Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden.
Department of Genetics, Stanford University, Stanford, CA, USA.
Nat Rev Mol Cell Biol. 2019 May;20(5):285-302. doi: 10.1038/s41580-018-0094-y.
Protein subcellular localization is tightly controlled and intimately linked to protein function in health and disease. Capturing the spatial proteome - that is, the localizations of proteins and their dynamics at the subcellular level - is therefore essential for a complete understanding of cell biology. Owing to substantial advances in microscopy, mass spectrometry and machine learning applications for data analysis, the field is now mature for proteome-wide investigations of spatial cellular regulation. Studies of the human proteome have begun to reveal a complex architecture, including single-cell variations, dynamic protein translocations, changing interaction networks and proteins localizing to multiple compartments. Furthermore, several studies have successfully harnessed the power of comparative spatial proteomics as a discovery tool to unravel disease mechanisms. We are at the beginning of an era in which spatial proteomics finally integrates with cell biology and medical research, thereby paving the way for unbiased systems-level insights into cellular processes. Here, we discuss current methods for spatial proteomics using imaging or mass spectrometry and specifically highlight global comparative applications. The aim of this Review is to survey the state of the field and also to encourage more cell biologists to apply spatial proteomics approaches.
蛋白质亚细胞定位受到严格控制,并与健康和疾病中的蛋白质功能密切相关。因此,捕获空间蛋白质组(即在亚细胞水平上蛋白质及其动态的定位)对于全面了解细胞生物学至关重要。由于显微镜、质谱和用于数据分析的机器学习应用的重大进展,该领域现在已经成熟,可以进行蛋白质组范围的空间细胞调控研究。对人类蛋白质组的研究已经开始揭示出一种复杂的结构,包括单细胞变化、动态蛋白质易位、不断变化的相互作用网络以及定位于多个隔室的蛋白质。此外,一些研究已经成功地利用比较空间蛋白质组学作为一种发现工具来揭示疾病机制。我们正处在一个时代的开端,即空间蛋白质组学最终与细胞生物学和医学研究相结合,从而为细胞过程的无偏系统级见解铺平道路。在这里,我们讨论了使用成像或质谱进行空间蛋白质组学的当前方法,并特别强调了全局比较应用。本综述的目的是调查该领域的现状,并鼓励更多的细胞生物学家应用空间蛋白质组学方法。