SWATH-MS as a tool for biomarker discovery: From basic research to clinical applications

Diagnostic Accuracy of Novel Protein Biomarkers in Saliva to Detect Periodontitis Using Untargeted 'SWATH' Mass Spectrometry

AIM

To discover new salivary biomarkers to diagnose periodontitis and evaluate the impact of age and smoking on predictive capacity.

MATERIAL AND METHODS

Saliva samples were collected from 44 healthy periodontal individuals and 41 with periodontitis. Samples were analysed by sequential window acquisition of all theoretical mass spectra (SWATH-MS), and proteins were identified by employing the UniProt database. The diagnostic capacity of the molecules was determined with generalized additive models. The models obtained were single-protein unadjusted and adjusted for age and smoking status, besides two-protein combinations.

RESULTS

Eight single salivary proteins had a bias-corrected accuracy (bc-ACC) of 78.8%-86.8% (bc-sensitivity/bc-specificity of 62.5%-86.9%/60.9%-98.1%) to diagnose periodontitis. Predictive capacity increased more by adjusting for age (bc-ACC: 94.1%-98.2%; bc-sensitivity/bc-specificity: 90.2%-98.6%/93.6%-97.2%) than smoking (bc-ACC: 83.9%-90.4%; bc-sensitivity/bc-specificity: 73.6%-89.9%/76.2%-96.4%). These proteins were keratin, type II cytoskeletal 1, protein S100-A8, β-2-microglobulin, neutrophil defensin 1, lysozyme C, ubiquitin-60S ribosomal protein L40, isoform 2 of tropomyosin α-3 chain and resistin. Two dual combinations showed bc-sensitivity/bc-specificity of > 90%: β-2-microglobulin with profilin-1, and lysozyme C with zymogen granule protein 16 homologue B.

CONCLUSIONS

New salivary biomarkers show good or excellent ability to diagnose periodontitis. Age has a more significant influence on the accuracy of the single biomarkers than smoking, with results comparable to two-protein combinations.

Using SWATH-MS to identify new molecular biomarkers in gingival crevicular fluid for detecting periodontitis and its response to treatment

AIM

To identify new biomarkers to detect untreated and treated periodontitis in gingival crevicular fluid (GCF) using sequential window acquisition of all theoretical mass spectra (SWATH-MS).

MATERIALS AND METHODS

GCF samples were collected from 44 periodontally healthy subjects and 40 with periodontitis (Stages III-IV). In the latter, 25 improved clinically 2 months after treatment. Samples were analysed using SWATH-MS, and proteins were identified by the UniProt human-specific database. The diagnostic capability of the proteins was determined with generalized additive models to distinguish the three clinical conditions.

RESULTS

In the untreated periodontitis vs. periodontal health modelling, five proteins showed excellent or good bias-corrected (bc)-sensitivity/bc-specificity values of >80%. These were GAPDH, ZG16B, carbonic anhydrase 1, plasma protease inhibitor C1 and haemoglobin subunit beta. GAPDH with MMP-9, MMP-8, zinc-α-2-glycoprotein and neutrophil gelatinase-associated lipocalin and ZG16B with cornulin provided increased bc-sensitivity/bc-specificity of >95%. For distinguishing treated periodontitis vs. periodontal health, most of these proteins and their combinations revealed a predictive ability similar to previous modelling. No model obtained relevant results to differentiate between periodontitis conditions.

CONCLUSIONS

New single and dual GCF protein biomarkers showed outstanding results in discriminating untreated and treated periodontitis from periodontal health. Periodontitis conditions were indistinguishable. Future research must validate these findings.

Characterization of the plasma proteomic profile of Fabry disease: Potential sex- and clinical phenotype-specific biomarkers

