TY - JOUR
T1 - Multiplex matrix network analysis of protein complexes in the human TCR signalosome
AU - Smith, Stephen E P
AU - Neier, Steven C.
AU - Reed, Brendan K.
AU - Davis, Tessa R.
AU - Sinnwell, Jason P.
AU - Eckel-Passow, Jeanette E.
AU - Sciallis, Gabriel F.
AU - Wieland, Carilyn N.
AU - Torgerson, Rochelle R.
AU - Gil, Diana
AU - Neuhauser, Claudia
AU - Schrum, Adam G.
N1 - 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.);
PY - 2016/8/2
Y1 - 2016/8/2
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84982794790&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84982794790&partnerID=8YFLogxK
U2 - 10.1126/scisignal.aad7279
DO - 10.1126/scisignal.aad7279
M3 - Article
C2 - 27485017
AN - SCOPUS:84982794790
SN - 1937-9145
VL - 9
JO - Science's STKE : signal transduction knowledge environment
JF - Science's STKE : signal transduction knowledge environment
IS - 439
M1 - rs7
ER -