This OMIP-034 (mass cytometry) comprehensively characterizes live human peripheral blood leukocytes from fresh, erythrocyte-depleted whole blood with a single measurement. Different from existing OMIP, it relies on mass cytometry rather than conventional, fluorescence cytometry and thereby combines 26 different markers in a single staining cocktail. The panel has been optimized with respect to marker selection, antibody clones used, pairing of reporter metal, and antibody on the background of isotope mass-dependent machine sensitivity and antigen abundance, avoiding background signals, and signal spill-over. The panel is designed for monitoring patients' leukocytes during immunomodulatory clinical studies in the field of chronic inflammatory, especially autoimmune diseases, but is likely to serve well in different settings, too. This OMIP-034 captures neutrophils, eosinophils, basophils, monocytes, dendritic cells, T and B lymphocytes and NK cells, and their subsets, and contains a selection of cell activation markers (Table 2). It permits leukocyte analyses in their original complexity without influences from density gradient centrifugation or cryopreservation (Table 1). Blank channels were included for the extension of the panel with up to eight markers of interest. The entire protocol takes 2 days, with 60- to 90-min acquisition time to generate data of ∼5 × 105 cell events. Monitoring of peripheral blood leukocytes (or white blood cells, WBC) is an important tool in immune diagnostics and biomarker screening, promising a better understanding of immune pathogenesis and pathology, and the identification of new treatment targets 1, 2. In autoimmune diseases, WBC are important mediators of autoreactivity and inflammation. At the same time, they also can serve as peripheral biosensors of inflammation, after they have been characteristically imprinted in the inflamed tissue. Thus, several WBC-based phenomena are associated with e.g. SLE, such as the presence of autoreactive T and B lymphocytes, over-activated monocytes as reflected by Siglec-1 expression 3 or cytokine production 4, abnormal NET-forming neutrophils 5, the expansion of Ab-secreting plasmablasts 6, 7, and general lymphopenia, to name some. Thus, WBC are proven to contain important information about the patient's illness 8, 9. Lately, new treatments have been developed for rheumatic diseases. These include blockade of proinflammatory TNF-α, IL-1, BAFF, or IL-6, IL-17, depletion of B cells, or blockade of costimulation, by chimeric or humanized monoclonal antibodies (mAb), along with “small molecule” signal transducer and proteasome inhibitors 10-12. Despite the fact that many of these treatments are specifically targeted, the exact modes of action leading to disease amelioration are not well understood. In many studies, about one-third of the treated patients does not sufficiently benefit from therapy, illustrating the need for personalized therapy concepts. Because WBC are directly involved in autoimmunity and inflammation, we and others 2, 13 consider qualitative and quantitative changes in their composition as promising candidates for potential biomarkers. Mass cytometry has been developed to analyze single cells according to their phenotype using routinely >35 parameters or prospectively 50–60 marker in a single panel 14-20. It relies on an immunoassay utilizing metal isotope-conjugated reporter Ab, which are detected by inductively coupled plasma mass spectrometry (ICP-MS). This approach facilitates the identification and characterization of hitherto undefined cellular entities, which might have gone unnoticed in previous immunophenotyping or clinical studies 21. Additional features such as cellular barcoding 17, 22, 23, dead cell exclusion 24, algorithms for data normalization 25 and analysis 26-28 help to make mass cytometry an increasingly standardized tool. The design of multiparametric panels for mass cytometry is complex and challenging but technically feasible when considering some priniciples similar to the flow cytometry approach. Besides the availability of preconjugated antibodies and consideration of economic aspects, the high, low, or bimodal expression of an antigen needs to match the sensitivity of the mass channel in the first place 29. Spill over issues have to be taken into account when pairing metal-isotpes with antibodies. Such signals can be minimized by not placing a low expressed marker into the channel with