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Major histocompatibility complex (MHC)-bound peptides that originate from tumor-specific genetic alterations, known as neoantigens, are an important class of anticancer therapeutic targets. Accurately predicting peptide presentation by MHC complexes is a key aspect of discovering therapeutically relevant neoantigens. Technological improvements in mass-spectrometry-based immunopeptidomics and advanced modeling techniques have vastly improved MHC presentation prediction over the past two decades. However, improvement in the sensitivity and specificity of prediction algorithms is needed for clinical applications such as the development of personalized cancer vaccines, the discovery of biomarkers for response to checkpoint blockade, and the quantification of autoimmune risk in gene therapies. Toward this end, we generated allele-specific immunopeptidomics data using 25 monoallelic cell lines and created Systematic HLA Epitope Ranking Pan Algorithm (SHERPA), a pan-allelic MHC-peptide algorithm for predicting MHC-peptide binding and presentation. In contrast to previously published large-scale monoallelic data, we used an HLA-null K562 parental cell line and a stable transfection of HLA alleles to better emulate native presentation. Our dataset includes five previously unprofiled alleles that expand MHC-binding pocket diversity in the training data and extend allelic coverage in under profiled populations. To improve generalizability, SHERPA systematically integrates 128 monoallelic and 384 multiallelic samples with publicly available immunoproteomics data and binding assay data. Using this dataset, we developed two features that empirically estimate the propensities of genes and specific regions within gene bodies to engender immunopeptides to represent antigen processing. Using a composite model constructed with gradient boosting decision trees, multiallelic deconvolution, and 2.15 million peptides encompassing 167 alleles, we achieved a 1.44-fold improvement of positive predictive value compared with existing tools when evaluated on independent monoallelic datasets and a 1.15-fold improvement when evaluating on tumor samples. With a high degree of accuracy, SHERPA has the potential to enable precision neoantigen discovery for future clinical applications., Graphical Abstract, Highlights • Generated 25 stably transfected monoallelic cell lines and applied immunopeptidomics. • Harmonized 512 public immunopeptidomic samples through systematic reprocessing. • Developed pan-allele MHC-binding algorithm (SHERPA) utilizing 167 human HLA alleles. • SHERPA demonstrates up to 1.44-fold increased precision over competing algorithms., In Brief Accurately identifying neoantigens is critical for many clinical applications. We generated immunopeptidomics data from 25 stably transfected monoallelic cell lines. Then, we systematically reprocessed a large corpus of public data to improve major histocompatibility complex (MHC) binding pocket diversity and to empirically learn the rules of antigen presentation. In applying these datasets, we trained SHERPA, an MHC binding and presentation prediction algorithm. SHERPA improves performance compared with existing tools by 1.44-fold in held-out monoallelic data and 1.11-fold for immunogenic epitopes.

We present the novel ReactomeGSA resource for comparative pathway analyses of multi-omics datasets. ReactomeGSA is accessible through Reactome's web interface and the novel ReactomeGSA R Bioconductor package with explicit support for scRNA-seq data. We showcase ReactomeGSA's functionality by characterizing the role of B cells in anti-tumour immunity. Combining multi-omics data of five TCGA studies reveals marked opposing effects of B cells in different cancers. This showcases how ReactomeGSA can quickly derive novel biomedical insights by integrating large multi-omics datasets., Graphical Abstract Highlights ReactomeGSA is a novel tool for multi-species, multi-omics pathway analysis. Its quantitative pathway analysis methods offer high statistical power. Combining data of five TCGA studies shows B cells have opposing effects in cancers. ReactomeGSA reveals differences in key pathways between transcript- and protein-level., Pathway analyses are key methods to analyze 'omics experiments. Nevertheless, integrating data from different 'omics technologies and different species still requires considerable bioinformatics knowledge. Here we present the novel ReactomeGSA resource for comparative pathway analyses of multi-omics datasets. ReactomeGSA can be used through Reactome's existing web interface and the novel ReactomeGSA R Bioconductor package with explicit support for scRNA-seq data. Data from different species is automatically mapped to a common pathway space. Public data from ExpressionAtlas and Single Cell ExpressionAtlas can be directly integrated in the analysis. ReactomeGSA greatly reduces the technical barrier for multi-omics, cross-species, comparative pathway analyses. We used ReactomeGSA to characterize the role of B cells in anti-tumor immunity. We compared B cell rich and poor human cancer samples from five of the Cancer Genome Atlas (TCGA) transcriptomics and two of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteomics studies. B cell-rich lung adenocarcinoma samples lacked the otherwise present activation through NFkappaB. This may be linked to the presence of a specific subset of tumor associated IgG+ plasma cells that lack NFkappaB activation in scRNA-seq data from human melanoma. This showcases how ReactomeGSA can derive novel biomedical insights by integrating large multi-omics datasets.

Cross-linking MS (XL-MS) has been recognized as an effective source of information about protein structures and interactions. In contrast to regular peptide identification, XL-MS has to deal with a quadratic search space, where peptides from every protein could potentially be cross-linked to any other protein. To cope with this search space, most tools apply different heuristics for search space reduction. We introduce a new open-source XL-MS database search algorithm, OpenPepXL, which offers increased sensitivity compared with other tools. OpenPepXL searches the full search space of an XL-MS experiment without using heuristics to reduce it. Because of efficient data structures and built-in parallelization OpenPepXL achieves excellent runtimes and can also be deployed on large compute clusters and cloud services while maintaining a slim memory footprint. We compared OpenPepXL to several other commonly used tools for identification of noncleavable labeled and label-free cross-linkers on a diverse set of XL-MS experiments. In our first comparison, we used a data set from a fraction of a cell lysate with a protein database of 128 targets and 128 decoys. At 5% FDR, OpenPepXL finds from 7% to over 50% more unique residue pairs (URPs) than other tools. On data sets with available high-resolution structures for cross-link validation OpenPepXL reports from 7% to over 40% more structurally validated URPs than other tools. Additionally, we used a synthetic peptide data set that allows objective validation of cross-links without relying on structural information and found that OpenPepXL reports at least 12% more validated URPs than other tools. It has been built as part of the OpenMS suite of tools and supports Windows, macOS, and Linux operating systems. OpenPepXL also supports the MzIdentML 1.2 format for XL-MS identification results. It is freely available under a three-clause BSD license at https://openms.org/openpepxl.

MSstatsTMT implements a general statistical approach for relative protein quantification and tests for differential abundance in mass spectrometry-based experiments with TMT labeling. It is applicable to experiments with multiple conditions, multiple biological replicate runs and multiple technical replicate runs, and unbalanced designs. Evaluation on a controlled mixture, simulated datasets, and three biological investigations with diverse designs demonstrated that MSstatsTMT balanced the sensitivity and the specificity of detecting differentially abundant proteins, in large-scale experiments with multiple biological mixtures., Graphical Abstract Highlights • Statistical approach for differential abundance analysis for proteomic experiments with TMT labeling. • Applicable to large-scale experiments with complex or unbalanced design. • An open-source R/Bioconductor package compatible with popular data processing tools., Tandem mass tag (TMT) is a multiplexing technology widely-used in proteomic research. It enables relative quantification of proteins from multiple biological samples in a single MS run with high efficiency and high throughput. However, experiments often require more biological replicates or conditions than can be accommodated by a single run, and involve multiple TMT mixtures and multiple runs. Such larger-scale experiments combine sources of biological and technical variation in patterns that are complex, unique to TMT-based workflows, and challenging for the downstream statistical analysis. These patterns cannot be adequately characterized by statistical methods designed for other technologies, such as label-free proteomics or transcriptomics. This manuscript proposes a general statistical approach for relative protein quantification in MS- based experiments with TMT labeling. It is applicable to experiments with multiple conditions, multiple biological replicate runs and multiple technical replicate runs, and unbalanced designs. It is based on a flexible family of linear mixed-effects models that handle complex patterns of technical artifacts and missing values. The approach is implemented in MSstatsTMT, a freely available open-source R/Bioconductor package compatible with data processing tools such as Proteome Discoverer, MaxQuant, OpenMS, and SpectroMine. Evaluation on a controlled mixture, simulated datasets, and three biological investigations with diverse designs demonstrated that MSstatsTMT balanced the sensitivity and the specificity of detecting differentially abundant proteins, in large-scale experiments with multiple biological mixtures.

Single-cell transcriptomics has revolutionized our understanding of basic biology and disease. Since transcript levels often do not correlate with protein expression, it is crucial to complement transcriptomics approaches with proteome analyses at single-cell resolution. Despite continuous technological improvements in sensitivity, mass-spectrometry-based single-cell proteomics ultimately faces the challenge of reproducibly comparing the protein expression profiles of thousands of individual cells. Here, we combine two hitherto opposing analytical strategies, DIA and Tandem-Mass-Tag (TMT)-multiplexing, to generate highly reproducible, quantitative proteome signatures from ultralow input samples. We developed a novel, identification-independent proteomics data-analysis pipeline that allows to quantitatively compare DIA-TMT proteome signatures across hundreds of samples independent of their biological origin to identify cell types and single protein knockouts. These proteome signatures overcome the need to impute quantitative data due to accumulating detrimental amounts of missing data in standard multibatch TMT experiments. We validate our approach using integrative data analysis of different human cell lines and standard database searches for knockouts of defined proteins. Our data establish a novel and reproducible approach to markedly expand the numbers of proteins one detects from ultralow input samples., Graphical Abstract, Highlights • DIA-TMT provides reproducible, quantitative proteome signatures at high throughput. • Proteome signature inferred cell type characterization is highly accurate. • Proteome signatures accurately highlight underrepresented cell types. • ID-independent DIA-TMT is more reproducible than standard DDA acquisition strategies., In Brief Proteomics faces the challenge of reproducibly comparing the protein expression profiles across large sample cohorts. Here, we combined two hitherto opposing analytical strategies, DIA and isobaric labeling to generate highly reproducible, quantitative “proteome signatures”. These signatures decouple peptide identification from quantification to quantitatively compare hundreds of samples. DIA-TMT data provides complete quantitative signatures independent of peptide identification that distinguish cell types down to single protein knockouts in high-throughput even at ultralow input.

