Using iRT, a normalized retention time for more targeted measurement of peptides

An Update on MRMAssayDB: A Comprehensive Resource for Targeted Proteomics Assays in the Community

Precise multiplexed quantification of proteins in biological samples can be achieved by targeted proteomics using multiple or parallel reaction monitoring (MRM/PRM). Combined with internal standards, the method achieves very good repeatability and reproducibility enabling excellent protein quantification and allowing longitudinal and cohort studies. A laborious part of performing such experiments lies in the preparation steps dedicated to the development and validation of individual protein assays. Several public repositories host information on targeted proteomics assays, including NCI’s Clinical Proteomic Tumor Analysis Consortium assay portals, PeptideAtlas SRM Experiment Library, SRMAtlas, PanoramaWeb, and PeptideTracker, with all offering varying levels of details. We introduced MRMAssayDB in 2018 as an integrated resource for targeted proteomics assays. The Web-based application maps and links the assays from the repositories, includes comprehensive up-to-date protein and sequence annotations, and provides multiple visualization options on the peptide and protein level. We have extended MRMAssayDB with more assays and extensive annotations. Currently it contains >828 000 assays covering >51 000 proteins from 94 organisms, of which >17 000 proteins are present in >2400 biological pathways, and >48 000 mapping to >21 000 Gene Ontology terms. This is an increase of about four times the number of assays since introduction. We have expanded annotations of interaction, biological pathways, and disease associations. A newly added visualization module for coupled molecular structural annotation browsing allows the user to interactively examine peptide sequence and any known PTMs and disease mutations, and map all to available protein 3D structures. Because of its integrative approach, MRMAssayDB enables a holistic view of suitable proteotypic peptides and commonly used transitions in empirical data. Availability: http://mrmassaydb.proteincentre.com.

Optimised data-independent acquisition strategy recaptures the classification of early-stage hepatocellular carcinoma based on data-dependent acquisition

Proteomics is increasingly used for exploring disease biomarkers and therapeutic targets. The data-independent acquisition (DIA) method collects all peptide signals in a sample, and provides a convenient way to archive disease-related molecular features for further exploration. In this study, we first established a high-coverage human hepatocellular carcinoma (HCC) spectral library containing 9393 protein groups, 119,903 peptides. Furthermore, we optimised the DIA method with respect to four key parameters: settings for mass spectrometry acquisition, gradient length, amount of sample loading, and length of analytical column. More than 6000 proteins from HepG2 cells could be stably quantified using the optimised one-shot DIA approach with a 2 h gradient time. One-shot DIA identified a similar number of proteins as did multi-fraction data-dependent acquisition (DDA) from the same group of HCC samples, but at a quarter of the total acquisition time. DIA data could recapture the classification results obtained from DDA data, thus paving the way for large-scale, multi-centre proteomics analysis of clinical samples. SIGNIFICANCE: The organ-specific spectral library for HCC and the optimised 2 h DIA approach met the urgent demands for large-scale quantitative proteomics analysis of HCC clinical samples. Compared with multi-fraction-DDA, the optimised one-shot DIA could reach a similar identification while consuming shorter acquisition time, thus making it possible to analyse thousands of clinical samples.

Mass spectrometry-based approaches to study lanthanides and lanthanide-dependent proteins in the phyllosphere

Rare-earth elements (REEs) were recently discovered to be biologically significant. The finding was originally made with the methanol dehydrogenase XoxF, which depends on REEs for its activity, and reports of lanthanide-utilizing bacteria have since expanded. Environmental proteomics allows the identification of proteins specifically induced by the presence of lanthanides or can provide insights into the preferred use of lanthanide-dependent and -independent isoenzymes, for example. Here we describe protocols for the growth and subsequent mass spectrometry-based proteome analysis of bacteria obtained from controlled artificial media and from the phyllosphere of the model plant Arabidopsis thaliana. In addition, the use of inductively coupled plasma mass spectrometry (ICP-MS) is described for the quantification of REEs in biological samples.

Use of Hybrid Data-Dependent and -Independent Acquisition Spectral Libraries Empowers Dual-Proteome Profiling

In the context of bacterial infections, it is imperative that physiological responses can be studied in an integrated manner, meaning a simultaneous analysis of both the host and the pathogen responses. To improve the sensitivity of detection, data-independent acquisition (DIA)-based proteomics was found to outperform data-dependent acquisition (DDA) workflows in identifying and quantifying low-abundant proteins. Here, by making use of representative bacterial pathogen/host proteome samples, we report an optimized hybrid library generation workflow for DIA mass spectrometry relying on the use of data-dependent and in silico-predicted spectral libraries. When compared to searching DDA experiment-specific libraries only, the use of hybrid libraries significantly improved peptide detection to an extent suggesting that infection-relevant host-pathogen conditions could be profiled in sufficient depth without the need of a priori bacterial pathogen enrichment when studying the bacterial proteome. Proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD017904 and PXD017945.

Automated Workflow for Peptide-Level Quantitation from DIA/SWATH-MS Data

Data-independent acquisition (DIA) is a powerful method to acquire spectra from all ionized precursors of a sample. Considering the complexity of the highly multiplexed spectral data, sophisticated workflows have been developed to obtain peptides quantification. Here we describe an open-source and easy-to-use workflow to obtain a quantitative matrix from multiple DIA runs. This workflow requires as prior information an "assay library," which contains the MS coordinates of peptides. It consists of OpenSWATH, pyProphet, and DIAlignR software. For the ease of installation and to isolate operating system-related dependency, docker-based containerization is utilized in this workflow.

SWATH-MS Protocols in Human Diseases

Identification of molecular biomarkers for human diseases is one of the most important disciplines in translational science as it helps to elucidate their origin and early progression. Thus, it is a key factor in better diagnosis, prognosis, and treatment. Proteomics can help to solve the problem of sample complexity when the most common primary sample specimens were analyzed: organic fluids of easy access. The latest developments in high-throughput and label-free quantitative proteomics (SWATH-MS), together with more advanced liquid chromatography, have enabled the analysis of large sample sets with the sensitivity and depth needed to succeed in this task. In this chapter, we show different sample processing methods (major protein depletion, digestion, etc.) and a micro LC-SWATH-MS protocol to identify/quantify several proteins in different types of samples (serum/plasma, saliva, urine, tears).

