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Discrimination of Seven Immune Cell Subsets by Two-fluorochrome Flow Cytometry
Published on: March 5, 2019
Comprehensive immune profiling of human peripheral blood mononuclear cells using two complementary spectral flow
Maartje H Rietdijk1, Leo C Kuhnen1, Marlous van den Braber1
1Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Molecular Cell Biology and Immunology, Cancer Center Amsterdam, Amsterdam institute for Immunology and Infectious diseases, Amsterdam, Netherlands.
Abstract:
Comprehensive immune profiling is essential for immunomonitoring studies aimed at identification of diagnostic and prognostic biomarkers. Peripheral blood mononuclear cells (PBMCs) undergo phenotypic and functional changes during disease, making them invaluable for the characterization of immune cell composition in both cross-sectional and longitudinal immune monitoring studies. We have developed a comprehensive immunophenotyping method based on two complementary panels of 60 unique markers to characterize B cells, T cells, innate lymphoid cells (ILCs), γδ T cells, mucosal-associated invariant T (MAIT) cells, natural killer (NK) cells, monocytes, dendritic cells (DCs) and several of their subsets, including their functional status, within human peripheral blood mononuclear cells (PBMCs). Dividing the markers over two complementary panels allows for inclusion of many more markers than currently allowed for single panels by state-of-the-art spectral flow cytometers, enabling the immunophenotyping of a broad spectrum of immune cell subsets at great analytical depth. This includes rare subsets and subsets that require an extensive combination of markers to be resolved, combined with the option of assessing differentiation, activation and exhaustion. The method enables the resolution of more than 50 distinct populations, and detailed exploration of differentiation, activation and exhaustion of these subsets, as is demonstrated here on PBMC samples from healthy donors, glioblastoma, inflammatory bowel disease and COVID-19 patients. The protocol supports both manual and unsupervised data analysis approaches and is suitable for large-scale immunomonitoring studies requiring standardized, reproducible multi-batch workflows.

