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 data analysis. Here, we introduce Hierarchical Stochastic Neighbor Embedding (HSNE), a computational approach that constructs a hierarchy of non-linear similarities. We integrated HSNE into the Cytosplore+HSNE framework to facilitate interactive exploration and analysis of the hierarchy by a set of corresponding two-dimensional plots with a stepwise increase in detail up to the single-cell level. We validated its discovery potential by re-analyzing a study on gastrointestinal disorders and two other publicly available mass cytometry datasets. We found that Cytosplore+HSNE efficiently identifies rare cell populations, missed in a previous analysis, without a need for downsampling and in a very short time span. Thus, Cytosplore+HSNE offers single-cell resolution while exploring mass cytometry datasets on tens of millions of cells on a standard computer.