The presentation of post-translationally modified (PTM) peptides by cell surface HLA molecules has the potential to increase the diversity of targets for surveilling T cells. Although immunopeptidomics studies routinely identify thousands of HLA-bound peptides from cell lines and tissue samples, in-depth analyses of the proportion and nature of peptides bearing one or more PTMs remains challenging. Here we have analyzed HLA-bound peptides from a variety of allotypes and assessed the distribution of mass spectrometry-detected PTMs, finding deamidation of asparagine or glutamine to be highly prevalent. Given that asparagine deamidation may arise either spontaneously or through enzymatic reaction, we assessed allele-specific and global motifs flanking the modified residues. Notably, we found that the N-linked glycosylation motif NX(S/T) was highly abundant across asparagine-deamidated HLA-bound peptides. This finding, demonstrated previously for a handful of deamidated T cell epitopes, implicates a more global role for the retrograde transport of nascently N-glycosylated polypeptides from the ER and their subsequent degradation within the cytosol to form HLA-ligand precursors. Chemical inhibition of Peptide:N-Glycanase (PNGase), the endoglycosidase responsible for the removal of glycans from misfolded and retrotranslocated glycoproteins, greatly reduced presentation of this subset of deamidated HLA-bound peptides. Importantly, there was no impact of PNGase inhibition on peptides not containing a consensus NX(S/T) motif. This indicates that a large proportion of HLA-I bound asparagine deamidated peptides are generated from formerly glycosylated proteins that have undergone deglycosylation via the ER-associated protein degradation (ERAD) pathway. The information herein will help train deamidation prediction models for HLA-peptide repertoires and aid in the design of novel T cell therapeutic targets derived from glycoprotein antigens.

Dynamic tyrosine phosphorylation is fundamental to a myriad of cellular processes. However, the inherently low abundance of tyrosine phosphorylation in the proteome and the inefficient enrichment of phosphotyrosine(pTyr)-containing peptides has led to poor pTyr peptide identification and quantitation, critically hindering researchers' ability to elucidate signaling pathways regulated by tyrosine phosphorylation in systems where cellular material is limited. The most popular approaches to wide-scale characterization of the tyrosine phosphoproteome use pTyr enrichment with pan-specific, anti-pTyr antibodies from a large amount of starting material. Methods that decrease the amount of starting material and increase the characterization depth of the tyrosine phosphoproteome while maintaining quantitative accuracy and precision would enable the discovery of tyrosine phosphorylation networks in rarer cell populations. To achieve these goals, the BOOST (Broad-spectrum Optimization Of Selective Triggering) method leveraging the multiplexing capability of tandem mass tags (TMT) and the use of pervanadate (PV) boost channels (cells treated with the broad-spectrum tyrosine phosphatase inhibitor PV) selectively increased the relative abundance of pTyr-containing peptides. After PV boost channels facilitated selective fragmentation of pTyr-containing peptides, TMT reporter ions delivered accurate quantitation of each peptide for the experimental samples while the quantitation from PV boost channels was ignored. This method yielded up to 6.3-fold boost in pTyr quantification depth of statistically significant data derived from contrived ratios, compared with TMT without PV boost channels or intensity-based label-free (LF) quantitation while maintaining quantitative accuracy and precision, allowing quantitation of over 2300 unique pTyr peptides from only 1 mg of T cell receptor-stimulated Jurkat T cells. The BOOST strategy can potentially be applied in analyses of other post-translational modifications where treatments that broadly elevate the levels of those modifications across the proteome are available.

DIA profiles of complex biological matrices such as tissues can contain quantitative interferences, and the interferences at the MS1 and the MS2 signals are often independent. We developed a statistical procedure incorporating both MS1 and MS2 quantitative information of DIA. We benchmarked the performance of the MS1-MS2-combined method to the individual use of MS1 or MS2 in DIA. In the majority of the comparisons, the combined method outperformed the individual use of MS1 or MS2., Graphical Abstract Highlights Modern DIA methods contain high quality MS1 and MS2. We developed a statistical procedure incorporating MS1 and MS2. Benchmarking, the combined method outperformed the individual use of MS1 or MS2., In bottom-up, label-free discovery proteomics, biological samples are acquired in a data-dependent (DDA) or data-independent (DIA) manner, with peptide signals recorded in an intact (MS1) and fragmented (MS2) form. While DDA has only the MS1 space for quantification, DIA contains both MS1 and MS2 at high quantitative quality. DIA profiles of complex biological matrices such as tissues or cells can contain quantitative interferences, and the interferences at the MS1 and the MS2 signals are often independent. When comparing biological conditions, the interferences can compromise the detection of differential peptide or protein abundance and lead to false positive or false negative conclusions. We hypothesized that the combined use of MS1 and MS2 quantitative signals could improve our ability to detect differentially abundant proteins. Therefore, we developed a statistical procedure incorporating both MS1 and MS2 quantitative information of DIA. We benchmarked the performance of the MS1-MS2-combined method to the individual use of MS1 or MS2 in DIA using four previously published controlled mixtures, as well as in two previously unpublished controlled mixtures. In the majority of the comparisons, the combined method outperformed the individual use of MS1 or MS2. This was particularly true for comparisons with low fold changes, few replicates, and situations where MS1 and MS2 were of similar quality. When applied to a previously unpublished investigation of lung cancer, the MS1-MS2-combined method increased the coverage of known activated pathways. Since recent technological developments continue to increase the quality of MS1 signals (e.g. using the BoxCar scan mode for Orbitrap instruments), the combination of the MS1 and MS2 information has a high potential for future statistical analysis of DIA data.

Top-down proteomics studies intact proteoform mixtures and offers important advantages over more common bottom-up proteomics technologies, as it avoids the protein inference problem. However, achieving complete molecular characterization of investigated proteoforms using existing technologies remains a fundamental challenge for top-down proteomics. Here, we benchmark the performance of ultraviolet photodissociation (UVPD) using 213 nm photons generated by a solid-state laser applied to the study of intact proteoforms from three organisms. Notably, the described UVPD setup applies multiple laser pulses to induce ion dissociation, and this feature can be used to optimize the fragmentation outcome based on the molecular weight of the analyzed biomolecule. When applied to complex proteoform mixtures in high-throughput top-down proteomics, 213 nm UVPD demonstrated a high degree of complementarity with the most employed fragmentation method in proteomics studies, higher-energy collisional dissociation (HCD). UVPD at 213 nm offered higher average proteoform sequence coverage and degree of proteoform characterization (including localization of post-translational modifications) than HCD. However, previous studies have shown limitations in applying database search strategies developed for HCD fragmentation to UVPD spectra which contains up to nine fragment ion types. We therefore performed an analysis of the different UVPD product ion type frequencies. From these data, we developed an ad hoc fragment matching strategy and determined the influence of each possible ion type on search outcomes. By paring down the number of ion types considered in high-throughput UVPD searches from all types down to the four most abundant, we were ultimately able to achieve deeper proteome characterization with UVPD. Lastly, our detailed product ion analysis also revealed UVPD cleavage propensities and determined the presence of a product ion produced specifically by 213 nm photons. All together, these observations could be used to better elucidate UVPD dissociation mechanisms and improve the utility of the technique for proteomic applications.

The impact of different approaches for spectral library generation used by DIA was analyzed on identification and quantification level using a gold standard spike-in data set. We included approaches using repetitive measurements of the original samples as well as methods applying peptide and protein pre-fractionation before library generation. The comparisons show, that prefractionation generally increases the number of identified peptides and protein groups, whereas the ground truth quantification ratios could be well approximated using libraries generated by repetitive measurements., Graphical Abstract Highlights Provision of data generated on the basis of a gold standard spike-in sample set. Choice of spectral library has great impact on identification and quantification. DIA is superior to DDA in quantification reproducibility, specificity and accuracy. DIA outperforms DDA in the quantification of low protein amounts. Quantification on peptide level is generally preferable., Currently data-dependent acquisition (DDA) is the method of choice for mass spectrometry-based proteomics discovery experiments, but data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirements to perform a DIA analysis is the availability of suitable spectral libraries for peptide identification and quantification. Several studies were performed addressing the evaluation of spectral library performance for protein identification in DIA measurements. But so far only few experiments estimate the effect of these libraries on the quantitative level. In this work we created a gold standard spike-in sample set with known contents and ratios of proteins in a complex protein matrix that allowed a detailed comparison of DIA quantification data obtained with different spectral library approaches. We used in-house generated sample-specific spectral libraries created using varying sample preparation approaches and repeated DDA measurement. In addition, two different search engines were tested for protein identification from DDA data and subsequent library generation. In total, eight different spectral libraries were generated, and the quantification results compared with a library free method, as well as a default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding DIA analysis results was inspected, but also the number of expected and identified differentially abundant protein groups and their ratios. We found, that while libraries of prefractionated samples were generally larger, there was no significant increase in DIA identifications compared with repetitive non-fractionated measurements. Furthermore, we show that the accuracy of the quantification is strongly dependent on the applied spectral library and whether the quantification is based on peptide or protein level. Overall, the reproducibility and accuracy of DIA quantification is superior to DDA in all applied approaches. Data has been deposited to the ProteomeXchange repository with identifiers PXD012986, PXD012987, PXD012988 and PXD014956.

Receptors expressed on the plasma membrane and their interacting partners critically regulate cellular communication during homeostasis and disease, and as such represent main therapeutic targets. Despite its importance for drug development, receptor-ligand proteomics has remained a daunting field, in part because of the challenges associated to the study of membrane-expressed proteins. Here, to enable sensitive detection of receptor-ligand interactions in high throughput, we implement a new platform, the Conditioned Media AlphaScreen, for interrogation of a library consisting of most single transmembrane human proteins. Using this method to study key immune receptors, we identify and further validate the interleukin receptor IL20RA as the first binding partner for the checkpoint inhibitor B7-H3. Further, KIR2DL5, a natural killer cell protein that had remained orphan, is uncovered as a functional binding partner for the poliovirus receptor (PVR). This interaction is characterized using orthogonal assays, which demonstrate that PVR specifically engages KIR2DL5 on natural killer cells leading to inhibition of cytotoxicity. Altogether, these results reveal unappreciated links between protein families that may importantly influence receptor-driven functions during disease. Applicable to any target of interest, this technology represents a versatile and powerful approach for elucidation of receptor-ligand interactomes, which is essential to understand basic aspects of the biology of the plasma membrane proteins and ultimately inform the development of novel therapeutic strategies.

