Probability state modeling of memory CD8⁺ T-cell differentiation

Abstract

Flow cytometric analysis enables the simultaneous single-cell interrogation of multiple biomarkers for phenotypic and functional identification of heterogeneous populations. Analysis of polychromatic data has become increasingly complex with more measured parameters. Furthermore, manual gating of multiple populations using standard analysis techniques can lead to errors in data interpretation and difficulties in the standardization of analyses. To characterize high-dimensional cytometric data, we demonstrate the use of probability state modeling (PSM) to visualize the differentiation of effector/memory CD8⁺ T cells. With this model, four major CD8⁺ T-cell subsets can be easily identified using the combination of three markers, CD45RA, CCR7 (CD197), and CD28, with the selection markers CD3, CD4, CD8, and side scatter (SSC). PSM enables the translation of complex multicolor flow cytometric data to pathway-specific cell subtypes, the capability of developing averaged models of healthy donor populations, and the analysis of phenotypic heterogeneity. In this report, we also illustrate the heterogeneity in memory T-cell subpopulations as branched differentiation markers that include CD127, CD62L, CD27, and CD57.

Keywords: CD8 T cells; CDP; CM; Computational model; Data analysis; EF; EM; EP; Flow cytometry; Immunomics; PBMCs; PCA; PSM; Probability state modeling; SPLOM; SSC; central memory; control definition point; effector memory; expression profile; peripheral blood mononuclear cells; principal components analysis; probability state modeling; scatterplot matrix; side scatter; terminal effector.