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Tandem mass spectrometry (MS/MS) is the gold standard for intact glycopeptide identification, enabling peptide sequence elucidation and site-specific localization of glycan compositions. Beam-type collisional activation is generally sufficient for N- glycopeptides, while electron-driven dissociation is crucial for site localization in O- glycopeptides. Modern glycoproteomic methods often employ multiple dissociation techniques within a single LC-MS/MS analysis, but this approach frequently sacrifices sensitivity when analyzing multiple glycopeptide classes simultaneously. Here we explore the utility of intelligent data acquisition for glycoproteomics through real-time library searching (RTLS) to match oxonium ion patterns for on-the-fly selection of the appropriate dissociation method. By matching dissociation method with glycopeptide class, this autonomous dissociation-type selection (ADS) generates equivalent numbers of N- glycopeptide identifications relative to traditional beam-type collisional activation methods while also yielding comparable numbers of site-localized O- glycopeptide identifications relative to conventional electron transfer dissociation-based methods. The ADS approach represents a step forward in glycoproteomics throughput by enabling site-specific characterization of both N- and O- glycopeptides within the same LC-MS/MS acquisition.

The invasive leading edge represents a potential gateway for tumor metastasis. The role of fibroblasts from the tumor edge in promoting cancer invasion and metastasis has not been comprehensively elucidated. We hypothesize that cross-talk between tumor and stromal cells within the tumor microenvironment results in activation of key biological pathways depending on their position in the tumor (edge vs. core). Here we highlight phenotypic differences between tumor-adjacent-fibroblasts (TAF) from the invasive edge and tumor core fibroblasts from the tumor core, established from human lung adenocarcinomas. A multiomics approach that includes genomics, proteomics, and O-glycoproteomics was used to characterize cross-talk between TAFs and cancer cells. These analyses showed that O-glycosylation, an essential posttranslational modification resulting from sugar metabolism, alters key biological pathways including the cyclin-dependent kinase 4 (CDK4) and phosphorylated retinoblastoma protein axis in the stroma and indirectly modulates proinvasive features of cancer cells. In summary, the O-glycoproteome represents a new consideration for important biological processes involved in tumor-stroma cross-talk and a potential avenue to improve the anticancer efficacy of CDK4 inhibitors., Significance: A multiomics analysis of spatially distinct fibroblasts establishes the importance of the stromal O-glycoproteome in tumor-stroma interactions at the leading edge and provides potential strategies to improve cancer treatment. See related commentary by De Wever, p. 537., (©2021 American Association for Cancer Research.)

Ion-ion reactions are valuable tools in mass-spectrometry-based peptide and protein sequencing. To boost the generation of sequence-informative fragment ions from low charge-density precursors, supplemental activation methods, via vibrational and photoactivation, have become widely adopted. However, long-lived radical peptide cations undergo intramolecular hydrogen atom transfer from c-type ions to z • -type ions. Here we investigate the degree of hydrogen transfer for thousands of unique peptide cations where electron transfer dissociation (ETD) was performed and was followed by beam-type collisional activation (EThcD), resonant collisional activation (ETcaD), or concurrent infrared photoirradiation (AI-ETD). We report on the precursor charge density and the local amino acid environment surrounding bond cleavage to illustrate the effects of intramolecular hydrogen atom transfer for various precursor ions. Over 30% of fragments from EThcD spectra comprise distorted isotopic distributions, whereas over 20% of fragments from ETcaD have distorted distributions and less than 15% of fragments derived from ETD and AI-ETD reveal distorted isotopic distributions. Both ETcaD and EThcD give a relatively high degree of hydrogen migration, especially when D, G, N, S, and T residues were directly C-terminal to the cleavage site. Whereas all postactivation methods boost the number of c- and z • -type fragment ions detected, the collision-based approaches produce higher rates of hydrogen migration, yielding fewer spectral identifications when only c- and z • -type ions are considered. Understanding hydrogen rearrangement between c- and z • -type ions will facilitate better spectral interpretation.

Secreted polypeptides represent a fundamental axis of intercellular communication. Here, we present a protocol for the cell type-specific biotinylation, enrichment, and proteomic profiling of secreted plasma proteins directly in mice. This protocol uses conditional "turn-on" adeno-associated viruses expressing an endoplasmic reticulum-targeted biotin ligase to globally biotinylate proteins of the secretory pathway in a cell type-specific manner. Biotinylated secreted proteins can be directly purified from blood plasma and analyzed by SDS-PAGE gel or shotgun proteomics. For complete information on the generation and use of this protocol, please refer to Wei et al. (2021)., Competing Interests: The authors declare no competing interests., (© 2021 The Author(s).)

