The data-independent acquisition (DIA) approach has recently been introduced as a novel mass spectrometric method that promises to combine the high content aspect of shotgun proteomics with the reproducibility and precision of selected reaction monitoring. Here, we evaluate, whether SWATH-MS type DIA effectively translates into a better protein profiling as compared with the established shotgun proteomics.We implemented a novel DIA method on the widely used Orbitrap platform and used retention-time-normalized (iRT) spectral libraries for targeted data extraction using Spectronaut. We call this combination hyper reaction monitoring (HRM). Using a controlled sample set, we show that HRM outperformed shotgun proteomics both in the number of consistently identified peptides across multiple measurements and quantification of differentially abundant proteins. The reproducibility of HRM in peptide detection was above 98%, resulting in quasi complete data sets compared with 49% of shotgun proteomics.Utilizing HRM, we profiled acetaminophen (APAP)1-treated three-dimensional human liver microtissues. An early onset of relevant proteome changes was revealed at subtoxic doses of APAP. Further, we detected and quantified for the first time human NAPQI-protein adducts that might be relevant for the toxicity of APAP. The adducts were identified on four mitochondrial oxidative stress related proteins (GATM, PARK7, PRDX6, and VDAC2) and two other proteins (ANXA2 and FTCD).Our findings imply that DIA should be the preferred method for quantitative protein profiling.

Comprehensive, reproducible and precise analysis of large sample cohorts is one of the key objectives of quantitative proteomics. Here, we present an implementation of data-independent acquisition using its parallel acquisition nature that surpasses the limitation of serial MS2 acquisition of data-dependent acquisition on a quadrupole ultra-high field Orbitrap mass spectrometer. In deep single shot data-independent acquisition, we identified and quantified 6,383 proteins in human cell lines using 2-or-more peptides/protein and over 7100 proteins when including the 717 proteins that were identified on the basis of a single peptide sequence. 7739 proteins were identified in mouse tissues using 2-or-more peptides/protein and 8121 when including the 382 proteins that were identified based on a single peptide sequence. Missing values for proteins were within 0.3 to 2.1% and median coefficients of variation of 4.7 to 6.2% among technical triplicates. In very complex mixtures, we could quantify 10,780 proteins and 12,192 proteins when including the 1412 proteins that were identified based on a single peptide sequence. Using this optimized DIA, we investigated large-protein networks before and after the critical period for whisker experience-induced synaptic strength in the murine somatosensory cortex 1-barrel field. This work shows that parallel mass spectrometry enables proteome profiling for discovery with high coverage, reproducibility, precision and scalability.

Quantitative phosphoproteomics has transformed investigations of cell signaling, but it remains challenging to scale the technology for high-throughput analyses. Here we report a rapid and reproducible approach to analyze hundreds of phosphoproteomes using data-independent acquisition (DIA) with an accurate site localization score incorporated into Spectronaut. DIA-based phosphoproteomics achieves an order of magnitude broader dynamic range, higher reproducibility of identification, and improved sensitivity and accuracy of quantification compared to state-of-the-art data-dependent acquisition (DDA)-based phosphoproteomics. Notably, direct DIA without the need of spectral libraries performs close to analyses using project-specific libraries, quantifying > 20,000 phosphopeptides in 15 min single-shot LC-MS analysis per condition. Adaptation of a 3D multiple regression model-based algorithm enables global determination of phosphorylation site stoichiometry in DIA. Scalability of the DIA approach is demonstrated by systematically analyzing the effects of thirty kinase inhibitors in context of epidermal growth factor (EGF) signaling showing that specific protein kinases mediate EGF-dependent phospho-regulation.

We established a robust capillary-flow data-independent acquisition MS platform capable of measuring 31 plasma proteomes per day without the need of repeated acquisition of the same sample. We acquired 1508 samples of the DiOGenes study (multicentered, Europa-wide caloric restriction weight loss and maintenance study of overweight and obese, non-diabetic participants). This was achieved using a single analytical column. Comprehensive biological reactions to weight loss and maintenance were observed.

Optimization of chromatography and data analysis resulted in more than 10 000 proteins in a single shot at a validated FDR of 1% (two-species test) and revealed deep insights into the testis cancer physiology.

