Learning cell identity from single-cell data presently relies on human experts. Here, we present Marker Enrichment Modeling (MEM), an algorithm that objectively describes cells by quantifying contextual feature enrichment and reporting a human and machine-readable text label. MEM outperformed traditional metrics in describing immune and cancer cell subsets from fluorescence and mass cytometry. MEM provides a quantitative language to communicate characteristics of new and established cytotypes observed in complex tissues.

The flood of high-dimensional data resulting from mass cytometry experiments that measure more than 40 features of individual cells has stimulated creation of new single cell computational biology tools. These tools draw on advances in the field of machine learning to capture multi-parametric relationships and reveal cells that are easily overlooked in traditional analysis. Here, we introduce a workflow for high dimensional mass cytometry data that emphasizes unsupervised approaches and visualizes data in both single cell and population level views. This workflow includes three central components that are common across mass cytometry analysis approaches: 1) distinguishing initial populations, 2) revealing cell subsets, and 3) characterizing subset features. In the implementation described here, viSNE, SPADE, and heatmaps were used sequentially to comprehensively characterize and compare healthy and malignant human tissue samples. The use of multiple methods helps provide a comprehensive view of results, and the largely unsupervised workflow facilitates automation and helps researchers avoid missing cell populations with unusual or unexpected phenotypes. Together, these methods develop a framework for future machine learning of cell identity.

Key Points• hi , CD127 lo Tregs), expresses the interleukin-2 (IL-2)/STAT5 pathway and cell-cycle commitment genes. Furthermore, in vitro-expanded Tregs become functional and take on the characteristics of Treg B. Collectively, this study identifies human Treg subpopulations that can be used as predictive biomarkers for response to IST in AA and potentially other autoimmune diseases. We also show that Tregs from AA patients are IL-2-sensitive and expandable in vitro, suggesting novel therapeutic approaches such as low-dose IL-2 therapy and/or expanded autologous Tregs and meriting further exploration. (Blood. 2016;128(9):1193-1205

Subpopulations of cells that escape anti-cancer treatment can cause relapse in cancer patients. Therefore, measurements of cellular-level tumor heterogeneity could enable improved anti-cancer treatment regimens. Cancer exhibits altered cellular metabolism, which affects the autofluorescence of metabolic cofactors NAD(P)H and FAD. The optical redox ratio (fluorescence intensity of NAD(P)H divided by FAD) reflects global cellular metabolism. The fluorescence lifetime (amount of time a fluorophore is in the excited state) is sensitive to microenvironment, particularly protein-binding. High-resolution imaging of the optical redox ratio and fluorescence lifetimes of NAD(P)H and FAD (optical metabolic imaging) enables single-cell analyses. In this study, mice with FaDu tumors were treated with the antibody therapy cetuximab or the chemotherapy cisplatin and imaged in vivo two days after treatment. Results indicate that fluorescence lifetimes of NAD(P)H and FAD are sensitive to early response (two days post-treatment, P < .05), compared with decreases in tumor size (nine days post-treatment, P < .05). Frequency histogram analysis of individual optical metabolic imaging parameters identifies subpopulations of cells, and a new heterogeneity index enables quantitative comparisons of cellular heterogeneity across treatment groups for individual variables. Additionally, a dimensionality reduction technique (viSNE) enables holistic visualization of multivariate optical measures of cellular heterogeneity. These analyses indicate increased heterogeneity in the cetuximab and cisplatin treatment groups compared with the control group. Overall, the combination of optical metabolic imaging and cellular-level analyses provide novel, quantitative insights into tumor heterogeneity.

The monocyte phagocyte system (MPS) includes numerous monocyte, macrophage, and dendritic cell (DC) populations that are heterogeneous, both phenotypically and functionally. In this study, we sought to characterize those diverse MPS phenotypes with mass cytometry (CyTOF). To identify a deep phenotype of monocytes, macrophages, and DCs, a panel was designed to measure 38 identity, activation, and polarization markers, including CD14, CD16, HLA-DR, CD163, CD206, CD33, CD36, CD32, CD64, CD13, CD11b, CD11c, CD86, and CD274. MPS diversity was characterized for 1) circulating monocytes from healthy donors, 2) monocyte-derived macrophages further polarized in vitro (i.e., M-CSF, GM-CSF, IL-4, IL-10, IFN-γ, or LPS long-term stimulations), 3) monocyte-derived DCs, and 4) myeloid-derived suppressor cells (MDSCs), generated in vitro from bone marrow and/or peripheral blood. Known monocyte subsets were detected in peripheral blood to validate the panel and analysis pipeline. Then, using various culture conditions and stimuli before CyTOF analysis, we constructed a multidimensional framework for the MPS compartment, which was registered against historical M1 or M2 macrophages, monocyte subsets, and DCs. Notably, MDSCs generated in vitro from bone marrow expressed more S100A9 than when generated from peripheral blood. Finally, to test the approach in vivo, peripheral blood from patients with melanoma ( = 5) was characterized and observed to be enriched for MDSCs with a phenotype of CD14HLA-DRS100A9 (3% of PBMCs in healthy donors, 15.5% in patients with melanoma, < 0.02). In summary, mass cytometry comprehensively characterized phenotypes of human monocyte, MDSC, macrophage, and DC subpopulations in both in vitro models and patients.