Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia.
Methods Mol Biol. 2020;2139:169-178. doi: 10.1007/978-1-0716-0528-8_13.
Most quantitative proteomics experiments either target a limited number of selected proteins for quantification or quantify proteins on a broad scale in an untargeted manner. However, we recently demonstrated that experiments that have both targeted and untargeted components can be particularly advantageous. Using a combined targeted and untargeted liquid chromatography-tandem mass spectrometry data acquisition strategy termed TDA/DDA (shorthand for targeted data acquisition/data-dependent acquisition), which we applied to a model quantitative plant proteomics experiment performed on Arabidopsis, we demonstrated improved quantification of both targeted and untargeted proteins relative to purely untargeted experiments performed using conventional data-dependent acquisition (Hart-Smith et al. Front Plant Sci 8:1669, 2017). This suggests that many quantitative proteomics datasets earmarked for collection using data-dependent acquisition are likely to benefit from the use of TDA/DDA instead.This chapter describes how TDA/DDA liquid chromatography-tandem mass spectrometry methods can be created on commonly used mass spectrometric instrument platforms. It described how, using freely available software, tandem mass spectrometry inclusion lists designed to target proteins of hypothesized interest can be generated. Best practice implementation of these inclusion lists in TDA/DDA strategies is then described. Relative to conventional data-dependent acquisition, the liquid chromatography-tandem mass spectrometry methods created using these guidelines increase the chances of quantifying targeted proteins and can produce widespread improvements in the reproducibility of untargeted protein quantification, without compromising the total numbers of proteins quantified. They are compatible with different quantitative proteomics methodologies, including metabolic labeling, chemical labeling and label-free approaches, and can be used to create tailored assay libraries to aid the interpretation of quantitative proteomics data collected using data-independent acquisition.
大多数定量蛋白质组学实验要么针对有限数量的选定蛋白质进行定量，要么以非靶向方式大规模定量蛋白质。然而，我们最近证明，同时具有靶向和非靶向成分的实验可能特别有利。我们使用了一种称为 TDA/DDA（靶向数据采集/数据依赖采集的简称）的组合靶向和非靶向液相色谱-串联质谱数据采集策略，将其应用于我们在拟南芥上进行的模型定量植物蛋白质组学实验中，与使用传统数据依赖采集进行的纯非靶向实验相比，我们证明了靶向和非靶向蛋白质的定量都得到了改善（Hart-Smith 等人，《植物科学前沿》8:1669，2017 年）。这表明，许多原本计划使用数据依赖采集收集的定量蛋白质组学数据集可能受益于 TDA/DDA 的使用。
本章介绍了如何在常用的质谱仪器平台上创建 TDA/DDA 液相色谱-串联质谱方法。描述了如何使用免费提供的软件，生成旨在靶向假设感兴趣的蛋白质的串联质谱包含列表。然后描述了这些包含列表在 TDA/DDA 策略中的最佳实施。与传统的数据依赖采集相比，使用这些指南创建的液相色谱-串联质谱方法增加了定量靶向蛋白质的机会，并且可以广泛提高非靶向蛋白质定量的重现性，而不会影响定量的蛋白质总数。它们与不同的定量蛋白质组学方法学兼容，包括代谢标记、化学标记和无标记方法，并且可以用于创建定制的测定文库，以帮助解释使用数据独立采集收集的定量蛋白质组学数据。
