Best Practices for Optimization of Phytoplankton Analysis in Natural Waters Using CytoSense Flow Cytometers
Year: 2025
Type: article
Abstract: The use of flow cytometry to investigate phytoplankton functional groups is rapidly expanding worldwide, using lab- or ship-based instruments or autonomous environmental monitoring platforms. Automation, coupled with greater autonomy, allows for higher spatial and temporal resolution of phytoplankton groups, enhancing understanding of their dynamics and patterns, generating large datasets. The level of resolution is determined by both instrumental capabilities and optimization of its acquisition settings. Sharing these datasets with the scientific community, whether to improve global phytoplankton distribution resolution or facilitate the intercomparison of environmental indicators among monitoring laboratories, strongly relies on quality-controlled instruments and standardized data acquisition and analysis. This article focuses on CytoSense-type (CytoBuoy, NL) flow cytometers, which operate by recording the optical pulse shapes of particles as they pass through a laser beam. Different configurations such as laser wavelength and power, sheath fluid management, sample inlet design, and dataset output format were not considered, in order to focus on optimization and protocol standardization to resolve the whole phytoplankton size spectrum, from the smallest autofluorescing prokaryotes to colonies and chain-forming species. In this study, coincidence, PMT voltage, trigger threshold optimization, and regular quality control procedures are considered and discussed, using datasets from three types of instruments and two contrasted marine coastal waters as case studies. The primary goal of this study is to establish a framework to guide and support the exploration and application of this type of flow cytometer, ultimately achieving a reliable and optimal resolution for sample acquisition of natural waters.
Source: Cytometry Part A
DOI: 10.1002/cyto.a.24964
Authors: Clémentine Gallot, Zéline Hubert, Lumi Haraguchi, Hedy Aardema, Luis Felipe Artigas, Amel Bellaaj Zouari, Arnaud Cauvin, Raffaella Casotti, Véronique Créach, Georges Dubelaar, Alexandre Epinoux, Gérald Grégori, Oliver Grosso, Joanna Kolasinki, Harrie Kools, Rob Lievaart, Arnaud P. Louchart, Glaucia Moreira Fragoso, Maialen Palazot, Machteld Rijkeboer, Kévin Robache, Joseph Rolland, Thomas Rutten, Melilotus Thyssen
Institutions: Centre National de la Recherche Scientifique, Université de Toulon, Aix-Marseille Université, Institut Méditerranéen d’Océanologie, Institut de Recherche pour le Développement, Université de Lille, Laboratoire d’Océanologie et de Géosciences, Finnish Environment Institute, ETH Zurich, Max Planck Institute for Chemistry, Institut National des Sciences et Technologies de la Mer, Stazione Zoologica Anton Dohrn, Centre for Environment, Fisheries and Aquaculture Science, Ifremer, University of Reunion Island, Écologie Marine Tropicale des Océans Pacifique et Indien, Netherlands Institute of Ecology, Norwegian University of Science and Technology, Rijkswaterstaat
Language: English
Subfield: Oceanography
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,970
Funders: Aix-Marseille Université, Institut Français de Recherche pour l'Exploitation de la Mer, Université du Littoral Côte d'Opale, Flotte Océanographique Française, European Commission, Agence Nationale de la Recherche, Centre National de la Recherche Scientifique, Horizon 2020 Framework Programme
Awards: G1190173965, OBSERVING AND MAPPING MARINE ECOSYSTEMS – NEXT GENERATION TOOLS, G2759228925, G30865144, G3585693841, G434337789, Transdisciplinary graduate school for marIne, Fisheries and SEAfood sciences, G4937468798, Joint European Research Infrastructure of Coastal Observatories - Design Study, G6849128849, G714691507, G7288108391, G7527216246, G8411898346, Joint European Research Infrastructure of Coastal Observatories: Science, Service, Sustainability - JERICO-S3
OpenAlex ID: W4415239106
