Vincent van Unen

Mass cytometry allows high-resolution dissection of the cellular composition of the immune system. However, the high-dimensionality, large size, and non-linear structure of the data poses considerable challenges for the data analysis. In particular, dimensionality reduction-based techniques like t-SNE offer single-cell resolution but are limited in the number of cells that can be analyzed. Here we introduce Hierarchical Stochastic Neighbor Embedding (HSNE) for the analysis of mass cytometry data sets. HSNE constructs a hierarchy of non-linear similarities that can be interactively explored with a stepwise increase in detail up to the single-cell level. We apply HSNE to a study on gastrointestinal disorders and three other available mass cytometry data sets. We find that HSNE efficiently replicates previous observations and identifies rare cell populations that were previously missed due to downsampling. Thus, HSNE removes the scalability limit of conventional t-SNE analysis, a feature that makes it highly suitable for the analysis of massive high-dimensional data sets.

In this work, we find that CD8 + T cells expressing inhibitory killer cell immunoglobulin-like receptors (KIRs) are the human equivalent of Ly49 + CD8 + regulatory T cells in mice and are increased in the blood and inflamed tissues of patients with a variety of autoimmune diseases. Moreover, these CD8 + T cells efficiently eliminated pathogenic gliadin-specific CD4 + T cells from the leukocytes of celiac disease patients in vitro. We also find elevated levels of KIR + CD8 + T cells, but not CD4 + regulatory T cells, in COVID-19 patients, correlating with disease severity and vasculitis. Selective ablation of Ly49 + CD8 + T cells in virus-infected mice led to autoimmunity after infection. Our results indicate that in both species, these regulatory CD8 + T cells act specifically to suppress pathogenic T cells in autoimmune and infectious diseases.