System-Wide Profiling of Protein Complexes Via Size Exclusion Chromatography-Mass Spectrometry (SEC-MS)

In living cells, most proteins are organized in stable or transient functional assemblies, protein complexes, which control a multitude of vital cellular processes such as cell cycle progression, metabolism, and signal transduction. Over several decades, specific protein complexes have been analyzed by structural biology methods, initially X-ray crystallography and more recently single particle cryoEM. In parallel, mass spectrometry (MS)-based methods including in vitro affinity-purification coupled to MS or in vivo protein proximity-dependent labeling methods have proven particularly effective to detect complexes, thus nominating new assemblies for structural analysis. Those approaches, however, are either of limited in throughput or require specifically engineered protein systems.In this chapter, we present protocols for a workflow that supports the parallel analysis of multiple complexes from the same biological sample with respect to abundance, subunit composition, and stoichiometry. It consists of the separation of native complexes by size-exclusion chromatography (SEC) and the subsequent mass spectrometric analysis of the proteins in consecutive SEC fractions. In particular, we describe (1) optimized conditions to achieve native protein complex separation by SEC, (2) the preparation of the SEC fractions for MS analysis, (3) the acquisition of the MS data at high throughput via SWATH/DIA (data-independent analysis) mass spectrometry and short chromatographic gradients, and (4) a set of bioinformatic tools for the targeted analysis of protein complexes. Altogether, the parallel measurement of a high number of complexes from a single biological sample results in unprecedented system-level insights into the remodeling of cellular protein complexes in response to perturbations of a broad range of cellular systems.

The modulation of the burn wound environment by negative pressure wound therapy: Insights from the proteome

Negative pressure wound therapy has been used to promote wound healing in a variety of settings, including as an adjunct to silver-impregnated dressings in the acute management of paediatric burns. Fluid aspirated by the negative pressure wound therapy system represents a potentially insightful research matrix for understanding the burn wound microenvironment and the intervention's biochemical mechanisms of action. The aim of this study was to characterize the proteome of wound fluid collected using negative pressure wound therapy from children with small-area thermal burns. Samples were obtained as part of a randomized controlled trial investigating the clinical efficacy of adjunctive negative pressure wound therapy. They were compared with blister fluid specimens from paediatric burn patients matched according to demographic and injury characteristics. Protein identification and quantification were performed via liquid chromatography tandem mass spectrometry and sequential window acquisition of all theoretical mass spectra data-independent acquisition. Proteins and biological pathways that were unique to or enriched in negative pressure wound therapy fluid samples were evaluated using principal components, partial least squares-discriminant, and gene ontology enrichment analyses. Eight viable samples of negative pressure wound therapy fluid were collected and analyzed with eight matched blister fluid samples. A total of 502 proteins were quantitatively profiled in the negative pressure wound therapy fluid, of which 444 (88.4%) were shared with blister fluid. Several proteins exhibited significant abundance differences between fluid types, with negative pressure wound therapy fluid showing a higher abundance of matrix metalloproteinase-9, arginase-1, low affinity immunoglobulin gamma Fc region receptor III-A, filamin-A, alpha-2-macroglobulin, and hemoglobin subunit alpha. The results lend support to the hypothesis that negative pressure wound therapy augments wound healing through the modulation of factors involved in the inflammatory response, granulation tissue synthesis, and extracellular matrix maintenance. Data are available via ProteomeXchange with identifier PXD023168.

Convergent network effects along the axis of gene expression during prostate cancer progression

Background

Tumor-specific genomic aberrations are routinely determined by high-throughput genomic measurements. It remains unclear how complex genome alterations affect molecular networks through changing protein levels and consequently biochemical states of tumor tissues.

Results

Here, we investigate the propagation of genomic effects along the axis of gene expression during prostate cancer progression. We quantify genomic, transcriptomic, and proteomic alterations based on 105 prostate samples, consisting of benign prostatic hyperplasia regions and malignant tumors, from 39 prostate cancer patients. Our analysis reveals the convergent effects of distinct copy number alterations impacting on common downstream proteins, which are important for establishing the tumor phenotype. We devise a network-based approach that integrates perturbations across different molecular layers, which identifies a sub-network consisting of nine genes whose joint activity positively correlates with increasingly aggressive tumor phenotypes and is predictive of recurrence-free survival. Further, our data reveal a wide spectrum of intra-patient network effects, ranging from similar to very distinct alterations on different molecular layers.

Conclusions

This study uncovers molecular networks with considerable convergent alterations across tumor sites and patients. It also exposes a diversity of network effects: we could not identify a single sub-network that is perturbed in all high-grade tumor regions.

Absolute quantification of transcription factors in human erythropoiesis using selected reaction monitoring mass spectrometry

Quantitative changes in transcription factor (TF) abundance regulate dynamic cellular processes, including cell fate decisions. Protein copy number provides information about the relative stoichiometry of TFs that can be used to determine how quantitative changes in TF abundance influence gene regulatory networks. In this protocol, we describe a targeted selected reaction monitoring (SRM)-based mass-spectrometry method to systematically measure the absolute protein concentration of nuclear TFs as human hematopoietic stem and progenitor cells differentiate along the erythropoietic lineage.

For complete details on the use and execution of this protocol, please refer to Gillespie et al. (2020).

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Protocol for absolute quantification of TFs in human erythropoiesis

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Selected reaction monitoring mass-spectrometry parameters for each peptide

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Validated SRM assays corresponding to >100 TFs

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Copy number reveals the relative stoichiometries of TFs during erythropoiesis

Thermostability profiling of MHC-bound peptides: a new dimension in immunopeptidomics and aid for immunotherapy design

The features of peptide antigens that contribute to their immunogenicity are not well understood. Although the stability of peptide-MHC (pMHC) is known to be important, current assays assess this interaction only for peptides in isolation and not in the context of natural antigen processing and presentation. Here, we present a method that provides a comprehensive and unbiased measure of pMHC stability for thousands of individual ligands detected simultaneously by mass spectrometry (MS). The method allows rapid assessment of intra-allelic and inter-allelic differences in pMHC stability and reveals profiles of stability that are broader than previously appreciated. The additional dimensionality of the data facilitated the training of a model which improves the prediction of peptide immunogenicity, specifically of cancer neoepitopes. This assay can be applied to any cells bearing MHC or MHC-like molecules, offering insight into not only the endogenous immunopeptidome, but also that of neoepitopes and pathogen-derived sequences.

