Smith Stephen E P, Neier Steven C, Reed Brendan K, Davis Tessa R, Sinnwell Jason P, Eckel-Passow Jeanette E, Sciallis Gabriel F, Wieland Carilyn N, Torgerson Rochelle R, Gil Diana, Neuhauser Claudia, Schrum Adam G
Department of Immunology, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA.
Sci Signal. 2016 Aug 2;9(439):rs7. doi: 10.1126/scisignal.aad7279.
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.
多蛋白复合物通过广泛的相互作用网络传导细胞信号,但从小型临床活检组织的细胞中分析这些网络的能力有限。为了解决这一问题,我们将一种适应性多重矩阵系统应用于从细胞系或人类患者样本中分离出的生理相关信号蛋白复合物。聚焦于近端T细胞受体(TCR)信号小体,我们评估了210对通过暴露表面表位检测到的共享复合物中的蛋白质(PiSCES)。在用负载超抗原的抗原呈递细胞刺激Jurkat细胞后,该系统产生了高维数据,能够可视化网络活性。一个综合分析平台通过应用无监督层次聚类、主成分分析、具有经验临界值分析的自适应非参数分析和加权相关网络分析,生成了PiSCES生物特征。我们从对照患者或患有自身免疫性皮肤病斑秃的患者的4毫米皮肤打孔活检组织中生成了PiSCES生物特征。该分析将疾病患者与对照区分开来,检测到自身免疫患者中基础TCR信号增强,并识别出一个可能指示疾病的潜在信号网络特征。因此,PiSCES生物特征的生成代表了一种方法,可提供有关包括临床活检组织中低丰度原代细胞在内的样本中蛋白质信号网络活性的信息。