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Multiplex matrix network analysis of protein complexes in the human TCR signalosome

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Sci. Signal.  02 Aug 2016:
Vol. 9, Issue 439, pp. rs7
DOI: 10.1126/scisignal.aad7279

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Personalized signaling complexes

The response of a cell to ligands that activate cell surface receptors depends on the resulting protein-protein interactions that occur. The formation of different protein complexes leads to the activation of different intracellular signaling pathways and different cellular outputs. Thus, knowing which protein-protein interactions occur can be clinically important. To analyze these complexes in patient samples, Smith et al. devised a multiplex, antibody-based method and used it to capture complexes from T cell lysates and identify binding partners by flow cytometry. Mathematical analyses of these data enabled the construction of protein-protein interaction networks and revealed the relative abundances of particular complexes between resting and stimulated cells. Application of this technique to T cells from skin biopsies identified protein complexes that differed in their relative abundance between control donors and patients with an autoimmune skin disease. This type of analysis brings basic research a step closer to personalized therapy and may be useful as a diagnostic tool.

Abstract

Multiprotein complexes transduce cellular signals through extensive interaction networks, but the ability to analyze these networks in cells from small clinical biopsies is limited. To address this, we applied an adaptable multiplex matrix system to physiologically relevant signaling protein complexes isolated from a cell line or from human patient samples. Focusing on the proximal T cell receptor (TCR) signalosome, we assessed 210 pairs of PiSCES (proteins in shared complexes detected by exposed surface epitopes). Upon stimulation of Jurkat cells with superantigen-loaded antigen-presenting cells, this system produced high-dimensional data that enabled visualization of network activity. A comprehensive analysis platform generated PiSCES biosignatures by applying unsupervised hierarchical clustering, principal component analysis, an adaptive nonparametric with empirical cutoff analysis, and weighted correlation network analysis. We generated PiSCES biosignatures from 4-mm skin punch biopsies from control patients or patients with the autoimmune skin disease alopecia areata. This analysis distinguished disease patients from the controls, detected enhanced basal TCR signaling in the autoimmune patients, and identified a potential signaling network signature that may be indicative of disease. Thus, generation of PiSCES biosignatures represents an approach that can provide information about the activity of protein signaling networks in samples including low-abundance primary cells from clinical biopsies.

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