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Mass cytometry has pioneered40-parameter single-cell analyses that allow for the characterization of complex cellular networks at unprecedented depth. Up to 135 parameters can be simultaneously detected, but limited availability of metal tags suitable for labeling of specific probes prevents optimal exploitation of the analytical capacity of mass cytometers. To this end, we here establish the application of elemental silver nanoparticles (AgNP) of different size for reporting cell surface antigens on human leukocytes in mass cytometry assays. The mass channels at 107 Da and 109 Da are uniquely occupied by silver isotopes and do not interfere with other mass cytometry reagents. Streptavidin-coated AgNP (SA-AgNP) facilitated distinct and specific detection of various antigens, such as CD8, CD244 and CD294 on peripheral blood leukocytes pre-incubated with respective biotinylated primary antibodies. Signal intensities elicited by 40 nm-sized AgNP allowed specific detection of the low abundance antigen CD25 on both, peripheral blood regulatory T cells and CD25

Multiparametric fluorescence and mass cytometry offers new perspectives to disclose and to monitor the high diversity of cell populations in the peripheral blood for biomarker research. While high-end cytometric devices are currently available to detect theoretically up to 120 individual parameters at the single cell level, software tools are needed to analyze these complex datasets automatically in acceptable time and without operator bias or knowledge. We developed an automated analysis pipeline, immunoClust, for uncompensated fluorescence and mass cytometry data, which consists of two parts. First, cell events of each sample are grouped into individual clusters. Subsequently, a classification algorithm assorts these cell event clusters into populations comparable between different samples. The clustering of cell events is designed for datasets with large event counts in high dimensions as a global unsupervised method, sensitive to identify rare cell types even when next to large populations. Both parts use model-based clustering with an iterative expectation maximization algorithm and the integrated classification likelihood to obtain the clusters. A detailed description of both algorithms is presented. Testing and validation was performed using 1) blood cell samples of defined composition that were depleted of particular cell subsets by magnetic cell sorting, 2) datasets of the FlowCAP III challenges to identify populations of rare cell types and 3) high-dimensional fluorescence and mass-cytometry datasets for comparison with conventional manual gating procedures. In conclusion, the immunoClust-algorithm is a promising tool to standardize and automate the analysis of high-dimensional cytometric datasets. As a prerequisite for interpretation of such data, it will support our efforts in developing immunological biomarkers for chronic inflammatory disorders and therapy recommendations in personalized medicine. immunoClust is implemented as an R-package and is provided as source code from www.bioconductor.org.