Multiplexed proteomics has emerged as a powerful tool to measure relative protein expression levels across multiple conditions. The relative protein abundances are inferred by comparing the signals generated by isobaric tags, which encode the samples' origins. Intuitively, the trust associated with a protein measurement depends on the similarity of ratios from the protein's peptides and the signal-strength of these measurements. However, typically the average peptide ratio is reported as the estimate of relative protein abundance, which is only the most likely ratio with a very naive model. Moreover, there is no sense on the confidence in these measurements. Here, we present a mathematically rigorous approach that integrates peptide signal strengths and peptide-measurement agreement into an estimation of the true protein ratio and the associated confidence (BACIQ). The main advantages of BACIQ are: (1) It removes the need to threshold reported peptide signal based on an arbitrary cut-off, thereby reporting more measurements from a given experiment; (2) Confidence can be assigned without replicates; (3) For repeated experiments BACIQ provides confidence intervals for the union, not the intersection, of quantified proteins; (4) For repeated experiments, BACIQ confidence intervals are more predictive than confidence intervals based on protein measurement agreement. To demonstrate the power of BACIQ we reanalyzed previously published data on subcellular protein movement on treatment with an Exportin-1 inhibiting drug. We detect ∼2× more highly significant movers, down to subcellular localization changes of ∼1%. Thus, our method drastically increases the value obtainable from quantitative proteomics experiments, helping researchers to interpret their data and prioritize resources. To make our approach easily accessible we distribute it via a Python/Stan package.

MS-based immunopeptidomics is maturing into an automatized and high-throughput technology, producing small- to large-scale datasets of clinically relevant major histocompatibility complex (MHC) class I-associated and class II-associated peptides. Consequently, the development of quality control (QC) and quality assurance systems capable of detecting sample and/or measurement issues is important for instrument operators and scientists in charge of downstream data interpretation. Here, we created MhcVizPipe (MVP), a semiautomated QC software tool that enables rapid and simultaneous assessment of multiple MHC class I and II immunopeptidomic datasets generated by MS, including datasets generated from large sample cohorts. In essence, MVP provides a rapid and consolidated view of sample quality, composition, and MHC specificity to greatly accelerate the “pass–fail” QC decision-making process toward data interpretation. MVP parallelizes the use of well-established immunopeptidomic algorithms (NetMHCpan, NetMHCIIpan, and GibbsCluster) and rapidly generates organized and easy-to-understand reports in HTML format. The reports are fully portable and can be viewed on any computer with a modern web browser. MVP is intuitive to use and will find utility in any specialized immunopeptidomic laboratory and proteomics core facility that provides immunopeptidomic services to the community., Graphical Abstract, Highlights • MhcVizPipe is a software tool for QC in immunopeptidomics. • It provides numerical scores for QC analysis of tumor biopsies. • It performs rapid QC analysis of large clinical immunopeptidomic sample cohorts. • It generates organized and easy-to-understand reports in HTML format., In Brief Automated QC software tools capable of detecting sample and/or measurement issues are important for downstream data interpretation. We created MhcVizPipe, the first semiautomated GUI-based QC software tool that enables rapid and simultaneous assessment of multiple MHC class I and II immunopeptidomic datasets generated by MS. MVP is intuitive to use, generate QC reports in HTML formats, and will find utility in any immunopeptidomic laboratory.

Antibodies against posttranslational modifications (PTMs) such as lysine acetylation, ubiquitin remnants, or phosphotyrosine have resulted in significant advances in our understanding of the fundamental roles of these PTMs in biology. However, the roles of a number of PTMs remain largely unexplored due to the lack of robust enrichment reagents. The addition of N-acetylglucosamine to serine and threonine residues (O-GlcNAc) by the O-GlcNAc transferase (OGT) is a PTM implicated in numerous biological processes and disease states but with limited techniques for its study. Here, we evaluate a new mixture of anti-O-GlcNAc monoclonal antibodies for the immunoprecipitation of native O-GlcNAcylated peptides from cells and tissues. The anti-O-GlcNAc antibodies display good sensitivity and high specificity toward O-GlcNAc-modified peptides and do not recognize O-GalNAc or GlcNAc in extended glycans. Applying this antibody-based enrichment strategy to synaptosomes from mouse brain tissue samples, we identified over 1300 unique O-GlcNAc-modified peptides and over 1000 sites using just a fraction of sample preparation and instrument time required in other landmark investigations of O-GlcNAcylation. Our rapid and robust method greatly simplifies the analysis of O-GlcNAc signaling and will help to elucidate the role of this challenging PTM in health and disease., Graphical Abstract, Highlights • Anti-O-GlcNAc antibodies are fast and simple enrichment reagents. • Anti-O-GlcNAc antibodies are sensitive and achieve significant depth of coverage. • Anti-O-GlcNAc antibodies are specific for singular O-GlcNAc modifications. • Anti-O-GlcNAc antibody enrichment techniques can be applied to cells and tissues. • HCD product-triggered EThcD data acquisition improves depth of coverage., In Brief O-GlcNAc, the single N-acetylglucosamine coupled to serine and threonines of nucleocytoplasmic proteins, is an enigmatic posttranslational modification implicated in most cellular functions. Methods to study the O-GlcNAc modified proteome have been challenging, in part, due to limitations in its enrichment from native tissue. Here, we characterize novel antibodies for the enrichment of native O-GlcNAc modified peptides. Our enrichment strategy shows strong sensitivity and excellent specificity for O-GlcNAc. We expect these reagents to be a significant advancement for the field.

Protein–protein interactions mediated by intrinsically disordered regions are often based on short linear motifs (SLiMs). SLiMs are implicated in signal transduction and gene regulation yet remain technically laborious and notoriously challenging to study. Here, we present an optimized method for a protein interaction screen on a peptide matrix (PRISMA) in combination with quantitative MS. The protocol was benchmarked with previously described SLiM-based protein–protein interactions using peptides derived from EGFR, SOS1, GLUT1, and CEBPB and extended to map binding partners of kinase activation loops. The detailed protocol provides practical considerations for setting up a PRISMA screen and subsequently implementing PRISMA on a liquid-handling robotic platform as a cost-effective high-throughput method. Optimized PRISMA can be universally applied to systematically study SLiM-based interactions and associated post-translational modifications or mutations to advance our understanding of the largely uncharacterized interactomes of intrinsically disordered protein regions., Graphical Abstract, Highlights • Optimized protocol for analysis of peptide–protein interactions with peptide arrays. • Detection of interactions affected by mutations or post-translational modifications. • Mapping of interaction sites with overlapping peptide sequences. • Implementation on a liquid-handling robotic platform., In Brief A universal and optimized peptide matrix pull-down approach enables cost-effective and reproducible high-throughput interactome analysis of peptides with reduced analysis time. Our data provide proof of principle for the wide applicability of our streamlined protocol to map and detect motif-based protein interactions influenced by mutations and post-translational modifications.

Proteomics methodology has expanded to include protein structural analysis, primarily through cross-linking mass spectrometry (XL-MS) and hydrogen–deuterium exchange mass spectrometry (HX-MS). However, while the structural proteomics community has effective tools for primary data analysis, there is a need for structure modeling pipelines that are accessible to the proteomics specialist. Integrative structural biology requires the aggregation of multiple distinct types of data to generate models that satisfy all inputs. Here, we describe IMProv, an app in the Mass Spec Studio that combines XL-MS data with other structural data, such as cryo-EM densities and crystallographic structures, for integrative structure modeling on high-performance computing platforms. The resource provides an easily deployed bundle that includes the open-source Integrative Modeling Platform program (IMP) and its dependencies. IMProv also provides functionality to adjust cross-link distance restraints according to the underlying dynamics of cross-linked sites, as characterized by HX-MS. A dynamics-driven conditioning of restraint values can improve structure modeling precision, as illustrated by an integrative structure of the five-membered Polycomb Repressive Complex 2. IMProv is extensible to additional types of data., Graphical Abstract, Highlights • A resource for streamlining integrative structural modeling using XL-MS. • A method for contextualizing crosslink restraints with HX-MS data. • Improved model precision arises from dynamics-controlled cross-link restraints. • A complete structural model of the PRC2 complex., In Brief Proteomics has much to contribute to the structural analysis of cells, particularly considering the new developments in computational structural prediction. Cross-linking MS data can use structural building blocks (from empirical or modeled sources) to assemble models of higher-order complexes involving many proteins. In this work, we present a resource (IMProv) that reduces the barrier to integrative structural modeling and improves the precision of modeling exercises by incorporating dynamics (in the form of hydrogen/deuterium exchange data) to condition the cross-linking restraints.

Patient-derived organoids recently emerged as promising ex vivo 3D culture models recapitulating histological and molecular characteristics of original tissues, thus proteomic profiling of organoids could be valuable for function investigation and clinical translation. However, organoids are usually cultured in murine Matrigel (served as scaffolds and matrix), which brings an issue to separate organoids from Matrigel. Because of the complex compositions of Matrigel and thousands of identical peptides shared between Matrigel and organoids, insufficiently dissolved Matrigel could influence proteomic analysis of organoids in multiple ways. Thus, how to dissolve Matrigel matrix and recovery organoid cells efficiently is vital for sample preparation. Here, we comprehensively compared three popular Matrigel dissolving methods (cell recovery solution, dispase, and PBS–EDTA buffer) and investigated the effect of undissolved Matrigel proteins on proteomic profiles of organoids. By integrative analysis of label-free proteomes of Matrigel and stable isotope labeling by amino acids in cell culture proteomes of organoids collected by three methods, respectively, we found that dispase showed an optimal efficiency, with the highest peptide yield and the highest incorporation ratio of stable isotope labeling by amino acids in cell culture labels (97.1%), as well as with the least potential Matrigel contaminants. To help analysis of proteomic profiles of organoids collected by the other two methods, we identified 312 high-confidence Matrigel contaminants, which could be filtered out to attenuate Matrigel interference with minimal loss of biological information. Together, our study identifies bioinformatics and experimental approaches to eliminate interference of Matrigel contaminants efficiently, which will be valuable for basic and translational proteomic research using organoid models., Graphical Abstract, Highlights • A comprehensive comparison of Matrigel dissolving methods on organoid proteomics. • Matrigel leftover influences protein identification and quantification for organoids. • Dispase is a satisfying method for proteomic sample preparation of organoids. • Exclusion of high-confidence Matrigel contaminants attenuates Matrigel interference., In Brief Matrigel has a complex composition, and insufficient separation of organoids from Matrigel could influence proteomic profiling of organoids significantly. Here, we performed an in-depth quantitative comparison of three Matrigel dissolving methods, and dispase was identified as a satisfying method in multiple ways. A list of high-confidence Matrigel contaminants was also identified, to help eliminate interference of undissolved Matrigel in proteomic analysis of organoids, collected by cell recovery solution or PBS–EDTA buffer.

