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

Stephen E P Smith, Steven C. Neier, Brendan K. Reed, Tessa R. Davis, Jason P. Sinnwell, Jeanette E. Eckel-Passow, Gabriel F. Sciallis, Carilyn N. Wieland, Rochelle R. Torgerson, Diana Gil, Claudia Neuhauser, Adam G. Schrum

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


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 lowabundance primary cells from clinical biopsies.

Original languageEnglish (US)
Article numberrs7
JournalScience signaling
Issue number439
StatePublished - Aug 2 2016

Bibliographical note

Funding Information:
This work was supported by NIH grants R01GM103841 (to A.G.S., J.E.E.-P., D.G., and C.N.), MH102244 (to S.E.P.S.), and T32AI7425 (to S.C.N. and B.K.R.);


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