FLEXIQuant-LF to quantify protein modification extent in label-free proteomics data

Improvements in LC-MS/MS methods and technology have enabled the identification of thousands of modified peptides in a single experiment. However, protein regulation by post-translational modifications (PTMs) is not binary, making methods to quantify the modification extent crucial to understanding the role of PTMs. Here, we introduce FLEXIQuant-LF, a software tool for large-scale identification of differentially modified peptides and quantification of their modification extent without knowledge of the types of modifications involved. We developed FLEXIQuant-LF using label-free quantification of unmodified peptides and robust linear regression to quantify the modification extent of peptides. As proof of concept, we applied FLEXIQuant-LF to data-independent-acquisition (DIA) data of the anaphase promoting complex/cyclosome (APC/C) during mitosis. The unbiased FLEXIQuant-LF approach to assess the modification extent in quantitative proteomics data provides a better understanding of the function and regulation of PTMs. The software is available at https://github.com/SteenOmicsLab/FLEXIQuantLF.

Phosphoproteomics reveals novel modes of function and inter-relationships among PIKKs in response to genotoxic stress

The DNA damage response (DDR) is a complex signaling network that relies on cascades of protein phosphorylation, which are initiated by three protein kinases of the family of PI3-kinase-related protein kinases (PIKKs): ATM, ATR, and DNA-PK. ATM is missing or inactivated in the genome instability syndrome, ataxia-telangiectasia (A-T). The relative shares of these PIKKs in the response to genotoxic stress and the functional relationships among them are central questions in the genome stability field. We conducted a comprehensive phosphoproteomic analysis in human wild-type and A-T cells treated with the double-strand break-inducing chemical, neocarzinostatin, and validated the results with the targeted proteomic technique, selected reaction monitoring. We also matched our results with 34 published screens for DDR factors, creating a valuable resource for identifying strong candidates for novel DDR players. We uncovered fine-tuned dynamics between the PIKKs following genotoxic stress, such as DNA-PK-dependent attenuation of ATM. In A-T cells, partial compensation for ATM absence was provided by ATR and DNA-PK, with distinct roles and kinetics. The results highlight intricate relationships between these PIKKs in the DDR.

Tau PTM Profiles Identify Patient Heterogeneity and Stages of Alzheimer's Disease

To elucidate the role of Tau isoforms and post-translational modification (PTM) stoichiometry in Alzheimer's disease (AD), we generated a high-resolution quantitative proteomics map of 95 PTMs on multiple isoforms of Tau isolated from postmortem human tissue from 49 AD and 42 control subjects. Although Tau PTM maps reveal heterogeneity across subjects, a subset of PTMs display high occupancy and frequency for AD, suggesting importance in disease. Unsupervised analyses indicate that PTMs occur in an ordered manner, leading to Tau aggregation. The processive addition and minimal set of PTMs associated with seeding activity was further defined by analysis of size-fractionated Tau. To summarize, features in the Tau protein critical for disease intervention at different stages of disease are identified, including enrichment of 0N and 4R isoforms, underrepresentation of the C terminus, an increase in negative charge in the proline-rich region (PRR), and a decrease in positive charge in the microtubule binding domain (MBD).

A comprehensive spectral assay library to quantify the Escherichia coli proteome by DIA/SWATH-MS

Data-Independent Acquisition (DIA) is a method to improve consistent identification and precise quantitation of peptides and proteins by mass spectrometry (MS). The targeted data analysis strategy in DIA relies on spectral assay libraries that are generally derived from a priori measurements of peptides for each species. Although Escherichia coli (E. coli) is among the best studied model organisms, so far there is no spectral assay library for the bacterium publicly available. Here, we generated a spectral assay library for 4,014 of the 4,389 annotated E. coli proteins using one- and two-dimensional fractionated samples, and ion mobility separation enabling deep proteome coverage. We demonstrate the utility of this high-quality library with robustness in quantitation of the E. coli proteome and with rapid-chromatography to enhance throughput by targeted DIA-MS. The spectral assay library supports the detection and quantification of 91.5% of all E. coli proteins at high-confidence with 56,182 proteotypic peptides, making it a valuable resource for the scientific community. Data and spectral libraries are available via ProteomeXchange (PXD020761, PXD020785) and SWATHAtlas (SAL00222-28).

Integrated Omics of Metastatic Colorectal Cancer

We integrate the genomics, proteomics, and phosphoproteomics of 480 clinical tissues from 146 patients in a Chinese colorectal cancer (CRC) cohort, among which 70 had metastatic CRC (mCRC). Proteomic profiling differentiates three CRC subtypes characterized by distinct clinical prognosis and molecular signatures. Proteomic and phosphoproteomic profiling of primary tumors alone successfully distinguishes cases with metastasis. Metastatic tissues exhibit high similarities with primary tumors at the genetic but not the proteomic level, and kinase network analysis reveals significant heterogeneity between primary colorectal tumors and their liver metastases. In vivo xenograft-based drug tests using 31 primary and metastatic tumors show personalized responses, which could also be predicted by kinase-substrate network analysis no matter whether tumors carry mutations in the drug-targeted genes. Our study provides a valuable resource for better understanding of mCRC and has potential for clinical application.

Deep Learning in Proteomics

Proteomics, the study of all the proteins in biological systems, is becoming a data-rich science. Protein sequences and structures are comprehensively catalogued in online databases. With recent advancements in tandem mass spectrometry (MS) technology, protein expression and post-translational modifications (PTMs) can be studied in a variety of biological systems at the global scale. Sophisticated computational algorithms are needed to translate the vast amount of data into novel biological insights. Deep learning automatically extracts data representations at high levels of abstraction from data, and it thrives in data-rich scientific research domains. Here, a comprehensive overview of deep learning applications in proteomics, including retention time prediction, MS/MS spectrum prediction, de novo peptide sequencing, PTM prediction, major histocompatibility complex-peptide binding prediction, and protein structure prediction, is provided. Limitations and the future directions of deep learning in proteomics are also discussed. This review will provide readers an overview of deep learning and how it can be used to analyze proteomics data.

Enzymatic Dissociation Induces Transcriptional and Proteotype Bias in Brain Cell Populations

Different cell isolation techniques exist for transcriptomic and proteotype profiling of brain cells. Here, we provide a systematic investigation of the influence of different cell isolation protocols on transcriptional and proteotype profiles in mouse brain tissue by taking into account single-cell transcriptomics of brain cells, proteotypes of microglia and astrocytes, and flow cytometric analysis of microglia. We show that standard enzymatic digestion of brain tissue at 37 °C induces profound and consistent alterations in the transcriptome and proteotype of neuronal and glial cells, as compared to an optimized mechanical dissociation protocol at 4 °C. These findings emphasize the risk of introducing technical biases and biological artifacts when implementing enzymatic digestion-based isolation methods for brain cell analyses.

