The evolution of mass spectrometry (MS) and analytical techniques has led to the demand for proteome analysis with high proteome coverage in single-shot measurements. Focus has been placed on data-independent acquisition (DIA)-MS and ion mobility spectrometry as techniques for deep proteome analysis. We aimed to expand the proteome coverage by single-shot measurements using optimizing high-field asymmetric waveform ion mobility spectrometry parameters in DIA-MS. With our established proteome analysis system, more than 10,000 protein groups were identified from HEK293 cell digests within 120 min of MS measurement time. Additionally, we applied our approach to the analysis of host proteins in mouse feces and detected as many as 892 host protein groups (771 upregulated/121 downregulated proteins) in a mouse model of repeated social defeat stress (R-SDS) used in studying depression. Interestingly, 285 proteins elevated by R-SDS were related to mental disorders. The fecal host protein profiling by deep proteome analysis may help us understand mental illness pathologies noninvasively. Thus, our approach will be helpful for an in-depth comparison of protein expression levels for biological and medical research because it enables the analysis of highly proteome coverage in a single-shot measurement.

The yeast, Saccharomyces cerevisiae, is a widely used model system for investigating conserved biological functions and pathways. Advancements in sample multiplexing have facilitated the study of the yeast proteome, yet many yeast proteins remain uncharacterized or only partially characterized. Yeast deletion strain collections are powerful resources for yeast proteome studies, uncovering the effects of gene function, genetic interactions, and cellular stresses. As complex biological systems cannot be understood by simply analyzing the individual components, a systems approach is often required in which a protein is represented as a component of large, interacting networks. Here, we evaluate the current state of yeast proteome analysis using isobaric tag-based sample multiplexing (TMTpro16) to profile the proteomes of 75 yeast deletion strains for which we measured the abundance of nearly 5000 proteins. Using statistical approaches, we highlighted covariance and regulation subnetworks and the enrichment of gene ontology classifications for covarying and coregulated proteins. This dataset presents a resource that is amenable to further data mining to study individual deletion strains, pathways, proteins, and/or interactions thereof while serving as a template for future network-based investigations using yeast deletion strain collections.

Advances in library-based methods for peptide detection from data-independent acquisition (DIA) mass spectrometry have made it possible to detect and quantify tens of thousands of peptides in a single mass spectrometry run. However, many of these methods rely on a comprehensive, high-quality spectral library containing information about the expected retention time and fragmentation patterns of peptides in the sample. Empirical spectral libraries are often generated through data-dependent acquisition and may suffer from biases as a result. Spectral libraries can be generated in silico, but these models are not trained to handle all possible post-translational modifications. Here, we propose a false discovery rate-controlled spectrum-centric search workflow to generate spectral libraries directly from gas-phase fractionated DIA tandem mass spectrometry data. We demonstrate that this strategy is able to detect phosphorylated peptides and can be used to generate a spectral library for accurate peptide detection and quantitation in wide-window DIA data. We compare the results of this search workflow to other library-free approaches and demonstrate that our search is competitive in terms of accuracy and sensitivity. These results demonstrate that the proposed workflow has the capacity to generate spectral libraries while avoiding the limitations of other methods.

Spectrum clustering is a powerful strategy to minimize redundant mass spectra by grouping them based on similarity, with the aim of forming groups of mass spectra from the same repeatedly measured analytes. Each such group of near-identical spectra can be represented by its so-called consensus spectrum for downstream processing. Although several algorithms for spectrum clustering have been adequately benchmarked and tested, the influence of the consensus spectrum generation step is rarely evaluated. Here, we present an implementation and benchmark of common consensus spectrum algorithms, including spectrum averaging, spectrum binning, the most similar spectrum, and the best-identified spectrum. We have analyzed diverse public data sets using two different clustering algorithms (spectra-cluster and MaRaCluster) to evaluate how the consensus spectrum generation procedure influences downstream peptide identification. The BEST and BIN methods were found the most reliable methods for consensus spectrum generation, including for data sets with post-translational modifications (PTM) such as phosphorylation. All source code and data of the present study are freely available on GitHub at https://github.com/statisticalbiotechnology/representative-spectra-benchmark.

Absolute (molar) quantification of clinically relevant proteins determines their reference values in liquid and solid biopsies. The FastCAT (for Fast-track QconCAT) method employs multiple short (<50 kDa), stable-isotope labeled chimeric proteins (CPs) composed of concatenated quantotypic (Q)-peptides representing the quantified proteins. Each CP also comprises scrambled sequences of reference (R)-peptides that relate its abundance to a single protein standard (bovine serum albumin, BSA). FastCAT not only alleviates the need to purify CP or use sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) but also improves the accuracy, precision, and dynamic range of the absolute quantification by grouping Q-peptides according to the expected abundance of the target proteins. We benchmarked FastCAT against the reference method of MS Western and tested it in the direct molar quantification of neurological markers in human cerebrospinal fluid at the low ng/mL level.

