Proteomics on an Orbitrap benchtop mass spectrometer using all-ion fragmentation

Unlocking the future potential of SWATH-MS: Advancing non-target screening workflow for the qualitative and quantitative analysis of emerging contaminants

SWATH-MS offers a robust data-independent acquisition method for complex proteomics and metabolomics. This study presents a detailed non-target screening workflow utilizing SWATH-MS to detect and analyze emerging contaminants (ECs) in aquatic environments. Our workflow, covering peak picking, alignment, prioritization, structure identification, and quantification, effectively identified all qualifying peaks from 298 standard compounds with different concentrations, discarding any that did not meet the criteria. In extracts of real water samples spiked at 100 and 10 ng/mL, our workflow prioritized 2083 and 1328 features, respectively. Following structure identification, these features were assigned confidence levels ranging from 1 to 5. Of these, 215 and 92 spiked standards achieved level 1. The remaining standards were not recognized as level 1 due to low intensities or poor peak shapes that failed to meet certain criteria. Additionally, using fragment ion peak areas for quantification significantly improved the linearity of standard curves, enhancing R2 values for ∼63 % of the standards. Incorporating fragment ion data improved quantification accuracy, increasing compounds within the 80 %-120 % range from 78 % to 90 % at 100 ng/mL and within the 50 %-150 % range from 36 % to 69 % at 10 ng/mL. These findings underscore SWATH-MS's potential to enhance monitoring of ECs and ecological risk assessments, providing critical insights for environmental management.

WNK1 signalling regulates amino acid transport and mTORC1 activity to sustain acute myeloid leukaemia growth

The lack of curative therapies for acute myeloid leukaemia (AML) remains an ongoing challenge despite recent advances in the understanding of the molecular basis of the disease. Here we identify the WNK1-OXSR1/STK39 pathway as a previously uncharacterised dependency in AML. We show that genetic depletion and pharmacological inhibition of WNK1 or its downstream phosphorylation targets OXSR1 and STK39 strongly reduce cell proliferation and induce apoptosis in leukaemia cells in vitro and in vivo. Furthermore, we show that the WNK1-OXSR1/STK39 pathway controls mTORC1 signalling via regulating amino acid uptake through a mechanism involving the phosphorylation of amino acid transporters, such as SLC38A2. Our findings underscore an important role of the WNK1-OXSR1/STK39 pathway in regulating amino acid uptake and driving AML progression.

Urine exosome biomarkers of obesity after Lekhana Basti treatment - Report of a pilot study

BACKGROUND

Obesity is a rising risk factor for various diseases including cardiovascular diseases and Cancer. The limitations of targeted obesity-treatment approaches employed in the clinic presently underscore the importance of developing integrative management strategies for identification of specific biomarkers of obesity.

OBJECTIVES

Given the specificity of exosome/extracellular vesicle (EV) biomarkers, we aimed here to identify the EV biomarkers of Ayurveda treatment - Lekhana Basti - for Obesity.

METHODOLOGY

A total of eighteen 24-h urine samples from 6 participants with BMI>30 kg/m2 were used in this study, collected over 3 time-points during the Lekhana basti (medicated enema for obesity) treatment. Urine EV were isolated using Polyethylene Glycol (PEG). The proteins were resolved by 1-d gel electrophoresis and identified using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) and quantified by label-free methods. Significant Protein-Protein Interactions, KEGG pathway analysis and enrichment, functional gene ontology (GO) annotation were identified and shortlisted in comparison to Obesity reference genes from DisGeNET.

RESULTS

With UniProt as a reference subsequent to LC-MS/MS-identification, a total of 210 exosome proteins were identified. Seventy-three proteins were overexpressed in pathway enrichment analysis. Further, GO functional annotation identified 15 common proteins involved. Finally, the 8 hub proteins associated with obesity were identified and their differential expression profile compared between three different time-points during Lekhana Basti treatment. Six protein markers overexpressed during obesity were downregulated post Lekhana Basti treatment, while 2 markers increased in abundance post-treatment.

CONCLUSION

To our knowledge, this is the first study to isolate and identify urine EV protein abundance profiles from obese female participants of India. The study results indicate significant changes in the differential expression profile of 8 hub proteins involved in obesity, after Lekhana Basti treatment. The biomarker signature of the pilot study indicates the role of Ayurveda treatment and the possible pathways involved in the treatment of Obesity. Further, this study underlines the specificity of urine exosomes/EV as diagnostic markers as well as the potential of Ayurveda treatment in effective management of obesity.

Quantitation of trace polyfunctional thiols in wine by liquid chromatography quadrupole Orbitrap high-resolution mass spectrometry in parallel reaction monitoring

Polyfunctional thiols are key contributors to wine aroma due to their extremely low odor thresholds, and their quantitative analysis remains challenging as a result of their ultratrace concentrations and high reactivity. This work presents the first method based on ultra-high-performance liquid chromatography (UHPLC) coupled to quadrupole Orbitrap high-resolution mass spectrometry (HRMS) in parallel reaction monitoring (PRM) mode for quantifying thiols at nanograms per liter (ng/L) levels in wine. Thiols in wine were derivatized using 4,4'-dithiodipyridine and isolated by liquid-liquid extraction. This protocol allowed rapid sample preparation with minimum labor input and low consumable expenses. Instrumental analysis was conducted using UHPLC-quadrupole Orbitrap HRMS in PRM mode. Twenty thiol analytes, including literature-known, recently identified, and novel thiols were selected and validated by the optimized method in three wine matrices. The overall analytical performances demonstrated by this method were equivalent, and in most cases, greater than many previously developed GC-MS or LC-MS methods. The validated method was applied to analyze a selection of wines in which 12 thiols were quantified.

Energy-Resolved In-Source Collison-Induced Dissociation for Isomer Discrimination

While mass spectrometry remains a gold-standard tool for analyte detection, characterization, and quantitation, isomer differentiation is often a challenge. Tandem mass spectrometry is a common approach to increase the selectivity of mass spectrometry and energy-resolved measurements can provide further improvements. However, not all mass spectrometers, especially those that are very compact and affordable, are amenable to such experiments. For instance, single-stage mass spectrometers with soft ionization provide no dissociation information and quadrupole ion trap instruments with resonant excitation do not necessarily provide as informative of energy-resolved curves, for instance when extensive sequential dissociation is responsible for much of the "fingerprint". In-source collision-induced dissociation (IS-CID) is one approach to overcoming these barriers to exploit the analytical selectivity of energy-resolved CID without the need for additional instrumentation; this approach could broaden the reach of these selectivity gains to additional user bases (e.g., educational settings, field portable devices). Here, we specifically investigate energy-resolved IS-CID with the goal of (1) comparing between energy-resolved appearance curves measured with true tandem mass spectrometry on a quadrupole time-of-flight instrument and those obtained using IS-CID, (2) evaluating the approach as a means of differentiating isomers/isobar sets, especially those with similar dissociation patterns, and (3) exploring additional analytical considerations relevant to method development and implementation. This proof-of-concept work establishes the analytical potential of this approach, opening doors for future method development for specific applications.

