OMIP‐024: Pan‐leukocyte immunophenotypic characterization of PBMC subsets in human samples

This phenotyping panel was developed to measure the relative frequencies of multiple leukocyte cell subsets in peripheral blood mononuclear cells (PBMC) from African infants and children, including the expression of immune activation and differentiation markers. It was optimized with the objective of obtaining the maximum information concerning the immune status and cell subsets that could influence the immune response to vaccines and infectious diseases using small volumes of pediatric samples. It was developed using cryopreserved PBMC from healthy HIV-uninfected and HIV-infected US adults, but it has also been tested with cryopreserved PBMC from US infants. Although we have not tested the panel on whole blood, it is likely that the panel could be used with whole blood following minimal optimization. Pediatric samples are often limited to small volumes of blood (typically 5 mL or less) and require high-level multicolor analyses to take full advantage of them. An 18-color panel was developed for phenotyping cryopreserved PBMC from African infants and children participating in a vaccine trial 1, 2. These valuable samples with very limited amount of cells are devoted primarily to the assessment of the antigen-specific cells induced by the vaccine. However, when there are additional cells, it is of interest to characterize the PBMC populations with a phenotyping panel (Table 1). Cell frequencies and differentiation/activation of particular cell subsets such as monocytes, NK cells, or T- and B-cell subsets may influence the immunogenicity of the vaccine and its efficacy 3, 4. These cell subsets, and additionally γδ T cells, may contribute to the mechanisms of vaccine induced protection 5-7 as well as the response to infectious diseases such as malaria 8-10. This is of special relevance in African settings where infants and children are continuously exposed to infections and unfavorable conditions for the immune system, such as malnutrition. In our case, comprehensive immunophenotypic characterization may shed light on the variability in efficacy of the RTS,S malaria vaccine candidate observed in clinical trials, included the phase III trial under study 1, 2. The panel examines the frequency of monocytes, B cells, plasmablasts, CD4+ and CD8+ T cells, regulatory T cells (Tregs), γδ T cells, NK T-like cells, NK cells, and dendritic cells, as well as memory and activation markers (Table 2). After viability (AViD) staining for the exclusion of dead cells, CD3, CD4, and CD8 markers were included for the gating of T cells, CD19 for B cells, and CD14 and CD16 for monocytes. NK cells and γδ T cells are of special interest for malaria immunology and vaccinology. Therefore, we prioritized the inclusion of CD56 (neural cell adhesion molecule NCAM) and CD16 (Fcγ-receptor IIIa) for defining five different NK cell subsets: the two more common subsets CD56dimCD16+ and CD56hiCD16+, and the unconventional CD56dimCD16−, CD56hiCD16+, and CD56-CD16+ subsets. Of importance, these subsets have been described to have different phenotypic, functional, and maturation profiles and certain diseases have been associated with its redistribution and expansion 11, 12. For the identification of γδ T cells, pan-γδ TCR and Vδ2 TCR markers were included to define the γδ T cells as Vδ2+ and Vδ2− (the latter are likely mainly Vδ1+, as Vδ1 and Vδ2 are the dominant Vδ genes in humans) 13. The inclusion of CD56 permitted also the identification NK T-like cells defined as CD56+CD3+. Due to the limitation on the number of colors available in the panel, only a few NK receptors could be included. CD57 was selected because it can also be used as a differentiation marker for T cells, and we also added the activating receptor NKG2C. Cytomegalovirus (CMV) infection has been shown to expand NKG2C+ cells and increase the expression of NKG2C on these cells 14, although the consequences on the immune response to vaccines or other infections are yet to be described. In addition to designing a panel that embraces the maximum of cell subsets, the panel had to be logistically feasible and avoid experimental complexity since it had to be performed in parallel to other flow cytometry assays using the same PBMC samples. Therefore, any intracellular markers were excluded to avoid the fixation and permeabilization steps necessary for the intracellular staining. For instance, to gate Tregs we used CD25 (IL-2Rα-chain) and CD127 (IL-7Rα-chain) markers without the inclusion of FoxP3, which requires intracellular staining. Previous studies proved that CD25highCD127− CD4+ T cells are a good correlate of Tregs 15-17, although this identification strategy may over- or underestimate its frequency. According to literature, about 13% of the CD25highCD127− CD4+ T cells may be FoxP3− 15-18. The memory and differentiation markers CD45RA and CCR7 were added to distinguish naïve (CD45RA+CCR7+), central memory (CD45RA−CCR7+), effector memory (CD45RA−CCR7−), and terminal effector memory (CD45RA+CCR7−) subsets 19-21. These cell subsets can be further characterized by the expression of two additional