Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Proteome Software, Inc. Portland, Oregon, USA.
Mol Cell Proteomics. 2020 Jul;19(7):1088-1103. doi: 10.1074/mcp.P119.001913. Epub 2020 Apr 20.
Data independent acquisition (DIA) is an attractive alternative to standard shotgun proteomics methods for quantitative experiments. However, most DIA methods require collecting exhaustive, sample-specific spectrum libraries with data dependent acquisition (DDA) to detect and quantify peptides. In addition to working with non-human samples, studies of splice junctions, sequence variants, or simply working with small sample yields can make developing DDA-based spectrum libraries impractical. Here we illustrate how to acquire, queue, and validate DIA data without spectrum libraries, and provide a workflow to efficiently generate DIA-only chromatogram libraries using gas-phase fractionation (GPF). We present best-practice methods for collecting DIA data using Orbitrap-based instruments and develop an understanding for why DIA using an Orbitrap mass spectrometer should be approached differently than when using time-of-flight instruments. Finally, we discuss several methods for analyzing DIA data without libraries.
数据非依赖性采集(DIA)是定量实验中替代标准鸟枪法蛋白质组学方法的一种有吸引力的选择。然而,大多数 DIA 方法需要使用基于数据依赖采集(DDA)来收集详尽的、特定于样本的谱图库,以检测和定量肽。除了处理非人类样本外,剪接接头、序列变体的研究,或者只是处理小样本产量,都可能使基于 DDA 的谱图库的开发变得不切实际。在这里,我们展示了如何在没有谱图库的情况下获取、排队和验证 DIA 数据,并提供了一种使用气相分馏(GPF)高效生成仅 DIA 色谱图库的工作流程。我们介绍了使用基于轨道阱的仪器收集 DIA 数据的最佳实践方法,并深入了解了为什么使用轨道阱质谱仪进行 DIA 与使用飞行时间仪器进行 DIA 应该采用不同的方法。最后,我们讨论了几种无需库即可分析 DIA 数据的方法。