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The Baxter Laboratory for Genetic Pharmacology, Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California 94305, USA
Address for correspondence: Omar D. Perez, The Baxter Laboratory for Genetic Pharmacology, Department of Microbiology & Immunology, Stanford University, 269 Campus Dr., CCSR 4225, Stanford, CA 94305. Voice: 650-724-4847; fax: 650-725-6193. email: operez{at}stanford.edu
Autoimmune disease pathologies are multifactorial with complex interactive networks of cells and chemical messengers that initiate cascades of aberrant cellular activity. Rheumatoid arthritis (RA) is a chronic inflammatory disease that is characterized by systemic inflammation, destruction of the joints, and production of autoantibodies recognizing dozens of putative autoantigens. The presence of autoreactive T cells in individuals leads to pathological autoimmunity by activating additional cellular constituents to mediate inflammation and joint destruction. The etiology of RA is unknown, and knowledge is lacking of the molecular mechanisms underlying the production and subsequent regulation of autoreactive T cells and predicting patient responses to treatments. Biochemical investigations into mechanisms of the disease have relied on animal models that are helpful in dissecting elements of the disease but that are not necessarily reflective of human RA development. The study of multiple activated signaling pathways in complex populations of cells, such as peripheral blood, at the single-cell level has not previously been possible. This article describes how intracellular phosphoepitope staining methodology in conjunction with surface-cell immunophenotyping can be used to deconvolute cellular subsets and allow functional characterization of patient-derived material. Multiparameter flow cytometric analysis allows for small subpopulations—representing different cellular subsets and differentiation or activation states—to be discerned and simultaneously assessed for intracellular biochemical activities. This article also describes how single-cell signal network analysis can be used to stratify patients and may be useful for understanding mechanisms of disease progression, treatment resistance, and development of diagnostic indicators.
Key Words: flow cytometry • phosphoproteomics • kinase • signal transduction • patient stratification This article has been cited by other articles:
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