Quality Control in the Mass Spectrometry Proteomics Core: A Practical Primer

The past decade has seen widespread advances in quality control (QC) materials and software tools focused specifically on mass spectrometry-based proteomics, yet the rate of adoption is inconsistent. Despite the fundamental importance of QC, it typically falls behind learning new techniques, instruments, or software. Considering how important QC is in a core setting where data is generated for non-mass spectrometry experts and confidence in delivered results is paramount, we have created this quick-start guide focusing on off-the-shelf QC materials and relatively easy-to-use QC software. We hope that by providing a background on the different levels of QC, different materials and their uses, describing QC design options, and highlighting some current QC software, implementing QC in a core setting will be easier than ever. There continues to be development in each of these areas (such as new materials and software), and the current generation of QC for mass spectrometry-based proteomics is more than capable of conveying confidence in results as well as minimizing laboratory downtime by guiding experimental, technical, and analytical troubleshooting from sample to results.

Deep Plasma Proteomics with Data-Independent Acquisition: Clinical Study Protocol Optimization with a COVID-19 Cohort

Plasma proteomics is a precious tool in human disease research but requires extensive sample preparation in order to perform in-depth analysis and biomarker discovery using traditional data-dependent acquisition (DDA). Here, we highlight the efficacy of combining moderate plasma prefractionation and data-independent acquisition (DIA) to significantly improve proteome coverage and depth while remaining cost-efficient. Using human plasma collected from a 20-patient COVID-19 cohort, our method utilizes commonly available solutions for depletion, sample preparation, and fractionation, followed by 3 liquid chromatography-mass spectrometry/MS (LC-MS/MS) injections for a 360 min total DIA run time. We detect 1321 proteins on average per patient and 2031 unique proteins across the cohort. Differential analysis further demonstrates the applicability of this method for plasma proteomic research and clinical biomarker identification, identifying hundreds of differentially abundant proteins at biological concentrations as low as 47 ng/L in human plasma. Data are available via ProteomeXchange with the identifier PXD047901. In summary, this study introduces a streamlined, cost-effective approach to deep plasma proteome analysis, expanding its utility beyond classical research environments and enabling larger-scale multiomics investigations in clinical settings. Our comparative analysis revealed that fractionation, whether the samples were pooled or separate postfractionation, significantly improved the number of proteins quantified. This underscores the value of fractionation in enhancing the depth of plasma proteome analysis, thereby offering a more comprehensive landscape for biomarker discovery in diseases such as COVID-19.

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

Data-Independent Acquisition: A Milestone and Prospect in Clinical Mass Spectrometry-Based Proteomics

Data-independent acquisition (DIA) has revolutionized the field of mass spectrometry (MS)-based proteomics over the past few years. DIA stands out for its ability to systematically sample all peptides in a given m/z range, allowing an unbiased acquisition of proteomics data. This greatly mitigates the issue of missing values and significantly enhances quantitative accuracy, precision, and reproducibility compared to many traditional methods. This review focuses on the critical role of DIA analysis software tools, primarily focusing on their capabilities and the challenges they address in proteomic research. Advances in MS technology, such as trapped ion mobility spectrometry, or high field asymmetric waveform ion mobility spectrometry require sophisticated analysis software capable of handling the increased data complexity and exploiting the full potential of DIA. We identify and critically evaluate leading software tools in the DIA landscape, discussing their unique features, and the reliability of their quantitative and qualitative outputs. We present the biological and clinical relevance of DIA-MS and discuss crucial publications that paved the way for in-depth proteomic characterization in patient-derived specimens. Furthermore, we provide a perspective on emerging trends in clinical applications and present upcoming challenges including standardization and certification of MS-based acquisition strategies in molecular diagnostics. While we emphasize the need for continuous development of software tools to keep pace with evolving technologies, we advise researchers against uncritically accepting the results from DIA software tools. Each tool may have its own biases, and some may not be as sensitive or reliable as others. Our overarching recommendation for both researchers and clinicians is to employ multiple DIA analysis tools, utilizing orthogonal analysis approaches to enhance the robustness and reliability of their findings.

From flesh to bones: Multi-omics approaches in forensic science

Recent advancements in omics techniques have revolutionised the study of biological systems, enabling the generation of high-throughput biomolecular data. These innovations have found diverse applications, ranging from personalised medicine to forensic sciences. While the investigation of multiple aspects of cells, tissues or entire organisms through the integration of various omics approaches (such as genomics, epigenomics, metagenomics, transcriptomics, proteomics and metabolomics) has already been established in fields like biomedicine and cancer biology, its full potential in forensic sciences remains only partially explored. In this review, we have presented a comprehensive overview of state-of-the-art analytical platforms employed in omics research, with specific emphasis on their application in the forensic field for the identification of the cadaver and the cause of death. Moreover, we have conducted a critical analysis of the computational integration of omics approaches, and highlighted the latest advancements in employing multi-omics techniques for forensic investigations.

Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.

Comprehensive Overview of Bottom-Up Proteomics using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods to aid the novice and experienced researcher. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this work to serve as a basic resource for new practitioners in the field of shotgun or bottom-up proteomics.

Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape

The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.

Comparing extraction method efficiency for high-throughput palaeoproteomic bone species identification

High-throughput proteomic analysis of archaeological skeletal remains provides information about past fauna community compositions and species dispersals in time and space. Archaeological skeletal remains are a finite resource, however, and therefore it becomes relevant to optimize methods of skeletal proteome extraction. Ancient proteins in bone specimens can be highly degraded and consequently, extraction methods for well-preserved or modern bone might be unsuitable for the processing of highly degraded skeletal proteomes. In this study, we compared six proteomic extraction methods on Late Pleistocene remains with variable levels of proteome preservation. We tested the accuracy of species identification, protein sequence coverage, deamidation, and the number of post-translational modifications per method. We find striking differences in obtained proteome complexity and sequence coverage, highlighting that simple acid-insoluble proteome extraction methods perform better in highly degraded contexts. For well-preserved specimens, the approach using EDTA demineralization and protease-mix proteolysis yielded a higher number of identified peptides. The protocols presented here allowed protein extraction from ancient bone with a minimum number of working steps and equipment and yielded protein extracts within three working days. We expect further development along this route to benefit large-scale screening applications of relevance to archaeological and human evolution research.

