Using Visualization of t-Distributed Stochastic Neighbor Embedding To Identify Immune Cell Subsets in Mouse Tumors

Abstract

High-dimensional flow cytometry is proving to be valuable for the study of subtle changes in tumor-associated immune cells. As flow panels become more complex, detection of minor immune cell populations by traditional gating using biaxial plots, or identification of populations that display small changes in multiple markers, may be overlooked. Visualization of t-distributed stochastic neighbor embedding (viSNE) is an unsupervised analytical tool designed to aid the analysis of high-dimensional cytometry data. In this study we use viSNE to analyze the simultaneous binding of 15 fluorophore-conjugated Abs and one cell viability probe to immune cells isolated from syngeneic mouse MB49 bladder tumors, spleens, and tumor-draining lymph nodes to identify patterns of anti-tumor immune responses. viSNE maps identified populations in multidimensional space of known immune cells, including T cells, B cells, eosinophils, neutrophils, dendritic cells, and NK cells. Based on the expression of CD86 and programmed cell death protein 1, CD8⁺ T cells were divided into distinct populations. Additionally, both CD8⁺ T cells and CD8⁺ dendritic cells were identified in the tumor microenvironment. Apparent differences between splenic and tumor polymorphonuclear cells/granulocytic myeloid-derived suppressor cells are due to the loss of CD44 upon enzymatic digestion of tumors. In conclusion, viSNE is a valuable tool for high-dimensional analysis of immune cells in tumor-bearing mice, which eliminates gating biases and identifies immune cell subsets that may be missed by traditional gating.