The oxidation of methionine has emerged as an important post-translational modification of proteins. A number of studies have suggested that the oxidation of methionines in select proteins can have diverse impacts on cell physiology, ranging from detrimental effects on protein stability to functional roles in cell signaling. Despite its importance, the large-scale investigation of methionine oxidation in a complex matrix, such as the cellular proteome, has been hampered by technical limitations. We report a methodology, methionine oxidation by blocking (MobB), that allows for accurate and precise quantification of low levels of methionine oxidation typically observed in vivo. To demonstrate the utility of this methodology, we analyzed the brain tissues of young (6 m.o.) and old (20 m.o.) mice and identified over 280 novel sites for in vivo methionine oxidation. We further demonstrated that oxidation stoichiometries for specific methionine residues are highly consistent between individual animals and methionine sulfoxides are enriched in clusters of functionally related gene products including membrane and extracellular proteins. However, we did not detect significant changes in methionine oxidation in brains of old mice. Our results suggest that under normal conditions, methionine oxidation may be a biologically regulated process rather than a result of stochastic chemical damage.

Traumatic spinal cord injury (SCI) results in disruption of tissue integrity and loss of function. We hypothesize that glycosylation has a role in determining the occurrence of regeneration and that biomaterial treatment can influence this glycosylation response. We investigated the glycosylation response to spinal cord transection in Xenopus laevis and rat. Transected rats received an aligned collagen hydrogel. The response compared regenerative success, regenerative failure, and treatment in an established nonregenerative mammalian system. In a healthy rat spinal cord, ultraperformance liquid chromatography (UPLC) N-glycoprofiling identified complex, hybrid, and oligomannose N-glycans. Following rat SCI, complex and outer-arm fucosylated glycans decreased while oligomannose and hybrid structures increased. Sialic acid was associated with microglia/macrophages following SCI. Treatment with aligned collagen hydrogel had a minimal effect on the glycosylation response. In Xenopus, lectin histochemistry revealed increased levels of N-acetyl-glucosamine (GlcNAc) in premetamorphic animals. The addition of GlcNAc is required for processing complex-type glycans and is a necessary foundation for additional branching. A large increase in sialic acid was observed in nonregenerative animals. This work suggests that glycosylation may influence regenerative success. In particular, loss of complex glycans in rat spinal cord may contribute to regeneration failure. Targeting the glycosylation response may be a promising strategy for future therapies.

Public phosphorylation databases such as PhosphoSitePlus (PSP) and PeptideAtlas (PA) compile results from published papers or openly available mass spectrometry (MS) data. However, there is no database-level control for false discovery of sites, likely leading to the overestimation of true phosphosites. By profiling the human phosphoproteome, we estimate the false discovery rate (FDR) of phosphosites and predict a more realistic count of true identifications. We rank sites into phosphorylation likelihood sets and analyze them in terms of conservation across 100 species, sequence properties, and functional annotations. We demonstrate significant differences between the sets and develop a method for independent phosphosite FDR estimation. Remarkably, we report estimated FDRs of 84, 98, and 82% within sets of phosphoserine (pSer), phosphothreonine (pThr), and phosphotyrosine (pTyr) sites, respectively, that are supported by only a single piece of identification evidence─the majority of sites in PSP. We estimate that around 62 000 Ser, 8000 Thr, and 12 000 Tyr phosphosites in the human proteome are likely to be true, which is lower than most published estimates. Furthermore, our analysis estimates that 86 000 Ser, 50 000 Thr, and 26 000 Tyr phosphosites are likely false-positive identifications, highlighting the significant potential of false-positive data to be present in phosphorylation databases.

Liquid chromatography coupled to mass spectrometry is a key metabolomics/metabonomics technology. Reversed-phase liquid chromatography (RPLC) is very widely used as a separation step, but typically has poor retention of highly polar metabolites. Here, we evaluated the combination of two alternative methods for improving retention of polar metabolites based on 6-aminoquinoloyl-N-hydroxysuccinidimyl carbamate derivatization for amine groups, and ion-pairing chromatography (IPC) using tributylamine as an ion-pairing agent to retain acids. We compared both of these methods to RPLC and also to each other, for targeted analysis using a triple-quadrupole mass spectrometer, applied to a library of ca. 500 polar metabolites. IPC and derivatization were complementary in terms of their coverage: combined, they improved the proportion of metabolites with good retention to 91%, compared to just 39% for RPLC alone. The combined method was assessed by analyzing a set of liver extracts from aged male and female mice that had been treated with the polyphenol compound ampelopsin. Not only were a number of significantly changed metabolites detected, but also it could be shown that there was a clear interaction between ampelopsin treatment and sex, in that the direction of metabolite change was opposite for males and females.