Functional analysis and classification of phytoplankton based on data from an automated flow cytometer

Authors

  • Anthony Malkassian,

    Corresponding author
    1. Universite de la Mediterranee Aix-Marseille II, Laboratoire de Microbiologie, de Geochimie et d'Ecologie Marines, UMR 6117 CNRS - Observatoire des Sciences de l'Univers (OSU), Centre d'Oceanologie de Marseille, France
    • Laboratoire de Microbiologie, de Geochimie et d'Ecologie Marines, Universite de la Mediterranee Aix-Marseille II, UMR 6117 CNRS - Observatoire des Sciences de l'Univers (OSU), Centre d'Oceanologie de Marseille, France
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  • David Nerini,

    1. Universite de la Mediterranee Aix-Marseille II, Laboratoire de Microbiologie, de Geochimie et d'Ecologie Marines, UMR 6117 CNRS - Observatoire des Sciences de l'Univers (OSU), Centre d'Oceanologie de Marseille, France
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  • Mark A. van Dijk,

    1. Netherlands Institute of Ecology (NIOO-KNAW), Department of Microbial Ecology, Rijksstraatweg 6, 3631 AC Nieuwersluis, The Netherlands
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  • Melilotus Thyssen,

    1. Laboratoire d'Oceanologie et de Geosciences UMR 8187 Maison de la Recherche en Environnements Naturels Avenue Foch 62930 Wimereux, France
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  • Claude Mante,

    1. Universite de la Mediterranee Aix-Marseille II, Laboratoire de Microbiologie, de Geochimie et d'Ecologie Marines, UMR 6117 CNRS - Observatoire des Sciences de l'Univers (OSU), Centre d'Oceanologie de Marseille, France
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  • Gerald Gregori

    1. Universite de la Mediterranee Aix-Marseille II, Laboratoire de Microbiologie, de Geochimie et d'Ecologie Marines, UMR 6117 CNRS - Observatoire des Sciences de l'Univers (OSU), Centre d'Oceanologie de Marseille, France
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Abstract

Analytical flow cytometry (FCM) is well suited for the analysis of phytoplankton communities in fresh and sea waters. The measurement of light scatter and autofluorescence properties of particles by FCM provides optical fingerprints, which enables different phytoplankton groups to be separated. A submersible version of the CytoSense flow cytometer (the CytoSub) has been designed for in situ autonomous sampling and analysis, making it possible to monitor phytoplankton at a short temporal scale and obtain accurate information about its dynamics. For data analysis, a manual clustering is usually performed a posteriori: data are displayed on histograms and scatterplots, and group discrimination is made by drawing and combining regions (gating). The purpose of this study is to provide greater objectivity in the data analysis by applying a nonmanual and consistent method to automatically discriminate clusters of particles. In other words, we seek for partitioning methods based on the optical fingerprints of each particle. As the CytoSense is able to record the full pulse shape for each variable, it quickly generates a large and complex dataset to analyze. The shape, length, and area of each curve were chosen as descriptors for the analysis. To test the developed method, numerical experiments were performed on simulated curves. Then, the method was applied and validated on phytoplankton cultures data. Promising results have been obtained with a mixture of various species whose optical fingerprints overlapped considerably and could not be accurately separated using manual gating. © 2011 International Society for Advancement of Cytometry

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