Targeted analysis of data‐independent acquisition (DIA) data is a powerful mass spectrometric approach for comprehensive, reproducible and precise proteome quantitation. It requires a spectral library, which contains for all considered peptide precursor ions empirically determined fragment ion intensities and their predicted retention time (RT). RTs, however, are not comparable on an absolute scale, especially if heterogeneous measurements are combined. Here, we present a method for high‐precision prediction of RT, which significantly improves the quality of targeted DIA analysis compared to in silico RT prediction and the state of the art indexed retention time (iRT) normalization approach. We describe a high‐precision normalized RT algorithm, which is implemented in the Spectronaut software. We, furthermore, investigate the influence of nine different experimental factors, such as chromatographic mobile and stationary phase, on iRT precision. In summary, we show that using targeted analysis of DIA data with high‐precision iRT significantly increases sensitivity and data quality. The iRT values are generally transferable across a wide range of experimental conditions. Best results, however, are achieved if library generation and analytical measurements are performed on the same system.

Transport of solutes between the cytosol and the vacuolar lumen is of crucial importance for various functions of vacuoles, including ion homeostasis; detoxification; storage of different molecules such as amino acids, phosphate, and calcium ions; and proteolysis. To identify proteins that catalyze solute transport across the vacuolar membrane, the membrane proteome of purified Saccharomyces cerevisiae vacuoles was analyzed. Subtractive proteomics was used to distinguish contaminants from true vacuolar proteins by comparing the relative abundances of proteins in pure and crude preparations. A robust statistical analysis combining enrichment ranking with the double boundary iterative group analysis revealed that 148 proteins were significantly enriched in the pure vacuolar preparations. Among these proteins were well characterized vacuolar proteins, such as the subunits of the vacuolar H ؉ -ATPase, but also proteins that had not previously been assigned to a cellular location, many of which are likely novel vacuolar membrane transporters, e.g. for nucleosides and oligopeptides. Although the majority of contaminating proteins from other organelles were depleted from the pure vacuolar membranes, some proteins annotated to reside in other cellular locations were enriched along with the vacuolar proteins. In many cases the enrichment of these proteins is biologically relevant, and we discuss that a large group is involved in membrane fusion and protein trafficking to vacuoles and may have multiple localizations. Other proteins are degraded in vacuoles, and in some cases database annotations are likely to be incomplete or incorrect. Our work provides a wealth of information on vacuolar biology and a solid basis for further characterization of vacuolar functions. The pH difference of ϳ1.7 pH units between the vacuolar lumen and the cytosol is used as the driving force for substrate-proton antiport systems in the vacuolar membrane (1). Transport processes across the vacuolar membrane are important for many crucial functions of vacuoles: storage of organic molecules such as polyphosphates, mannans, and other carbohydrates (2, 3); detoxification, i.e. removal and accumulation of harmful substances such as heavy metals and drugs; and proton and ion homeostasis. Another major function of lysosomes and vacuoles is the intracellular proteolysis of cytosolic and membrane proteins (4, 5) and turnover of organelles, e.g. lipid bodies, mitochondria, peroxisomes, and portions of nuclei (6 -9). Hydrolytic proteins in the lumen of vacuoles, including proteases, lipases, phosphatases, and nucleases, carry out these functions. Whereas vacuolar luminal proteins have been studied fairly extensively (10, 11), our knowledge about the integral membrane proteins in the yeast vacuolar membrane is limited.

Profiling of biological relationships between different molecular layers dissects regulatory mechanisms that ultimately determine cellular function. To thoroughly assess the role of protein post‐translational turnover, we devised a strategy combining pulse stable isotope‐labeled amino acids in cells (pSILAC), data‐independent acquisition mass spectrometry (DIA‐MS), and a novel data analysis framework that resolves protein degradation rate on the level of mRNA alternative splicing isoforms and isoform groups. We demonstrated our approach by the genome‐wide correlation analysis between mRNA amounts and protein degradation across different strains of HeLa cells that harbor a high grade of gene dosage variation. The dataset revealed that specific biological processes, cellular organelles, spatial compartments of organelles, and individual protein isoforms of the same genes could have distinctive degradation rate. The protein degradation diversity thus dissects the corresponding buffering or concerting protein turnover control across cancer cell lines. The data further indicate that specific mRNA splicing events such as intron retention significantly impact the protein abundance levels. Our findings support the tight association between transcriptome variability and proteostasis and provide a methodological foundation for studying functional protein degradation.