Zhang Bo, Käll Lukas, Zubarev Roman A
From the ‡ Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Scheeles väg 2, SE-17177 Solna, Sweden.
§ Science for Life Laboratory, School of Biotechnology, Royal Institute of Technology-KTH, 17165 Solna, Sweden.
Mol Cell Proteomics. 2016 Apr;15(4):1467-78. doi: 10.1074/mcp.O115.055475. Epub 2016 Jan 4.
For historical reasons, most proteomics workflows focus on MS/MS identification but consider quantification as the end point of a comparative study. The stochastic data-dependent MS/MS acquisition (DDA) gives low reproducibility of peptide identifications from one run to another, which inevitably results in problems with missing values when quantifying the same peptide across a series of label-free experiments. However, the signal from the molecular ion is almost always present among the MS(1)spectra. Contrary to what is frequently claimed, missing values do not have to be an intrinsic problem of DDA approaches that perform quantification at the MS(1)level. The challenge is to perform sound peptide identity propagation across multiple high-resolution LC-MS/MS experiments, from runs with MS/MS-based identifications to runs where such information is absent. Here, we present a new analytical workflow DeMix-Q (https://github.com/userbz/DeMix-Q), which performs such propagation that recovers missing values reliably by using a novel scoring scheme for quality control. Compared with traditional workflows for DDA as well as previous DIA studies, DeMix-Q achieves deeper proteome coverage, fewer missing values, and lower quantification variance on a benchmark dataset. This quantification-centered workflow also enables flexible and robust proteome characterization based on covariation of peptide abundances.
由于历史原因，大多数蛋白质组学工作流程专注于串联质谱（MS/MS）鉴定，但将定量视为比较研究的终点。随机数据依赖型MS/MS采集（DDA）在不同运行之间的肽段鉴定中再现性较低，这不可避免地导致在一系列无标记实验中对同一肽段进行定量时出现缺失值问题。然而，分子离子的信号几乎总是存在于一级质谱（MS(1)）谱图中。与通常所认为的相反，缺失值不一定是在MS(1)水平进行定量的DDA方法的固有问题。挑战在于在多个高分辨率液相色谱-串联质谱（LC-MS/MS）实验中进行可靠的肽段身份传播，从基于MS/MS鉴定的运行到缺乏此类信息的运行。在此，我们提出了一种新的分析工作流程DeMix-Q（https://github.com/userbz/DeMix-Q），它通过使用一种新颖的质量控制评分方案来执行这种传播，从而可靠地恢复缺失值。与传统的DDA工作流程以及先前的数据独立采集（DIA）研究相比，DeMix-Q在一个基准数据集上实现了更深的蛋白质组覆盖、更少的缺失值和更低的定量方差。这种以定量为中心的工作流程还能够基于肽段丰度的协变进行灵活且稳健的蛋白质组表征。