Proteomics: Its Promise and Pitfalls in Shaping Precision Medicine in Solid Organ Transplantation

Solid organ transplantation is an established treatment of choice for end-stage organ failure. However, all transplant patients are at risk of developing complications, including allograft rejection and death. Histological analysis of graft biopsy is still the gold standard for evaluation of allograft injury, but it is an invasive procedure and prone to sampling errors. The past decade has seen an increased number of efforts to develop minimally invasive procedures for monitoring allograft injury. Despite the recent progress, limitations such as the complexity of proteomics-based technology, the lack of standardization, and the heterogeneity of populations that have been included in different studies have hindered proteomic tools from reaching clinical transplantation. This review focuses on the role of proteomics-based platforms in biomarker discovery and validation in solid organ transplantation. We also emphasize the value of biomarkers that provide potential mechanistic insights into the pathophysiology of allograft injury, dysfunction, or rejection. Additionally, we forecast that the growth of publicly available data sets, combined with computational methods that effectively integrate them, will facilitate a generation of more informed hypotheses for potential subsequent evaluation in preclinical and clinical studies. Finally, we illustrate the value of combining data sets through the integration of 2 independent data sets that pinpointed hub proteins in antibody-mediated rejection.

Dear-DIAXMBD: Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics

Data-independent acquisition (DIA) technology for protein identification from mass spectrometry and related algorithms is developing rapidly. The spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising direction. In this paper, we proposed an untargeted analysis method, Dear-DIA^XMBD, for direct analysis of DIA data. Dear-DIA^XMBD first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms, then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes, and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and peptides. We show that Dear-DIA^XMBD performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms. Dear-DIA^XMBD is publicly available at https://github.com/jianweishuai/Dear-DIA-XMBD.

Oligonucleotide Catabolism-Derived Gluconucleosides in Caenorhabditis elegans

Nucleosides are essential cornerstones of life, and nucleoside derivatives and synthetic analogues have important biomedical applications. Correspondingly, production of non-canonical nucleoside derivatives in animal model systems is of particular interest. Here, we report the discovery of diverse glucose-based nucleosides in Caenorhabditis elegans and related nematodes. Using a mass spectrometric screen based on all-ion fragmentation in combination with total synthesis, we show that C. elegans selectively glucosylates a series of modified purines but not the canonical purine and pyrimidine bases. Analogous to ribonucleosides, the resulting gluconucleosides exist as phosphorylated and non-phosphorylated forms. The phosphorylated gluconucleosides can be additionally decorated with diverse acyl moieties from amino acid catabolism. Syntheses of representative variants, facilitated by a novel 2'-O- to 3'-O-dibenzyl phosphoryl transesterification reaction, demonstrated selective incorporation of different nucleobases and acyl moieties. Using stable-isotope labeling, we further show that gluconucleosides incorporate modified nucleobases derived from RNA and possibly DNA breakdown, revealing extensive recycling of oligonucleotide catabolites. Gluconucleosides are conserved in other nematodes, and biosynthesis of specific subsets is increased in germline mutants and during aging. Bioassays indicate that gluconucleosides may function in stress response pathways.

A Streamlined High-Throughput Plasma Proteomics Platform for Clinical Proteomics with Improved Proteome Coverage, Reproducibility, and Robustness

Mass spectrometry-based clinical proteomics requires high throughput, reproducibility, robustness, and comprehensive coverage to serve the needs of clinical diagnosis, prognosis, and personalized medicine. Oftentimes these requirements are contradictory to each other. We report the development of a streamlined High-Throughput Plasma Proteomics (sHTPP) platform for untargeted profiling of the blood plasma proteome, which includes 96-well plates and simplified procedures for sample preparation, disposable trap column for peptide loading, robust liquid chromatographic system for separation, data-independent acquisition in tandem mass spectrometry, and DIA-NN, FragPipe, and in-house peptide spectral library-based data analysis. Using the optimized platform at a throughput of 60 samples per day, over 600 protein groups including 57 FDA-approved biomarkers can be consistently identified from whole human plasma, and more than 85% of the detected proteins have 100% completeness in quantitative values across 300 samples. The balance achieved between proteome coverage, throughput, and reproducibility of this sHTPP platform makes it promising in clinical settings, where a large number of samples are to be measured quickly and reliably to support various needs of clinical medicine.

Prediction of peptide mass spectral libraries with machine learning

The recent development of machine learning methods to identify peptides in complex mass spectrometric data constitutes a major breakthrough in proteomics. Longstanding methods for peptide identification, such as search engines and experimental spectral libraries, are being superseded by deep learning models that allow the fragmentation spectra of peptides to be predicted from their amino acid sequence. These new approaches, including recurrent neural networks and convolutional neural networks, use predicted in silico spectral libraries rather than experimental libraries to achieve higher sensitivity and/or specificity in the analysis of proteomics data. Machine learning is galvanizing applications that involve large search spaces, such as immunopeptidomics and proteogenomics. Current challenges in the field include the prediction of spectra for peptides with post-translational modifications and for cross-linked pairs of peptides. Permeation of machine-learning-based spectral prediction into search engines and spectrum-centric data-independent acquisition workflows for diverse peptide classes and measurement conditions will continue to push sensitivity and dynamic range in proteomics applications in the coming years.

Mass Difference Matching Unfolds Hidden Molecular Structures of Dissolved Organic Matter

Ultrahigh-resolution Fourier transform mass spectrometry (FTMS) has revealed unprecedented details of natural complex mixtures such as dissolved organic matter (DOM) on a molecular formula level, but we lack approaches to access the underlying structural complexity. We here explore the hypothesis that every DOM precursor ion is potentially linked with all emerging product ions in FTMS² experiments. The resulting mass difference (Δm) matrix is deconvoluted to isolate individual precursor ion Δm profiles and matched with structural information, which was derived from 42 Δm features from 14 in-house reference compounds and a global set of 11 477 Δm features with assigned structure specificities, using a dataset of ∼18 000 unique structures. We show that Δm matching is highly sensitive in predicting potential precursor ion identities in terms of molecular and structural composition. Additionally, the approach identified unresolved precursor ions and missing elements in molecular formula annotation (P, Cl, F). Our study provides first results on how Δm matching refines structural annotations in van Krevelen space but simultaneously demonstrates the wide overlap between potential structural classes. We show that this effect is likely driven by chemodiversity and offers an explanation for the observed ubiquitous presence of molecules in the center of the van Krevelen space. Our promising first results suggest that Δm matching can both unfold the structural information encrypted in DOM and assess the quality of FTMS-derived molecular formulas of complex mixtures in general.