Fabry disease (FD) is a X-linked rare lysosomal storage disorder caused by deficient α-galactosidase A (α-GalA) activity. Early diagnosis and the prediction of disease course are complicated by the clinical heterogeneity of FD, as well as by the frequently inconclusive biochemical and genetic test results that do not correlate with clinical course. We sought to identify potential biomarkers of FD to better understand the underlying pathophysiology and clinical phenotypes. We compared the plasma proteomes of 50 FD patients and 50 matched healthy controls using DDA and SWATH-MS. The >30 proteins that were differentially expressed between the 2 groups included proteins implicated in processes such as inflammation, heme and haemoglobin metabolism, oxidative stress, coagulation, complement cascade, glucose and lipid metabolism, and glycocalyx formation. Stratification by sex revealed that certain proteins were differentially expressed in a sex-dependent manner. Apolipoprotein A-IV was upregulated in FD patients with complications, especially those with chronic kidney disease, and apolipoprotein C-III and fetuin-A were identified as possible markers of FD with left ventricular hypertrophy. All these proteins had a greater capacity to identify the presence of complications in FD patients than lyso-GB3, with apolipoprotein A-IV standing out as being more sensitive and effective in differentiating the presence and absence of chronic kidney disease in FD patients than renal markers such as creatinine, glomerular filtration rate and microalbuminuria. Identification of these potential biomarkers can help further our understanding of the pathophysiological processes that underlie the heterogeneous clinical manifestations associated with FD.

Comprehensive Urinary Proteome Profiling Analysis Identifies Diagnosis and Relapse Surveillance Biomarkers for Bladder Cancer

Bladder cancer (BCa) is the predominant malignancy of the urinary system. Herein, a comprehensive urine proteomic feature was initially established for the noninvasive diagnosis and recurrence monitoring of bladder cancer. 279 cases (63 primary BCa, 87 nontumor controls (NT), 73 relapsed BCa (BCR), and 56 nonrelapsed BCa (BCNR)) were collected to screen urinary protein biomarkers. 4761 and 3668 proteins were qualified and quantified by DDA and sequential window acquisition of all theoretical mass spectra (SWATH-MS) analysis in two discovery sets, respectively. Upregulated proteins were validated by multiple reaction monitoring (MRM) in two independent combined sets. Using the multi-support vector machine-recursive feature elimination (mSVM-RFE) algorithm, a model comprising 13 proteins exhibited good performance between BCa and NT with an AUC of 0.821 (95% CI: 0.675-0.967), 90.9% sensitivity (95% CI: 72.7-100%), and 73.3% specificity (95% CI: 53.3-93.3%) in the diagnosis test set. Meanwhile, an 11-marker classifier significantly distinguished BCR from BCNR with 75.0% sensitivity (95% CI: 50.0-100%), 81.8% specificity (95% CI: 54.5-100%), and an AUC of 0.784 (95% CI: 0.609-0.959) in the test cohort for relapse surveillance. Notably, six proteins (SPR, AK1, CD2AP, ADGRF1, GMPS, and C8A) of 24 markers were newly reported. This paper reveals novel urinary protein biomarkers for BCa and offers new theoretical insights into the pathogenesis of bladder cancer (data identifier PXD044896).

Concomitant investigation of crustacean amphipods lipidome and metabolome during the molting cycle by Zeno SWATH data-independent acquisition coupled with electron activated dissociation and machine learning

BACKGROUND

DIA (Data-Independent Acquisition) is a powerful technique in Liquid Chromatography coupled with high-resolution tandem Mass Spectrometry (LC-MS/MS) initially developed for proteomics studies and recently emerging in metabolomics and lipidomics. It provides a comprehensive and unbiased coverage of molecules with improved reproducibility and quantitative accuracy compared to Data-Dependent Acquisition (DDA). Combined with the Zeno trap and Electron-Activated Dissociation (EAD), DIA enhances data quality and structural elucidation compared to conventional fragmentation under CID. These tools were applied to study the lipidome and metabolome of the freshwater amphipod Gammarus fossarum, successfully discriminating stages and highlighting significant biological features. Despite being underused, DIA, along with the Zeno trap and EAD, holds great potential for advancing research in the omics field.

RESULTS

DIA combined with the Zeno trap enhances detection reproducibility compared to conventional DDA, improving fragmentation spectra quality and putative identifications. LC coupled with Zeno-SWATH-DIA methods were used to characterize molecular changes in reproductive cycle of female gammarids. Multivariate data analysis including Principal Component Analysis and Partial Least Square Discriminant Analysis successfully identified significant features. EAD fragmentation helped to identify unknown features and to confirm their molecular structure using fragmentation spectra database annotation or machine learning. EAD database matching accurately annotated five glycerophospholipids, including the position of double bonds on fatty acid chain moieties. SIRIUS database predicted structures of unknown features based on experimental fragmentation spectra to compensate for database incompleteness.

