Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden.
Nat Protoc. 2022 Aug;17(8):1832-1867. doi: 10.1038/s41596-022-00699-2. Epub 2022 Jun 22.
The molecular functions of a protein are defined by its inherent properties in relation to its environment and interaction network. Within a cell, this environment and network are defined by the subcellular location of the protein. Consequently, it is crucial to know the localization of a protein to fully understand its functions. Recently, we have developed a mass spectrometry- (MS) and bioinformatics-based pipeline to generate a proteome-wide resource for protein subcellular localization across multiple human cancer cell lines ( www.subcellbarcode.org ). Here, we present a detailed wet-lab protocol spanning from subcellular fractionation to MS-sample preparation and analysis. A key feature of this protocol is that it includes all generated cell fractions without discarding any material during the fractionation process. We also describe the subsequent quantitative MS-data analysis, machine learning-based classification, differential localization analysis and visualization of the output. For broad applicability, we evaluated the pipeline by using MS data generated by two different peptide pre-fractionation approaches, namely high-resolution isoelectric focusing and high-pH reverse-phase fractionation, as well as direct analysis without pre-fractionation by using long-gradient liquid chromatography-MS. Moreover, an R package covering the dry-lab part of the method was developed and made available through Bioconductor. The method is straightforward and robust, and the entire protocol, from cell harvest to classification output, can be performed within 1-2 weeks. The protocol enables accurate classification of proteins to 15 compartments and 4 neighborhoods, visualization of the output data and differential localization analysis including treatment-induced protein relocalization, condition-dependent localization or cell type-specific localization. The SubCellBarCode package is freely available at https://bioconductor.org/packages/devel/bioc/html/SubCellBarCode.html .
蛋白质的分子功能与其环境和相互作用网络的固有特性有关。在细胞内，这个环境和网络由蛋白质的亚细胞位置定义。因此，了解蛋白质的定位对于充分理解其功能至关重要。最近，我们开发了一种基于质谱（MS）和生物信息学的方法，用于生成蛋白质在多种人类癌细胞系中的亚细胞定位的蛋白质组广泛资源（www.subcellbarcode.org）。在这里，我们提供了一个详细的实验方案，涵盖了从亚细胞分级分离到 MS 样品制备和分析的全过程。该方案的一个关键特点是它包含了所有生成的细胞级分，而在分级分离过程中不丢弃任何材料。我们还描述了随后的定量 MS 数据分析、基于机器学习的分类、差异定位分析和输出的可视化。为了广泛的适用性，我们通过使用两种不同的肽预分级方法（即高分辨率等电聚焦和高 pH 反相分级分离）以及直接分析而不进行预分级的长梯度液相色谱-MS 生成的 MS 数据来评估该方法。此外，还开发了一个涵盖该方法干燥实验部分的 R 包，并通过 Bioconductor 提供。该方法简单可靠，从细胞收获到分类输出，整个方案可以在 1-2 周内完成。该方案能够将蛋白质准确分类到 15 个区室和 4 个邻域，可视化输出数据，并进行差异定位分析，包括治疗诱导的蛋白质重定位、条件依赖性定位或细胞类型特异性定位。SubCellBarCode 包可在 https://bioconductor.org/packages/devel/bioc/html/SubCellBarCode.html 免费获取。
