Maximizing Peptide Identification Events in Proteomic Workflows Using Data-Dependent Acquisition (DDA)

Abstract

Current analytical strategies for collecting proteomic data using data-dependent acquisition (DDA) are limited by the low analytical reproducibility of the method. Proteomic discovery efforts that exploit the benefits of DDA, such as providing peptide sequence information, but that enable improved analytical reproducibility, represent an ideal scenario for maximizing measureable peptide identifications in "shotgun"-type proteomic studies. Therefore, we propose an analytical workflow combining DDA with retention time aligned extracted ion chromatogram (XIC) areas obtained from high mass accuracy MS1 data acquired in parallel. We applied this workflow to the analyses of sample matrixes prepared from mouse blood plasma and brain tissues and observed increases in peptide detection of up to 30.5% due to the comparison of peptide MS1 XIC areas following retention time alignment of co-identified peptides. Furthermore, we show that the approach is quantitative using peptide standards diluted into a complex matrix. These data revealed that peptide MS1 XIC areas provide linear response of over three orders of magnitude down to low femtomole (fmol) levels. These findings argue that augmenting "shotgun" proteomic workflows with retention time alignment of peptide identifications and comparative analyses of corresponding peptide MS1 XIC areas improve the analytical performance of global proteomic discovery methods using DDA. Molecular & Cellular Proteomics 13: 10.1074/ mcp.M112.026500, 329-338, 2014.Label-free methods in mass spectrometry-based proteomics, such as those used in common "shotgun" proteomic studies, provide peptide sequence information as well as relative measurements of peptide abundance (1-3). A common data acquisition strategy is based on data-dependent acquisition (DDA) 1 where the most abundant precursor ions are selected for tandem mass spectrometry (MS/MS) analysis (1-2). DDA attempts to minimize redundant peptide precursor selection and maximize the depth of proteome coverage (2). However, the analytical reproducibility of peptide identifications obtained using DDA-based methods result in Ͻ75% overlap between technical replicates (3-4). Comparisons of peptide identifications between replicate analyses have shown that the rate of new peptide identifications increases sharply following two replicate sample injections and gradually tapers off after approximately five replicate injections (4). This phenomenon is due, in part, to the semirandom sampling of peptides in a DDA experiment (5).Alternate label-free methods focused on measuring the abundance of intact peptide ions, such as the accurate mass and time tag (AMT) approach (6 -8, 42), are aimed at differential analyses of extracted ion chromatogram (XIC) areas integrated from high mass accuracy peptide precursor mass spectra (MS1 spectra) exhibiting discrete chromatographic elution times. This method is particularly powerful for the analysis of post-translationally modified (PTM) peptides as pairing the low abundance of PTM candidates with the variabl...

DeMix-Q: Quantification-Centered Data Processing Workflow

“…Today, one or another variant of the accurate mass and time tag-based PIP is employed in many MS 1 -based LFQ algorithms for analyzing DDA data (6,21). MS feature matching (22,23) and targeted extraction of ion chromatograms (XIC) (24,25) represent examples of such variants. Although these algorithms are not free from certain generic drawbacks (26), they allow large-scale comparison of DDA-analyzed complex proteomes (23,27,28).…”

A mass spectrometry-based proteomic analysis of Homer2-interacting proteins in the mouse brain

“…A search of the PeptideAtlas compendium (build: Mouse 2016-01) [87,88] revealed that eEF-2k, Rac1, and TrpC1 proteins were detected by mass spectrometry in mouse brain fractions or neural cell lines, which could indicate that our protein solubilization procedure was too mild to solubilize these proteins. Interestingly, we previously detected peptides corresponding to DGL-α and mGluR1-α in mouse brain tissue, but after an enrichment for PSD proteins [89]. …”

“…MS1 full-scan filtering enabled us to measure relative quantitative differences in protein abundance between samples that do not share identical peptide identification events [89]. Accordingly, we used this method to detect Homer2-interacting proteins that were enriched in WT versus Homer2 KO co-IP samples.…”

Approaches to identify kinase dependencies in cancer signalling networks

“…There are several software tools developed to obtain quantitative information from MS platforms (e.g. ), and readers interested in their characteristics and the challenges still associated with these tools are referred to a recent review .…”