The ability to comprehensively explore the impact of bio-active molecules on human samples at the single-cell level can provide great insight for biomedical research. Mass cytometry enables quantitative single-cell analysis with deep dimensionality, but currently lacks high-throughput capability. Here we report a method termed mass-tag cellular barcoding (MCB) that increases mass cytometry throughput by sample multiplexing. 96-well format MCB was used to characterize human peripheral blood mononuclear cell (PBMC) signaling dynamics, cell-to-cell communication, the signaling variability between 8 donors, and to define the impact of 27 inhibitors on this system. For each compound, 14 phosphorylation sites were measured in 14 PBMC types, resulting in 18,816 quantified phosphorylation levels from each multiplexed sample. This high-dimensional systems-level inquiry allowed analysis across cell-type and signaling space, reclassified inhibitors, and revealed off-target effects. MCB enables high-content, high-throughput screening, with potential applications for drug discovery, pre-clinical testing, and mechanistic investigation of human disease.

We have performed an in-depth single-cell phenotypic characterization of high-grade serous ovarian cancer (HGSOC) by multiparametric mass cytometry (CyTOF). Using a CyTOF antibody panel to interrogate features of HGSOC biology, combined with unsupervised computational analysis, we identified noteworthy cell types co-occurring across the tumors. In addition to a dominant cell subset, each tumor harbored rarer cell phenotypes. One such group co-expressed E-cadherin and vimentin (EV), suggesting their potential role in epithelial mesenchymal transition, which was substantiated by pairwise correlation analyses. Furthermore, tumors from patients with poorer outcome had an increased frequency of another rare cell type that co-expressed vimentin, HE4, and cMyc. These poorer-outcome tumors also populated more cell phenotypes, as quantified by Simpson's diversity index. Thus, despite the recognized genomic complexity of the disease, the specific cell phenotypes uncovered here offer a focus for therapeutic intervention and disease monitoring.

Background Activation of Toll-Like Receptors (TLRs) induces inflammatory responses involved in immunity to pathogens and autoimmune pathogenesis, such as in Systemic Lupus Erythematosus (SLE). Although TLRs are differentially expressed across the immune system, a comprehensive analysis of how multiple immune cell subsets respond in a system-wide manner has previously not been described. Objective To characterize TLR activation across multiple immune cell subsets and individuals, with the goal of establishing a reference framework against which to compare pathological processes. Methods Peripheral whole blood samples were stimulated with TLR ligands, and analyzed by mass cytometry simultaneously for surface marker expression, activation states of intracellular signaling proteins, and cytokine production. We developed a novel data visualization tool to provide an integrated view of TLR signaling networks with single-cell resolution. We studied seventeen healthy volunteer donors and eight newly diagnosed untreated SLE patients. Results Our data revealed the diversity of TLR-induced responses within cell types, with TLR ligand specificity. Subsets of NK and T cells selectively induced NF-κB in response to TLR2 ligands. CD14hi monocytes exhibited the most polyfunctional cytokine expression patterns, with over 80 distinct cytokine combinations. Monocytic TLR-induced cytokine patterns were shared amongst a group of healthy donors, with minimal intra- and inter- individual variability. Furthermore, autoimmune disease altered baseline cytokine production, as newly diagnosed untreated SLE patients shared a distinct monocytic chemokine signature, despite clinical heterogeneity. Conclusion Mass cytometry analysis defined a systems-level reference framework for human TLR activation, which can be applied to study perturbations in inflammatory disease, such as SLE.