Targeted proteomics on its way to discovery

For a long time, targeted and discovery proteomics covered different corners of the detection spectrum, with targeted proteomics focused on small target sets. This changed with the recent advances in highly multiplexed analysis. While discovery proteomics still pushes higher numbers of identified and quantified proteins, the advances in targeted proteomics rose to cover large parts of less complex proteomes or proteomes with low protein detection counts due to dynamic range restrictions, like the blood proteome. These new developments will impact, especially on the field of biomarker discovery and the possibility of using targeted proteomics for diagnostic purposes. This article is protected by copyright. All rights reserved.

A Sensitive and Controlled Data-Independent Acquisition Method for Proteomic Analysis of Cell Therapies

Mass spectrometry (MS)-based proteomic measurements are uniquely poised to impact the development of cell and gene therapies. With the adoption of rigorous instrumental performance qualifications (PQs), large-scale proteomics can move from a research to a manufacturing control tool. Especially suited, data-independent acquisition (DIA) approaches have distinctive qualities to extend multiattribute method (MAM) principles to characterize the proteome of cell therapies. Here, we describe the development of a DIA method for the sensitive identification and quantification of proteins on a Q-TOF instrument. Using the improved acquisition parameters, we defined a control strategy and highlighted some metrics to improve the reproducibility of SWATH acquisition-based proteomic measurements. Finally, we applied the method to analyze the proteome of Jurkat cells that here serves as a model for human T-cells. Raw and processed data were deposited in PRIDE (PXD029780).

Investigation of Effects of the Spectral Library on Analysis of diaPASEF Data

Targeted analysis of data-independent acquisition (DIA) data needs a spectral library, which is generated by data-dependent acquisition (DDA) experiments or directly from DIA data. A comparison of the DDA library and DIA library in analyzing DIA data has been reported. However, the effects of different spectral libraries on the analysis of diaPASEF data have not been investigated. Here, we generate different spectral libraries with varying proteome coverage to analyze parallel accumulation-serial fragmentation (diaPASEF) data. Besides, we also employ the library-free strategy. The library, constructed by extensive fractionation DDA experiments, produces the highest numbers of precursors and proteins but with a high percentage of missing values. The library-free strategy identifies 10-20% fewer proteins than the library-based method but with a high degree of data completeness. A further study shows that the library-free strategy, although it identifies fewer proteins than the library-based method, leads to similar biological conclusions as the library-based method.

Fast Screening and Identification of Illegal Adulterated Glucocorticoids in Dietary Supplements and Herbal Products Using UHPLC-QTOF-MS With All-Ion Fragmentation Acquisition Combined With Characteristic Fragment Ion List Classification

Ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) with all-ion fragmentation (AIF) acquisition was established for an identification and quantification of illegal adulterated glucocorticoids in dietary supplements and herbal products. Next, a novel method called characteristic fragment ion list classification (CFILC) was developed for a fast screening of adulterated compounds. CFILC could provide the characteristic ions comprehensively and completely through direct extract from the MS² library instead of finding them manually. This is time-saving and provides fast screening results with a high confidence level by filtering of a pre-calculated threshold of similarity scores for illegal adulterants that are not included in the library as well as for new emerging structural analogs. The obtained results demonstrated the great qualitative and quantitative strength of this approach, providing a promising and powerful method for a routine fast screening of illegal adulterated glucocorticoids.

Bottom-up/cross-linking mass spectrometry via simplified sample processing on anion-exchange solid-phase extraction spin column

We introduce a simple single-column protein digestion method for low-microgram-level samples containing sodium dodecyl sulfate and Coomassie dye that can be completed within a few hours.

Deep representation features from DreamDIAXMBD improve the analysis of data-independent acquisition proteomics

We developed DreamDIA^XMBD (denoted as DreamDIA), a software suite based on a deep representation model for data-independent acquisition (DIA) data analysis. DreamDIA adopts a data-driven strategy to capture comprehensive information from elution patterns of peptides in DIA data and achieves considerable improvements on both identification and quantification performance compared with other state-of-the-art methods such as OpenSWATH, Skyline and DIA-NN. Specifically, in contrast to existing methods which use only 6 to 10 selected fragment ions from spectral libraries, DreamDIA extracts additional features from hundreds of theoretical elution profiles originated from different ions of each precursor using a deep representation network. To achieve higher coverage of target peptides without sacrificing specificity, the extracted features are further processed by nonlinear discriminative models under the framework of positive-unlabeled learning with decoy peptides as affirmative negative controls. DreamDIA is publicly available at https://github.com/xmuyulab/DreamDIA-XMBD for high coverage and accuracy DIA data analysis.

Liquid Chromatography-Mass Spectrometry for Clinical Metabolomics: An Overview

Metabolomics is a discipline that offers a comprehensive analysis of metabolites in biological samples. In the last decades, the notable evolution in liquid chromatography and mass spectrometry technologies has driven an exponential progress in LC-MS-based metabolomics. Targeted and untargeted metabolomics strategies are important tools in health and medical science, especially in the study of disease-related biomarkers, drug discovery and development, toxicology, diet, physical exercise, and precision medicine. Clinical and biological problems can now be understood in terms of metabolic phenotyping. This overview highlights the current approaches to LC-MS-based metabolomics analysis and its applications in the clinical research.

First Direct Evidence of Interpartner Hydride/Deuteride Exchanges for Stored Sodiated Arginine/Fructose-6-phosphate Complex Anions within Salt-Solvated Structures

Mass spectrometric investigations of noncovalent binding between low molecular weight compounds revealed the existence of gas-phase (GP) noncovalent complex (NCC) ions involving zwitterionic structures. ESI MS is used to prove the formation of stable sodiated NCC anions between fructose (F6P) and arginine (R) moieties. Theoretical calculations indicate a folded solvated salt (i.e., sodiated carboxylate interacting with phosphate) rather than a charge-solvated form. Under standard CID conditions, [(F6P+R-H+Na)-H]^- competitively forms two major product ions (PIs) through partner splitting [(R-H+Na) loss] and charge-induced cross-ring cleavage while preserving the noncovalent interactions (noncovalent product ions (NCPIs)). MS/MS experiments combined with in-solution proton/deuteron exchanges (HDXs) demonstrated an unexpected labeling of PIs, i.e., a correlated D-enrichment/D-depletion. An increase in activation time up to 3000 ms favors such processes when limited to two H/D exchanges. These results are rationalized by interpartner hydride/deuteride exchanges (⟨HDX⟩) through stepwise isomerization/dissociation of sodiated NCC-d11 anions. In addition, the D-enrichment/D-depletion discrepancy is further explained by back HDX with residual water in LTQ (selective for the isotopologue NCPIs as shown by PI relaxation experiments). Each isotopologue leads to only one back HDX unlike multiple HDXs generally observed in GP. This behavior shows that NCPIs are zwitterions with charges solvated by a single water molecule, thus generating a back HDX through a relay mechanism, which quenches the charges and prevents further back HDX. By estimating back HDX impact on D-depletion, the interpartner ⟨HDX⟩ complementarity was thus illustrated. This is the first description of interpartner ⟨HDX⟩ and selective back HDX validating salt-solvated structures.

