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.
| Original language | English |
|---|---|
| Article number | 114210 |
| Journal | Analytical Biochemistry |
| Volume | 627 |
| Number of pages | 13 |
| ISSN | 0003-2697 |
| DOIs | |
| Publication status | Published - 2021 |
Bibliographical note
Funding Information:Financial support to this study was provided by the Danish Cancer Society , grant no. R72-A4531 and R146-A9531-16-S2 , and Herlev-Gentofte hospital research grant .
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Flow cytometry
- Immune monitoring
- Immunology
- Myelodysplastic syndrome
- Unsupervised clustering
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