Flow Cytometric Kinase Profiling in Autoimmune Disease

Stanford University, Stanford CA

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Abstract

There is a need to measure complex populations of immune system cells and phenotype them not only for their cell lineage status but for their relative activation state. Changes in intracellular protein levels, subcellular localization, or activation state are considered to be reflective of a cell's capabilities or functions. Importantly this Project employs equipment that at a basic level is familiar to nearly all academic research centers, including fluorescence activated cell sorters (FACS) in a highly state of the art approach by upgrading our capabilities significantly for a more complete analysis at a higher level of resolution than previously attempted --11 color multivariate analysis of autoimmune phenotypes in RA and EAE. Populations of cells can be distinguished by phosphoprotein analysis using Flow Cytometry. We will apply our approaches first on cell lines (where the appropriate kinase or phosphoproteins are suspected to be active or to exist), and then on primary murine tissues. Since we can verify multiple kinases simultaneously and in multiple cell types due to the polychromatic discriminatory power of flow cytometry, we can at the same time begin to develop a comprehensive catalog of kinase profiles correlated both with other kinases as well as within different subsets of cells, under various standard activation conditions, and as measured against several different standard cytokines. It is postulated that in murine RA autoimmune disease significant differences exist in the signaling systems as reflected by changes in the phosphorylation patterns of known kinases and that these differences can be correlated to disease indices during progression. We will apply our research to the study of blood components of tractable animal models of two distinct autoimmune diseases, rheumatoid arthritis (RA, a disease mediated predominantly by T lymphocytes) and EAE (mediated by both T cell and B cell components). We expect significant progress given our preliminary data and past progress in the area.