Sense and Nonsense of Elevated Column Temperature in Proteomic Bottom-up LC-MS Analyses

Elevated column temperature represents a simple means for improving chromatographic separation of peptides. Here, we demonstrated the advantages of the column temperature in peptide separation using state-of-the-art columns. More importantly, we also determined how temperature can impair proteomic bottom-up analyses. We found that an elevated temperature in combination with the acidic pH of the mobile phase induced in-column peptide hydrolysis with high specificity to Asp and accelerated five modification reactions of amino acids. The positive effects of temperature dominated in the 30 min long gradients since the column operated at 90 °C provided the largest number of identified peptides and proteins. However, the adverse effects of temperature on peptide integrity in longer liquid chromatography-mass spectrometry (LC-MS) analyses required its reduction to obtain optimum results. The largest number of peptides was identified using the column maintained at 75 °C in 60 min long gradients, at 60 °C in 120 min long gradients, and at 45 °C in 240 min long gradients. Our results indicate that no universal column temperature exists for bottom-up LC-MS analyses. Quite the contrary, the temperature setting must be selected rationally to exploit the full capabilities of the state-of-the-art mass spectrometers in proteomic LC-MS analyses, with the gradient time being a critical factor.

Development of a Parallel Reaction Monitoring Mass Spectrometry Assay for the Detection of SARS-CoV-2 Spike Glycoprotein and Nucleoprotein

There is an urgent need for robust and high-throughput methods for SARS-CoV-2 detection in suspected patient samples to facilitate disease management, surveillance, and control. Although nucleic acid detection methods such as reverse transcription polymerase chain reaction (RT-PCR) are the gold standard, during the current pandemic, the deployment of RT-PCR tests has been extremely slow, and key reagents such as PCR primers and RNA extraction kits are at critical shortages. Rapid point-of-care viral antigen detection methods have been previously employed for the diagnosis of respiratory viruses such as influenza and respiratory syncytial viruses. Therefore, the direct detection of SARS-CoV-2 viral antigens in patient samples could also be used for diagnosis of active infection, and alternative methodologies for specific and sensitive viral protein detection should be explored. Targeted mass spectrometry techniques have enabled the identification and quantitation of a defined subset of proteins/peptides at single amino acid resolution with attomole level sensitivity and high reproducibility. Herein, we report a targeted mass spectrometry assay for the detection of SARS-CoV-2 spike protein and nucleoprotein in a relevant biological matrix. Recombinant full-length spike protein and nucleoprotein were digested and proteotypic peptides were selected for parallel reaction monitoring (PRM) quantitation using a high-resolution Orbitrap instrument. A spectral library, which contained seven proteotypic peptides (four from spike protein and three from nucleoprotein) and the top three to four transitions, was generated and evaluated. From the original spectral library, we selected two best performing peptides for the final PRM assay. The assay was evaluated using mock test samples containing inactivated SARS-CoV-2 virions, added to in vitro derived mucus. The PRM assay provided a limit of detection of ∼200 attomoles and a limit of quantitation of ∼ 390 attomoles. Extrapolating from the test samples, the projected titer of virus particles necessary for the detection of SARS-CoV-2 spike and nucleoprotein detection was approximately 2 × 105 viral particles/mL, making it an attractive alternative to RT-PCR assays. Potentially, mass spectrometry-based methods for viral antigen detection may deliver higher throughput and could serve as a complementary diagnostic tool to RT-PCR. Furthermore, this assay could be used to evaluate the presence of SARS-CoV-2 in archived or recently collected biological fluids, in vitro-derived research materials, and wastewater samples.

Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies

Cancer has no borders: Generation and analysis of molecular data across multiple centers worldwide is necessary to gain statistically significant clinical insights for the benefit of patients. Here we conceived and standardized a proteotype data generation and analysis workflow enabling distributed data generation and evaluated the quantitative data generated across laboratories of the international Cancer Moonshot consortium. Using harmonized mass spectrometry (MS) instrument platforms and standardized data acquisition procedures, we demonstrate robust, sensitive, and reproducible data generation across eleven international sites on seven consecutive days in a 24/7 operation mode. The data presented from the high-resolution MS1-based quantitative data-independent acquisition (HRMS1-DIA) workflow shows that coordinated proteotype data acquisition is feasible from clinical specimens using such standardized strategies. This work paves the way for the distributed multi-omic digitization of large clinical specimen cohorts across multiple sites as a prerequisite for turning molecular precision medicine into reality.

Lysine and Arginine Protein Post-translational Modifications by Enhanced DIA Libraries: Quantification in Murine Liver Disease

Proteoforms containing post-translational modifications (PTMs) represent a degree of functional diversity only harnessed through analytically precise simultaneous quantification of multiple PTMs. Here we present a method to accurately differentiate an unmodified peptide from its PTM-containing counterpart through data-independent acquisition-mass spectrometry, leveraging small precursor mass windows to physically separate modified peptidoforms from each other during MS2 acquisition. We utilize a lysine and arginine PTM-enriched peptide assay library and site localization algorithm to simultaneously localize and quantify seven PTMs including mono-, di-, and trimethylation, acetylation, and succinylation in addition to total protein quantification in a single MS run without the need to enrich experimental samples. To evaluate biological relevance, this method was applied to liver lysate from differentially methylated nonalcoholic steatohepatitis (NASH) mouse models. We report that altered methylation and acetylation together with total protein changes drive the novel hypothesis of a regulatory function of PTMs in protein synthesis and mRNA stability in NASH.

Spliced Peptides and Cytokine-Driven Changes in the Immunopeptidome of Melanoma

Antigen recognition by CD8⁺ T cells is governed by the pool of peptide antigens presented on the cell surface in the context of HLA class I complexes. Studies have shown not only a high degree of plasticity in the immunopeptidome, but also that a considerable fraction of all presented peptides is generated through proteasome-mediated splicing of noncontiguous regions of proteins to form novel peptide antigens. Here, we used high-resolution mass spectrometry combined with new bioinformatic approaches to characterize the immunopeptidome of melanoma cells in the presence or absence of IFNγ. In total, we identified more than 60,000 peptides from a single patient-derived cell line (LM-MEL-44) and demonstrated that IFNγ induced changes in the peptidome, with an overlap of only approximately 50% between basal and treated cells. Around 6% to 8% of the peptides were identified as cis-spliced peptides, and 2,213 peptides (1,827 linear and 386 cis-spliced peptides) were derived from known melanoma-associated antigens. These peptide antigens were equally distributed between the constitutive- and IFNγ-induced peptidome. We next examined additional HLA-matched patient-derived cell lines to investigate how frequently these peptides were identified and found that a high proportion of both linear and spliced peptides was conserved between individual patient tumors, drawing on data amassing to more than 100,000 peptide sequences. Several of these peptides showed in vitro immunogenicity across multiple patients with melanoma. These observations highlight the breadth and complexity of the repertoire of immunogenic peptides that can be exploited therapeutically and suggest that spliced peptides are a major class of tumor antigens.

