Parker Sarah J, Raedschelders Koen, Van Eyk Jennifer E
Department of Medicine, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA; Advanced Clinical Biosystems Research Institute, Los Angeles, CA, USA; Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Proteomics. 2015 May;15(9):1486-502. doi: 10.1002/pmic.201400448. Epub 2015 Feb 10.
Aberrant cell signaling events either drive or compensate for nearly all pathologies. A thorough description and quantification of maladaptive signaling flux in disease is a critical step in drug development, and complex proteomic approaches can provide valuable mechanistic insights. Traditional proteomics-based signaling analyses rely heavily on in vitro cellular monoculture. The characterization of these simplified systems generates a rich understanding of the basic components and complex interactions of many signaling networks, but they cannot capture the full complexity of the microenvironments in which pathologies are ultimately made manifest. Unfortunately, techniques that can directly interrogate signaling in situ often yield mass-limited starting materials that are incompatible with traditional proteomics workflows. This review provides an overview of established and emerging techniques that are applicable to context-dependent proteomics. Analytical approaches are illustrated through recent proteomics-based studies in which selective sample acquisition strategies preserve context-dependent information, and where the challenge of minimal starting material is met by optimized sensitivity and coverage. This review is organized into three major technological themes: (i) LC methods in line with MS; (ii) antibody-based approaches; (iii) MS imaging with a discussion of data integration and systems modeling. Finally, we conclude with future perspectives and implications of context-dependent proteomics.
异常的细胞信号转导事件几乎驱动或补偿了所有病理状况。对疾病中适应不良的信号通量进行全面描述和定量是药物开发的关键步骤,而复杂的蛋白质组学方法可以提供有价值的机制见解。传统的基于蛋白质组学的信号分析严重依赖体外细胞单培养。对这些简化系统的表征有助于深入了解许多信号网络的基本组成部分和复杂相互作用,但它们无法捕捉到病理状况最终显现的微环境的全部复杂性。不幸的是,能够直接原位询问信号的技术通常会产生与传统蛋白质组学工作流程不兼容的起始材料量有限的情况。本综述概述了适用于上下文相关蛋白质组学的既定技术和新兴技术。通过最近基于蛋白质组学的研究说明了分析方法,其中选择性样本采集策略保留了上下文相关信息,并且通过优化的灵敏度和覆盖范围应对了起始材料最少的挑战。本综述分为三个主要技术主题:(i)与质谱联用的液相色谱方法;(ii)基于抗体的方法;(iii)质谱成像,并讨论了数据整合和系统建模。最后,我们总结了上下文相关蛋白质组学的未来前景和意义。