Most proteomic studies use liquid chromatography coupled to tandem mass spectrometry to identify and quantify the peptides generated by the proteolysis of a biological sample. However, with the current methods it remains challenging to rapidly, consistently, reproducibly, accurately, and sensitively detect and quantify large fractions of proteomes across multiple samples. Here we present a new strategy that systematically queries sample sets for the presence and quantity of essentially any protein of interest. It consists of using the information available in fragment ion spectral libraries to mine the complete fragment ion maps generated using a data-independent acquisition method. For this study, the data were acquired on a fast, high resolution quadrupole-quadrupole time-offlight (TOF) instrument by repeatedly cycling through 32 consecutive 25-Da precursor isolation windows (swaths). This SWATH MS acquisition setup generates, in a single sample injection, time-resolved fragment ion spectra for all the analytes detectable within the 400 -1200 m/z precursor range and the user-defined retention time window. We show that suitable combinations of fragment ions extracted from these data sets are sufficiently specific to confidently identify query peptides over a dynamic range of 4 orders of magnitude, even if the precursors of the queried peptides are not detectable in the survey scans. We also show that queried peptides are quantified with a consistency and accuracy comparable with that of selected reaction monitoring, the gold standard proteomic quantification method. Moreover, targeted data extraction enables ad libitum quantification refinement and dynamic extension of protein probing by iterative re-mining of the once-and-forever acquired data sets. This combination of unbiased, broad range precursor ion fragmentation and targeted data extraction alleviates most constraints of present proteomic methods and should be equally applicable to the comprehensive analysis of other classes of analytes, beyond proteomics. Molecular & Cellular Proteomics 11: 10.1074/mcp.O111.016717, 1-17, 2012. Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS)1 is considered the method of choice for the identification and quantification of proteins and proteomes (1-4) and for the analysis of metabolites, lipids, glycans, and many other types of (bio)molecules. For proteomics, two main LC-MS/MS strategies have been used thus far. They have in common that the sample proteins are converted by proteolysis into peptides, which are then separated by (capillary) liquid chromatography. They differ in the mass spectrometric method used. The first and most widely used strategy is known as shotgun or discovery proteomics. For this method, the MS instrument is operated in data-dependent acquisition (DDA) mode, where fragment ion (MS2) spectra for selected precursor ions detectable in a survey (MS1) scan are generated (5). The resulting fragment ion spectra are then assigned to their corresponding peptide sequences by sequence data...

OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data.

The consistent and accurate quantification of proteins by mass spectrometry (MS)-based proteomics depends on the performance of instruments, acquisition methods and data analysis software. In collaboration with the software developers, we evaluated OpenSWATH, SWATH2.0, Skyline, Spectronaut and DIA-Umpire, five of the most widely used software methods for processing data from SWATH-MS (sequential window acquisition of all theoretical fragment ion spectra), a method that uses data-independent acquisition (DIA) for label-free protein quantification. We analyzed high-complexity test datasets from hybrid proteome samples of defined quantitative composition acquired on two different MS instruments using different SWATH isolation windows setups. For consistent evaluation we developed LFQbench, an R-package to calculate metrics of precision and accuracy in label-free quantitative MS, and report the identification performance, robustness and specificity of each software tool. Our reference datasets enabled developers to improve their software tools. After optimization, all tools provided highly convergent identification and reliable quantification performance, underscoring their robustness for label-free quantitative proteomics.

We present a data-independent acquisition mass spectrometry method, ultradefinition (UD) MS(E). This approach utilizes ion mobility drift time-specific collision-energy profiles to enhance precursor fragmentation efficiency over current MS(E) and high-definition (HD) MS(E) data-independent acquisition techniques. UDMS(E) provided high reproducibility and substantially improved proteome coverage of the HeLa cell proteome compared to previous implementations of MS(E), and it also outperformed a state-of-the-art data-dependent acquisition workflow. Additionally, we report a software tool, ISOQuant, for processing label-free quantitative UDMS(E) data.

