Groen Arnoud J, Sancho-Andrés Gloria, Breckels Lisa M, Gatto Laurent, Aniento Fernando, Lilley Kathryn S
Cambridge Centre for Proteomics, Cambridge Systems Biology Centre, Department of Biochemistry, University of Cambridge , 80 Tennis Court Road, Cambridge CB2 1GA, United Kingdom.
J Proteome Res. 2014 Feb 7;13(2):763-76. doi: 10.1021/pr4008464. Epub 2014 Jan 17.
Knowledge of protein subcellular localization assists in the elucidation of protein function and understanding of different biological mechanisms that occur at discrete subcellular niches. Organelle-centric proteomics enables localization of thousands of proteins simultaneously. Although such techniques have successfully allowed organelle protein catalogues to be achieved, they rely on the purification or significant enrichment of the organelle of interest, which is not achievable for many organelles. Incomplete separation of organelles leads to false discoveries, with erroneous assignments. Proteomics methods that measure the distribution patterns of specific organelle markers along density gradients are able to assign proteins of unknown localization based on comigration with known organelle markers, without the need for organelle purification. These methods are greatly enhanced when coupled to sophisticated computational tools. Here we apply and compare multiple approaches to establish a high-confidence data set of Arabidopsis root tissue trans-Golgi network (TGN) proteins. The method employed involves immunoisolations of the TGN, coupled to probability-based organelle proteomics techniques. Specifically, the technique known as LOPIT (localization of organelle protein by isotope tagging), couples density centrifugation with quantitative mass-spectometry-based proteomics using isobaric labeling and targeted methods with semisupervised machine learning methods. We demonstrate that while the immunoisolation method gives rise to a significant data set, the approach is unable to distinguish cargo proteins and persistent contaminants from full-time residents of the TGN. The LOPIT approach, however, returns information about many subcellular niches simultaneously and the steady-state location of proteins. Importantly, therefore, it is able to dissect proteins present in more than one organelle and cargo proteins en route to other cellular destinations from proteins whose steady-state location favors the TGN. Using this approach, we present a robust list of Arabidopsis TGN proteins.
了解蛋白质的亚细胞定位有助于阐明蛋白质功能，并理解在离散亚细胞区域发生的不同生物学机制。以细胞器为中心的蛋白质组学能够同时定位数千种蛋白质。尽管这些技术已成功实现细胞器蛋白质目录的构建，但它们依赖于对目标细胞器的纯化或显著富集，而这对许多细胞器来说是无法实现的。细胞器分离不完全会导致错误发现和错误分配。测量特定细胞器标记物沿密度梯度分布模式的蛋白质组学方法能够根据与已知细胞器标记物的共迁移情况来确定未知定位的蛋白质，而无需进行细胞器纯化。当与先进的计算工具结合使用时，这些方法会得到极大增强。在这里，我们应用并比较了多种方法来建立拟南芥根组织反式高尔基体网络（TGN）蛋白质的高可信度数据集。所采用的方法包括TGN的免疫分离，并结合基于概率的细胞器蛋白质组学技术。具体而言，称为LOPIT（通过同位素标记进行细胞器蛋白质定位）的技术将密度离心与基于定量质谱的蛋白质组学相结合，使用等压标记和靶向方法以及半监督机器学习方法。我们证明，虽然免疫分离方法产生了大量数据集，但该方法无法区分货物蛋白和持续性污染物与TGN的常驻蛋白。然而，LOPIT方法能够同时返回有关许多亚细胞区域以及蛋白质稳态位置的信息。因此，重要的是，它能够区分存在于多个细胞器中的蛋白质以及正在运往其他细胞目的地的货物蛋白与稳态位置有利于TGN的蛋白质。使用这种方法，我们列出了一份可靠的拟南芥TGN蛋白质清单。
