Research Techniques Made Simple: Mass Cytometry Analysis Tools for Decrypting the Complexity of Biological Systems

Mass cytometry by time-of-flight experiments allow analysis of over 40 functional and phenotypic cellular markers simultaneously at the single-cell level. The data dimensionality escalation accentuates limitations, inherent to manual analysis, as being subjective, labor-intensive, slow, and often incapable of showing the detailed features of each unique cell within populations. The subsequent challenge of examining, visualizing, and presenting mass cytometry data has motivated continuous development of dimensionality reduction methods. As a result, an increasing recognition of the inherent diversity and complexity of cellular networks is emerging, with the discovery of unexpected cell subpopulations, hierarchies, and developmental pathways, such as those existing within the immune system. Here, we briefly review some frequently used and accessible mass cytometry data analysis tools, including principal component analysis (PCA); spanning-tree progression analysis of density-normalized events (SPADE); t-distributed stochastic neighbor embedding (t-SNE)-based visualization (viSNE); automatic classification of cellular expression by nonlinear stochastic embedding (ACCENSE); and cluster identification, characterization, and regression (CITRUS). Mass cytometry, used together with these innovative analytic tools, has the power to lead to key discoveries in investigative dermatology, including but not limited to identifying signaling phenotypes with predictive value for early diagnosis, prognosis, or relapse and a thorough characterization of intratumor heterogeneity and disease-resistant cell populations, that may ultimately unveil novel therapeutic approaches.