Aqueous Fibronectin Correlates With Severity of Macular Edema and Visual Acuity in Patients With Branch Retinal Vein Occlusion: A Proteome Study

Purpose

Large-scale protein analysis may bring important insights into molecular changes following branch retinal vein occlusion (BRVO). Using proteomic techniques this study compared aqueous humor samples from patients with BRVO to age-matched controls.

Methods

Aqueous humor samples from treatment naive patients with BRVO complicated by macular edema (n = 19) and age-matched controls (n = 18) were analyzed with label-free quantification nano liquid chromatography - tandem mass spectrometry (LFQ nLC-MS/MS). The severity of macular edema was measured as central retinal thickness (CRT) with optical coherence tomography. Control samples were obtained prior to cataract surgery. Proteins were filtered by requiring quantification in at least 50% of the samples in each group without imputation of missing values. Significantly changed proteins were identified with a permutation-based calculation with a false discovery rate at 0.05.

Results

In BRVO, 52 proteins were differentially expressed. Regulated proteins were involved in cell adhesion, coagulation, and acute-phase response. Apolipoprotein C-III, complement C3, complement C5, complement factor H, fibronectin, and fibrinogen chains were increased in BRVO and correlated with CRT. Fibronectin also correlated with best corrected visual acuity (BCVA) and vascular endothelial growth factor (VEGF). Monocyte differentiation antigen CD14 (CD14) and lipopolysaccharide-binding protein (LBP) were upregulated in BRVO. Contactin-1 and alpha-enolase were downregulated in BRVO and correlated negatively with CRT.

Conclusions

Multiple proteins, including complement factors, fibrinogen chains, and apolipoprotein C-III, correlated with CRT, indicating a multifactorial response. Fibronectin correlated with BCVA, CRT, and VEGF. Fibronectin may reflect the severity of BRVO. The proinflammatory proteins CD14 and LBP were upregulated in BRVO.

Identification of newly formed toxic chemicals in E-cigarette aerosols with Orbitrap mass spectrometry and implications on E-cigarette control

The increasing use of electronic nicotine delivery systems (ENDS) is of concern due to multiple emerging adverse health effects. Most analyses of the harmful chemicals of ENDS have targeted metals or carbonyls generated by thermal decomposition of carrier liquids such as propylene glycol. However, new complex compounds not routinely identified and with unknown health consequences could be formed. ENDS aerosol samples were collected by the direct aerosol droplet deposition method. Untargeted analysis was performed using Orbitrap mass spectrometry with high mass accuracy. We identified more than 30 "features" in the aerosol characterized by pairs of the mass-to-charge ratio "m/z" of the compound and the retention time. We identified several compounds containing nicotine and propylene glycol (NIC-PG), whose abundance relative to nicotine increased along with vaping power used. On the basis of the prediction by the Environmental Protection Agency Toxicity Estimation Software Tool, these compounds exert developmental toxicity. In addition, a nitrogen-containing compound, likely tributylamine (a known lung irritant), was identified based on the molecular weight. This compound has not been previously identified in ENDS e-liquids and aerosols. ENDS produce not only small toxic compounds such as aldehydes, but also large complex toxic compounds such as NIC-PG. Predicted development toxicity for NIC-PG is concerning for fetal development in pregnant women who use ENDS, children exposed to secondhand or thirdhand ENDS aerosols, and teenage ENDS users whose brains are still developing. The strong positive association between NIC-PG levels and ENDS power output supports regulating high-powered ENDS.

Proteomic insights into synaptic signaling in the brain: the past, present and future

Chemical synapses in the brain connect neurons to form neural circuits, providing the structural and functional bases for neural communication. Disrupted synaptic signaling is closely related to a variety of neurological and psychiatric disorders. In the past two decades, proteomics has blossomed as a versatile tool in biological and biomedical research, rendering a wealth of information toward decoding the molecular machinery of life. There is enormous interest in employing proteomic approaches for the study of synapses, and substantial progress has been made. Here, we review the findings of proteomic studies of chemical synapses in the brain, with special attention paid to the key players in synaptic signaling, i.e., the synaptic protein complexes and their post-translational modifications. Looking toward the future, we discuss the technological advances in proteomics such as data-independent acquisition mass spectrometry (DIA-MS), cross-linking in combination with mass spectrometry (CXMS), and proximity proteomics, along with their potential to untangle the mystery of how the brain functions at the molecular level. Last but not least, we introduce the newly developed synaptomic methods. These methods and their successful applications marked the beginnings of the synaptomics era.

Extending the Depth of Human Plasma Proteome Coverage Using Simple Fractionation Techniques

Human plasma is one of the most widely used tissues in clinical analysis, and plasma-based biomarkers are used for monitoring patient health status and/or response to medical treatment to avoid unnecessary invasive biopsy. Data-driven plasma proteomics has suffered from a lack of throughput and detection sensitivity, largely due to the complexity of the plasma proteome and in particular the enormous quantitative dynamic range, estimated to be between 9 and 13 orders of magnitude between the lowest and the highest abundance protein. A major challenge is to identify workflows that can achieve depth of plasma proteome coverage while minimizing the complexity of the sample workup and maximizing the sample throughput. In this study, we have performed intensive depletion of high-abundant plasma proteins or enrichment of low-abundant proteins using the Agilent multiple affinity removal liquid chromatography (LC) column-Human 6 (Hu6), the Agilent multiple affinity removal LC column-Human 14 (Hu14), and ProteoMiner followed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS PAGE) and C18 prefractionation techniques. We compared the performance of each of these fractionation approaches to identify the method that satisfies requirements for analysis of clinical samples and to include good plasma proteome coverage in combination with reasonable sample output. In this study, we report that one-dimensional (1D) gel-based prefractionation allows parallel sample processing and no loss of proteome coverage, compared with serial chromatographic separation, and significantly accelerates analysis time, particularly important for large clinical projects. Furthermore, we show that a variety of methodologies can achieve similarly high plasma proteome coverage, allowing flexibility in method selection based on project-specific needs. These considerations are important in the effort to accelerate plasma proteomics research so as to provide efficient, reliable, and accurate diagnoses, population-based health screening, clinical research studies, and other clinical work.