one mass unit higher than the highly expressed marker. Additional signals of oxides in channels 16 mass units higher than the reporter metal can preferentially be detected from metal ions with a higher oxygen affinity. Utilizing recommended buffers and reagents for mass cytometry and a careful sample preparation lead to low background signals and optimal results. This OMIP quantifies and characterizes major WBC subtypes. Extremely rare subsets such as stem cells or type 2 mDC could be detected as well, but require inclusion of additional markers (e.g., CD34), greater starting cell numbers and longer acquisition times, for solid statistics or even pre-enrichment strategies. With the development of more cell-type specific mass cytometry panels, we envisage the standardized, routine application of mass cytometry in clinical studies in the area of chronic inflammation. Our mass cytometric panel covers a wide range of cell surface markers allowing the identification of all major leukocyte subsets as demonstrated in Figure 1. This panel is partially overlapping with OMIP-003 30, −004 31, −006 32, −007 33, −010 34, −013 35, −015 36, −017 37, −023 38, and −024 39 that utilize fluorochrome-tagged Ab and contain fewer markers, which often focus on specific subsets of leukocytes such as T cells, B cells, or NK cells. Analysis of OMIP-034 by manual gating. Leukocytes were stained with the 27 antibodies and analyzed by mass cytometry. First gates were successively applied to discriminate single, live cells from cell debris, cell aggregates and dead cells (plot 1–3), as before 14. Four-element beads added to samples prior to data acquisition for later normalization 25 were excluded according to their unique 140Ce signal (plot 4). Total leukocytes (L) were gated based on CD45 expression (plot 5). CD15+CD16+ neutrophilic granulocytes (N) were gated from total CD45+ cells (plot 6). Eosinophilic granulocytes (E) were identified as CD16-Siglec8+ cells after excluding neutrophilic granulocytes (plot 6–7). Basophilic granulocytes (Baso) were identified among CD45+CD3-HLA-DR- cells as CD123++CD38++ cells (plot 8–9). CD45+CD19+CD20+ B cells (B) were identified (plot 10) and further subclassified into CD27-IgD+ naïve, CD27+IgD+, CD27+IgD−, and CD27−IgD− memory B cell subsets (plot 11), respectively. Plasmablasts/plasma cells (P) were selected from the CD45+CD3−CD36− population (plot 12), after exclusion of neutrophilic granulocytes (plot 13) according to the co-expression of high levels of CD38 and CD27 (plot 14). The two major subsets of CD56+CD16+ and CD56++CD16+/− natural killer cells (NK) were determined within the CD45+CD20−CD3−CD56+HLA-DR− population (plot 15–17). Monocytes (M) were selected from CD45+CD20−CD3−CD36+CD15−HLA-DR+CD123−CD11c+ cells, after exclusion of CD1c+CD11c+ mDC (plot 20) and split into CD14++CD16+/− inflammatory and CD14+/−CD16+resident subsets (plot 18–21). Dendritic cells were selected from the CD45+CD19−CD20−CD3−CD14−HLA-DR+ population (plot 10, 24–26) and were further divided into CD303+CD123+CD1c−CD11c− pDCs (plot 26–27) and CD303−CD123−CD1c+CD11c+ mDCs (plot 26,28). Type I interferon imprinting was studied by measuring CD169 expression by monocytes and DC (plot 22,23,29,30). The histograms show an overlay of CD169 signal from a normal donor (red curve) and an active systemic lupus erythematosus patient (blue curve). CD45+CD16+/−CD3+ T cells (T) (plot 31) were divided into CD4+, CD8+ and CD4+CD8+ “double-positive” and CD4−CD8− “double-negative” T cells 32. On either CD4+ or CD8+ cells, CD45RA+CD45RO- naïve and terminal differentiated effector, and CD45RA-CD45RO+ central and effector memory T cells were gated (plot 33, 37). For an in-depth analysis, CD45RA was plotted against CD197 to allow discrimination of CD45RA+CD197+ naïve, CD45RA−CD197+ central memory and CD45RA−CD197− effector memory and CD45RA+CD197−terminal differentiated effector T cells (plot 34, 38). CD4+CD25+CD127low regulatory T cells (Tregs) were gated from CD4+ T cells (plot 35). Among T cells a subpopulation of CD8+CD56+ cells was defined (plot 39). Expression of CD69 and CD25 were used to identify activated/resident CD8+ and CD4+ T cells (plot 36,40). The authors thank Axel R. Schulz for fruitful discussions. Additional Supporting Information may be found in the online version of this article. Supporting Information Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. 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