Coisolation of Peptide Pairs for Peptide Identification and MS/MS-Based Quantification.

Stable-isotope labeling with amino acids in cell culture (SILAC)-based metabolic labeling is a widely adopted proteomics approach that enables quantitative comparisons among a variety of experimental conditions. Despite its quantitative capacity, SILAC experiments analyzed with data-dependent acquisition (DDA) do not fully leverage peptide pair information for identification and suffer from undersampling compared to label-free proteomic experiments. Herein, we developed a DDA strategy that coisolates and fragments SILAC peptide pairs and uses y-ions for their relative quantification. To facilitate the analysis of this type of data, we adapted the Comet sequence database search engine to make use of SILAC peptide paired fragments and developed a tool to annotate and quantify MS/MS spectra of coisolated SILAC pairs. This peptide pair coisolation approach generally improved expectation scores compared to the traditional DDA approach. Fragment ion quantification performed similarly well to precursor quantification in the MS1 and achieved more quantifications. Lastly, our method enables reliable MS/MS quantification of SILAC proteome mixtures with overlapping isotopic distributions. This study shows the feasibility of the coisolation approach. Coupling this approach with intelligent acquisition strategies has the potential to improve SILAC peptide sampling and quantification.

IsobaricQuant enables cross-platform quantification, visualization, and filtering of isobarically-labeled peptides.

In mass spectrometry (MS)-based quantitative proteomics, labeling with isobaric mass tags such as iTRAQ and TMT can substantially improve sample throughput and reduce peptide missing values. Nonetheless, the quantification of labeled peptides tends to suffer from reduced accuracy due to the co-isolation of co-eluting precursors of similar mass-to-charge. Acquisition approaches such as multistage MS3 or ion mobility separation address this problem, yet are difficult to audit and limited to expensive instrumentation. Here we introduce IsobaricQuant, an open-source software tool for quantification, visualization, and filtering of peptides labeled with isobaric mass tags, with specific focus on precursor interference. IsobaricQuant is compatible with MS2 and MS3 acquisition strategies, has a viewer that allows assessing interference, and provides several scores to aid the filtering of scans with compression. We demonstrate that IsobaricQuant quantifications are accurate by comparing it with commonly used software. We further show that its QC scores can successfully filter out scans with reduced quantitative accuracy at MS2 and MS3 levels, removing inaccurate peptide quantifications and decreasing protein CVs. Finally, we apply IsobaricQuant to a PISA dataset and show that QC scores improve the sensitivity of the identification of protein targets of a kinase inhibitor. IsobaricQuant is available at https://github.com/Villen-Lab/isobaricquant.

Plug-and-play analysis of the human phosphoproteome by targeted high-resolution mass spectrometry.

Systematic approaches to studying cellular signaling require phosphoproteomic techniques that reproducibly measure the same phosphopeptides across multiple replicates, conditions, and time points. Here we present a method to mine information from large-scale, heterogeneous phosphoproteomics data sets to rapidly generate robust targeted mass spectrometry (MS) assays. We demonstrate the performance of our method by interrogating the IGF-1/AKT signaling pathway, showing that even rarely observed phosphorylation events can be consistently detected and precisely quantified.