An Introduction to Automated Flow Cytometry Gating Tools and Their Implementation

Area of Science:

• Biotechnology
• Computational Biology
• Immunology

Background:

• Current flow cytometry (FCM) enables high-parameter cell analysis, generating vast datasets.
• Manual data processing via gating is time-consuming and prone to technical variability, especially in large-scale experiments.
• This variability can become prohibitive for achieving specific clinical or research objectives.

Purpose of the Study:

• To provide an overview of automated flow cytometry analysis methods.
• To demonstrate the implementation of FLOw Clustering without K as an accessible computational tool.
• To highlight the benefits of computational assistance for flow cytometry data analysis.

Main Methods:

• Overview of automated flow cytometry analysis techniques.
• Implementation and application of the FLOw Clustering without K algorithm.
• Comparison of automated analysis with traditional manual gating.

Main Results:

• Automated FCM analysis, particularly with tools like FLOw Clustering without K, significantly reduces analysis time.
• Computational methods demonstrate higher reproducibility compared to manual gating.
• Automated analysis can facilitate the discovery of previously unidentified cellular populations.

Conclusions:

• Automated flow cytometry analysis provides a powerful, efficient, and reproducible alternative to manual gating.
• Computational tools are accessible and beneficial for researchers handling large FCM datasets.
• Implementing computational assistance is recommended for large-scale flow cytometry experiments to enhance data interpretation and discovery.