SIGNIFICANCE

Reproducible detection of features and confident identification of putative compounds are pivotal stages within analytical pipelines. The DIA approach combined with Zeno pulsing enhances detection sensitivity and targeted fragmentation with EAD in positive polarity provides orthogonal fragmentation information. In our study, Zeno-DIA and EAD thereby facilitated a comprehensive and insightful exploration of pertinent biological molecules associated with the reproductive cycle of gammarids. The developed methodology holds great promises for identifying informative biomarkers on the health status of an environmental sentinel species.

Computational inhibition of S100A8 (calgranulin A) as a potential non-invasive biomarker for rheumatoid arthritis

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic joint pain and inflammation, loss of mobility, which affects the quality of life. The etiology of RA is unknown, and there is no isolated cause for the development of this illness. Synovial inflammation, autoantibody generation and bone degradation leading to deformity are classic characteristics of RA. The search for a non-invasive biomarker is crucial for helping patients receive standard therapies leading to faster treatments, as diagnosis depends on invasive biopsies. Therefore, in the present investigation, using transcriptomics and proteomic approaches, potential genes involved in crucial networks in RA were identified. Gene expression datasets of two tissue types i.e. whole blood and synovial tissue were retrieved from the GEO database and used for transcriptomic analyses. Using SWATH-MS analysis, differentially expressed proteins were identified from collected saliva of RA patients. Through bioinformatics analysis, S100A8, also known as Calprotectin (a complex of S100A8/A9) or Calgranulin A, was found to be common among all tissue types. S100A8 was then further quantified in saliva of healthy volunteers and RA patients, where it was found that the protein's level in healthy controls was lowered when compared to RA patients. Through in-silico characterization, potential plant-based inhibitors of S100A8 were also identified, to reduce its pro-inflammatory effects. Thus, it may be important in the pathophysiology of RA and, in the future, might help guide targeted treatment. as well as act as a non-invasive diagnostic biomarker platform.

Personalized Drug Therapy: Innovative Concept Guided With Proteoformics

Personalized medicine can reduce adverse effects, enhance drug efficacy, and optimize treatment outcomes, which represents the essence of personalized medicine in the pharmacy field. Protein drugs are crucial in the field of personalized drug therapy and are currently the mainstay, which possess higher target specificity and biological activity than small-molecule chemical drugs, making them efficient in regulating disease-related biological processes, and have significant potential in the development of personalized drugs. Currently, protein drugs are designed and developed for specific protein targets based on patient-specific protein data. However, due to the rapid development of two-dimensional gel electrophoresis and mass spectrometry, it is now widely recognized that a canonical protein actually includes multiple proteoforms, and the differences between these proteoforms will result in varying responses to drugs. The variation in the effects of different proteoforms can be significant and the impact can even alter the intended benefit of a drug, potentially making it harmful instead of lifesaving. As a result, we propose that protein drugs should shift from being targeted through the lens of protein (proteomics) to being targeted through the lens of proteoform (proteoformics). This will enable the development of personalized protein drugs that are better equipped to meet patients' specific needs and disease characteristics. With further development in the field of proteoformics, individualized drug therapy, especially personalized protein drugs aimed at proteoforms as a drug target, will improve the understanding of disease mechanisms, discovery of new drug targets and signaling pathways, provide a theoretical basis for the development of new drugs, aid doctors in conducting health risk assessments and making more cost-effective targeted prevention strategies conducted by artificial intelligence/machine learning, promote technological innovation, and provide more convenient treatment tailored to individualized patient profile, which will benefit the affected individuals and society at large.

Pine wilt disease: what do we know from proteomics?

Pine wilt disease (PWD) is a devastating forest disease caused by the pinewood nematode (PWN), Bursaphelenchus xylophilus, a migratory endoparasite that infects several coniferous species. During the last 20 years, advances have been made for understanding the molecular bases of PWN-host trees interactions. Major advances emerged from transcriptomic and genomic studies, which revealed some unique features related to PWN pathogenicity and constituted fundamental data that allowed the development of postgenomic studies. Here we review the proteomic approaches that were applied to study PWD and integrated the current knowledge on the molecular basis of the PWN pathogenicity. Proteomics has been useful for understanding cellular activities and protein functions involved in PWN-host trees interactions, shedding light into the mechanisms associated with PWN pathogenicity and being promising tools to better clarify host trees PWN resistance/susceptibility.

