be counted within only a few minutes. Photosynthesis pigment autofluorescence was used to separate algal cells from detritus and to classify the phytoplankton organisms into different pigment groups. Chlorophyll fluorescence ratio (CFR) at different excitation wave lengths proved to be a sensitive tool. As expected, the diversity of CFRdetermined pigment groups decreased with increasing acidification. Some groups were acid-insensitive and occurred even at pH below 3.0. However, picoplanktic cyanobacteria ( Synechoccocus/Synechocystis -like particles [SLPs]) were absent at pHs below 4.5-4.0, with their accompanying high metal concentrations. Thus, the reappearance of cyanobacterial picoplankton may serve as a first major restoration goal in strongly acidified lakes. Protein staining using fluorescein isothiocyanate enables fast estimates of phytoplankton biomass and establishment of biomass spectra as an estimate of the integrity of plankton communities. Although the phytoplankton investigations presented are only snapshots of the situation on the sampling days, the feasibility of flow cytometrical methods for preparing phytoplankton biomass spectra has been demonstrated. The completeness of such biomass spectra, such as the presence or absence of SLP-picoplankters, as well as the variances around regression lines (linear or parabolic), may serve as goals in restoring lakes acidified to different degrees.

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Section: Protein Staining and Photometrical Quantification Of Cellulamentioning

confidence: 99%

Section: Protein Staining and Photometrical Quantification Of Cellulamentioning

confidence: 99%

Deriving Restoration Goals for Acidified Lakes from Ataxonomic Phytoplankton Studies

Steinberg

Schäfer

Beisker

et al. 1998

Restoration Ecology

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“…However, these methods are restricted in taxonomical discrimination (Franqueira et al, 2000;Steinberg et al, 1996). Digital image analysis has a greater potential for discrimination, but shortcomings have limited our attempts of integrating this method into phytoplankton research until now (Bayerand et al, 2001;Gray et al, 2002;Rines, 1999;Walker, 1999;Walker et al, 1998Walker et al, , 2002.…”

Section: Introductionmentioning

confidence: 96%

Automatic analysis of aqueous specimens for phytoplankton structure recognition and population estimation

Rodenacker

Hense

Jütting

et al. 2006

Microsc. Res. Tech.

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KEY WORDSUtermöhl plankton chambers; digital image analysis; fluorescence analysis; quantification; image archiving system ABSTRACT An automatic microscope image acquisition, evaluation, and recognition system was developed for the analysis of Utermöhl plankton chambers in terms of taxonomic algae recognition. The system called PLASA (Plankton Structure Analysis) comprises (1) fully automatic archiving (optical fixation) of aqueous specimens as digital bright field and fluorescence images, (2) phytoplankton analysis and recognition, and (3) training facilities for new taxa. It enables characterization of aqueous specimens by their populations. The system is described in detail with emphasis on image analytical aspects. Plankton chambers are scanned by sizable grids, divers objective(s), and up to four fluorescence spectral bands. Acquisition positions are focused and digitized by a TV camera and archived on disk. The image data sets are evaluated by a large set of quantitative features. Automatic classifications for a number of organisms are developed and embedded in the program. Interactive programs for the design of training sets were additionally implemented. A long-term sampling period of 23 weeks from two ponds at two different locations each was performed to generate a reliable data set for training and testing purposes. These data were used to present this system's results for phytoplankton structure characterization. PLASA represents an automatic system, comprising all steps from specimen processing to algae identification up to species level and quantification.

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“…plants exist within the clade of the green algae). Identification of functional groups by SFCM is possible because groups differ in their autofluorescent photosynthetic pigments [ 24 ], leading to differences in cell absorption and emission profiles [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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Scanning flow cytometry (SFCM) is characterized by the measurement of time-resolved pulses of fluorescence and scattering, enabling the high-throughput quantification of phytoplankton morphology and pigmentation. Quantifying variation at the single cell and colony level improves our ability to understand dynamics in natural communities. Automated high-frequency monitoring of these communities is presently limited by the absence of repeatable, rapid protocols to analyse SFCM datasets, where images of individual particles are not available. Here we demonstrate a repeatable, semi-automated method to (1) rapidly clean SFCM data from a phytoplankton community by removing signals that do not belong to live phytoplankton cells, (2) classify individual cells into trait clusters that correspond to functional groups, and (3) quantify the biovolumes of individual cells, the total biovolume of the whole community and the total biovolumes of the major functional groups. Our method involves the development of training datasets using lab cultures, the use of an unsupervised clustering algorithm to identify trait clusters, and machine learning tools (random forests) to (1) evaluate variable importance, (2) classify data points, and (3) estimate biovolumes of individual cells. We provide example datasets and R code for our analytical approach that can be adapted for analysis of datasets from other flow cytometers or scanning flow cytometers.

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Ataxonomic Assessment of Phytoplankton Integrity by Means of Flow Cytometry

Cited by 8 publications

References 28 publications

Deriving Restoration Goals for Acidified Lakes from Ataxonomic Phytoplankton Studies

Deriving Restoration Goals for Acidified Lakes from Ataxonomic Phytoplankton Studies

Automatic analysis of aqueous specimens for phytoplankton structure recognition and population estimation

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

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