Targeted proteomics by selected/multiple reaction monitoring (S/MRM) or, on a larger scale, by SWATH (sequential window acquisition of all theoretical spectra) MS (mass spectrometry) typically relies on spectral reference libraries for peptide identification. Quality and coverage of these libraries are therefore of crucial importance for the performance of the methods. Here we present a detailed protocol that has been successfully used to build high-quality, extensive reference libraries supporting targeted proteomics by SWATH MS. We describe each step of the process, including data acquisition by discovery proteomics, assertion of peptide-spectrum matches (PSMs), generation of consensus spectra and compilation of MS coordinates that uniquely define each targeted peptide. Crucial steps such as false discovery rate (FDR) control, retention time normalization and handling of post-translationally modified peptides are detailed. Finally, we show how to use the library to extract SWATH data with the open-source software Skyline. The protocol takes 2-3 d to complete, depending on the extent of the library and the computational resources available.

Unbiased data-independent acquisition (DIA) strategies have gained increased popularity in the field of quantitative proteomics. The integration of ion mobility separation (IMS) into DIA workflows provides an additional dimension of separation to liquid chromatography-mass spectrometry (LC-MS), and it increases the achievable analytical depth of DIA approaches. Here we provide a detailed protocol for a label-free quantitative proteomics workflow based on ion mobility-enhanced DIA, which synchronizes precursor ion drift times with collision energies to improve precursor fragmentation efficiency. The protocol comprises a detailed description of all major steps including instrument setup, filter-aided sample preparation, LC-IMS-MS analysis and data processing. Our protocol can handle proteome samples of any complexity, and it enables a highly reproducible and accurate precursor intensity-based label-free quantification of up to 5,600 proteins across multiple runs in complete cellular lysates. Depending on the number of samples to be analyzed, the protocol takes a minimum of 3 d to complete from proteolytic digestion to data evaluation.

SWATH-MS is a data-independent acquisition method that generates, in a single measurement, a complete recording of the fragment ion spectra of all the analytes in a biological sample for which the precursor ions are within a predetermined m/z versus retention time window. To assess the performance and suitability of SWATH-MS-based protein quantification for clinical use, we compared SWATH-MS and SRM-MS-based quantification of N-linked glycoproteins in human plasma, a commonly used sample for biomarker discovery. Using dilution series of isotopically labeled heavy peptides representing biomarker candidates, the LOQ of SWATH-MS was determined to reach 0.0456 fmol at peptide level by targeted data analysis, which corresponds to a concentration of 5-10 ng protein/mL in plasma, while SRM reached a peptide LOQ of 0.0152 fmol. Moreover, the quantification of endogenous glycoproteins using SWATH-MS showed a high degree of reproducibility, with the mean CV of 14.90%, correlating well with SRM results (R(2) = 0.9784). Overall, SWATH-MS measurements showed a slightly lower sensitivity and a comparable reproducibility to state-of-the-art SRM measurements for targeted quantification of the N-glycosites in human blood. However, a significantly larger number of peptides can be quantified per analysis. We suggest that SWATH-MS analysis combined with N-glycoproteome enrichment in plasma samples is a promising integrative proteomic approach for biomarker discovery and verification.

The combination of stable isotope labeling (SIL) with mass spectrometry (MS) allows comparison of the abundance of thousands of proteins in complex mixtures. However, interpretation of the large data sets generated by these techniques remains a challenge because appropriate statistical standards are lacking. Here, we present a generally applicable model that accurately explains the behavior of data obtained using current SIL approaches, including 18O, iTRAQ, and SILAC labeling, and different MS instruments. The model decomposes the total technical variance into the spectral, peptide, and protein variance components, and its general validity was demonstrated by confronting 48 experimental distributions against 18 different null hypotheses. In addition to its general applicability, the performance of the algorithm was at least similar than that of other existing methods. The model also provides a general framework to integrate quantitative and error information fully, allowing a comparative analysis of the results obtained from different SIL experiments. The model was applied to the global analysis of protein alterations induced by low H2O2 concentrations in yeast, demonstrating the increased statistical power that may be achieved by rigorous data integration. Our results highlight the importance of establishing an adequate and validated statistical framework for the analysis of high-throughput data.