The Role of Data-Independent Acquisition for Glycoproteomics

Data-independent acquisition (DIA) is now an emerging method in bottom–up proteomics and capable of achieving deep proteome coverage and accurate label-free quantification. However, for post-translational modifications, such as glycosylation, DIA methodology is still in the early stage of development. The full characterization of glycoproteins requires site-specific glycan identification as well as subsequent quantification of glycan structures at each site. The tremendous complexity of glycosylation represents a significant analytical challenge in glycoproteomics. This review focuses on the development and perspectives of DIA methodology for N- and O-linked glycoproteomics and posits that DIA-based glycoproteomics could be a method of choice to address some of the challenging aspects of glycoproteomics. First, the current challenges in glycoproteomics and the basic principles of DIA are briefly introduced. DIA-based glycoproteomics is then summarized and described into four aspects based on the actual samples. Finally, we discussed the important challenges and future perspectives in the field. We believe that DIA can significantly facilitate glycoproteomic studies and contribute to the development of future advanced tools and approaches in the field of glycoproteomics.

•

Protein glycosylation and challenges in glycoproteomics.

•

Data-independent acquisition for deglycosylated and intact N-linked glycopeptides.

•

Unbiased screening of oxonium ions from all glycopeptide precursors.

•

Glyco–data-independent acquisition on mucin-type O-glycopeptides.

Avant-garde: an automated data-driven DIA data curation tool

Several challenges remain in data-independent acquisition (DIA) data analysis, such as to confidently identify peptides, define integration boundaries, remove interferences, and control false discovery rates. In practice, a visual inspection of the signals is still required, which is impractical with large datasets. We present Avant-garde as a tool to refine DIA (and parallel reaction monitoring) data. Avant-garde uses a novel data-driven scoring strategy: signals are refined by learning from the dataset itself, using all measurements in all samples to achieve the best optimization. We evaluate the performance of Avant-garde using benchmark DIA datasets and show that it can determine the quantitative suitability of a peptide peak, and reach the same levels of selectivity, accuracy, and reproducibility as manual validation. Avant-garde is complementary to existing DIA analysis engines and aims to establish a strong foundation for subsequent analysis of quantitative mass spectrometry data.

Critical Review of Existing MHC I Immunopeptidome Isolation Methods

Major histocompatibility complex class I (MHC I) plays a crucial role in the development of adaptive immune response in vertebrates. MHC molecules are cell surface protein complexes loaded with short peptides and recognized by the T-cell receptors (TCR). Peptides associated with MHC are named immunopeptidome. The MHC I immunopeptidome is produced by the proteasome degradation of intracellular proteins. The knowledge of the immunopeptidome repertoire facilitates the creation of personalized antitumor or antiviral vaccines. A huge number of publications on the immunopeptidome diversity of different human and mouse biological samples-plasma, peripheral blood mononuclear cells (PBMCs), and solid tissues, including tumors-appeared in the scientific journals in the last decade. Significant immunopeptidome identification efficiency was achieved by advances in technology: the immunoprecipitation of MHC and mass spectrometry-based approaches. Researchers optimized common strategies to isolate MHC-associated peptides for individual tasks. They published many protocols with differences in the amount and type of biological sample, amount of antibodies, type and amount of insoluble support, methods of post-fractionation and purification, and approaches to LC-MS/MS identification of immunopeptidome. These parameters have a large impact on the final repertoire of isolated immunopeptidome. In this review, we summarize and compare immunopeptidome isolation techniques with an emphasis on the results obtained.

Recent advances in analytical strategies for mass spectrometry-based lipidomics

Lipids are vital biological molecules and play multiple roles in cellular function of mammalian organisms such as cellular membrane anchoring, signal transduction, material trafficking and energy storage. Driven by the biological significance of lipids, lipidomics has become an emerging science in the field of omics. Lipidome in biological systems consists of hundreds of thousands of individual lipid molecules that possess complex structures, multiple categories, and diverse physicochemical properties assembled by different combinations of polar headgroups and hydrophobic fatty acyl chains. Such structural complexity poses a huge challenge for comprehensive lipidome analysis. Thanks to the great innovations in chromatographic separation techniques and the continuous advances in mass spectrometric detection tools, analytical strategies for lipidomics have been highly diversified so that the depth and breadth of lipidomics have been greatly enhanced. This review will present the current state of mass spectrometry-based analytical strategies including untargeted, targeted and pseudotargeted lipidomics. Recent typical applications of lipidomics in biomarker discovery, pathogenic mechanism and therapeutic strategy are summarized, and the challenges facing to the field of lipidomics are also discussed.

Proteomics and Informatics for Understanding Phases and Identifying Biomarkers in COVID-19 Disease

The emergence of novel coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 coronavirus, has necessitated the urgent development of new diagnostic and therapeutic strategies. Rapid research and development, on an international scale, has already generated assays for detecting SARS-CoV-2 RNA and host immunoglobulins. However, the complexities of COVID-19 are such that fuller definitions of patient status, trajectory, sequelae, and responses to therapy are now required. There is accumulating evidence-from studies of both COVID-19 and the related disease SARS-that protein biomarkers could help to provide this definition. Proteins associated with blood coagulation (D-dimer), cell damage (lactate dehydrogenase), and the inflammatory response (e.g., C-reactive protein) have already been identified as possible predictors of COVID-19 severity or mortality. Proteomics technologies, with their ability to detect many proteins per analysis, have begun to extend these early findings. To be effective, proteomics strategies must include not only methods for comprehensive data acquisition (e.g., using mass spectrometry) but also informatics approaches via which to derive actionable information from large data sets. Here we review applications of proteomics to COVID-19 and SARS and outline how pipelines involving technologies such as artificial intelligence could be of value for research on these diseases.

