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. 2020 Dec 18;105(5):404-410.
doi: 10.1002/cyto.a.24288. Online ahead of print.

Development of a 43 color panel for the characterization of conventional and unconventional T-cell subsets, B cells, NK cells, monocytes, dendritic cells, and innate lymphoid cells using spectral flow cytometry

Affiliations

Development of a 43 color panel for the characterization of conventional and unconventional T-cell subsets, B cells, NK cells, monocytes, dendritic cells, and innate lymphoid cells using spectral flow cytometry

Fairooz Sahir et al. Cytometry A. .

Expression of concern in

Abstract

Although many flow cytometers can analyze 30-50 parameters, it is still challenging to develop a 40+ color panel for the phenotyping of immune cells using fluorochrome conjugated antibodies due to limitations in the availability of spectrally unique fluorochromes that can be excited by the commonly used laser lines (UV, Violet, Blue, Green/Yellow-green, and Red). Spectral flowcytometry is capable of differentiating fluorochromes with significant overlap in the emission spectra, enabling the use of spectrally similar fluorochrome pairs such as Brilliant Blue 515 and FITC in a single panel. We have developed a 43 color panel to characterize most of the immune subsets within the peripheral immune system, including conventional T cells, unconventional T cells such as invariant natural killer T cells (iNKT), Gamma delta (γδ) T-cell subsets (TCR Vδ2, TCR Vγ9) and mucosal-associated invariant T cells (MAIT), B-cell subsets, natural killer (NK) cells, plasmacytoid dendritic cells, dendritic cell subsets, hematopoietic progenitor cells, basophils, and innate lymphoid cell (ILC) subsets (CD117, CRTH2). The panel includes surface markers to analyze activation (CD38, HLA-DR, ICOS/CD278), differentiation (CD45RA, CD27, CD28, CD57), expression of cytokine and chemokine receptors (CD25, CD127, CCR10, CCR6, CCR4, CXCR3, CXCR5, CRTH2/CD294), and co-inhibitory molecules and exhaustion (PD-1, CD223/LAG-3, TIGIT), which enables a deep characterization of PBMCs from peripheral blood. Cells were analyzed on a 5-laser Cytek Aurora and data analysis was done using FlowJo. This panel can help to make a thorough interpretation of immune system, specifically when specimen quantity is low. The panel has not been completely optimized but would rather act as a guide toward the development of a workflow for optimized multicolor immunophenotyping panel.

Keywords: dendritic cells; immunophenotyping; innate lymphoid cells; spectral flow cytometry; unconventional T cells.

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Conflict of interest statement

The authors declare that there are no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Manual gating strategy for the identification of 131 immune subsets from PBMCs. Live, single cells from seven technical replicates were concatenated to a single FCS file before gating. Bregs, regulatory B cells; CM, central memory; DC, dendritic cells; DP, double positive; EM, effector memory; EMRA, effector memory re‐expressing RA; ILC, innate lymphoid cells; Neg, negative; Pos, positive; Tregs, regulatory T cells; pDC, plasmacytoid dendritic cells; SP, single positive [Color figure can be viewed at wileyonlinelibrary.com]

References

    1. Rodriguez L, Pekkarinen PT, Lakshmikanth T, Tan Z, Consiglio CR, Pou C, et al. Systems‐level Immunomonitoring from acute to recovery phase of severe COVID‐19. Cell Rep Med. 2020;1(5):100078. - PMC - PubMed
    1. Park LM, Lannigan J, Jaimes MC. OMIP‐069: forty‐color full Spectrum flow cytometry panel for deep Immunophenotyping of major cell subsets in human peripheral blood. Cytom A. 2020;97(10):1044–1051. - PMC - PubMed
    1. Monaco G, Chen H, Poidinger M, Chen J, de Magalhaes J, Larbi A. flowAI: automatic and interactive anomaly discerning tools for flow cytometry data. Bioinformatics. 2016;32(16):2473–2480. - PubMed
    1. McInnes L, Healy J, Melville J. UMAP: uniform manifold approximation and projection for dimension reduction. arXiv. 2018;1802:03426.
    1. Belkina AC, Ciccolella CO, Anno R, Halpert R, Spidlen J, Snyder‐Cappione JE. Automated optimized parameters for T‐distributed stochastic neighbor embedding improve visualization and analysis of large datasets. Nat Commun. 2019;10:5415. - PMC - PubMed

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