We report O-Pair Search, an approach to identify O-glycopeptides and localize O-glycosites. Using paired collision- and electron-based dissociation spectra, O-Pair Search identifies O-glycopeptides via an ion-indexed open modification search and localizes O-glycosites using graph theory and probability-based localization. O-Pair Search reduces search times more than 2,000-fold compared to current O-glycopeptide processing software, while defining O-glycosite localization confidence levels and generating more O-glycopeptide identifications. Beyond the mucin-type O-glycopeptides discussed here, O-Pair Search also accepts user-defined glycan databases, making it compatible with many types of O-glycosylation. O-Pair Search is freely available within the open-source MetaMorpheus platform at https://github.com/smith-chem-wisc/MetaMorpheus .

The cell surface proteome, the surfaceome, is the interface for engaging the extracellular space in normal and cancer cells. Here we apply quantitative proteomics of N -linked glycoproteins to reveal how a collection of some 700 surface proteins is dramatically remodeled in an isogenic breast epithelial cell line stably expressing any of six of the most prominent proliferative oncogenes, including the receptor tyrosine kinases, EGFR and HER2, and downstream signaling partners such as KRAS, BRAF, MEK, and AKT. We find that each oncogene has somewhat different surfaceomes, but the functions of these proteins are harmonized by common biological themes including up-regulation of nutrient transporters, down-regulation of adhesion molecules and tumor suppressing phosphatases, and alteration in immune modulators. Addition of a potent MEK inhibitor that blocks MAPK signaling brings each oncogene-induced surfaceome back to a common state reflecting the strong dependence of the oncogene on the MAPK pathway to propagate signaling. Cell surface protein capture is mediated by covalent tagging of surface glycans, yet current methods do not afford sequencing of intact glycopeptides. Thus, we complement the surfaceome data with whole cell glycoproteomics enabled by a recently developed technique called activated ion electron transfer dissociation (AI-ETD). We found massive oncogene-induced changes to the glycoproteome and differential increases in complex hybrid glycans, especially for KRAS and HER2 oncogenes. Overall, these studies provide a broad systems-level view of how specific driver oncogenes remodel the surfaceome and the glycoproteome in a cell autologous fashion, and suggest possible surface targets, and combinations thereof, for drug and biomarker discovery., Competing Interests: Competing interest statement: K.K.L., L.L.K, and J.A.W. received research funding from Celgene Corporation but no personal financial gain or equity., (Copyright © 2020 the Author(s). Published by PNAS.)

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., (© 2019 Brademan et al.)

High-throughput top-down proteomic experiments directly identify proteoforms in complex mixtures, making high quality tandem mass spectra necessary to deeply characterize proteins with many sources of variation. Collision-based dissociation methods offer expedient data acquisition but often fail to extensively fragment proteoforms for thorough analysis. Electron-driven dissociation methods are a popular alternative approach, especially for precursor ions with high charge density. Combining infrared photoactivation concurrent with electron transfer dissociation (ETD) reactions, i.e., activated ion ETD (AI-ETD), can significantly improve ETD characterization of intact proteins, but benefits of AI-ETD have yet to be quantified in high-throughput top-down proteomics. Here, we report the first application of AI-ETD to LC-MS/MS characterization of intact proteins (<20 kDa), highlighting improved proteoform identification the method offers over higher energy-collisional dissociation (HCD), standard ETD, and ETD followed by supplemental HCD activation (EThcD). We identified 935 proteoforms from 295 proteins from human colorectal cancer cell line HCT116 using AI-ETD compared to 1014 proteoforms, 915 proteoforms, and 871 proteoforms with HCD, ETD, and EThcD, respectively. Importantly, AI-ETD outperformed each of the three other methods in MS/MS success rates and spectral quality metrics (e.g., sequence coverage achieved and proteoform characterization scores). In all, this four-method analysis offers the most extensive comparisons to date and demonstrates that AI-ETD both increases identifications over other ETD methods and improves proteoform characterization via higher sequence coverage, positioning it as a premier method for high-throughput top-down proteomics.