QuantPipe: A User-Friendly Pipeline Software Tool for DIA Data Analysis Based on the OpenSWATH-PyProphet-TRIC Workflow

Targeted analysis of data-independent acquisition (DIA) mass spectrometry data requires elegant software tools and strict statistical control. OpenSWATH-PyProphet-TRIC is a widely used DIA data analysis workflow. The OpenSWATH-PyProphet-TRIC workflow is typically executed by running command lines. Here, we present QuantPipe, which is a graphic interface software tool based on the OpenSWATH-PyProphet-TRIC workflow. In addition to OpenSWATH-PyProphet-TRIC functions, QuantPipe can convert the spectral library to the assay library and output peptides and protein intensities. We demonstrated that QuantPipe can be used to analyze SWATH-MS data from TripleTOF 5600 and TripleTOF 6600, phospho-SWATH-MS data, DIA data from Orbitrap instrument, and diaPASEF data from TimsTOF Pro instrument. The executable files, user manual, and source code of QuantPipe are freely available at https://github.com/tachengxmu/QuantPipe/releases.

PulseDIA: Data-Independent Acquisition Mass Spectrometry Using Multi-Injection Pulsed Gas-Phase Fractionation

The performance of data-independent acquisition (DIA) mass spectrometry (MS) depends on the separation efficiency of peptide precursors. In Orbitrap-based mass spectrometers, separation efficiency of peptide precursors is limited by the relatively slow scanning rate compared to time of flight (TOF)-based MS. Here, we present PulseDIA, a multi-injection gas-phase fractionation (GPF) strategy for enhanced DIA-MS. This is achieved by equally dividing the conventional DIA analysis covering the entire mass range into multiple injections for DIA analyses with complementary windows. Using mouse liver digests, the PulseDIA method identified up to 50% more peptides and 29% more protein groups than that by conventional DIA with the same length of effective gradient time. Compared to conventional multi-injection GPF, PusleDIA exhibited higher flexibility and identified up to 18% more peptides and 8% more protein groups using two injections. The gain of peptides per effective time unit was the highest in PulseDIA compared to conventional DIA and GPF. We further applied the PulseDIA method to profile the proteome of 18 human tissue samples (benign and malignant) from nine cholangiocarcinoma (CCA) patients. PulseDIA identified 7796 protein groups in these CCA samples, with a 14% increase of protein group identification compared to the conventional DIA method. The missing value for protein matrix dropped by 7% using PulseDIA compared to DIA. A total of 681 significantly altered proteins were detected in CCA samples using PulseDIA, including several dysregulated proteins, which were absent in the conventional DIA analysis. Taken together, we present PulseDIA as an enhanced DIA-MS method with improved sensitivity and reproducibility.

High-speed Analysis of Large Sample Sets - How Can This Key Aspect of the Omics Be Achieved?

High-speed analysis of large (prote)omics sample sets at the rate of thousands or millions of samples per day on a single platform has been a challenge since the beginning of proteomics. For many years, ESI-based MS methods have dominated proteomics because of their high sensitivity and great depth in analyzing complex proteomes. However, despite improvements in speed, ESI-based MS methods are fundamentally limited by their sample introduction, which excludes off-line sample preparation/fractionation because of the time required to switch between individual samples/sample fractions, and therefore being dependent on the speed of on-line sample preparation methods such as liquid chromatography. Laser-based ionization methods have the advantage of moving from one sample to the next without these limitations, being mainly restricted by the speed of modern sample stages, i.e. 10 ms or less between samples. This speed matches the data acquisition speed of modern high-performing mass spectrometers whereas the pulse repetition rate of the lasers (>1 kHz) provides a sufficient number of desorption/ionization events for successful ion signal detection from each sample at the above speed of the sample stages. Other advantages of laser-based ionization methods include the generally higher tolerance to sample additives and contamination compared with ESI MS, and the contact-less and pulsed nature of the laser used for desorption, reducing the risk of cross-contamination. Furthermore, new developments in MALDI have expanded its analytical capabilities, now being able to fully exploit high-performing hybrid mass analyzers and their strengths in sensitivity and MS/MS analysis by generating an ESI-like stable yield of multiply charged analyte ions. Thus, these new developments and the intrinsically high speed of laser-based methods now provide a good basis for tackling extreme sample analysis speed in the omics.

Guidance Document: Validation of a High-Performance Liquid Chromatography-Tandem Mass Spectrometry Immunopeptidomics Assay for the Identification of HLA Class I Ligands Suitable for Pharmaceutical Therapies

For more than two decades naturally presented, human leukocyte antigen (HLA)-restricted peptides (immunopeptidome) have been eluted and sequenced using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Since, identified disease-associated HLA ligands have been characterized and evaluated as potential active substances. Treatments based on HLA-presented peptides have shown promising results in clinical application as personalized T cell-based immunotherapy. Peptide vaccination cocktails are produced as investigational medicinal products under GMP conditions. To support clinical trials based on HLA-presented tumor-associated antigens, in this study the sensitive LC-MS/MS HLA class I antigen identification pipeline was fully validated for our technical equipment according to the current US Food and Drug Administration (FDA) and European Medicines Agency (EMA) guidelines.The immunopeptidomes of JY cells with or without spiked-in, isotope labeled peptides, of peripheral blood mononuclear cells of healthy volunteers as well as a chronic lymphocytic leukemia and a bladder cancer sample were reliably identified using a data-dependent acquisition method. As the LC-MS/MS pipeline is used for identification purposes, the validation parameters include accuracy, precision, specificity, limit of detection and robustness.

Treasure hunt for peptides with undefined chemical modifications: Proteomics identification of differential albumin adducts of 2-nitroimidazole-indocyanine green in hypoxic tumor

2-Nitroimidazole is a well-known chemical probe targeting hypoxic environments of solid tumors, and its derivatives are widely used as imaging agents to investigate tissue and tumor hypoxia. However, the underlying chemistry for the hypoxia-detection capability of 2-nitroimidazole is still unclear. In this study, we deployed a biotin conjugate of 2-nitroimidazole-indocyanine green (2-nitro-ICG) for the investigation of in vivo hypoxia-probing mechanism of 2-nitro-ICG compounds. By implementing mass spectrometry-based proteomics and exhaustive data mining, we report that 2-nitro-ICG and its fragments modify mouse serum albumin as the primary protein target but at two structurally distinct sites and possibly via two different mechanisms. The identification of probe-modified peptides not only contributes to the understanding of the in vivo metabolism of 2-nitroimidazole compounds but also demonstrates a competent analytical workflow that enables the search for peptides with undefined modifications in complex proteome digests.