The extracellular matrix is a key component of tissues, yet it is underrepresented in proteomic datasets. Identification and evaluation of proteins in the extracellular matrix (ECM) has proved challenging due to the insolubility of many ECM proteins in traditional protein extraction buffers. Here we separate the decellularization and ECM extraction steps of several prominent methods for evaluation under real-world conditions. The results are used to optimize a two-fraction ECM extraction method. Approximately one dozen additional parameters are tested, and recommendations for analysis based on overall ECM coverage or specific ECM classes are given. Compared with a standard in-solution digest, the optimized method yielded a fourfold improvement in unique ECM peptide identifications., Graphical abstract, Highlights • Decellularization drastically improves ECM coverage. • ECM coverage is the highest with a dual chemical/enzymatic digestion method. • SPEED is the best tested single-shot method for ECM identification. • A fourfold improvement in ECM peptide IDs was achieved compared with standard methods., Due to their insolubility, many extracellular matrix (ECM) proteins are resistant to typical extraction and require optimized extraction methods for proteomic analysis. A wide variety of decellularization and ECM extraction methods have been published, but it remains unclear how these methods perform compared with one another on a complex, ECM-rich sample. A direct comparison of both cell and ECM extraction methods on a whole organism sample and four additional organs serves as a reference for future ECM proteomics.

Understanding the dynamics of the human proteome is crucial for developing biomarkers to be used as measurable indicators for disease severity and progression, patient stratification, and drug development. The Proximity Extension Assay (PEA) is a technology that translates protein information into actionable knowledge by linking protein-specific antibodies to DNA-encoded tags. In this report we demonstrate how we have combined the unique PEA technology with an innovative and automated sample preparation and high-throughput sequencing readout enabling parallel measurement of nearly 1500 proteins in 96 samples generating close to 150,000 data points per run. This advancement will have a major impact on the discovery of new biomarkers for disease prediction and prognosis and contribute to the development of the rapidly evolving fields of wellness monitoring and precision medicine., Graphical Abstract, Highlights • Novel tool for protein discovery and quantification. • PEA technology in combination with high-throughput sequencing read-out. • Parallel measurement of nearly 1500 proteins. • Innovative, miniaturized, and automated sample preparation protocol., In Brief Wik et al. presents a groundbreaking method based on the PEA technology in combination with NGS readout to measure nearly 1500 validated proteins in parallel. The protocol contains an innovative and automated sample preparation workflow generating close to 150,000 data points per run while consuming minimal amount of sample. Using dual DNA tags as readout enables high multiplex detection with exceptional specificity. This protocol meets all the requirements for a proteomic tool of the 21st century healthcare.

Unbiased and sensitive quantification of low abundance small proteins in human plasma (e.g. hormones, immune factors, metabolic regulators) remains an unmet need. These small protein factors are typically analyzed individually and using antibodies that can lack specificity. Mass spectrometry (MS)-based proteomics has the potential to address these problems, however the analysis of plasma by MS is plagued by the extremely large dynamic range of this body fluid, with protein abundances spanning at least 13 orders of magnitude. Here we describe an enrichment assay (SPEA), that greatly simplifies the plasma dynamic range problem by enriching small-proteins of 2–10 kDa, enabling the rapid, specific and sensitive quantification of >100 small-protein factors in a single untargeted LC-MS/MS acquisition. Applying this method to perform deep-proteome profiling of human plasma we identify C5ORF46 as a previously uncharacterized human plasma protein. We further demonstrate the reproducibility of our workflow for low abundance protein analysis using a stable-isotope labeled protein standard of insulin spiked into human plasma. SPEA provides the ability to study numerous important hormones in a single rapid assay, which we applied to study the intermittent fasting response and observed several unexpected changes including decreased plasma abundance of the iron homeostasis regulator hepcidin.

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has produced extensive mass spectrometry-based proteomics data for selected breast, colon, and ovarian tumors from The Cancer Genome Atlas (TCGA). We have incorporated the CPTAC proteomics data into the cBioPortal to support easy exploration and integrative analysis of these proteomic datasets in the context of the clinical and genomics data from the same tumors. cBioPortal is an open source platform for exploring, visualizing, and analyzing multidimensional cancer genomics and clinical data. The public instance of the cBioPortal (http://cbioportal.org/) hosts more than 200 cancer genomics studies, including all of the data from TCGA. Its biologist-friendly interface provides many rich analysis features, including a graphical summary of gene-level data across multiple platforms, correlation analysis between genes or other data types, survival analysis, and per-patient data visualization. Here, we present the integration of the CPTAC mass spectrometry-based proteomics data into the cBioPortal, consisting of 77 breast, 95 colorectal, and 174 ovarian tumors that already have been profiled by TCGA for mutations, copy number alterations, gene expression, and DNA methylation. As a result, the CPTAC data can now be easily explored and analyzed in the cBioPortal in the context of clinical and genomics data. By integrating CPTAC data into cBioPortal, limitations of TCGA proteomics array data can be overcome while also providing a user-friendly web interface, a web API, and an R client to query the mass spectrometry data together with genomic, epigenomic, and clinical data.

Mass spectrometry based proteomics is the method of choice for quantifying genome-wide differential changes of protein expression in a wide range of biological and biomedical applications. Protein expression changes need to be reliably derived from many measured peptide intensities and their corresponding peptide fold changes. These peptide fold changes vary considerably for a given protein. Numerous instrumental setups aim to reduce this variability, whereas current computational methods only implicitly account for this problem. We introduce a new method, MS-EmpiRe, which explicitly accounts for the noise underlying peptide fold changes. We derive data set-specific, intensity-dependent empirical error fold change distributions, which are used for individual weighing of peptide fold changes to detect differentially expressed proteins (DEPs). In a recently published proteome-wide benchmarking data set, MS-EmpiRe doubles the number of correctly identified DEPs at an estimated FDR cutoff compared with state-of-the-art tools. We additionally confirm the superior performance of MS-EmpiRe on simulated data. MS-EmpiRe requires only peptide intensities mapped to proteins and, thus, can be applied to any common quantitative proteomics setup. We apply our method to diverse MS data sets and observe consistent increases in sensitivity with more than 1000 additional significant proteins in deep data sets, including a clinical study over multiple patients. At the same time, we observe that even the proteins classified as most insignificant by other methods but significant by MS-EmpiRe show very clear regulation on the peptide intensity level. MS-EmpiRe provides rapid processing (< 2 min for 6 LC-MS/MS runs (3 h gradients)) and is publicly available under github.com/zimmerlab/MS-EmpiRe with a manual including examples.

Recent advances in the multi-omics characterization necessitate knowledge integration across different data types that go beyond individual biomarker discovery. In this study, we apply independent component analysis (ICA) to human breast cancer proteogenomics data to retrieve mechanistic information. We show that as an unsupervised feature extraction method, ICA was able to construct signatures with known biological relevance on both transcriptome and proteome levels. Moreover, proteome and transcriptome signatures can be associated by their respective correlation with patient clinical features, providing an integrated description of phenotype-related biological processes. Our results demonstrate that the application of ICA to proteogenomics data could lead to pathway-level knowledge discovery. Potential extension of this approach to other data and cancer types may contribute to pan-cancer integration of multi-omics information.

The label-free proteome quantification (LFQ) is multistep workflow collectively defined by quantification tools and subsequent data manipulation methods that has been extensively applied in current biomedical, agricultural, and environmental studies. Despite recent advances, in-depth and high-quality quantification remains extremely challenging and requires the optimization of LFQs by comparatively evaluating their performance. However, the evaluation results using different criteria (precision, accuracy, and robustness) vary greatly, and the huge number of potential LFQs becomes one of the bottlenecks in comprehensively optimizing proteome quantification. In this study, a novel strategy, enabling the discovery of the LFQs of simultaneously enhanced performance from thousands of workflows (integrating 18 quantification tools with 3,128 manipulation chains), was therefore proposed. First, the feasibility of achieving simultaneous improvement in the precision, accuracy, and robustness of LFQ was systematically assessed by collectively optimizing its multistep manipulation chains. Second, based on a variety of benchmark datasets acquired by various quantification measurements of different modes of acquisition, this novel strategy successfully identified a number of manipulation chains that simultaneously improved the performance across multiple criteria. Finally, to further enhance proteome quantification and discover the LFQs of optimal performance, an online tool (https://idrblab.org/anpela/) enabling collective performance assessment (from multiple perspectives) of the entire LFQ workflow was developed. This study confirmed the feasibility of achieving simultaneous improvement in precision, accuracy, and robustness. The novel strategy proposed and validated in this study together with the online tool might provide useful guidance for the research field requiring the mass-spectrometry-based LFQ technique.

Here we present IPSA, an innovative web-based spectrum annotator that visualizes and characterizes peptide tandem mass spectra. A tool for the scientific community, IPSA can visualize peptides collected using a wide variety of experimental and instrumental configurations. Annotated spectra are customizable via a selection of interactive features and can be exported as editable scalable vector graphics to aid in the production of publication-quality figures. Single spectra can be analyzed through provided web forms, whereas data for multiple peptide spectral matches can be uploaded using the Proteomics Standards Initiative file formats mzTab, mzIdentML, and mzML. Alternatively, peptide identifications and spectral data can be provided using generic file formats. IPSA provides supports for annotating spectra collecting using negative-mode ionization and facilitates the characterization of experimental MS/MS performance through the optional export of fragment ion statistics from one to many peptide spectral matches. This resource is made freely accessible at http://interactivepeptidespectralannotator.com, whereas the source code and user guides are available at https://github.com/coongroup/IPSA for private hosting or custom implementations.

Gene-set analysis (GSA) summarizes information from molecule to gene-set level to facilitate biological interpretation of molecular profiling experiments. We present a statistical framework for single sample GSA of multiple 'omic data. MOGSA learns the most variant biomolecules and integrates data sets to generate gene set scores (GSS) for each sample. It extracts the contribution of individual data sets and biomolecules to GSS. MOGSA is rigorously benchmarked with simulated and real biological data and is implemented in the Bioconductor package “mogsa.”, Graphical Abstract Highlights MOGSA is a new integrative multi 'omics single sample gene set analysis method. Integrating diverse biomolecular data increases the power to discover gene sets. MOGSA evaluates the contribution of biomolecules, samples and data sets to gene sets. The method and tutorial are available in the R/Bioconductor package mogsa., Gene-set analysis (GSA) summarizes individual molecular measurements to more interpretable pathways or gene-sets and has become an indispensable step in the interpretation of large-scale omics data. However, GSA methods are limited to the analysis of single omics data. Here, we introduce a new computation method termed multi-omics gene-set analysis (MOGSA), a multivariate single sample gene-set analysis method that integrates multiple experimental and molecular data types measured over the same set of samples. The method learns a low dimensional representation of most variant correlated features (genes, proteins, etc.) across multiple omics data sets, transforms the features onto the same scale and calculates an integrated gene-set score from the most informative features in each data type. MOGSA does not require filtering data to the intersection of features (gene IDs), therefore, all molecular features, including those that lack annotation may be included in the analysis. Using simulated data, we demonstrate that integrating multiple diverse sources of molecular data increases the power to discover subtle changes in gene-sets and may reduce the impact of unreliable information in any single data type. Using real experimental data, we demonstrate three use-cases of MOGSA. First, we show how to remove a source of noise (technical or biological) in integrative MOGSA of NCI60 transcriptome and proteome data. Second, we apply MOGSA to discover similarities and differences in mRNA, protein and phosphorylation profiles of a small study of stem cell lines and assess the influence of each data type or feature on the total gene-set score. Finally, we apply MOGSA to cluster analysis and show that three molecular subtypes are robustly discovered when copy number variation and mRNA data of 308 bladder cancers from The Cancer Genome Atlas are integrated using MOGSA. MOGSA is available in the Bioconductor R package “mogsa.”

