OMIP‐042: 21‐color flow cytometry to comprehensively immunophenotype major lymphocyte and myeloid subsets in human peripheral blood

This 21-color flow cytometry-based OMIP 1 enables simultaneous quantification of monocytes, basophils, granulocytes, dendritic cells, natural killer cells, B cells, and all well-defined T and T helper cell subsets in the human peripheral blood (Table 1). This panel captures the major phenotypes described in the NIH Human Immunology Project 2, 3 with additional markers for deep T cell analysis 4. We specifically designed this panel for analysis of peripheral blood from patients involved in our clinical trials of novel agents for the treatment of graft versus host disease (GVHD) after allogeneic hematopoietic stem cell transplantation (alloHSCT). We have optimized this panel for the analysis of 1 × 106 fresh or previously frozen peripheral blood mononuclear cells (PBMCs). We initially designed this panel for the analysis of the PBMCs from patients who have undergone alloHSCT, particularly those enrolled in drug studies for the prevention and treatment of GVHD. Prior studies in humans and animal models have implicated many immune cell types in the initiation and progression of GVHD, and the data have, at times, conflicted, depending on species, model, and individual laboratories. In particular, prior studies have identified imbalances in T regulatory cells (Tregs), T follicular helper (Tfh) cells, T helper type 1 (Th1), type 2 (Th2), type 17 (Th17), myeloid derived suppressor cells (MDSCs), natural killer cells (NKs), dendritic cells (DCs), and others in modulating engraftment, GVHD, and treatment responses 5, 6. Accordingly, we aimed to develop a standardized panel to capture all major human lymphoid and myeloid populations with deep T cell phenotyping in a single analysis, thus reducing experimental variability, redundancy, and the need for a high quantity of input cells (Table 2). As to the last point, post-HSCT patients typically have few circulating leukocytes until hematopoietic engraftment and reconstitution. Thus, multiple flow cytometry panels and/or CyTOF analyses pose a greater challenge than a single, comprehensive flow-based panel. Beyond our HSCT-focused studies, this panel should find broad application in the study of many inflammatory and neoplastic conditions. Of note, this panel uses antibodies targeting exclusively surface receptors, making fixation and permeabilization unnecessary. After gating on FSC/SSC, single, live cells (Fig. 1A), PBMCs broadly segregate into T cells (CD3 + CD20-), B cells (CD3-CD20+), and non-B/T cells (CD3-CD20-), the latter of which includes dendritic cells, natural killer cells, myeloid, and progenitor populations (Fig. 1B; full gating strategy Supporting Information Table S3). To further define non-lymphoid phenotypes described in the Human Immunology Project, we included CD14, CD16, HLADR, CD56, CD123, and CD11c surface markers. First, CD14 and HLADR distinguish monocytes (CD14 + HLADR±) and dendritic cells (CD14-HLADR+) from other granulocytes and NKs (CD14-HLADR-) (Fig. 1C, Non-B/T). Within this latter NK/granulocyte population, CD123 expression denotes basophils and CD56 identifies natural killer cells (Fig. 1C, NK/Granulocytes). NK cells further segregate into at least three populations according to CD56 and CD16 density (Fig. 1C, NKs) 7. Within the CD14-HLADR+ DC population, CD11c and CD123 distinguish plasmacytoid DCs and monocytic DCs (Fig. 1C, DCs). Finally, within the CD14+ monocyte population, CD16 and HLADR identify at least three populations: classical monocytes (HLADR + CD16-), non-classical monocytes (HLADR + CD16+), and a subset containing MDSCs (HLADR-CD16-) (Fig. 1C, Monocytes). Of note, further analyses of chemokine receptor expression can be performed on any non-B/T subset, which may have particular relevance in diseased states (data not shown). Example gating strategy for major immune cell subsets on stained PBMCs from healthy donors. [Color figure can be viewed at wileyonlinelibrary.com] Basic T cell markers include CD4 and CD8 (Fig. 1D, T cells). Next, a combination of cell surface markers, including