Data, Reagents, Assays and Merits of Proteomics for SARS-CoV-2 Research and Testing

As the COVID-19 pandemic continues to spread, thousands of scientists around the globe have changed research direction to understand better how the virus works and to find out how it may be tackled. The number of manuscripts on preprint servers is soaring and peer-reviewed publications using MS-based proteomics are beginning to emerge. To facilitate proteomic research on SARS-CoV-2, the virus that causes COVID-19, this report presents deep-scale proteomes (10,000 proteins; >130,000 peptides) of common cell line models, notably Vero E6, Calu-3, Caco-2, and ACE2-A549 that characterize their protein expression profiles including viral entry factors such as ACE2 or TMPRSS2. Using the 9 kDa protein SRP9 and the breast cancer oncogene BRCA1 as examples, we show how the proteome expression data can be used to refine the annotation of protein-coding regions of the African green monkey and the Vero cell line genomes. Monitoring changes of the proteome on viral infection revealed widespread expression changes including transcriptional regulators, protease inhibitors, and proteins involved in innate immunity. Based on a library of 98 stable-isotope labeled synthetic peptides representing 11 SARS-CoV-2 proteins, we developed PRM (parallel reaction monitoring) assays for nano-flow and micro-flow LC-MS/MS. We assessed the merits of these PRM assays using supernatants of virus-infected Vero E6 cells and challenged the assays by analyzing two diagnostic cohorts of 24 (+30) SARS-CoV-2 positive and 28 (+9) negative cases. In light of the results obtained and including recent publications or manuscripts on preprint servers, we critically discuss the merits of MS-based proteomics for SARS-CoV-2 research and testing.

Breast Cancer Classification Based on Proteotypes Obtained by SWATH Mass Spectrometry

Accurate classification of breast tumors is vital for patient management decisions and enables more precise cancer treatment. Here, we present a quantitative proteotyping approach based on sequential windowed acquisition of all theoretical fragment ion spectra (SWATH) mass spectrometry and establish key proteins for breast tumor classification. The study is based on 96 tissue samples representing five conventional breast cancer subtypes. SWATH proteotype patterns largely recapitulate these subtypes; however, they also reveal varying heterogeneity within the conventional subtypes, with triple negative tumors being the most heterogeneous. Proteins that contribute most strongly to the proteotype-based classification include INPP4B, CDK1, and ERBB2 and are associated with estrogen receptor (ER) status, tumor grade status, and HER2 status. Although these three key proteins exhibit high levels of correlation with transcript levels (R > 0.67), general correlation did not exceed R = 0.29, indicating the value of protein-level measurements of disease-regulated genes. Overall, this study highlights how cancer tissue proteotyping can lead to more accurate patient stratification.

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Proteotyping of 96 breast tumors by SWATH mass spectrometry

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Three key proteins for breast tumor classification

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Varying degrees of heterogeneity within conventional breast cancer subtypes

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Generally modest correlation between protein and transcript levels in tumor tissue

DPHL: A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to generate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.

Quantitative Interactomics in Primary T Cells Provides a Rationale for Concomitant PD-1 and BTLA Coinhibitor Blockade in Cancer Immunotherapy

Deciphering how TCR signals are modulated by coinhibitory receptors is of fundamental and clinical interest. Using quantitative interactomics, we define the composition and dynamics of the PD-1 and BTLA coinhibitory signalosomes in primary effector T cells and at the T cell-antigen-presenting cell interface. We also solve the existing controversy regarding the role of the SHP-1 and SHP-2 protein-tyrosine phosphatases in mediating PD-1 coinhibition. PD-1 predominantly recruits SHP-2, but when absent, it recruits SHP-1 and remains functional. In contrast, BTLA predominantly recruits SHP-1 and to a lesser extent SHP-2. By separately analyzing the PD-1-SHP-1 and PD-1-SHP-2 complexes, we show that both dampen the TCR and CD28 signaling pathways equally. Therefore, our study illustrates how comparison of coinhibitory receptor signaling via quantitative interactomics in primary T cells unveils their extent of redundancy and provides a rationale for designing combinations of blocking antibodies in cancer immunotherapy on the basis of undisputed modes of action.

Normalized retention time for scheduled liquid chromatography-multistage mass spectrometry analysis of epitranscriptomic modifications

Investigations into post-transcriptional modifications of RNA and their regulatory proteins have revealed pivotal roles of these modifications in cellular functions. A robust method for the quantitative analysis of modified nucleosides in RNA may facilitate the assessment about their functions in RNA biology and disease etiology. Here, we developed a sensitive nano-liquid chromatography-multistage mass spectrometry (nLC-MS3) method for profiling simultaneously 27 modified ribonucleosides. We employed normalized retention time (iRT) and scheduled selected-reaction monitoring (SRM) to achieve high-throughput analysis, where we assigned iRT values for modified ribonucleosides based on their relative elution times with respect to the four canonical ribonucleosides. The iRT scores allowed for reliable predictions of retention times for modified ribonucleosides with the use of two types of stationary phase materials and various mobile phase gradients. The method enabled the identification of 20 modified ribonucleosides with the use of the enzymatic digestion mixture of 2.5 ng total RNA and facilitated robust quantification of modified cytidine derivatives in total RNA. Together, we established a scheduled SRM-based method for high-throughput analysis of modified ribonucleosides with the use of a few nanograms of RNA.

A mouse SWATH-mass spectrometry reference spectral library enables deconvolution of species-specific proteomic alterations in human tumour xenografts

SWATH-mass spectrometry (MS) enables accurate and reproducible proteomic profiling in multiple model organisms including the mouse. Here, we present a comprehensive mouse reference spectral library (MouseRefSWATH) that permits quantification of up to 10,597 proteins (62.2% of the mouse proteome) by SWATH-MS. We exploit MouseRefSWATH to develop an analytical pipeline for species-specific deconvolution of proteomic alterations in human tumour xenografts (XenoSWATH). This method overcomes the challenge of high sequence similarity between mouse and human proteins, facilitating the study of host microenvironment-tumour interactions from 'bulk tumour' measurements. We apply the XenoSWATH pipeline to characterize an intraductal xenograft model of breast ductal carcinoma in situ and uncover complex regulation consistent with stromal reprogramming, where the modulation of cell migration pathways is not restricted to tumour cells but also operates in the mouse stroma upon progression to invasive disease. MouseRefSWATH and XenoSWATH open new opportunities for in-depth and reproducible proteomic assessment to address wide-ranging biological questions involving this important model organism.