SWATH-MS has been widely used for proteomics analysis given its high-throughput and reproducibility but ensuring consistent quantification of analytes across large-scale studies of heterogeneous samples such as human-plasma remains challenging. Heterogeneity in large-scale studies can be caused by large time intervals between data-acquisition, acquisition by different operators or instruments, intermittent repair or replacement of parts, such as the liquid chromatography column, all of which affect retention time (RT) reproducibility and successively performance of SWATH-MS data analysis. Here, we present a novel algorithm for retention time alignment of SWATH-MS data based on direct alignment of raw MS2 chromatograms using a hybrid dynamic programming approach. The algorithm does not impose a chronological order of elution and allows for alignment of elution-order swapped peaks. Furthermore, allowing RT-mapping in a certain window around coarse global fit makes it robust against noise. On a manually validated dataset, this strategy outperforms the current state-of-the-art approaches. In addition, on a real-world clinical data, our approach outperforms global alignment methods by mapping 98% of peaks compared to 67% cumulatively and DIAlignR can reduce alignment error up to 30-fold for extremely distant runs. The robustness of technical parameters used in this pairwise alignment strategy has also been demonstrated. The source code is released under the BSD license at https://github.com/Roestlab/DIAlignR.Abbreviations:AUCArea Under the CurveDIAData-independent acquisitionLCLiquid chromatographyLOESSLocal weighted regressionRSEResidual Standard ErrorRTRetention timeXICExtracted ion chromatogramsData Availability:Raw chromatograms and features extracted by OpenSWATH are available on PeptideAtlas.Servername: ftp.peptideatlas.orgUsername: PASS01280Password: KQ2592b

Protein quantification by label-free shotgun proteomics experiments is plagued by a multitude of error sources. Typical pipelines for identifying differentially expressed proteins use intermediate filters in an attempt to control the error rate. However, they often ignore certain error sources and, moreover, regard filtered lists as completely correct in subsequent steps. These two indiscretions can easily lead to a loss of control of the false discovery rate (FDR). We propose a probabilistic graphical model, Triqler, that propagates error information through all steps, employing distributions in favor of point estimates, most notably for missing value imputation. The model outputs posterior probabilities for fold changes between treatment groups, highlighting uncertainty rather than hiding it. We analyzed 3 engineered datasets and achieved FDR control and high sensitivity, even for truly absent proteins. In a bladder cancer clinical dataset we discovered 35 proteins at 5% FDR, whereas the original study discovered 1 and MaxQuant/Perseus 4 proteins at this threshold. Compellingly, these 35 proteins showed enrichment for functional annotation terms, whereas the top ranked proteins reported by MaxQuant/Perseus showed no enrichment. The model executes in minutes and is freely available at https://pypi.org/project/triqler/.

Determining changes in protein expression and post-translational modifications (PTMs) is crucial for elucidating cellular signal transduction and disease mechanisms. Conventional antibody-based approaches have inherent problems such as the limited availability of high-quality antibodies and batch-to-batch variation. Top-down mass spectrometry (MS)-based proteomics has emerged as the most powerful method for characterization and quantification of protein modifications. Nevertheless, robust methods to simultaneously determine changes in protein expression and PTMs remain lacking. Herein, we have developed a straightforward and robust top-down liquid chromatography (LC)/MS-based targeted proteomics platform for simultaneous quantification of protein expression and PTMs with high throughput and high reproducibility. We employed this method to analyze the sarcomeric subproteome from various muscle types of different species, which successfully revealed skeletal muscle heterogeneity and cardiac developmental changes in sarcomeric protein isoform expression and PTMs. As demonstrated, this targeted top-down proteomics platform offers an excellent 'antibody-independent' alternative for the accurate quantification of sarcomeric protein expression and PTMs concurrently in complex mixtures, which is generally applicable to different species and various tissue types.

The open (mass tolerant) search of tandem mass spectra of peptides shows great potential in the comprehensive detection of post-translational modifications (PTMs) in shotgun proteomics. However, this search strategy has not been widely used by the community, and one bottleneck of it is the lack of appropriate algorithms for automated and reliable post-processing of the coarse and error-prone search results. Here we present PTMiner, a software tool for confident filtering and localization of modifications (mass shifts) detected in an open search. After mass-shift-grouped false discovery rate (FDR) control of peptide-spectrum matches (PSMs), PTMiner uses an empirical Bayesian method to localize modifications through iterative learning of the prior probabilities of each type of modification occurring on different amino acids. The performance of PTMiner was evaluated on three data sets, including simulated data, chemically synthesized peptide library data and modified-peptide spiked-in proteome data. The results showed that PTMiner can effectively control the PSM FDR and accurately localize the modification sites. At 1% real false localization rate (FLR), PTMiner localized 93%, 84 and 83% of the modification sites in the three data sets, respectively, far higher than two open search engines we used and an extended version of the Ascore localization algorithm. We then used PTMiner to analyze a draft map of human proteome containing 25 million spectra from 30 tissues, and confidently identified over 1.7 million modified PSMs at 1% FDR and 1% FLR, which provided a system-wide view of both known and unknown PTMs in the human proteome.

In quantitative mass spectrometry, the method by which peptides are grouped into proteins can have dramatic effects on downstream analyses. Here we describe gpGrouper, an inference and quantitation algorithm that offers an alternative method for assignment of protein groups by gene locus and improves pseudo-absolute iBAQ quantitation by weighted distribution of shared peptide areas. We experimentally show that distributing shared peptide quantities based on unique peptide peak ratios improves quantitation accuracy compared with conventional winner-take-all scenarios. Furthermore, gpGrouper seamlessly handles two-species samples such as patient-derived xenografts (PDXs) without ignoring the host species or species-shared peptides. This is a critical capability for proper evaluation of proteomics data from PDX samples, where stromal infiltration varies across individual tumors. Finally, gpGrouper calculates peptide peak area (MS1) based expression estimates from multiplexed isobaric data, producing iBAQ results that are directly comparable across label-free, isotopic, and isobaric proteomics approaches.

The depth of proteomic analyses is often limited by the overwhelming proportion of confounding background ions that compromise the identification and quantification of low abundance peptides. To alleviate these limitations, we present a new high field asymmetric waveform ion mobility spectrometry (FAIMS) interface that can be coupled to the Orbitrap Tribrid mass spectrometers. The interface provides several advantages over previous generations of FAIMS devices, including ease of operation, robustness, and high ion transmission. Replicate LC-FAIMS-MS/MS analyses (n = 100) of HEK293 protein digests showed stable ion current over extended time periods with uniform peptide identification on more than 10,000 distinct peptides. For complex tryptic digest analyses, the coupling of FAIMS to LC-MS/MS enabled a 30% gain in unique peptide identification compared with non-FAIMS experiments. Improvement in sensitivity facilitated the identification of low abundance peptides, and extended the limit of detection by almost an order of magnitude. The reduction in chimeric MS/MS spectra using FAIMS also improved the precision and the number of quantifiable peptides when using isobaric labeling with tandem mass tag (TMT) 10-plex reagent. We compared quantitative proteomic measurements for LC-MS/MS analyses performed using synchronous precursor selection (SPS) and LC-FAIMS-MS/MS to profile the temporal changes in protein abundance of HEK293 cells following heat shock for periods up to 9 h. FAIMS provided 2.5-fold increase in the number of quantifiable peptides compared with non-FAIMS experiments (30,848 peptides from 2,646 proteins for FAIMS versus 12,400 peptides from 1,229 proteins with SPS). Altogether, the enhancement in ion transmission and duty cycle of the new FAIMS interface extended the depth and comprehensiveness of proteomic analyses and improved the precision of quantitative measurements.

Human leukocyte antigen (HLA) molecules are cell-surface glycoproteins that present peptide antigens on the cell surface for surveillance by T lymphocytes, which contemporaneously seek signs of disease. Mass spectrometric analysis allows us to identify large numbers of these peptides (the immunopeptidome) following affinity purification of solubilized HLA–peptide complexes. However, in recent years, there has been a growing awareness of the “dark side” of the immunopeptidome: unconventional peptide epitopes, including neoepitopes, which elude detection by conventional search methods because their sequences are not present in reference protein databases (DBs). Here, we establish a bioinformatics workflow to aid identification of peptides generated by noncanonical translation of mRNA or by genome variants. The workflow incorporates both standard transcriptomics software and novel computer programs to produce cell line–specific protein DBs based on three-frame translation of the transcriptome. The final protein DB also includes sequences resulting from variants determined by variant calling on the same RNA-Seq data. We then searched our experimental data against both transcriptome-based and standard DBs using PEAKS Studio (Bioinformatics Solutions, Inc). Finally, further novel software helps to compare the various result sets arising for each sample, pinpoint putative genomic origins for unconventional sequences, and highlight potential neoepitopes. We applied the workflow to study the immunopeptidome of the acute myeloid leukemia cell line THP-1, using RNA-Seq and immunopeptidome data. We confidently identified over 14,000 peptides from three replicates of purified HLA peptides derived from THP-1 cells using the conventional UniProt human proteome. Using the transcriptome-based DB generated using our workflow, we recapitulated >85% of these and also identified 1029 unconventional peptides not explained by UniProt, including 16 sequences caused by nonsynonymous variants. Our workflow, which we term “immunopeptidogenomics,” can provide DBs, which include pertinent unconventional sequences and allow neoepitope discovery, without becoming too large to search. Immunopeptidogenomics is a step toward unbiased search approaches that are needed to illuminate the dark side of the immunopeptidome., Graphical Abstract, Highlights • Our workflow uses RNA-Seq data to generate a bespoke database for immunopeptidomics. • Peptides (7%) in the THP-1 immunopeptidome are derived from noncanonical antigens. • These include peptides from alternative reading frames or noncoding genomic regions., In Brief Here, we describe a workflow for the inclusion of noncanonical antigen sources in a database for the analysis of immunopeptidomics data. The approach utilizes a series of executable programs that interrogate RNA-Seq data from the tissue of cell-based starting material for the immunopeptidomics experiment to generate a bespoke searchable FASTA database. We show the application of this workflow for the THP-1 leukemia cell line with the identification of a number of validated cancer-associated peptide antigens.

