Development and computational analysis of high dimensional spectral flow cytometry data for the resolution of innate lymphoid cells in the mammary tumor microenvironment.

Spectral flow cytometry has ushered in a new era in immunology. Through the improvement of the resolution of surface and intracellular protein expression, this approach enables in depth characterization of rare immune cell subsets, such as innate lymphoid cells (ILCs), in health and disease. Due to their heterogeneity, the identification of ILCs requires the use of many lineage marker antibodies for non-ILC exclusion, together with the analysis of several transcription factor expression profiles for ILC subset distinction. Such intricacies toward their identification and their scarcity in tissues have been key factors directly limiting their characterization, particularly during tumor development and progression. We developed, optimized and validated a 25-parameter spectral flow cytometric panel for the identification of mouse ILC subsets and characterization of their phenotype and proliferation capabilities in mouse mammary tumors. The use of conjugated antibodies coupled to different fluorochromes for the analysis of lineage marker expression further allows the identification and characterization of γδ T cells, CD4 and CD8 αβ T cells, as well as CD19 B cells. Furthermore, we built a bioinformatics pipeline for unbiased immune cell clustering and marker expression analysis. We assessed this panel and downstream bioinformatics analyses on two spectral flow cytometers and found no difference in immune cell identification and clustering save for slight variations in marker intensity, inherent to the specificities of the instrument. These findings highlight the robustness of our developed approach for the identification of innate lymphoid cells in tumors, a method that can be easily implemented for day-to-day analysis of ILCs and other rare immune cell subsets.