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Updated: Nov 4, 2025
Discrimination of Seven Immune Cell Subsets by Two-fluorochrome Flow Cytometry
Published on: March 5, 2019
A user's guide to multicolor flow cytometry panels for comprehensive immune profiling
Staffan Holmberg-Thyden1, Kirsten Grønbæk2, Anne Ortved Gang3
1Dept. of Hematology, Copenhagen University Hospital, Rigshospitalet, Denmark; T-cells and Cancer, Experimental & Translational Immunology (XTI), Health Technology, Technical University of Denmark, Denmark.
Insights
This study introduces a systematic workflow to standardize multicolor flow cytometry, reducing errors and improving immune system analysis. Unsupervised clustering aids in identifying critical cell subpopulations in hematological cancer patients.
Area of Science:
- Immunology
- Biotechnology
- Data Science
Background:
- Multicolor flow cytometry is vital for immune system research, yielding multiparametric data from patient samples.
- Instrument variability and analytical errors complicate flow cytometry, impacting data reliability.
- Standardization is needed to address performance variations in flow cytometry instruments.
Purpose of the Study:
- To present a systematic workflow for optimizing multicolor flow cytometry panels for specific equipment.
- To improve the accuracy and reproducibility of flow cytometry analysis in health and disease.
- To identify significant cell subpopulations using advanced data analysis techniques.
Main Methods:
- Developed a systematic workflow for pairing colors to markers, optimized for specific flow cytometry instruments.
- Designed four comprehensive flow cytometry panels for hematological cancer patient samples.
- Incorporated quality control, antibody titration, compensation, and cell staining protocols.
- Applied unsupervised clustering techniques to analyze large datasets from multicolor flow cytometry.
Main Results:
- The workflow demonstrated effective optimization of flow cytometry panels.
- Standardized protocols addressed common analytical errors and instrument variations.
- Unsupervised clustering identified novel cell subpopulations missed by conventional gating.
- The approach was exemplified with panels for hematological cancer research.
Conclusions:
- The presented workflow enhances the reliability and accuracy of multicolor flow cytometry.
- Systematic panel design and advanced data analysis overcome key limitations in the field.
- This method facilitates deeper insights into the immune system, particularly in complex diseases like hematological cancers.
Abstract:
Multicolor flow cytometry is an essential tool for studying the immune system in health and disease, allowing users to extract longitudinal multiparametric data from patient samples. The process is complicated by substantial variation in performance between each flow cytometry instrument, and analytical errors are therefore common. Here, we present an approach to overcome such limitations by applying a systematic workflow for pairing colors to markers optimized for the equipment intended to run the experiments. The workflow is exemplified by the design of four comprehensive flow cytometry panels for patients with hematological cancer. Methods for quality control, titration of antibodies, compensation, and staining of cells for obtaining optimal results are also addressed. Finally, to handle the large amounts of data generated by multicolor flow cytometry, unsupervised clustering techniques are used to identify significant subpopulations not detected by conventional sequential gating.