multiple chemokine receptors, identifies T cell activation, T regulatory cells (Tregs), T cell memory status, and all major Th subsets 3, 4, 8, 9. Of note, HLADR and CD38 expression identifies T cell activation status within any subset 10, with an example shown for all CD4+ cells. Within the CD4+ T cell population, Tregs identify as CD25 + CD127-/lo, a population highly correlated with Tregs traditionally defined as FOXP3+ CD4+ 8, 11, 12. CD45RA and CCR7 further define CD4 and CD8 T cells into four major subsets: T effector cells (Teff; CD45RA + CCR7-), naïve T cells (Tnaive; CD45RA + CCR7+), T effector memory cells (Tem; CD45RA-CCR7-), and T central memory cells (Tcm; CD45RA-CCR7+; ungated) (Fig. 1D, second panel and Fig. 1E, first panel). Within the CD4+ T memory population (i.e., all cells that are CD20-CD3 + CD4 + CD45RA-), various chemokine receptors distinguish Th1, Th2, Th9, Th17, Th22, a subset containing T follicular helper cells (Tfh), and T GM-CSF-secreting (ThGM-CSF) cells 4. First, within the T memory population, CCR10 and CXCR5 expression identify the subset containing Tfh cells (CCR10-CXCR5+) (Fig. 1D, Tem and Tcm CD4 cells). Within the CCR10 +/− CXCR5- Th subset, Th9 cells can be identified as CCR6 + CCR4- (Fig. 1D, Th subset). Further gating on CCR6, CCR4, CXCR3, and CCR10 distinguishes the remaining Th subsets: Th1 (CXCR5-CCR6-CXCR3 + CCR10-), Th2 (CXCR5-CCR6-CXCR3-CCR10-), ThGM-CSF (CXCR5-CCR6-CXCR3-CCR10+), Th17 (CXCR5-CCR6 + CCR4 + CXCR3 +/− CCR10-), and Th22 (CXCR5-CCR6 + CCR4 + CXCR3-CCR10+) 3, 4 (Fig. 1D, Th22_Th17 and Th1_Th2_ThGM). Although further subsets of CD8+ T cells are not rigorously defined, high-dimensional analysis with t stochastic neighbor embedding (tSNE) revealed differences in normal human PBMCs according to chemokine and Fc receptor expression (Fig. 1E). In this example, tSNE discriminated distinct populations of CD8+ Tem cells, which on further examination, segregated according to CCR6, CCR4, and HLADR/CD38 expression. Interestingly, a single prior report has postulated this CD8+ CCR6+ Tem subset as a modulator of mucosal immunity 13, and another report identified CD8 + CCR4+ cells as potential mediators of synovial inflammation in rheumatoid arthritis 14. Thus, this high-color flow panel allows high-dimensional data visualization techniques to uncover unknown and/or poorly-defined cell types in both normal and diseased states. In summary, our 21-color panel provides a powerful tool for in-depth analysis of lymphoid and myeloid cells in the human peripheral blood with deep T cell analysis and coverage of most populations defined in the NIH's Human Immunology Project. Future panels could substitute certain T cell markers (e.g., CCR4, CXCR5, and CCR10) in favor of increased B cell discrimination (e.g., CD19, CD27, and IgD). Of note, by comparison to CyTOF, which can simultaneously detect 20–40 antigens, this panel requires fewer input cells, less acquisition time, and less money, while still permitting worthwhile high-dimensional analysis. PBMCs were obtained from healthy donors. The use of human tissue in this study was approved by the Institutional Review Board at Washington University in St. Louis. This panel builds on OMIPs −024, −015, and −030, which identify pan-leukocytes, T regulatory cells without intracellular staining, and all major T helper subsets, respectively. This single 21-color panel identifies the majority of subsets described in these three OMIPs, captures the major lymphoid and myeloid immunophenotypes defined in the NIH's Human Immunology Project 3, and uniquely allows for detailed chemokine receptor analysis on non-B/T cell subsets. The authors have no conflict of interest to declare. This work was supported by National Institutes of Health, National Cancer Institute grant P50 CA171963, National Institutes of Health, Loan Repayment Program (to KS), and the Dermatology Foundation (to KS). Additional Supporting Information may be found in the online version of this article. Supporting MIFlowCyt checklist Supporting Online Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. 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