Long Marcus J C, Liu Jinmin, Aye Yimon
University of Lausanne (UNIL) Switzerland.
NCCR Chemical Biology, University of Geneva (UNIGE) Switzerland.
RSC Chem Biol. 2022 Dec 2;4(2):110-120. doi: 10.1039/d2cb00214k. eCollection 2023 Feb 8.
First established in the seventies, proteomics, chemoproteomics, and most recently, spatial/proximity-proteomics technologies have empowered researchers with new capabilities to illuminate cellular communication networks that govern sophisticated decision-making processes. With an ever-growing inventory of these advanced proteomics tools, the onus is upon the researchers to understand their individual advantages and limitations, such that we can ensure rigorous implementation and conclusions derived from critical data interpretations backed up by orthogonal series of functional validations. This perspective-based on the authors' experience in applying varied proteomics workflows in complex living models-underlines key book-keeping considerations, comparing and contrasting most-commonly-deployed modern proteomics profiling technologies. We hope this article stimulates thoughts among expert users and equips new-comers with practical knowhow of what has become an indispensable tool in chemical biology, drug discovery, and broader life-science investigations.
蛋白质组学最早建立于20世纪70年代,随后出现了化学蛋白质组学,最近又发展出空间/邻近蛋白质组学技术,这些技术为研究人员提供了新的能力,以阐明控制复杂决策过程的细胞通讯网络。随着这些先进蛋白质组学工具的不断增加,研究人员有责任了解它们各自的优势和局限性,以便我们能够确保严格实施,并从由一系列正交功能验证支持的关键数据解释中得出结论。基于作者在复杂生命模型中应用各种蛋白质组学工作流程的经验,这一观点强调了关键的记录考量,对最常用的现代蛋白质组学分析技术进行了比较和对比。我们希望本文能激发专家用户的思考,并为新手提供实用的专业知识,了解蛋白质组学已成为化学生物学、药物发现及更广泛生命科学研究中不可或缺的工具。