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Illustration of automated flow cytometry data acquisition in natural waters
This dataset illustrates the Best Practices in flow cytometry for studying phytoplankton. The use of flow cytometry to collect datasets on phytoplankton functional groups is rapidly expanding worldwide, with deployments on scientific vessels, ships of opportunity, buoys, and autonomous environmental monitoring platforms. Automated flow cytometry for aquatic photosynthetic microbes enables precise quantification across the full-size range of phytoplankton while offering enhanced autonomy.
Sharing these datasets with the scientific community—whether to improve the resolution of global phytoplankton distribution or to facilitate the intercomparison of environmental indicators among monitoring laboratories—requires quality-controlled instruments and standardized data acquisition. Although flow cytometers all operate on the principle of recording the optical pulse shapes of particles passing through a laser beam, their configurations vary in terms of laser wavelength and power, sheath fluid management, sample inlet, and dataset output format.
The characterization of phytoplankton communities and their optical representation on cytograms has been standardized by experts, with resolution depending on the optimal configuration of the instrument in use. This dataset exemplifies the application of best practices for optimizing the settings of autonomous pulse shape-recording flow cytometers, such as those developed by CytoBuoy. We address key aspects such as particle counting limits, the coincidence phenomenon, trigger threshold optimization, and regular quality control procedures, illustrating these principles with datasets from two types of instruments. The primary goal is to establish a methodological framework that guides and supports the exploration and application of this type of flow cytometer, ultimately achieving reliable and optimal sample acquisition resolution.
Disciplines
Biological oceanography, Environment
Keywords
In Situ/Laboratory Instruments>Chemical Meters/Analyzers>FLOW CYTOMETRY, automated flow cytometry, EARTH SCIENCE SERVICES>DATA ANALYSIS, product quality assessment, quality control, interoperability of data, best practices, regular maintenance, trigger optimisation, coincidence risk
Devices
Two experiments were conducted, on two different CytoSense instruments:
- testing coincidence threshold
- testing trigger level optimisation
The instruments are of different technological advancement (2015 vs 2019). They were from different laboratories and thus analysed varied ecosystems (Eastern English Channel coastal waters-ECC vs Mediterranean coastal waters - MED).
Instruments: CytoSense CS-2015-68 and CytoSense CS-2019-93
CS-2015-68:
● Laser: 488 nm - 120 mW
● PMTs: FWS (left and right) SWS FLR ( > 652 nm) FLO (552 - 652 nm)
CS-2019-93:
● Laser: 488 nm - 60 mW
● PMTs: FWS (left and right) SWS FLR (668 - 726 nm) FLO (604 - 644 nm) FLY (553 - 577 nm)
Data
File | Size | Format | Processing | Access | |
---|---|---|---|---|---|
The readme.txt file describes the directory structure and the column headers of the files provided for the dataset. | 6 Ko | TEXT | Raw data | ||
File with listmode of each 2 µm polystyrene red fluorescent bead acquisition throughout year 2024. | 1 Mo | CSV | Quality controlled data | ||
Lists measured concentration (Real_concentration_ml) and the calculated theoretical concentration (Theoretical_concentration_ml) for the 2 µm beads used for the coincidence experiment made with CS-201 | 2 Ko | CSV | Quality controlled data | ||
In each trigger level folder (e.g. FLR5), there is a listmode file for each group (phytoplankton functional group or background signal). | 37 Mo | CSV | Quality controlled data |