Application of SWATH Mass Spectrometry and Machine Learning in the Diagnosis of Inflammatory Bowel Disease Based on the Stool Proteome

Inflammatory bowel disease (IBD) flare-ups exhibit symptoms that are similar to other diseases and conditions, making diagnosis and treatment complicated. Currently, the gold standard for diagnosing and monitoring IBD is colonoscopy and biopsy, which are invasive and uncomfortable procedures, and the fecal calprotectin test, which is not sufficiently accurate. Therefore, it is necessary to develop an alternative method. In this study, our aim was to provide proof of concept for the application of Sequential Window Acquisition of All Theoretical Mass Spectra-Mass spectrometry (SWATH-MS) and machine learning to develop a non-invasive and accurate predictive model using the stool proteome to distinguish between active IBD patients and symptomatic non-IBD patients. Proteome profiles of 123 samples were obtained and data processing procedures were optimized to select an appropriate pipeline. The differentially abundant analysis identified 48 proteins. Utilizing correlation-based feature selection (Cfs), 7 proteins were selected for proceeding steps. To identify the most appropriate predictive machine learning model, five of the most popular methods, including support vector machines (SVMs), random forests, logistic regression, naive Bayes, and k-nearest neighbors (KNN), were assessed. The generated model was validated by implementing the algorithm on 45 prospective unseen datasets; the results showed a sensitivity of 96% and a specificity of 76%, indicating its performance. In conclusion, this study illustrates the effectiveness of utilizing the stool proteome obtained through SWATH-MS in accurately diagnosing active IBD via a machine learning model.

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.

Peptidomics Unveils Distinct Acetylation Patterns of Histone and Annexin A1 in Differentiated Thyroid Cancer

Thyroid cancer is a common malignancy of the endocrine system. Nodules are routinely evaluated for malignancy risk by fine needle aspiration biopsy (FNAB), and in cases such as follicular lesions, differential diagnosis between benign and malignant nodules is highly uncertain. Therefore, the discovery of new biomarkers for this disease could be helpful in improving diagnostic accuracy. Thyroid nodule biopsies were subjected to a precipitation step with both the insoluble and supernatant fractions subjected to proteome and peptidome profiling. Proteomic analysis identified annexin A1 as a potential biomarker of thyroid cancer malignancy, with its levels increased in malignant samples. Also upregulated were the acetylated peptides of annexin A1, revealed by the peptidome analysis of the supernatant fraction. In addition, supernatant peptidomic analysis revealed a number of acetylated histone peptides that were significantly elevated in the malignant group, suggesting higher gene transcription activity in malignant tissue. Two of these peptides were found to be robust malignancy predictors, with an area under the receiver operating a characteristic curve (ROC AUC) above 0.95. Thus, this combination of proteomics and peptidomics analyses improved the detection of malignant lesions and also provided new evidence linking thyroid cancer development to heightened transcription activity. This study demonstrates the importance of peptidomic profiling in complementing traditional proteomics approaches.

Windows Scanning Multiomics: Integrated Metabolomics and Proteomics

Metabolomics and proteomics offer significant advantages in understanding biological mechanisms at two hierarchical levels. However, conventional single omics analysis faces challenges due to the high demand for specimens and the complexity of intrinsic associations. To obtain comprehensive and accurate system biological information, we developed a multiomics analytical method called Windows Scanning Multiomics (WSM). In this method, we performed simultaneous extraction of metabolites and proteins from the same sample, resulting in a 10% increase in the coverage of the identified biomolecules. Both metabolomics and proteomics analyses were conducted by using ultrahigh-performance liquid chromatography mass spectrometry (UPLC-MS), eliminating the need for instrument conversions. Additionally, we designed an R-based program (WSM.R) to integrate mathematical and biological correlations between metabolites and proteins into a correlation network. The network created from simultaneously extracted biomolecules was more focused and comprehensive compared to those from separate extractions. Notably, we excluded six pairs of false-positive relationships between metabolites and proteins in the network established using simultaneously extracted biomolecules. In conclusion, this study introduces a novel approach for multiomics analysis and data processing that greatly aids in bioinformation mining from multiomics results. This method is poised to play an indispensable role in systems biology research.