Here we report the first demonstration of near-complete sequence coverage of intact proteins using activated ion-electron transfer dissociation (AI-ETD), a method that leverages concurrent infrared photoactivation to enhance electron-driven dissociation. AI-ETD produces mainly c/z-type product ions and provides comprehensive (77-97%) protein sequence coverage, outperforming HCD, ETD, and EThcD for all proteins investigated. AI-ETD also maintains this performance across precursor ion charge states, mitigating charge-state dependence that limits traditional approaches.

The ability to localize phosphosites to specific amino acid residues is crucial to translating phosphoproteomic data into biological meaningful contexts. In a companion manuscript ( Anal. Chem. 2017 , DOI: 10.1021/acs.analchem.7b00213 ), we described a new implementation of activated ion electron transfer dissociation (AI-ETD) on a quadrupole-Orbitrap-linear ion trap hybrid MS system (Orbitrap Fusion Lumos), which greatly improved peptide fragmentation and identification over ETD and other supplemental activation methods. Here we present the performance of AI-ETD for identifying and localizing sites of phosphorylation in both phosphopeptides and intact phosphoproteins. Using 90 min analyses we show that AI-ETD can identify 24,503 localized phosphopeptide spectral matches enriched from mouse brain lysates, which more than triples identifications from standard ETD experiments and outperforms ETcaD and EThcD as well. AI-ETD achieves these gains through improved quality of fragmentation and MS/MS success rates for all precursor charge states, especially for doubly protonated species. We also evaluate the degree to which phosphate neutral loss occurs from phosphopeptide product ions due to the infrared photoactivation of AI-ETD and show that modifying phosphoRS (a phosphosite localization algorithm) to include phosphate neutral losses can significantly improve localization in AI-ETD spectra. Finally, we demonstrate the utility of AI-ETD in localizing phosphosites in α-casein, an ∼23.5 kDa phosphoprotein that showed eight of nine known phosphorylation sites occupied upon intact mass analysis. AI-ETD provided the greatest sequence coverage for all five charge states investigated and was the only fragmentation method to localize all eight phosphosites for each precursor. Overall, this work highlights the analytical value AI-ETD can bring to both bottom-up and top-down phosphoproteomics.

Each year over 90 million units of blood are transfused worldwide. Our dependence on this blood supply mandates optimized blood management and storage. During storage, red blood cells undergo degenerative processes resulting in altered metabolic characteristics which may make blood less viable for transfusion. However, not all stored blood spoils at the same rate, a difference that has been attributed to variable rates of energy usage and metabolism in red blood cells. Specific metabolite abundances are heritable traits; however, the link between heritability of energy metabolism and red blood cell storage profiles is unclear. Herein we performed a comprehensive metabolomics and proteomics study of red blood cells from 18 mono- and di-zygotic twin pairs to measure heritability and identify correlations with ATP and other molecular indices of energy metabolism. Without using affinity-based hemoglobin depletion, our work afforded the deepest multi-omic characterization of red blood cell membranes to date (1280 membrane proteins and 330 metabolites), with 119 membrane protein and 148 metabolite concentrations found to be over 30% heritable. We demonstrate a high degree of heritability in the concentration of energy metabolism metabolites, especially glycolytic metabolites. In addition to being heritable, proteins and metabolites involved in glycolysis and redox metabolism are highly correlated, suggesting that crucial energy metabolism pathways are inherited en bloc at distinct levels. We conclude that individuals can inherit a phenotype composed of higher or lower concentrations of these proteins together. This can result in vastly different red blood cells storage profiles which may need to be considered to develop precise and individualized storage options. Beyond guiding proper blood storage, this intimate link in heritability between energy and redox metabolism pathways may someday prove useful in determining the predisposition of an individual toward metabolic diseases., (© 2016 by The American Society for Biochemistry and Molecular Biology, Inc.)

Three studies demonstrate the potential of state-of-the-art mass spectrometry-based proteomics for rapid, deep characterization of proteomes., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Electron transfer dissociation (ETD) has been broadly adopted and is now available on a variety of commercial mass spectrometers. Unlike collisional activation techniques, optimal performance of ETD requires considerable user knowledge and input. ETD reaction duration is one key parameter that can greatly influence spectral quality and overall experiment outcome. We describe a calibration routine that determines the correct number of reagent anions necessary to reach a defined ETD reaction rate. Implementation of this automated calibration routine on two hybrid Orbitrap platforms illustrate considerable advantages, namely, increased product ion yield with concomitant reduction in scan rates netting up to 75% more unique peptide identifications in a shotgun experiment. Graphical Abstract ᅟ.

