Gating can be one of the more time-consuming parts of cytometry analysis. When gate positions need to be changed for every sample in an experiment, it can take orders of magnitude longer.
Two automatic tools built into CellEngine, CellCarta’s cytometry analysis platform, can be leveraged to increase speed and efficiency:
- Automatic gate tailoring uses a machine learning algorithm to adjust gate placement. After defining a population hierarchy, select one or more gates to be adjusted to fit each sample’s data automatically. A set of manually gated reference files provide a model for the algorithm to position gates in the rest of the data. When faced with datasets with high variability, automatic gate tailoring is one of the most powerful tools to expedite analysis.
- Automatic percentile gating precisely moves a gate vertex or edge to a specified percentile of one or more control files. For example, you can move a range gate to the 99th percentile of an unstimulated or isotype control. This approach facilitates comparative analysis of samples; for example, when comparing normalized outcomes donor-by-donor.
In order to streamline the gating process most efficiently, there are several important points to remember when using these tools.
1 – Understanding Automatic Gating Tools in Flow Cytometry with CellEngine
Experimental design is a fundamental factor for successful cytometry analysis. Autogating in CellEngine streamlines the analysis process, but the best results come from well-designed trials and high-quality data.
Taking time to design clear objectives, include appropriate controls, and optimize experimental conditions ensures that the results from autogating are meaningful and useful.
For example, including an unstimulated control in an intracellular cytokine or phospho-flow panel provides an ideal control for using percentile gating.
Picking dyes that provide good separation of populations improves automatic gate tailoring’s performance. By creating a well-designed foundation, researchers can extract meaningful insights from complex datasets.
2 – Quality control is indispensable for Flow Cytometry Analysis
One of the most important aspects of high-quality analysis is high-quality data. To generate high-quality data, build quality control processes into your workflows to ensure reliability and accuracy, and identify issues promptly.
For example, verify staining via positive controls, use beads to calibrate and verify cytometer performance, and reduce technical variability as much as possible.
This maximizes the potential from each dataset, whether you use automatic tools in analysis or not.
3 – How to Implement Automatic Gating in CellEngine
Automatic gate tailoring uses one or more files as a reference for setting gate positions. Training data must be representative of the overall dataset, and carefully gated for best results.
An experiment with different cell types, disease states, or experimental conditions should include examples of all of those in the training data. This enhances the algorithm’s ability to properly adjust gates.
Consistent choices when gating the training data will also reduce variability.
Combining meticulous experiment design, trustworthy data, and carefully curated training data with automated tools increases efficiency and accuracy.
By incorporating these best practices, researchers can harness the full power of autogating tools to optimize their workflows.
If you’re interested in trying autogating, you can sign up for a free two-month trial of CellEngine at https://cellengine.com/.
About the author:
Anita Ray is a technical application specialist with CellEngine. She has ten years of experience in cytometry and worked in translational immuno-oncology prior to joining the CellEngine team.
