Murine brain tumor microenvironment immunophenotyping using mass cytometry

Abstract

Here, we present a mass cytometry protocol optimized to examine the phenotype of immune cells within the mouse glioma microenvironment, using a Sleeping Beauty transposon-mediated mouse glioma model. We describe antibody conjugation and titrations for analysis of immune cells. We then detail mouse brain tumor tissue collection and processing, staining, followed by data acquisition, analysis, and gating strategy. This protocol can be applied to any brain tumor-harboring mouse model. For complete details on the use and execution of this protocol, please refer to Alghamri et al. (2021).

Keywords: Antibody; Cancer; Cell Biology; Flow Cytometry/Mass Cytometry; Immunology; Model Organisms; Neuroscience; Single Cell.

Graphical abstract

Figure 1

Representation of a serial dilution The serial dilution step allows one to make the different antibody solutions required for the titration. The tube on the far left depicts the initial solution, where the antibody is in a 1:50 dilution from the stock. The subsequent steps involve pipetting 50 μL from one tube to the next to create the further dilutions. Finally, the tube on the far right contains the most diluted solution.

Figure 2

Example selection of optimal concentration of antibodies from titration data The titration step is used to determine the best concentration of antibody for use in the staining process for the main experiment. This CD11b, CD162, and CD4 titration data is an example of the gates generated from these antibodies at different concentrations. The blue arrow indicates it as the selected concentration for further use in identifying the positive cell populations. The red and blue boxes in the selected graphs identify the positive and negative cell populations, respectively.

Figure 3

Completed percoll gradient From top to bottom, the tube will contain a white layer (tumor cells, debris, and dead cells), a pink layer (30% SIP media solution), a white-cloudy layer (immune cells), a clear layer (70% Percoll solution), and a dark red layer (red blood cells and debris).

Figure 4

Recommended gating strategy using the using Gaussian parameters for data cleanup

Figure 5

t-distributed stochastic neighbor embedding (t-SNE) clustering of immune cells infiltrating glioma tumor microenvironment tSNE-based clustering represents an excellent identification and visualization of mass cytometry data. Each panel represent feature plot of expression of lineage specific marker.