MS is the most effective method to directly identify peptides presented on human leukocyte antigen (HLA) molecules. However, current standard approaches often use 500 million or more cells as input to achieve high coverage of the immunopeptidome, and therefore, these methods are not compatible with the often limited amounts of tissue available from clinical tumor samples. Here, we evaluated microscaled basic reversed-phase fractionation to separate HLA peptide samples offline followed by ion mobility coupled to LC–MS/MS for analysis. The combination of these two separation methods enabled identification of 20% to 50% more peptides compared with samples analyzed without either prior fractionation or use of ion mobility alone. We demonstrate coverage of HLA immunopeptidomes with up to 8107 distinct peptides starting with as few as 100 million cells. The increased sensitivity obtained using our methods can provide data useful to improve HLA-binding prediction algorithms as well as to enable detection of clinically relevant epitopes such as neoantigens., Graphical Abstract, Highlights • Deep immunopeptidome coverage using liquid and gas phase separation. • Up to 50% more HLA-I peptides using microscaled basic reversed-phase fractionation. • Ion mobility separation (FAIMS) increases HLA-I peptide identifications by up to 58%. • Increased sensitivity provided by these methods enables detection of neoantigens., In Brief Here, we evaluated off-line microscaled basic reversed-phase fractionation as well as the use of ion mobility coupled to LC–MS/MS for analysis of peptides presented on HLA-I. The two separation methods enabled identification of 20% to 50% more peptides compared with samples analyzed without either prior fractionation or use of ion mobility alone starting with as few as 100 million cells. The increased sensitivity obtained using our methods can enable detection of low abundant but clinically relevant epitopes such as neoantigens.

Proteogenomics approaches often struggle with the distinction between true and false peptide-to-spectrum matches as the database size enlarges. However, features extracted from tandem mass spectrometry intensity predictors can enhance the peptide identification rate and can provide extra confidence for peptide-to-spectrum matching in a proteogenomics context. To that end, features from the spectral intensity pattern predictors MS2PIP and Prosit were combined with the canonical scores from MaxQuant in the Percolator postprocessing tool for protein sequence databases constructed out of ribosome profiling and nanopore RNA-Seq analyses. The presented results provide evidence that this approach enhances both the identification rate as well as the validation stringency in a proteogenomic setting., Graphical abstract, Highlights • First proteogenomics with PSM rescoring using machine learning–predicted spectra • Demonstrated on both ribosome profiling and nanopore RNA-Seq–derived databases • Rescoring leads to elevated stringency and increased identification rates • Rescoring compensates for the search space size issues in proteogenomics, In Brief Proteogenomics suffers from statistical issues as the sequencing information inflates the database size. To compensate for this, rescoring with the machine learning–based spectrum predictors MS2PIP and Prosit was implemented in a proteogenomics approach. This was demonstrated for both ribosome profiling and nanopore RNA-Seq derived databases. Postprocessing with Percolator showed that these techniques result in recovered and often even elevated stringency levels and identification rates. In this way, it allows to validate novel proteoforms through proteogenomics with unsurpassed confidence levels.

We developed a simple and rapid method to enrich protein N-terminal peptides, in which the protease TrypN is first employed to generate protein N-terminal peptides without Lys or Arg and internal peptides with two positive charges at their N termini, and then, the N-terminal peptides with or without N-acetylation are separated from the internal peptides by strong cation exchange chromatography according to a retention model based on the charge/orientation of peptides. This approach was applied to 20 μg of human HEK293T cell lysate proteins to profile the N-terminal proteome. On average, 1550 acetylated and 200 unmodified protein N-terminal peptides were successfully identified in a single LC/MS/MS run with less than 3% contamination with internal peptides, even when we accepted only canonical protein N termini registered in the Swiss-Prot database. Because this method involves only two steps, protein digestion and chromatographic separation, without the need for tedious chemical reactions, it should be useful for comprehensive profiling of protein N termini, including proteoforms with neo-N termini., Graphical Abstract, Highlights • Isolation of protein N-terminal peptides without chemical reaction • Both N-acetylated and unmodified protein N termini can be isolated • Two-step isolation consisting of TrypN digestion and strong cation exchange separation • Less than 3% contamination with internal peptides, In Brief N-acetylated and unmodified protein N-terminal peptides were successfully isolated by strong cation exchange chromatographic separation of TrypN-digested peptides without chemical reaction. From 20 μg of human HEK293T cell lysate proteins, 1550 acetylated and 200 unmodified protein N-terminal peptides were identified in a single LC/MS/MS run with less than 3% contamination with internal peptides, even when we accepted only canonical protein N termini registered in the Swiss-Prot database.

Reversed-phase solid-phase extraction (SPE) techniques are commonly used for desalting samples before LC/MS/MS in shotgun proteomics. However, hydrophilic peptides are often lost during the desalting step under the standard SPE conditions. Here, we describe a simple protocol in which a stop-and-go extraction tip packed with a poly(styrene-divinylbenzene) copolymer disc is used at 4 °C during sample loading without any organic solvent. Using this method, which we designate as the CoolTip protocol, we identified 2.9-fold more tryptic peptides and 6.1-fold more tryptic phosphopeptides from HeLa lysates than the standard SPE protocol for hydrophilic peptides, with a mobile phase of less than 8% acetonitrile in LC/MS/MS. There was no decrease in the recovery of hydrophobic peptides. CoolTip also provided better quantitative reproducibility in LC/MS/MS analysis. We anticipate that this protocol will provide improved performance in many kinds of shotgun proteomics experiments., Graphical Abstract, Highlights • CoolTip, a StageTip with a poly(styrene-divinylbenzene) disc operated at 4 °C. • Identification of more 6.1-fold hydrophilic phosphopeptides from HeLa lysates. • No decrease in the recovery of hydrophobic peptides using the CoolTip protocol. • Better reproducibility in quantitative LC/MS/MS analysis., In Brief A desalting protocol to increase the recovery of hydrophilic peptides and phosphopeptides was developed by using SDB-StageTip at 4 °C. Using this protocol, 2.9-fold more tryptic peptides and 6.1-fold more tryptic phosphopeptides from HeLa lysates were identified for hydrophilic peptides.

Mass spectrometry (MS) is the state-of-the-art methodology for capturing the breadth and depth of the immunopeptidome across human leukocyte antigen (HLA) allotypes and cell types. The majority of studies in the immunopeptidomics field are discovery driven. Hence, data-dependent tandem MS (MS/MS) acquisition (DDA) is widely used, as it generates high-quality references of peptide fingerprints. However, DDA suffers from the stochastic selection of abundant ions that impairs sensitivity and reproducibility. In contrast, in data-independent acquisition (DIA), the systematic fragmentation and acquisition of all fragment ions within given isolation m/z windows yield a comprehensive map for a given sample. However, many DIA approaches commonly require generating comprehensive DDA-based spectrum libraries, which can become impractical for studying noncanonical and personalized neoantigens. Because the amount of HLA peptides eluted from biological samples such as small tissue biopsies is typically not sufficient for acquiring both meaningful DDA data necessary for generating comprehensive spectral libraries and DIA MS measurements, the implementation of DIA in the immunopeptidomics translational research domain has remained limited. We implemented a DIA immunopeptidomics workflow and assessed its sensitivity and accuracy by matching DIA data against libraries with growing complexity—from sample-specific libraries to libraries combining 2 to 40 different immunopeptidomics samples. Analyzing DIA immunopeptidomics data against a complex multi-HLA spectral library resulted in a two-fold increase in peptide identification compared with sample-specific library and in a three-fold increase compared with DDA measurements, yet with no detrimental effect on the specificity. Furthermore, we demonstrated the implementation of DIA for sensitive personalized neoantigen discovery through the analysis of DIA data with predicted MS/MS spectra of clinically relevant HLA ligands. We conclude that a comprehensive multi-HLA library for DIA approach in combination with MS/MS prediction is highly advantageous for clinical immunopeptidomics, especially when low amounts of biological samples are available., Graphical Abstract, Highlights • So far, DIA in the immunopeptidomics translational research has been limited. • DIA immunopeptidomics data were analyzed against a complex multi-HLA spectral library. • This resulted in improved sensitivity with no detrimental effect on the specificity. • We implemented DIA for clinical antigen discovery combined with predicted MS/MS spectra., In Brief The implementation of DIA in the immunopeptidomics translational research domain has remained limited because the amount of HLA peptides eluted from clinical samples is typically not sufficient for acquiring both meaningful DDA data for generating comprehensive spectral libraries and DIA MS measurements. We implemented a DIA immunopeptidomics workflow and assessed its sensitivity and accuracy with libraries of growing complexity and multi-HLA libraries. In addition, we demonstrated the analysis of DIA data with predicted MS/MS spectra of clinically relevant HLA ligands.

Reversed-phase HPLC is the most commonly applied peptide-separation technique in MS-based proteomics. Particle-packed capillary columns are predominantly used in nanoflow HPLC systems. Despite being the broadly applied standard for many years, capillary columns are still expensive and suffer from short lifetimes, particularly in combination with ultra-high-pressure chromatography systems. For this reason, and to achieve maximum performance, many laboratories produce their own in-house packed columns. This typically requires a considerable amount of time and trained personnel. Here, we present a new packing system for capillary columns enabling rapid, multiplexed column packing with pressures reaching up to 3000 bar. Requiring only a conventional gas pressure supply and methanol as the driving fluid, our system replaces the traditional setup of helium-pressured packing bombs. By using 10× multiplexing, we have reduced the production time to just under 2 min for several 50 cm columns with 1.9-µm particle size, speeding up the process of column production 40 to 800 times. We compare capillary columns with various inner diameters and lengths packed under different pressure conditions with our newly designed, broadly accessible high-pressure packing station., Graphical Abstract, Highlights • We present a newly designed high-pressure packing station for capillary columns. • Detailed part lists and manufacturing instructions included. • 40 to 800 times faster packing of capillary columns for high-performance proteomics. • Columns produced with the presented setup have state-of-the-art quality metrics., In Brief Capillary columns in nanoflow application are a basis of high-performance LC-MS–based proteomics but are difficult or expensive to procure. We present a new high-pressure packing station for capillary columns (1000–3000 bar) together with detailed instructions for reproducing our setup. Compared with previous publications, this produces capillary columns 40 to 800 times faster at undiminished chromatographic performance.