Data on nucleoid-associated proteins isolated from Mycoplasma Gallisepticum in different growth phases

Mycoplasma gallisepticum (MG) is one of the smallest free-living and self-replicating organisms, it is characterized by lack of cell wall and reduced genome size. As a result of genome reduction, MG has a limited variety of DNA-binding proteins and transcription factors. To investigate the dynamic changes of the proteomic profile of MG nucleoid, that may assist in revealing its mechanisms of functioning, regulation of chromosome organization and stress adaptation, a quantitative proteomic study was performed on MG nucleoids obtained from the cell culture in logarithmic and stationary phases of synchronous growth. MG cells were grown on a liquid medium with a 9 h starvation period. Nucleoids were obtained from the cell culture at the 26^th and the 50^th hour (logarithmic and stationary growth phases respectively) by sucrose density gradient centrifugation. LC-MS analysis was carried out on an Ultimate 3000 RSLCnano HPLC system connected to a Fusion Lumos mass spectrometer, controlled by XCalibur software (Thermo Fisher Scientific) via a nanoelectrospray source (Thermo Fisher Scientific). For comprehensive peptide library generation one sample from each biological replicate was run in DDA mode. Then, all the samples were run in a single LC-MS DIA run. Identification of DDA files and DIA quantitation was performed with MaxQuant and Skyline software, correspondingly. All raw data generated from IDA and DDA acquisitions are presented in the PRIDE database with identifier PXD019077.

Proteomic and Metabolomic Characterization of COVID-19 Patient Sera

Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model was validated using 10 independent patients, 7 of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation, complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.

High-Throughput and Integrated Chemical Proteomic Approach for Profiling Phosphotyrosine Signaling Complexes

Phosphotyrosine (pTyr) signaling complexes are important resources of biomarkers and drug targets which often need to be profiled with enough throughput. Current profiling approaches are not feasible to meet this need due to either biased profiling by antibody-based detection or low throughput by traditional affinity purification-mass spectrometry approach (AP-MS), as exemplified by our previously developed photo-pTyr-scaffold approach. To address these limitations, we developed a 96-well microplate-based sample preparation and fast data independent proteomic analysis workflow. By assembling the photo-pTyr-scaffold probe into a 96-well microplate, we achieved steric hindrance-free photoaffinity capture of pTyr signaling complexes, selective enrichment under denaturing conditions, and efficient in-well digestion in a fully integrated manner. EGFR signaling complex proteins could be efficiently captured and identified by using 300 times less cell lysate and 100 times less photo-pTyr-scaffold probe as compared with our previous approach operated in an Eppendorf tube. Furthermore, the lifetime of the photo-pTyr-scaffold probe in a 96-well microplate was significantly extended from 1 week up to 1 month. More importantly, by combining with high-flow nano LC separation and data independent acquisition on the Q Exactive HF-X mass spectrometer, LC-MS time could be significantly reduced to only 35 min per sample without increasing sample loading amount and compromising identification and quantification performance. This new high-throughput proteomic approach allowed us to rapidly and reproducibly profile dynamic pTyr signaling complexes with EGF stimulation at five time points and EGFR inhibitor treatment at five different concentrations. We are therefore optimized for its generic application in biomarkers discovery and drug screening in a high-throughput fashion.

Proteome-wide Interrogation of Small GTPases Regulated by N6-Methyladenosine Modulators

N⁶-Methyladenosine (m⁶A) in messenger RNA (mRNA) regulates its stability, splicing, and translation efficiency. Here, we explored how the expression levels of small GTPase proteins are regulated by m⁶A modulators. We employed a high-throughput scheduled multiple-reaction monitoring (MRM)-based targeted proteomic approach to quantify systemically the changes in expression of small GTPase proteins in cells upon genetic ablation of METTL3 (the catalytic subunit of the major m⁶A methyltransferase complex), m⁶A demethylases (ALKBH5 and FTO), or m⁶A reader proteins (YTHDF1, YTHDF2, and YTHDF3). Depletions of METTL3 and ALKBH5 resulted in substantially diminished and augmented expression, respectively, of a subset of small GTPase proteins, including RHOB and RHOC. Our results also revealed that the stability of RHOB mRNA is significantly increased in cells depleted of METTL3, suggesting an m⁶A-elicited destabilization of this mRNA. Those small GTPases that are targeted by METTL3 and/or ALKBH5 also displayed higher discrepancies between protein and mRNA expression in paired primary/metastatic melanoma or colorectal cancer cells than those that are not. Together, this is the first comprehensive analysis of the alterations in small GTPase proteome regulated by epitranscriptomic modulators of m⁶A, and our study suggests the potential of an alternative therapeutic approach to target the currently "undruggable" small GTPases.

Identification of Metabolically Distinct Adipocyte Progenitor Cells in Human Adipose Tissues

Adipocyte progenitor cells (APCs) provide the reservoir of regenerative cells to produce new adipocytes, although their identity in humans remains elusive. Using FACS analysis, gene expression profiling, and metabolic and proteomic analyses, we identified three APC subtypes in human white adipose tissues. The APC subtypes are molecularly distinct but possess similar proliferative and adipogenic capacities. Adipocytes derived from APCs with high CD34 expression exhibit exceedingly high rates of lipid flux compared with APCs with low or no CD34 expression, while adipocytes produced from CD34^- APCs display beige-like adipocyte properties and a unique endocrine profile. APCs were more abundant in gluteofemoral compared with abdominal subcutaneous and omental adipose tissues, and the distribution of APC subtypes varies between depots and in patients with type 2 diabetes. These findings provide a mechanistic explanation for the heterogeneity of human white adipose tissue and a potential basis for dysregulated adipocyte function in type 2 diabetes.

