Related Experiment Video
Updated: Jan 26, 2026
15:10
From Fast Fluorescence Imaging to Molecular Diffusion Law on Live Cell Membranes in a Commercial Microscope
Published on: October 9, 2014
11.9K
Performance comparison of four commercially available cytometers using fluorescent, polystyrene, submicron-scale
Hannah R Safford1, Heather N Bischel1
1Department of Civil and Environmental Engineering, University of California Davis, 2001 Ghausi Hall, 480 Bainer Hall Drive, 95616, Davis, CA, United States.
Data in Brief
|April 19, 2019
Summary
Flow cytometry instrument variability impacts data comparison. This study analyzed bead data across four cytometers to highlight differences and promote data sharing for improved water quality assessment.
Area of Science:
- Environmental Science
- Analytical Chemistry
- Biotechnology
Background:
- Accurate flow cytometric data comparison necessitates understanding instrument-to-instrument variability.
- Key variability sources include laser configuration, detector setup, sample handling, and voltage settings.
Purpose of the Study:
- To investigate and quantify variability in flow cytometric data acquisition across different instruments.
- To provide a dataset for enhancing the reliability and comparability of flow cytometry in water quality applications.
Main Methods:
- Prepared suspensions of three sizes (0.2, 0.5, 0.8 μm) of fluorescent polystyrene beads.
- Acquired data from these suspensions using four distinct flow cytometers with consistent settings.
- Generated complete Flow Cytometry Standard (FCS) files for subsequent analysis.
Main Results:
- Demonstrated significant variability in cytometric fingerprints due to differences in instrument configurations.
- Visualized data variations graphically, illustrating the impact of instrument settings on bead detection.
- Collected and archived raw FCS data from the comparative experiment.
Conclusions:
- Instrument variability is a critical factor in flow cytometry data interpretation.
- Recommends standardized data sharing practices for robust reporting and inter-instrument comparison.
- Highlights the potential for shared datasets to advance computational methods in flow cytometry for water quality analysis.
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