SWATH-MS Analysis of Blood Plasma and Circulating Small Extracellular Vesicles Enables Detection of Putative Protein Biomarkers of Fertility in Young and Aged Dairy Cows

The development of biomarkers of fertility could provide benefits for the genetic improvement of dairy cows. Circulating small extracellular vesicles (sEVs) show promise as diagnostic or prognostic markers since their cargo reflects the metabolic state of the cell of origin; thus, they mirror the physiological status of the host. Here, we employed data-independent acquisition mass spectrometry to survey the plasma and plasma sEV proteomes of two different cohorts of Young (Peripubertal; n = 30) and Aged (Primiparous; n = 20) dairy cows (Bos taurus) of high- and low-genetic merit of fertility and known pregnancy outcomes (ProteomeXchange data set identifier PXD042891). We established predictive models of fertility status with an area under the curve of 0.97 (sEV; p value = 3.302e-07) and 0.95 (plasma; p value = 6.405e-08). Biomarker candidates unique to high-fertility Young cattle had a sensitivity of 0.77 and specificity of 0.67 (*p = 0.0287). Low-fertility biomarker candidates uniquely identified in sEVs from Young and Aged cattle had a sensitivity and specificity of 0.69 and 1.0, respectively (***p = 0.0005). Our bioinformatics pipeline enabled quantification of plasma and circulating sEV proteins associated with fertility phenotype. Further investigations are warranted to validate this research in a larger population, which may lead to improved classification of fertility status in cattle.

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.

Integration of data-independent acquisition (DIA) with co-fractionation mass spectrometry (CF-MS) to enhance interactome mapping capabilities

Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.

Cannflavins A and B with Anti-Ferroptosis, Anti-Glycation, and Antioxidant Activities Protect Human Keratinocytes in a Cell Death Model with Erastin and Reactive Carbonyl Species

Precursors of advanced glycation endproducts, namely, reactive carbonyl species (RCSs), are aging biomarkers that contribute to cell death. However, the impact of RCSs on ferroptosis-an iron-dependent form of cell death-in skin cells remains unknown. Herein, we constructed a cellular model (with human keratinocyte; HaCaT cells) to evaluate the cytotoxicity of the combinations of RCSs (including glyoxal; GO and methyglyoxal; MGO) and erastin (a ferroptosis inducer) using bioassays (measuring cellular lipid peroxidation and iron content) and proteomics with sequential window acquisition of all theoretical mass spectra. Additionally, a data-independent acquisition approach was used to characterize RCSs' and erastin's molecular network including genes, canonical pathways, and upstream regulators. Using this model, we evaluated the cytoprotective effects of two dietary flavonoids including cannflavins A and B against RCSs and erastin-induced cytotoxicity in HaCaT cells. Cannflavins A and B (at 0.625 to 20 µM) inhibited ferroptosis by restoring the cell viability (by 56.6-78.6% and 63.8-81.1%) and suppressing cellular lipid peroxidation (by 42.3-70.2% and 28.8-63.6%), respectively. They also alleviated GO + erastin- or MGO + erastin-induced cytotoxicity by 62.2-67.6% and 56.1-69.3%, and 35.6-54.5% and 33.8-62.0%, respectively. Mechanistic studies supported that the cytoprotective effects of cannflavins A and B are associated with their antioxidant activities including free radical scavenging capacity and an inhibitory effect on glycation. This is the first study showing that cannflavins A and B protect human keratinocytes from RCSs + erastin-induced cytotoxicity, which supports their potential applications as dietary interventions for aging-related skin conditions.

Identification of Plasma Biomarkers from Rheumatoid Arthritis Patients Using an Optimized Sequential Window Acquisition of All THeoretical Mass Spectra (SWATH) Proteomics Workflow

