Highlights d Analyses of 184 immune features define kinetics of immune responses to SARS-CoV-2 d Circulating T FH 1 cells in acute COVID-19 correlate with antibodies d sIL-6R levels are elevated in severe COVID-19 but do not correlate with IL-6 d Elevated IL-6 and IL-18 correlate with immune cell hyperactivation

As the size and complexity of high‐dimensional (HD) cytometry data continue to expand, comprehensive, scalable, and methodical computational analysis approaches are essential. Yet, contemporary clustering and dimensionality reduction tools alone are insufficient to analyze or reproduce analyses across large numbers of samples, batches, or experiments. Moreover, approaches that allow for the integration of data across batches or experiments are not well incorporated into computational toolkits to allow for streamlined workflows. Here we present Spectre, an R package that enables comprehensive end‐to‐end integration and analysis of HD cytometry data from different batches or experiments. Spectre streamlines the analytical stages of raw data pre‐processing, batch alignment, data integration, clustering, dimensionality reduction, visualization, and population labelling, as well as quantitative and statistical analysis. Critically, the fundamental data structures used within Spectre, along with the implementation of machine learning classifiers, allow for the scalable analysis of very large HD datasets, generated by flow cytometry, mass cytometry, or spectral cytometry. Using open and flexible data structures, Spectre can also be used to analyze data generated by single‐cell RNA sequencing or HD imaging technologies, such as Imaging Mass Cytometry. The simple, clear, and modular design of analysis workflows allow these tools to be used by bioinformaticians and laboratory scientists alike. Spectre is available as an R package or Docker container. R code is available on Github (https://github.com/immunedynamics/spectre).

One way sunlight causes skin cancer is by suppressing anti-tumor immunity. A major mechanism involves altering mast cell migration via the C-X-C motif chemokine receptor 4-C-X-C motif chemokine ligand 12 (CXCR4-CXCL12) chemokine pathway. We have discovered that pharmacologically blocking this pathway with the CXCR4 antagonist AMD3100 prevents both UV radiation-induced immune suppression and skin cancer. The majority of control mice receiving UV-only developed histopathologically confirmed squamous cell carcinomas. In contrast, skin tumor incidence and burden was significantly lower in AMD3100-treated mice. Perhaps most striking was that AMD3100 completely prevented the outgrowth of latent tumors that occurred once UV irradiation ceased. AMD3100 protection from UV immunosuppression and skin cancer was associated with reduced mast cell infiltration into the skin, draining lymph nodes, and the tumor itself. Thus a major target of CXCR4 antagonism was the mast cell. Our results indicate that interfering with UV-induced CXCL12 by antagonizing CXCR4 significantly inhibits skin tumor development by blocking UV-induced effects on mast cells. Hence, the CXCR4-CXCL12 chemokine pathway is a novel therapeutic target in the prevention of UV-induced skin cancer.

CD8+CD57+ terminal effector T (TTE) cells are a component of marrow-infiltrating lymphocytes and may contribute to the altered immune responses in multiple myeloma (MM) patients. We analyzed TTE cells in the bone marrow (BM) and peripheral blood (PB) of age-matched controls and patients with monoclonal gammopathy of undetermined significance (MGUS), smoldering MM (SMM), and newly diagnosed (ND) MM using flow cytometry, mass cytometry, and FlowSOM clustering. TTE cells are heterogeneous in all subjects, with BM containing both CD69− and CD69+ subsets, while only CD69− cells are found in PB. Within the BM-TTE compartment, CD69− and CD69+ cells are found in comparable proportions in controls, while CD69− cells are dominant in MGUS and SMM and predominantly either CD69− or CD69+ cells in NDMM. A positive relationship between CD69+TTE and CD69−TTE cells is observed in the BM of controls, lost in MGUS, and converted to an inverse relationship in NDMM. CD69−TTE cells include multiple oligoclonal expansions of T-cell receptor/Vβ families shared between BM and PB of NDMM. Oligoclonal expanded CD69−TTE cells from the PB include myeloma-reactive cells capable of killing autologous CD38hi plasma cells in vitro, involving degranulation and high expression of perforin and granzyme. In contrast to CD69−TTE cells, oligoclonal expansions are not evident within CD69+TTE cells, which possess low perforin and granzyme expression and high inhibitory checkpoint expression and resemble T resident memory cells. Both CD69−TTE and CD69+TTE cells from the BM of NDMM produce large amounts of the inflammatory cytokines interferon-γ and tumor necrosis factor α. The balance between CD69− and CD69+ cells within the BM-TTE compartment may regulate immune responses in NDMM and contribute to the clinical heterogeneity of the disease.