markers already present in the panel: the IL-7 receptor CD127, involved in homeostatic proliferation and in the survival of memory T cell precursors 20, 22; and CD57, indicative of cell senescence, failure to proliferate, and susceptibility to activation-induced cell death 23, 24, but also a correlate of high cytolytic potential 25. Initially, we considered adding CD28 instead of CD57, but they are mutually exclusive in the different memory subsets and CD57 is more relevant for NK cells in infectious diseases 26 and vaccine effector responses 27. CD38 and HLA-DR were included as cell activation markers. Differential expression of these two T-cell activation markers identifies subsets correlating with disease prognosis, particularly in HIV-1 infection 28. Another set of activation markers of interest was Bcl2 and Ki67, but we excluded them because they require intracellular staining. Additionally, CD38 staining allows the identification of plasmablast cells, which are defined as CD19+ with a high CD38 expression 21. Finally, negative gating using all the lineage markers together with HLA-DR staining allows measurement of dendritic cells as lineage—HLA-DR+ cells 21. The gating strategy for evaluating all these subsets with the developed panel is displayed in Figure 1. Additional information about the panel development and gating strategy is explained in the Supplementary materials. Our panel includes a wide range of markers to identify several human PBMC subsets and therefore covers a number of cell types and cell markers addressed individually by different OMIP panels: OMIP-007 29 since they examine NK cells, OMIP-013 30 because it examines differentiation of T cells, OMIP-015 18 due to the identification of Tregs and activated T cells without intracellular staining, and OMIP-019 31, OMIP-020 32, and OMIP-021 33 since all three comprise a characterization of γδ T cells, and additionally OMIP-019 and OMIP-021 include enumeration of NK T cells. Example of the staining and gating strategy. Cryopreserved PBMC from an HIV-infected and treated individual were stained with the complete panel. All gates were defined using fluorescence minus one (FMO) controls. A: Initial gating is done on FSC-H and FSC-A to discriminate singlets, followed by the exclusion of events collected during a period of time early in collection when fluctuations may occur. Dead cells are excluded by an amine reactive dye. Monocytes (classical CD14+CD16−, intermediate CD14+CD16+, and non-classical CD16+CD14dim) are identified as HLA-DR+ cells and subsequently by CD14 and CD16 staining. B: Lymphocytes are gated using FSC-A and SSC-A from a Boolean gate of live cells and not monocytes. Subsequent gating discriminates CD3+ cells, followed by identification of Vδ2+ and Vδ2− γδ T cells and CD4+ and CD8+ T cells. Within CD4+ T cells, Tregs are CD127− while expressing high levels of CD25. Within CD3− cells, five NK cell subsets are discriminated based on CD56 and CD16 expression (CD56hiCD16−, CD56dimCD16−, CD56hiCD16+, CD56dimCD16+, and CD56−CD16+) and B cells are identified as CD19+HLA-DR+. Within B cells, plasmablasts are discriminated by a high CD38 expression. Finally, using CD3 and CD56, NK T-like cells are identified from the lymphocyte gate. Of note, γδ T cells, Tregs, and NK T-like cells are not excluded from classical T cells and therefore are overlapping populations. C: After negative gating on all lineage markers (shown in Supporting Information), dendritic cells are identified by HLA-DR high expression. D: The expression level of CCR7, CD45RA, CD57, and CD127 is examined within CD4+ and CD8+ T-cell subsets as gated in (B) to provide insight into memory and differentiation of these cell subsets; HLA-DR and CD38 are evaluated in the same subsets to assess activation. Boolean gates are later created based on the gates shown to identify cells expressing various combinations of markers. The same gating strategy is applied for other cell subsets (shown in Supporting Information). E: Variable staining of CD57 and NKG2C in NK T-like cells and the different NK cell subsets to show different functional profiles (example gating on the CD56hiCD16−, CD56dimCD16+, and CD56−CD16+ cells). The authors thank Min Xu from Seattle Children's Hospital for kindly providing de-identified infant samples. The authors also wish to thank all the individuals enrolled in the Seattle Assay Control cohort, from which PBMC were used for optimization and testing of the panel. The authors acknowledge HVTN technicians Nathaniel Chartrand and Paul Newling for their help when the flow cytometer presented problems, and Stephen Voght for help with editing. Authors thank James B. Pendleton Charitable Trust for their generous equipment donation. They also thank the MAL067 Vaccine Immunology Workgroup (VIW) for their support for developing the panel for the pediatric phase III RTS,S immune correlates study, and particularly, Claudia Daubenberger for her inputs in the design of this panel. Additional Supporting Information may be found in the online version of this article. Supplementary Information Supplementary Information Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.