Concepts and strategies of soybean seed proteomics using the shotgun proteomics approach

Introduction: The last decade has yielded significant developments in the field of proteomics, especially in mass spectrometry (MS) and data analysis tools. In particular, a shift from gel-based to MS-based proteomics has been observed, thereby providing a platform with which to construct proteome atlases for all life forms. Nevertheless, the analysis of plant proteomes, especially those of samples that contain high-abundance proteins (HAPs), such as soybean seeds, remains challenging. Areas covered: Here, we review recent progress in soybean seed proteomics and highlight advances in HAPs depletion methods and peptide pre-fractionation, identification, and quantification methods. We also suggest a pipeline for future proteomic analysis, in order to increase the dynamic coverage of the soybean seed proteome. Expert opinion: Because HAPs limit the dynamic resolution of the soybean seed proteome, the depletion of HAPs is a prerequisite of high-throughput proteome analysis, and owing to the use of two-dimensional gel electrophoresis-based proteomic approaches, few soybean seed proteins have been identified or characterized. Recent advances in proteomic technologies, which have significantly increased the proteome coverage of other plants, could be used to overcome the current complexity and limitation of soybean seed proteomics.

Development of Targeted Mass Spectrometry-Based Approaches for Quantitation of Proteins Enriched in the Postsynaptic Density (PSD)

The postsynaptic density (PSD) is a structural, electron-dense region of excitatory glutamatergic synapses, which is involved in a variety of cellular and signaling processes in neurons. The PSD is comprised of a large network of proteins, many of which have been implicated in a wide variety of neuropsychiatric disorders. Biochemical fractionation combined with mass spectrometry analyses have enabled an in-depth understanding of the protein composition of the PSD. However, the PSD composition may change rapidly in response to stimuli, and robust and reproducible methods to thoroughly quantify changes in protein abundance are warranted. Here, we report on the development of two types of targeted mass spectrometry-based assays for quantitation of PSD-enriched proteins. In total, we quantified 50 PSD proteins in a targeted, parallel reaction monitoring (PRM) assay using heavy-labeled, synthetic internal peptide standards and identified and quantified over 2100 proteins through a pre-determined spectral library using a data-independent acquisition (DIA) approach in PSD fractions isolated from mouse cortical brain tissue.

Advancing untargeted metabolomics using data-independent acquisition mass spectrometry technology

Metabolomics quantitatively measures metabolites in a given biological system and facilitates the understanding of physiological and pathological activities. With the recent advancement of mass spectrometry (MS) technology, liquid chromatography-mass spectrometry (LC-MS) with data-independent acquisition (DIA) has been emerged as a powerful technology for untargeted metabolomics due to its capability to acquire all MS2 spectra and high quantitative accuracy. In this trend article, we first introduced the basic principles of several common DIA techniques including MS^E, all ion fragmentation (AIF), SWATH, and MSX. Then, we summarized and compared the data analysis strategies to process DIA-based untargeted metabolomics data, including metabolite identification and quantification. We think the advantages of the DIA technique will enable its broad application in untargeted metabolomics.

Omentin-regulated proteins combine a pro-inflammatory phenotype with an anti-inflammatory counterregulation in human adipocytes: A proteomics analysis

AIMS

Experimental and epidemiological studies reported controversial data on the role of omentin in type 2 diabetes and cardiovascular diseases. This study aimed to characterise the impact of omentin on the secretome of human adipocytes to analyse the enrichment of these proteins in metabolic and cellular signalling pathways underlying its physiological function.

MATERIAL/METHODS

Differentiated primary human adipocytes were treated without or with 500 or 2000 ng/mL omentin for 24 hours. The secretome was analysed by liquid chromatography coupled tandem-mass spectrometry. Differences in protein secretion between untreated and omentin-treated adipocytes were compared using a paired t-test. Other potential upstream regulators and the overrepresentation in canonical pathways of omentin-stimulated proteins were analysed using Ingenuity Pathway Analysis.

RESULTS

The supernatant of adipocytes contained 3493 proteins, of which 140 were differentially secreted by both concentrations of omentin compared with untreated adipocytes. Among the most strongly increased proteins, tumour necrosis factor-inducible gene 6 protein (TNFAIP6) was increased by 140-fold in the supernatant. Omentin-regulated proteins were overrepresented in seven canonical pathways including eukaryotic initiation factor 2 signalling, complement system, and inhibition of matrix metalloproteases. We further identified 25 other potential upstream activators of omentin-regulated proteins, mainly pro-inflammatory cytokines and transcription regulators including NFκB.

CONCLUSIONS

In differentiated human adipocytes, the release of the anti-inflammatory TNFAIP6 might be part of a counterregulatory response to the pro-inflammatory action of omentin. Omentin-regulated proteins were overrepresented in pathways indicating cellular stress, a pro-inflammatory environment and a crosstalk with other organs. Other potential activators of omentin-regulated proteins point towards a central role of NFκB activation in the omentin-induced secretory process.

Mass Spectrometry-Based Biomarkers in Drug Development

Advances in mass spectrometry, proteomics, protein bioanalytical approaches, and biochemistry have led to a rapid evolution and expansion in the area of mass spectrometry-based biomarker discovery and development. The last decade has also seen significant progress in establishing accepted definitions, guidelines, and criteria for the analytical validation, acceptance and qualification of biomarkers. These advances have coincided with a decreased return on investment for pharmaceutical research and development and an increasing need for better early decision making tools. Empowering development teams with tools to measure a therapeutic interventions impact on disease state and progression, measure target engagement and to confirm predicted pharmacodynamic effects is critical to efficient data-driven decision making. Appropriate implementation of a biomarker or a combination of biomarkers can enhance understanding of a drugs mechanism, facilitate effective translation from the preclinical to clinical space, enable early proof of concept and dose selection, and increases the efficiency of drug development. Here we will provide descriptions of the different classes of biomarkers that have utility in the drug development process as well as review specific, protein-centric, mass spectrometry-based approaches for the discovery of biomarkers and development of targeted assays to measure these markers in a selective and analytically precise manner.

A Guide to Mass Spectrometry-Based Quantitative Proteomics

Proteomics has become an attractive science in the postgenomic era, given its capacity to identify up to thousands of molecules in a single, complex sample and quantify them in an absolute and/or relative manner. The use of these techniques enables understanding of cellular and molecular mechanisms of diseases and other biological conditions, as well as identification and screening of protein biomarkers. Here we provide a straightforward, up-to-date compilation and comparison of the main quantitation techniques used in comparative proteomics such as in vitro and in vivo stable isotope labeling and label-free techniques. Additionally, this chapter includes common methods for data acquisition in proteomics and some appropriate methods for data processing. This compilation can serve as a reference for scientists who are new to, or already familiar with, quantitative proteomics.