Development of a Spectral Flow Cytometry Analysis Pipeline for High-dimensional Immune Cell Characterization

Flow cytometry is used for immune cell analysis for cell composition and function. Spectral flow cytometry allows for high-dimensional analysis of immune cells, overcoming limitations of conventional flow cytometry. However, analyzing data from large Ab panels is challenging using traditional biaxial gating strategies. We present, to our knowledge, a novel analysis pipeline to improve analysis of spectral flow cytometry. We employ this method to identify rare T cell populations in aging. We isolated splenocytes from young (2-3 mo old) and aged (18-19 mo old) female C57BL/6N mice and then stained these with a panel of 20 fluorescently labeled Abs. We performed spectral flow cytometry and then data processing and analysis using Python within a Jupyter Notebook environment to perform dimensionality reduction, batch correction, unsupervised clustering, and differential expression analysis. Our analysis of 3,776,804 T cells from 11 spleens revealed 35 distinct T cell clusters identified by surface marker expression. We observed significant differences between young and aged mice, with clusters enriched in one age group over the other. Naive, effector memory, and central memory CD8+ and CD4+ T cell subsets exhibited age-associated changes in abundance and marker expression. We also demonstrate the utility of our pipeline in a human PBMC dataset that used a 50-fluorescent color panel. By leveraging high-dimensional analysis methods, we provide insights into the immune aging process. This approach offers a robust and easily implemented analysis pipeline for spectral flow cytometry data that may facilitate the discovery of novel therapeutic targets for age-related immune dysfunction.