Rheumatoid arthritis (RA) is a systemic autoimmune and inflammatory disease. Plasma biomarkers are critical for understanding disease mechanisms, treatment effects, and diagnosis. Mass spectrometry-based proteomics is a powerful tool for unbiased biomarker discovery. However, plasma proteomics is significantly hampered by signal interference from high-abundance proteins, low overall protein coverage, and high levels of missing data from data-dependent acquisition (DDA). To achieve quantitative proteomics analysis for plasma samples with a balance of throughput, performance, and cost, we developed a workflow incorporating plate-based high abundance protein depletion and sample preparation, comprehensive peptide spectral library building, and data-independent acquisition (DIA) SWATH mass spectrometry-based methodology. In this study, we analyzed plasma samples from both RA patients and healthy donors. The results showed that the new workflow performance exceeded that of the current state-of-the-art depletion-based plasma proteomic platforms in terms of both data quality and proteome coverage. Proteins from biological processes related to the activation of systemic inflammation, suppression of platelet function, and loss of muscle mass were enriched and differentially expressed in RA. Some plasma proteins, particularly acute-phase reactant proteins, showed great power to distinguish between RA patients and healthy donors. Moreover, protein isoforms in the plasma were also analyzed, providing even deeper proteome coverage. This workflow can serve as a basis for further application in discovering plasma biomarkers of other diseases.

Urine SERPINC1/ORM1 as biomarkers for early detection of lupus nephritis in MRL-lpr mice

Background

To evaluate the usefulness of urine SERPINC1 and ORM1 as biomarkers for early detection of lupus nephritis (LN).

Methods

Using proteomics, we screened for potential urine biomarkers that differentiate LN from systemic lupus erythematosus (SLE) patients without nephritis. In addition, urine levels of target biomarkers were measured by ELISA in 13- and 23-week-old MRL-lpr (murine model for LN) and MRL/MpJ mice. Histological analysis was also performed on the kidneys of 23-week-old mice.

Results

Urine SERPINC1 and ORM1 were elevated in SLE patients with newly diagnosed LN compared with SLE patients without LN (SERPINC1, AUC=.892, P<.001; ORM1, AUC=.886, P<.001). Levels of urine SERPINC1 and ORM1 were also significantly higher in MRL-lpr mice than in MRL/MpJ mice at 13 and 23 weeks (SERPINC1: p<.01 and p<.001 at 13 and 23 weeks, respectively; ORM1: p<.01 at 13 and 23 weeks). In contrast, a significant difference in urine albumin between the two groups was only observed at 23 weeks (p<.001) not at 13 weeks (p=.83). Regarding the kidney pathology of MPL-lpr mice, urine ORM1 and urine albumin, but not urine SERPINC1, were positively correlated with the activity index (ORM1, rho =.879, p<.001; albumin, rho =.807, p=.003) and chronicity index (ORM1, rho =.947, p<.001; albumin, rho =.869, p<.001).

Conclusion

We propose that urine SERPINC1 and ORM1 are novel biomarkers for early LN.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

Proteomic analysis identifies subgroups of patients with active systemic lupus erythematosus

OBJECTIVE

Systemic lupus erythematosus (SLE) is a clinically and biologically heterogenous autoimmune disease. We aimed to investigate the plasma proteome of patients with active SLE to identify novel subgroups, or endotypes, of patients.

METHOD

Plasma was collected from patients with active SLE who were enrolled in the British Isles Lupus Assessment Group Biologics Registry (BILAG-BR). The plasma proteome was analysed using a data-independent acquisition method, Sequential Window Acquisition of All theoretical mass spectra mass spectrometry (SWATH-MS). Unsupervised, data-driven clustering algorithms were used to delineate groups of patients with a shared proteomic profile.

RESULTS

In 223 patients, six clusters were identified based on quantification of 581 proteins. Between the clusters, there were significant differences in age (p = 0.012) and ethnicity (p = 0.003). There was increased musculoskeletal disease activity in cluster 1 (C1), 19/27 (70.4%) (p = 0.002) and renal activity in cluster 6 (C6) 15/24 (62.5%) (p = 0.051). Anti-SSa/Ro was the only autoantibody that significantly differed between clusters (p = 0.017). C1 was associated with p21-activated kinases (PAK) and Phospholipase C (PLC) signalling. Within C1 there were two sub-clusters (C1A and C1B) defined by 49 proteins related to cytoskeletal protein binding. C2 and C6 demonstrated opposite Rho family GTPase and Rho GDI signalling. Three proteins (MZB1, SND1 and AGL) identified in C6 increased the classification of active renal disease although this did not reach statistical significance (p = 0.0617).

CONCLUSIONS

Unsupervised proteomic analysis identifies clusters of patients with active SLE, that are associated with clinical and serological features, which may facilitate biomarker discovery. The observed proteomic heterogeneity further supports the need for a personalised approach to treatment in SLE.