A Comprehensive Proteomic SWATH-MS Workflow for Profiling Blood Extracellular Vesicles: A New Avenue for Glioma Tumour Surveillance

Improving outcomes for diffuse glioma patients requires methods that can accurately and sensitively monitor tumour activity and treatment response. Extracellular vesicles (EV) are membranous nanoparticles that can traverse the blood-brain-barrier, carrying oncogenic molecules into the circulation. Measuring clinically relevant glioma biomarkers cargoed in circulating EVs could revolutionise how glioma patients are managed. Despite their suitability for biomarker discovery, the co-isolation of highly abundant complex blood proteins has hindered comprehensive proteomic studies of circulating-EVs. Plasma-EVs isolated from pre-operative glioma grade II-IV patients (n = 41) and controls (n = 11) were sequenced by Sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) and data extraction was performed by aligning against a custom 8662-protein library. Overall, 4054 proteins were measured in plasma-EVs. Differentially expressed proteins and putative circulating-EV markers were identified (adj. p-value < 0.05), including those reported in previous in-vitro and ex-vivo glioma-EV studies. Principal component analysis showed that plasma-EV protein profiles clustered according to glioma histological-subtype and grade, and plasma-EVs resampled from patients with recurrent tumour progression grouped with more aggressive glioma samples. The extensive plasma-EV proteome profiles achieved here highlight the potential for SWATH-MS to define circulating-EV biomarkers for objective blood-based measurements of glioma activity that could serve as ideal surrogate endpoints to assess tumour progression and allow more dynamic, patient-centred treatment protocols.

Acquiring and Analyzing Data Independent Acquisition Proteomics Experiments without Spectrum Libraries

Data independent acquisition (DIA) is an attractive alternative to standard shotgun proteomics methods for quantitative experiments. However, most DIA methods require collecting exhaustive, sample-specific spectrum libraries with data dependent acquisition (DDA) to detect and quantify peptides. In addition to working with non-human samples, studies of splice junctions, sequence variants, or simply working with small sample yields can make developing DDA-based spectrum libraries impractical. Here we illustrate how to acquire, queue, and validate DIA data without spectrum libraries, and provide a workflow to efficiently generate DIA-only chromatogram libraries using gas-phase fractionation (GPF). We present best-practice methods for collecting DIA data using Orbitrap-based instruments and develop an understanding for why DIA using an Orbitrap mass spectrometer should be approached differently than when using time-of-flight instruments. Finally, we discuss several methods for analyzing DIA data without libraries.

Immunopeptidomic Analysis Reveals That Deamidated HLA-bound Peptides Arise Predominantly from Deglycosylated Precursors

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.

The Age of Data-Driven Proteomics: How Machine Learning Enables Novel Workflows

A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open modification searches. These workflows are all challenging because of ambiguity in the identification stage; they either expand the search space and thus increase the ambiguity of identifications, or, in the case of DIA, they generate data that is inherently more ambiguous. In this context, machine learning-based predictive models are now generating considerable excitement in the field of proteomics because these predictive models hold great potential to drastically reduce the ambiguity in the identification process of the above-mentioned workflows. Indeed, the field has already produced classical machine learning and deep learning models to predict almost every aspect of a liquid chromatography-mass spectrometry (LC-MS) experiment. Yet despite all the excitement, thorough integration of predictive models in these challenging LC-MS workflows is still limited, and further improvements to the modeling and validation procedures can still be made. Therefore, highly promising recent machine learning developments in proteomics are pointed out in this viewpoint, alongside some of the remaining challenges.

Profiling tear proteomes of patients with unilateral relapsed Behcet's disease-associated uveitis using data-independent acquisition proteomics

Purpose

To explore whether unilateral relapse of Bechet’s disease-associated uveitis (BDU) causes differences in the tear proteome between the diseased and the contralateral quiescent eye and potential tear biomarkers for uveitis recurrence and disease monitoring.

Method

To minimize interindividual variations, bilateral tear samples were collected from the same patient (n = 15) with unilateral relapse of BDU. A data-independent acquisition (DIA) strategy was used to identify proteins that differed between active and quiescent eyes.

Results

A total of 1,797 confident proteins were identified in the tear samples, of which 381 (21.2%) were also highly expressed in various tissues and organs. Fifty-one (2.8%) proteins differed in terms of expression between tears in active and quiescent eyes, 9 (17.6%) of which were functionally related to immunity or inflammation. Alpha-1-acid glycoprotein 1 (fold change = 3.2, p = 0.007) was increased and Annexin A1 (fold change = −1.7, p < 0.001) was decreased in the tears of the active BDU eye compared to the contralateral quiescent eye.

Conclusions

A substantial amount of confident proteins were detected in the tears of BDU patients, including proteins that were deferentially expressed in the uveitis-relapsed eyes and the contralateral quiescent eyes. Some of these identified tear proteins play important roles in immune and inflammatory processes. Tear proteome might be a good source of biomarkers for uveitis.

Mass Spectrometric Mapping of Glycoproteins Modified by Tn-Antigen Using Solid-Phase Capture and Enzymatic Release

Tn-antigen (Tn), a single N-acetylgalactosamine (GalNAc) monosaccharide attached to protein Ser/Thr residues, is found on most cancer yet rarely detected in adult normal tissues as reported in previous studies, featuring it as one of the most distinctive signatures of cancer. Although it is important in cancer, Tn modified glycoproteins are not entirely clear owing to the lack of a suitable method. Knowing the Tn-glycosylated proteins and glycosylation sites are essential to the prevention, diagnosis, and therapy of cancer associated with the expression of Tn. Here, we introduce a method named EXoO-Tn for large-scale mapping of Tn-glycosylated proteins and glycosylation sites. EXoO-Tn utilizes solid-phase immobilization of proteolytic peptides of proteins, which modifies Tn by glycosyltransferase C1GalT1 with isotopically labeled UDP-Gal(¹³C₆), to tag and convert Tn to Gal(¹³C₆)-Tn, which gives rise to a unique glycan mass. The exquisite Gal(¹³C₆) modified Tn are then recognized by a human-gut-bacterial enzyme, OpeRATOR, and released at the N-termini of the Gal(¹³C₆)-Tn-occupied Ser/Thr residues from immobilized peptides to yield site-containing glycopeptides. The effectiveness of EXoO-Tn was benchmarked by analyzing Jurkat cells, where 947 Tn-glycosylation sites from 480 glycoproteins were mapped. The EXoO-Tn was further applied to the analysis of pancreatic cancer sera, where Tn-glycoproteins were identified. Given the significance of Tn in cancer, EXoO-Tn is anticipated to have broad translational and clinical utilities.

iSwathX 2.0 for Processing DDA Spectral Libraries for DIA Data Analysis

The iSwathX web application processes and normalizes mass spectrometry-based proteomics spectral libraries generated in the data-dependent acquisition (DDA) approach. These libraries are stored in various proteomics repositories such as PeptideAtlas and NIST, or are user-generated and provide reference data for data-independent acquisition (DIA) targeted data extraction and analysis. iSwathX 2.0 can efficiently normalize DDA data from different instruments, gathered at different instances, and make it compatible with specific DIA experiments. Novel functions for parallel processing of DDA libraries and DIA report files, along with various data visualizations, are available in iSwathX 2.0. The step-by-step protocols provided here describe how the libraries are uploaded, processed, visualized, and downloaded using various modules of the application. They also provide detailed guidelines on the use of DIA report files for data analysis and visualization. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Processing, combining, and visualizing two DDA libraries Basic Protocol 2: Parallel processing and combination of multiple DDA libraries Basic Protocol 3: Downstream processing, comparison, and visualization of DIA report files.