The sulfated glycosaminoglycans (GAGs) are long, linear polysaccharide chains that are typically found as the glycan portion of proteoglycans. These GAGs are characterized by repeating disaccharide units with variable sulfation and acetylation patterns along the chain. GAG length and modification patterns have profound impacts on growth factor signaling mechanisms central to numerous physiological processes. Electron activated dissociation tandem mass spectrometry is a very effective technique for assigning the structures of GAG saccharides; however, manual interpretation of the resulting complex tandem mass spectra is a difficult and time-consuming process that drives the development of computational methods for accurate and efficient sequencing. We have recently published GAGfinder, the first peak picking and elemental composition assignment algorithm specifically designed for GAG tandem mass spectra. Here, we present GAGrank, a novel network-based method for determining GAG structure using information extracted from tandem mass spectra using GAGfinder. GAGrank is based on Google’s PageRank algorithm for ranking websites for search engine output. In particular, it is an implementation of BiRank, an extension of PageRank for bipartite networks. In our implementation, the two partitions comprise every possible sequence for a given GAG composition and the tandem MS fragments found using GAGfinder. Sequences are given a higher ranking if they link to many important fragments. Using the simulated annealing probabilistic optimization technique, we optimized GAGrank’s parameters on ten training sequences. We then validated GAGrank’s performance on three validation sequences. We also demonstrated GAGrank’s ability to sequence isomeric mixtures using two mixtures at five different ratios., Graphical abstract, Highlights • GAGfinder assigns glycosaminoglycan EDD and NETD product ions. • GAGrank assigns the most probable sequence from the tandem MS. • GAGrank ranks nodes using a bipartite network’s structure. • GAGrank ranks glycosaminoglycan sequences based on their importance in the network., In Brief We demonstrate GAGrank, an algorithm that uses a bipartite graph model for sequencing glycosaminoglycans from EDD or NETD tandem mass spectra. The process involves first assigning glycosaminoglycan product ions using the GAGfinder algorithm. The second step is to rank possible sequences using GAGrank. We show GAGrank’s ability to sequence isomeric mixtures.

Advances in several key technologies, including MHC peptidomics, have helped fuel our understanding of basic immune regulatory mechanisms and the identification of T cell receptor targets for the development of immunotherapeutics. Isolating and accurately quantifying MHC-bound peptides from cells and tissues enables characterization of dynamic changes in the ligandome due to cellular perturbations. However, the current multistep analytical process is challenging, and improvements in throughput and reproducibility would enable rapid characterization of multiple conditions in parallel. Here, we describe a robust and quantitative method whereby peptides derived from MHC-I complexes from a variety of cell lines, including challenging adherent lines such as MC38, can be enriched in a semiautomated fashion on reusable, dry-storage, customized antibody cartridges. Using this method, a researcher, with very little hands-on time and in a single day, can perform up to 96 simultaneous enrichments at a similar level of quality as a manual workflow. TOMAHAQ (Triggered by Offset, Multiplexed, Accurate-mass, High-resolution, and Absolute Quantification), a targeted mass spectrometry technique that combines sample multiplexing and high sensitivity, was employed to characterize neoepitopes displayed on MHC-I by tumor cells and to quantitatively assess the influence of neoantigen expression and induced degradation on neoepitope presentation. This unique combination of robust semiautomated MHC-I peptide isolation and high-throughput multiplexed targeted quantitation allows for both the routine analysis of >4000 unique MHC-I peptides from 250 million cells using nontargeted methods, as well as quantitative sensitivity down to the low amol/μl level using TOMAHAQ targeted MS., Graphical abstract, Highlights • Semiautomated peptide immunoprecipitation on reusable antibody cartridges. • Application of TOMAHAQ for MHC-I detection and quantitation. • Routine analysis of >4000 unique MHC-I peptides from 250 million cells via automation. • Quantitative sensitivity down to the low amol/μl level using TOMAHAQ targeted MS., In Brief This manuscript highlights a new semiautomated MHC-I peptide immunoprecipitation method used in conjunction with the multiplexed quantitative technique TOMAHAQ (Triggered by Offset, Multiplexed, Accurate-mass, High-resolution, and Absolute Quantification) for the detection and quantitation of MHC-I peptides. This combination of techniques allows the routine analysis of >4000 unique MHC-I peptides from 250 million cells and in with targeted analysis, quantitative sensitivity down to the low amol/μl level.

The increasing consumption of high-fat foods combined with a lack of exercise is a major contributor to the burden of obesity in humans. Aerobic exercise such as running is known to provide metabolic benefits, but how the overconsumption of a high-fat diet (HFD) and exercise interact is not well characterized at the molecular level. Here, we examined the plasma proteome in mice for the effects of aerobic exercise as both a treatment and as a preventative regimen for animals on either a HFD or a healthy control diet. This analysis detected large changes in the plasma proteome induced by the HFD, such as increased abundance of SERPINA7, ALDOB, and downregulation of SERPINA1E and complement factor D (CFD; adipsin). Some of these changes were significantly reverted using exercise as a preventative measure but not as a treatment regimen. To determine if either the intensity or duration of exercise influenced the outcome, we compared high-intensity interval training and endurance running. Endurance running slightly outperformed high-intensity interval training exercise, but overall, both provided similar reversion in abundance of plasma proteins modulated by the HFD, including SERPINA7, apolipoprotein E, SERPINA1E, and CFD. Finally, we compared the changes induced by overconsumption of a HFD with previous data from mice fed on an isocaloric high-saturated fatty acid or polyunsaturated fatty acid diet. This identified several common changes, including not only increased apolipoprotein C-II and apolipoprotein E but also highlighted changes specific for overconsumption of a HFD (fructose-bisphosphate aldolase B, SERPINA7, and CFD), saturated fatty acid–based diets (SERPINA1E), or polyunsaturated fatty acid–based diets (haptoglobin). Together, these data highlight the importance of early intervention with exercise to revert HFD-induced phenotypes and suggest some of the molecular mechanisms leading to the changes in the plasma proteome generated by HFD consumption. Web-based interactive visualizations are provided for this dataset (larancelab.com/hfd-exercise), which give insight into diet and exercise phenotypic interactions on the plasma proteome., Graphical Abstract, Highlights • Plasma proteomics reveals 30% of proteins change in response to a high-fat diet (HFD). • Running exercise applied from the beginning of the HFD reverts some of these changes. • High-intensity interval training and endurance training provide similar benefits. • Some proteins were regulated by either HFD overconsumption or diet fat profile., In Brief Exercise is known to provide benefits, but how a high-fat diet (HFD) and exercise interact is not well characterized. Here, we examined the plasma proteome in mice for the effects of exercise as both a treatment and a preventative regimen for animals on either a HFD or a healthy control diet. Many changes in the plasma proteome were induced by the HFD, and these were significantly reverted using exercise as a preventative measure but not as a treatment regimen.

Histone post-translational modifications (hPTMs) are epigenetic marks that strongly affect numerous processes, including cell cycling and protein interactions. They have been studied by both antibody- and MS-based methods for years, but the analyses are still challenging, mainly because of the diversity of histones and their modifications arising from high contents of reactive amine groups in their amino acid sequences. Here, we introduce use of trimethylacetic anhydride (TMA) as a new reagent for efficient histone derivatization, which is a requirement for bottom–up proteomic hPTM analysis. TMA can derivatize unmodified amine groups of lysine residues and amine groups generated at peptide N-termini by trypsin digestion. The derivatization is facilitated by microwave irradiation, which also reduces incubation times to minutes. We demonstrate that histone derivatization with TMA reliably provides high yields of fully derivatized peptides and thus is an effective alternative to conventional methods. TMA afforded more than 98% and 99% labeling efficiencies for histones H4 and H3, respectively, thereby enabling accurate quantification of peptide forms. Trimethylacetylation substantially improves chromatographic separation of peptide forms, which is essential for direct quantification based on signals extracted from MS1 data. For this purpose, software widely applied by the proteomics community can be used without additional computational development. Thorough comparison with widely applied propionylation highlights the advantages of TMA-based histone derivatization for monitoring hPTMs in biological samples., Graphical abstract, Highlights • Microwave-assisted labeling of histones using TMA for bottom–up proteomics. • TMA enables discrimination of isobaric peptides by improved chromatographic behavior. • TMA facilitates histone quantification from MS1-level spectral information. • TMA provides excellent performance for monitoring hPTM pattern in the tissues., In Brief The discrimination of isobaric peptides represents a common challenge in histone characterization. This study reports TMA as a novel derivatization reagent for bottom–up proteomics of histone proteoforms. TMA substantially improves separation of positional isomers, which is a prerequisite for identification and quantification of post-translationally modified histone forms.

Over the past decade, modern methods of MS (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. Although MALDI-TOF MS has already revolutionized the way microorganisms are identified, recent years have witnessed also substantial progress in the development of liquid chromatography (LC)-MS based proteomics for microbiological applications. For example, LC-tandem MS (LC-MS(2)) has been proposed for microbial characterization by means of multiple discriminative peptides that enable identification at the species, or sometimes at the strain level. However, such investigations can be laborious and time-consuming, especially if the experimental LC-MS(2) data are tested against sequence databases covering a broad panel of different microbiological taxa. In this proof of concept study, we present an alternative bottom-up proteomics method for microbial identification. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC–MS measurements. Peptide masses are then extracted from MS(1) data and systematically tested against an in silico library of all possible peptide mass data compiled in-house. The library has been computed from the UniProt Knowledgebase covering Swiss-Prot and TrEMBL databases and comprises more than 12,000 strain-specific in silico profiles, each containing tens of thousands of peptide mass entries. Identification analysis involves computation of score values derived from correlation coefficients between experimental and strain-specific in silico peptide mass profiles and compilation of score ranking lists. The taxonomic positions of the microbial samples are then determined by using the best-matching database entries. The suggested method is computationally efficient – less than 2 mins per sample - and has been successfully tested by a test set of 39 LC-MS(1) peak lists obtained from 19 different microbial pathogens. The proposed method is rapid, simple and automatable and we foresee wide application potential for future microbiological applications.

