DeMix-Q: Quantification-Centered Data Processing Workflow

Abstract

Label-free quantification (LFQ) is one of the most efficient approaches for quantifying proteome differences between multiple states of a biological system. LFQ aims to reproducibly identify and quantify peptides through multiple liquid-chromatography-coupled tandem mass spectrometry (LC-MS/MS) experiments. In the popular data-dependent acquisition (DDA) approach named Top-N DDA, the appearance of a peptide-like signal in a "survey" mass spectrum triggers a tandem mass spectrometry (MS/MS) event, targeting the (N) most-abundant precursor ions. Previous studies have shown that, due to the limited speed of a mass spectrometer, the majority of peptide ions detected in MS 1 are not targeted in MS/MS, especially when a nonfractionated complex sample is analyzed (1, 2). This low sampling efficiency (Ͻ50%), combined with the stochastic nature of precursor selection and a limited efficiency of MS/MS identification (Ͻ70%) (3), frequently causes the absence of MS/MS identification for an individual peptide in some LC-MS/MS experiments ("runs") within a larger dataset, even when replicate measurements are made (4). This deficiency is known as the missing value problem in LFQ. The problem significantly limits the size of the DDA-acquired proteomics dataset across which reliable quantification can be made for each protein (5, 6).One of the causes of the missing value problem is the traditional focus on the process of identifying a peptide as opposed to its quantification. For historical reasons, peptide sequence identification has been considered the focal point and the most important step in the whole proteomics procedure, while quantification came as almost an afterthought (7,8). This dominant proteomics paradigm can be characterized as the identification-centered approach, also known as a spectrum-centric approach (9). Only gradually the missing value problem has been identified as one of the biggest drawbacks of the DDA approach (4, 5). To address the reproducibility issue in MS/MS identification, several alternative data acquisition strategies had been suggested, including targeted (10) and semi-targeted (11, 12) approaches. However, none of the improved DDA strategies has solved the missing value problem anywhere close to the data-independent acquisition (DIA) (13,14). The latter approach, however, typically provides somewhat lower depth and breadth of the proteome coverage than the DDA methods.In our opinion, the DDA-associated missing value problem is caused by the sequential execution of two independent processes: peptide identification by MS/MS and its quantification by MS 1 . At first glance, performing MS 1 -based quantification simultaneously with MS/MS identification should provide an obvious solution to the missing value problem. Since MS 1 spectra contain many more peptide ions than are selected for MS/MS in DDA (or identified in DIA), the peptide's mass information is practically always present when an iden-

DirectMS1: MS/MS-free identification of 1000 proteins of cellular proteomes in 5 minutes

“…However, most of these approaches fail to achieve the meaningful depth of quantitative proteome coverage within short experimental times. Many of the recent efforts were focused on hybrid experimental pipelines, in which MS/MS-based identification is combined with MS1-based quantitation 3,16 .…”

Focus on the spectra that matter by clustering of quantification data in shotgun proteomics

“…For each pair-wise retention time alignment, the MS1 features discovered by Dinosaur from the two corresponding runs were matched based on a set of numeric features, such as the difference between the observed and aligned retention time and precursor mass difference. At the same time, we matched MS1 features against decoy MS1 features [46], which were generated by shifting the precursor m/z of all MS1 features discovered by Dinosaur by 5 · 1.000508 Th in one of the runs. For a more detailed description of these decoy features and what constitutes good decoy features, see Supplementary Section 5.…”

“…This perhaps surprising use of Percolator -which normally is used for quality assessments of peptide-spectrum matches -allowed us to avoid fixed thresholds for individual numeric features, and instead derive error probabilities for feature-feature matches. This approach is an extension of the approach employed by DeMix-Q [46], where Percolator now provides a more natural means to feature weighting as well as probabilistic scoring.…”

“…Two well-recognized issues regarding the sensitivity of LFQ pipelines are that many MS1 features remain unassigned to peptides [46] or even to MS2 spectra [29] and that a large number of fragment spectra remain unidentified [37]. Match-between-runs (MBR) has proven to be an effective technique to propagate MS2 information to the MS1 features [5,46,1] and clustering of MS2 spectra from large repositories has allowed us to zoom in on frequently unidentified spectra for peptide identification [13]. Unsupervised clustering of MS2 spectra also significantly reduces the number of MS2 spectra that need to be searched [10,12,39].…”

“…A less highlighted issue concerning sensitivity is that conventional data analysis pipelines match the individual sample's spectra to peptide sequences with search engines as a first step [5,46]. This step directly limits the number of peptides and proteins that can ultimately be quantified.…”