The field of proteomics almost uniformly relies on peptide cation analysis, leading to an underrepresentation of acidic portions of proteomes, including relevant acidic posttranslational modifications. Despite the many benefits negative mode proteomics can offer, peptide anion analysis remains in its infancy due mainly to challenges with high-pH reversed-phase separations and a lack of robust fragmentation methods suitable for peptide anion characterization. Here, we report the first implementation of activated ion negative electron transfer dissociation (AI-NETD) on the chromatographic timescale, generating 7,601 unique peptide identifications from Saccharomyces cerevisiae in single-shot nLC-MS/MS analyses of tryptic peptides-a greater than 5-fold increase over previous results with NETD alone. These improvements translate to identification of 1,106 proteins, making this work the first negative mode study to identify more than 1,000 proteins in any system. We then compare the performance of AI-NETD for analysis of peptides generated by five proteases (trypsin, LysC, GluC, chymotrypsin, and AspN) for negative mode analyses, identifying as many as 5,356 peptides (1,045 proteins) with LysC and 4,213 peptides (857 proteins) with GluC in yeast-characterizing 1,359 proteins in total. Finally, we present the first deep-sequencing approach for negative mode proteomics, leveraging offline low-pH reversed-phase fractionation prior to online high-pH separations and peptide fragmentation with AI-NETD. With this platform, we identified 3,467 proteins in yeast with trypsin alone and characterized a total of 3,730 proteins using multiple proteases, or nearly 83% of the expressed yeast proteome. This work represents the most extensive negative mode proteomics study to date, establishing AI-NETD as a robust tool for large-scale peptide anion characterization and making the negative mode approach a more viable platform for future proteomic studies., (© 2015 by The American Society for Biochemistry and Molecular Biology, Inc.)

Top-down proteomics offers the potential for full protein characterization, but many challenges remain for this approach, including efficient protein separations and effective fragmentation of intact proteins. Capillary zone electrophoresis (CZE) has shown great potential for separation of intact proteins, especially for differentially modified proteoforms of the same gene product. To date, however, CZE has been used only with collision-based fragmentation methods. Here we report the first implementation of electron transfer dissociation (ETD) with online CZE separations for top-down proteomics, analyzing a mixture of four standard proteins and a complex protein mixture from the Mycobacterium marinum bacterial secretome. Using a multipurpose dissociation cell on an Orbitrap Elite system, we demonstrate that CZE is fully compatible with ETD as well as higher energy collisional dissociation (HCD), and that the two complementary fragmentation methods can be used in tandem on the electrophoretic time scale for improved protein characterization. Furthermore, we show that activated ion electron transfer dissociation (AI-ETD), a recently introduced method for enhanced ETD fragmentation, provides useful performance with CZE separations to greatly increase protein characterization. When combined with HCD, AI-ETD improved the protein sequence coverage by more than 200% for proteins from both standard and complex mixtures, highlighting the benefits electron-driven dissociation methods can add to CZE separations.

The ability to acquire highly accurate quantitative data is an increasingly important part of any proteomics experiment, whether shotgun or top-down approaches are used. We recently developed a quantitation strategy for peptides based on neutron encoding, or NeuCode SILAC, which uses closely spaced heavy isotope-labeled amino acids and high-resolution mass spectrometry to provide quantitative data. We reasoned that the strategy would also be applicable to intact proteins and could enable robust, multiplexed quantitation for top-down experiments. We used yeast lysate labeled with either (13)C6(15)N2-lysine or (2)H8-lysine, isotopologues of lysine that are spaced 36 mDa apart. Proteins having such close spacing cannot be distinguished during a medium resolution scan, but upon acquiring a high-resolution scan, the two forms of the protein with each amino acid are resolved and the quantitative information revealed. An additional benefit NeuCode SILAC provides for top down is that the spacing of the isotope peaks indicates the number of lysines present in the protein, information that aids in identification. We used NeuCode SILAC to quantify several hundred isotope distributions, manually identify and quantify proteins from 1:1, 3:1, and 5:1 mixed ratios, and demonstrate MS(2)-based quantitation using ETD.