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基于稳定同位素标记氨基酸的细胞培养技术对来自HIV-1感染细胞的外泌体进行蛋白质组学表征

SILAC Based Proteomic Characterization of Exosomes from HIV-1 Infected Cells.

作者信息

Cheruiyot Collins, Pataki Zemplen, Williams Robert, Ramratnam Bharat, Li Ming

机构信息

Brown University.

COBRE Center for Cancer Research, Lifespan Laboratories, Rhode Island and Miriam Hospitals; Division of Infectious Diseases, Department of Medicine, Warren Alpert Medical School, Brown University.

出版信息

J Vis Exp. 2017 Mar 3(121):54799. doi: 10.3791/54799.

Abstract

Proteomics is the large-scale analysis of proteins. Proteomic techniques, such as liquid chromatography tandem mass spectroscopy (LC-MS/MS), can characterize thousands of proteins at a time. These powerful techniques allow us to have a systemic understanding of cellular changes, especially when cells are subjected to various stimuli, such as infections, stresses, and specific test conditions. Even with recent developments, analyzing the exosomal proteome is time-consuming and often involves complex methodologies. In addition, the resultant large dataset often needs robust and streamlined analysis in order for researchers to perform further downstream studies. Here, we describe a SILAC-based protocol for characterizing the exosomal proteome when cells are infected with HIV-1. The method is based on simple isotope labeling, isolation of exosomes from differentially labeled cells, and mass spectrometry analysis. This is followed by detailed data mining and bioinformatics analysis of the proteomic hits. The resultant datasets and candidates are easy to understand and often offer a wealth of information that is useful for downstream analysis. This protocol is applicable to other subcellular compartments and a wide range of test conditions.

摘要

蛋白质组学是对蛋白质进行大规模分析。蛋白质组学技术,如液相色谱串联质谱法(LC-MS/MS),能够一次对数千种蛋白质进行表征。这些强大的技术使我们能够系统地了解细胞变化,尤其是当细胞受到各种刺激时,如感染、应激和特定测试条件。即便有了最近的进展,分析外泌体蛋白质组仍然耗时,且通常涉及复杂的方法。此外,由此产生的大型数据集往往需要强大且简化的分析,以便研究人员进行进一步的下游研究。在此,我们描述了一种基于稳定同位素标记氨基酸细胞培养法(SILAC)的方案,用于在细胞感染HIV-1时表征外泌体蛋白质组。该方法基于简单的同位素标记、从差异标记的细胞中分离外泌体以及质谱分析。随后对蛋白质组学结果进行详细的数据挖掘和生物信息学分析。所得数据集和候选物易于理解,通常提供大量对下游分析有用的信息。该方案适用于其他亚细胞区室和广泛的测试条件。

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