Proteomic and Metabolomic Characterization of COVID-19 Patient Sera

Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model was validated using 10 independent patients, 7 of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation, complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.

Salivary proteome signatures in the early and middle stages of human pregnancy with term birth outcome

The establishment and maintenance of pregnancy in humans proceed through a continuous change of biochemical and biophysical processes. It requires a constant interaction between the fetus and the maternal system. The present prospective study aims to elucidate changes in salivary proteome from the early to middle stages of term pregnancy, and establishing an expressional trajectory for modulated proteins. To date, a comprehensive characterization of the longitudinal salivary proteome in pregnancy has not been performed and it is our immediate interest. In the discovery phase, maternal saliva (N = 20) at 6–13, 18–21, and 26–29 weeks of gestation was analyzed using level-free proteomics (SWATH-MS) approach. The expression levels of 65 proteins were found to change significantly with gestational age and distributed into two distinct clusters with a unique expression trajectory. The results revealed that altered proteins are involved in maternal immune modulation, metabolism, and host defense mechanism. Further, verification of 12 proteins was employed using targeted mass spectrometry (MRM-MS) in a separate subset of saliva (N = 14). The MRM results of 12 selected proteins confirmed a similar expression pattern as in SWATH-MS analysis. Overall, the results not only demonstrate the longitudinal maternal saliva proteome for the first time but also set the groundwork for comparative analysis between term birth and adverse pregnancy outcomes.

Deciphering MET-dependent modulation of global cellular responses to DNA damage by quantitative phosphoproteomics

Increasing evidence suggests that interference with growth factor receptor tyrosine kinase (RTK) signaling can affect DNA damage response (DDR) networks, with a consequent impact on cellular responses to DNA‐damaging agents widely used in cancer treatment. In that respect, the MET RTK is deregulated in abundance and/or activity in a variety of human tumors. Using two proteomic techniques, we explored how disrupting MET signaling modulates global cellular phosphorylation response to ionizing radiation (IR). Following an immunoaffinity‐based phosphoproteomic discovery survey, we selected candidate phosphorylation sites for extensive characterization by targeted proteomics focusing on phosphorylation sites in both signaling networks. Several substrates of the DDR were confirmed to be modulated by sequential MET inhibition and IR, or MET inhibition alone. Upon combined treatment, for two substrates, NUMA1 S395 and CHEK1 S345, the gain and loss of phosphorylation, respectively, were recapitulated using invivo tumor models by immunohistochemistry, with possible utility in future translational research. Overall, we have corroborated phosphorylation sites at the intersection between MET and the DDR signaling networks, and suggest that these represent a class of proteins at the interface between oncogene‐driven proliferation and genomic stability.

In-depth mining of the immunopeptidome of an acute myeloid leukemia cell line using complementary ligand enrichment and data acquisition strategies

The identification of T cell epitopes derived from tumour specific antigens remains a significant challenge for the development of peptide-based vaccines and immunotherapies. The use of mass spectrometry-based approaches (immunopeptidomics) can provide powerful new avenues for the identification of such epitopes. In this study we report the use of complementary peptide antigen enrichment methods and a comprehensive mass spectrometric acquisition strategy to provide in-depth immunopeptidome data for the THP-1 cell line, a cell line used widely as a model of human leukaemia. To accomplish this, we combined robust experimental workflows that incorporated ultrafiltration or off-line reversed phase chromatography to enrich peptide ligand as well as a multifaceted data acquisition strategy using an Orbitrap Fusion LC-MS instrument. Using the combined datasets from the two ligand enrichment methods we gained significant depth in immunopeptidome coverage by identifying a total of 41,816 HLA class I peptides from THP-1 cells, including a significant number of peptides derived from different oncogenes or over expressed proteins associated with cancer. The physicochemical properties of the HLA-bound peptides dictated their recovery using the two ligand enrichment approaches and their distribution across the different precursor charge states considered in the data acquisition strategy. The data highlight the complementarity of the two enrichment procedures, and in cases where sample is not limiting, suggest that the combination of both approaches will yield the most comprehensive immunopeptidome information.

The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics

Skyline is a freely available, open-source Windows client application for accelerating targeted proteomics experimentation, with an emphasis on the proteomics and mass spectrometry community as users and as contributors. This review covers the informatics encompassed by the Skyline ecosystem, from computationally assisted targeted mass spectrometry method development, to raw acquisition file data processing, and quantitative analysis and results sharing.

Revealing Dynamic Protein Acetylation across Subcellular Compartments

Protein acetylation is a widespread post-translational modification implicated in many cellular processes. Recent advances in mass spectrometry have enabled the cataloging of thousands of sites throughout the cell; however, identifying regulatory acetylation marks have proven to be a daunting task. Knowledge of the kinetics and stoichiometry of site-specific acetylation is an important factor to uncover function. Here, an improved method of quantifying acetylation stoichiometry was developed and validated, providing a detailed landscape of dynamic acetylation stoichiometry within cellular compartments. The dynamic nature of site-specific acetylation in response to serum stimulation was revealed. In two distinct human cell lines, growth factor stimulation led to site-specific, temporal acetylation changes, revealing diverse kinetic profiles that clustered into several groups. Overlap of dynamic acetylation sites among two different human cell lines suggested similar regulatory control points across major cellular pathways that include splicing, translation, and protein homeostasis. Rapid increases in acetylation on protein translational machinery suggest a positive regulatory role under progrowth conditions. Finally, higher median stoichiometry was observed in cellular compartments where active acetyltransferases are well described. Data sets can be accessed through ProteomExchange via the MassIVE repository (ProteomExchange: PXD014453; MassIVE: MSV000084029).