Open searching has proven to be an effective strategy for identifying both known and unknown modifications in shotgun proteomics experiments. Rather than being limited to a small set of user-specified modifications, open searches identify peptides with any mass shift that may correspond to a single modification or a combination of several modifications. Here we present PTM-Shepherd, a bioinformatics tool that automates characterization of post-translational modification profiles detected in open searches based on attributes, such as amino acid localization, fragmentation spectra similarity, retention time shifts, and relative modification rates. PTM-Shepherd can also perform multiexperiment comparisons for studying changes in modification profiles, e.g., in data generated in different laboratories or under different conditions. We demonstrate how PTM-Shepherd improves the analysis of data from formalin-fixed and paraffin-embedded samples, detects extreme underalkylation of cysteine in some data sets, discovers an artifactual modification introduced during peptide synthesis, and uncovers site-specific biases in sample preparation artifacts in a multicenter proteomics profiling study., Graphical Abstract, Highlights • Comprehensive open-search annotation. • Sensitive modification detection. • Identification of batch effects. • Novel post-translational modification discovery., In Brief Proteomics open searches require extensive post hoc analysis to be useful. PTM-Shepherd provides comprehensive open search annotation from peptide-spectrum match lists, including modification ID, localization, and effects on spectral similarity (SS) and retention time (RT). These features can be used to find batch effect and new post-translational modifications, and inform subsequent closed searches. It is available as a standalone JAR and as part of the FragPipe suite.

Differential analysis of MS-data to identify biomarkers or to understand biology is a cornerstone in proteomics. DEqMS is a robust method for analysis of both labelled and label-free MS-data. The method takes into account the inherent dependence of protein variance on the number of PSMs or peptides used for quantification, thereby providing a more accurate variance estimation. Compared to current methods, DEqMS achieves better accuracy and statistical power in quantitative proteomics. The tool is available as user-friendly R package., Graphical Abstract Highlights DEqMS is a method for statistical analysis of quantitative MS-data. Variance estimates based on the actual MS-data structure. Improved statistical power and accuracy in protein differential analysis. DEqMS is available as a user-friendly R package in Bioconductor., Quantitative proteomics by mass spectrometry is widely used in biomarker research and basic biology research for investigation of phenotype level cellular events. Despite the wide application, the methodology for statistical analysis of differentially expressed proteins has not been unified. Various methods such as t test, linear model and mixed effect models are used to define changes in proteomics experiments. However, none of these methods consider the specific structure of MS-data. Choices between methods, often originally developed for other types of data, are based on compromises between features such as statistical power, general applicability and user friendliness. Furthermore, whether to include proteins identified with one peptide in statistical analysis of differential protein expression varies between studies. Here we present DEqMS, a robust statistical method developed specifically for differential protein expression analysis in mass spectrometry data. In all data sets investigated there is a clear dependence of variance on the number of PSMs or peptides used for protein quantification. DEqMS takes this feature into account when assessing differential protein expression. This allows for a more accurate data-dependent estimation of protein variance and inclusion of single peptide identifications without increasing false discoveries. The method was tested in several data sets including E. coli proteome spike-in data, using both label-free and TMT-labeled quantification. Compared with previous statistical methods used in quantitative proteomics, DEqMS showed consistently better accuracy in detecting altered protein levels compared with other statistical methods in both label-free and labeled quantitative proteomics data. DEqMS is available as an R package in Bioconductor.

We assess the ability of open searching (OS) to analyze bacterial glycoproteomes. We find OS readily identifies known and unknown glycans within samples. Using OS, we compared glycan use across eight Burkholderia species confirming the conservation of glycan use and highlight how low frequency glycans can be identified in OS data sets. Importantly, OS is not without caveats; leading to lower assignment scores and under reporting multiply glycosylated peptides. This supports the value of OS followed by focused searching approaches., Graphical Abstract Highlights • We demonstrate the use of open searching for the detection of atypical glycosylation in bacterial glycoproteomes. • We show that although open searching is able to detect unique glycoforms it is less sensitive for the detection of multiply glycosylated peptides than focused searches. • Using open search delta mass profiles, we demonstrate glycan use across bacterial glycoproteomes can be easily compared. • For the Burkholderia genus we confirm the use of similar glycans for protein glycosylation yet also highlight that species specific glycans do exist., Mass spectrometry has become an indispensable tool for the characterization of glycosylation across biological systems. Our ability to generate rich fragmentation of glycopeptides has dramatically improved over the last decade yet our informatic approaches still lag behind. Although glycoproteomic informatics approaches using glycan databases have attracted considerable attention, database independent approaches have not. This has significantly limited high throughput studies of unusual or atypical glycosylation events such as those observed in bacteria. As such, computational approaches to examine bacterial glycosylation and identify chemically diverse glycans are desperately needed. Here we describe the use of wide-tolerance (up to 2000 Da) open searching as a means to rapidly examine bacterial glycoproteomes. We benchmarked this approach using N-linked glycopeptides of Campylobacter fetus subsp. fetus as well as O-linked glycopeptides of Acinetobacter baumannii and Burkholderia cenocepacia revealing glycopeptides modified with a range of glycans can be readily identified without defining the glycan masses before database searching. Using this approach, we demonstrate how wide tolerance searching can be used to compare glycan use across bacterial species by examining the glycoproteomes of eight Burkholderia species (B. pseudomallei; B. multivorans; B. dolosa; B. humptydooensis; B. ubonensis, B. anthina; B. diffusa; B. pseudomultivorans). Finally, we demonstrate how open searching enables the identification of low frequency glycoforms based on shared modified peptides sequences. Combined, these results show that open searching is a robust computational approach for the determination of glycan diversity within bacterial proteomes.

Ion mobility helps resolve complex proteomics samples, but data structures can be unwieldy and lead to long post-acquisition analysis times. We adapted the fast search engine MSFragger for timsTOF data, and developed IonQuant for accurate quantification. These tools are part of a complete pipeline that is well suited for the analysis of timsTOF in terms of identification sensitivity, quantification accuracy, and runtimes. We additionally demonstrate complex analyses, including semi-enzymatic database search to monitor gas-phase fragmentation in early timsTOF data., Graphical Abstract Highlights • MSFragger now supports raw timsTOF PASEF data. • IonQuant performs fast and accurate feature detection and quantification. • MSFragger and IonQuant provide excellent performance for timsTOF PASEF data. • Flexibility allows for complex analyses, such as semi-enzymatic and open search., Ion mobility brings an additional dimension of separation to LC–MS, improving identification of peptides and proteins in complex mixtures. A recently introduced timsTOF mass spectrometer (Bruker) couples trapped ion mobility separation to TOF mass analysis. With the parallel accumulation serial fragmentation (PASEF) method, the timsTOF platform achieves promising results, yet analysis of the data generated on this platform represents a major bottleneck. Currently, MaxQuant and PEAKS are most used to analyze these data. However, because of the high complexity of timsTOF PASEF data, both require substantial time to perform even standard tryptic searches. Advanced searches (e.g. with many variable modifications, semi- or non-enzymatic searches, or open searches for post-translational modification discovery) are practically impossible. We have extended our fast peptide identification tool MSFragger to support timsTOF PASEF data, and developed a label-free quantification tool, IonQuant, for fast and accurate 4-D feature extraction and quantification. Using a HeLa data set published by Meier et al. (2018), we demonstrate that MSFragger identifies significantly (∼30%) more unique peptides than MaxQuant (1.6.10.43), and performs comparably or better than PEAKS X+ (∼10% more peptides). IonQuant outperforms both in terms of number of quantified proteins while maintaining good quantification precision and accuracy. Runtime tests show that MSFragger and IonQuant can fully process a typical two-hour PASEF run in under 70 min on a typical desktop (6 CPU cores, 32 GB RAM), significantly faster than other tools. Finally, through semi-enzymatic searching, we significantly increase the number of identified peptides. Within these semi-tryptic identifications, we report evidence of gas-phase fragmentation before MS/MS analysis.

Multiple reaction monitoring with isotope dilution assays were developed for quantification of 200 proteins in dried blood spots, with median inter-day CV of 8%. Developed assays were used to measure the concentration ranges of these 200 proteins in twenty same sex, same race and age matched individuals. These, precise, sensitive, and multiplexed assays can be applied for de novo biomarker discovery and for biomarker quantification or verification experiments., Graphical Abstract Highlights Repeatable quantification of 200 proteins in dried blood spots. Determined lower limit of quantification, repeatability, parallelism and stability. Protein stability in DBS stored at ambient temperatures for up to 2 months. Concentration ranges for 200 proteins in 20 healthy individuals., The use of protein biomarkers as surrogates for clinical endpoints requires extensive multilevel validation including development of robust and sensitive assays for precise measurement of protein concentration. Multiple reaction monitoring (MRM) is a well-established mass-spectrometric method that can be used for reproducible protein-concentration measurements in biological specimens collected via microsampling. The dried blood spot (DBS) microsampling technique can be performed non-invasively without the expertise of a phlebotomist, and can enhance analyte stability which facilitate the application of this technique in retrospective studies while providing lower storage and shipping costs, because cold-chain logistics can be eliminated. Thus, precise, sensitive, and multiplexed methods for measuring protein concentrations in DBSs can be used for de novo biomarker discovery and for biomarker quantification or verification experiments. To achieve this goal, MRM assays were developed for multiplexed concentration measurement of proteins in DBSs. The lower limit of quantification (LLOQ) was found to have a median total coefficient of variation (CV) of 18% for 245 proteins, whereas the median LLOQ was 5 fmol of peptide injected on column, and the median inter-day CV over 4 days for measuring endogenous protein concentration was 8%. The majority (88%) of the assays displayed parallelism, whereas the peptide standards remained stable throughout the assay workflow and after exposure to multiple freeze-thaw cycles. For 190 proteins, the measured protein concentrations remained stable in DBS stored at ambient laboratory temperature for up to 2 months. Finally, the developed assays were used to measure the concentration ranges for 200 proteins in twenty same sex, same race and age matched individuals.

We present a technological advancement for the estimation of the affinities of Protein-Protein Interactions (PPIs) in living cells. A novel set of vectors is introduced that enables a quantitative yeast two-hybrid system based on fluorescent fusion proteins. The vectors allow simultaneous quantification of the reaction partners (Bait and Prey) and the reporter at the single-cell level by flow cytometry. We validate the applicability of this system on a small but diverse set of PPIs (eleven protein families from six organisms) with different affinities; the dissociation constants range from 117 pm to 17 μm. After only two hours of reaction, expression of the reporter can be detected even for the weakest PPI. Through a simple gating analysis, it is possible to select only cells with identical expression levels of the reaction partners. As a result of this standardization of expression levels, the mean reporter levels directly reflect the affinities of the studied PPIs. With a set of PPIs with known affinities, it is straightforward to construct an affinity ladder that permits rapid classification of PPIs with thus far unknown affinities. Conventional software can be used for this analysis. To permit automated analysis, we provide a graphical user interface for the Python-based FlowCytometryTools package.