Multi-omics in Crohn's disease: New insights from inside

Crohn's disease (CD) is an inflammatory bowel disease (IBD) with complex clinical manifestations such as chronic diarrhea, weight loss and hematochezia. Despite the increasing incidence worldwide, cure of CD remains extremely difficult. The rapid development of high-throughput sequencing technology with integrated-omics analyses in recent years has provided a new means for exploring the pathogenesis, mining the biomarkers and designing targeted personalized therapeutics of CD. Host genomics and epigenomics unveil heredity-related mechanisms of susceptible individuals, while microbiome and metabolomics map host-microbe interactions in CD patients. Proteomics shows great potential in searching for promising biomarkers. Nonetheless, single omics technology cannot holistically connect the mechanisms with heterogeneity of pathological behavior in CD. The rise of multi-omics analysis integrates genetic/epigenetic profiles with protein/microbial metabolite functionality, providing new hope for comprehensive and in-depth exploration of CD. Herein, we emphasized the different omics features and applications of CD and discussed the current research and limitations of multi-omics in CD. This review will update and deepen our understanding of CD from integration of broad omics spectra and will provide new evidence for targeted individualized therapeutics.

Quantitative SWATH-based proteomic profiling of urine for the identification of endometrial cancer biomarkers in symptomatic women

BACKGROUND

A non-invasive endometrial cancer detection tool that can accurately triage symptomatic women for definitive testing would improve patient care. Urine is an attractive biofluid for cancer detection due to its simplicity and ease of collection. The aim of this study was to identify urine-based proteomic signatures that can discriminate endometrial cancer patients from symptomatic controls.

METHODS

This was a prospective case-control study of symptomatic post-menopausal women (50 cancers, 54 controls). Voided self-collected urine samples were processed for mass spectrometry and run using sequential window acquisition of all theoretical mass spectra (SWATH-MS). Machine learning techniques were used to identify important discriminatory proteins, which were subsequently combined in multi-marker panels using logistic regression.

RESULTS

The top discriminatory proteins individually showed moderate accuracy (AUC > 0.70) for endometrial cancer detection. However, algorithms combining the most discriminatory proteins performed well with AUCs > 0.90. The best performing diagnostic model was a 10-marker panel combining SPRR1B, CRNN, CALML3, TXN, FABP5, C1RL, MMP9, ECM1, S100A7 and CFI and predicted endometrial cancer with an AUC of 0.92 (0.96-0.97). Urine-based protein signatures showed good accuracy for the detection of early-stage cancers (AUC 0.92 (0.86-0.9)).

CONCLUSION

A patient-friendly, urine-based test could offer a non-invasive endometrial cancer detection tool in symptomatic women. Validation in a larger independent cohort is warranted.

Generating Detailed Spectral Libraries for Canine Proteomes Obtained from Serum and Urine

Domestic dogs (Canis lupus familiaris) are popular companion animals. Increase in medical expenses associated with them and demand for extending their lifespan in a healthy manner has created the need to develop new diagnostic technology. Companion dogs also serve as important animal models for non-clinical research as they can provide various biological phenotypes. Proteomics have been increasingly used on dogs and humans to identify novel biomarkers of various diseases. Despite the growing applications of proteomics in liquid biopsy in veterinary medicine, no publicly available spectral assay libraries have been created for the proteome of canine serum and urine. In this study, we generated spectral assay libraries for the two-representative liquid-biopsy samples using mid-pH fractionation that allows in-depth understanding of proteome coverage. The resultant canine serum and urine spectral assay libraries include 1,132 and 4,749 protein groups and 5,483 and 25,228 peptides, respectively. We built these complimentary accessible resources for proteomic biomarker discovery studies through ProteomeXchange with the identifier PXD034770.

SWATH proteomics analysis of placental tissue with intrahepatic cholestasis of pregnancy

INTRODUCTION

Intrahepatic cholestasis of pregnancy (ICP) usually occurs in the second and third trimesters. The disease's etiology and diagnostic criteria are currently unknown. Based on a sequence window to obtain all theoretical fragment ions (SWATH) proteomic approach, this study sought to identify potential proteins in placental tissue that may be involved in the pathogenesis of ICP and adverse fetal pregnancy outcomes.

METHODS

The postpartum placental tissue of pregnant women with ICP were chosen as the case group (ICP group) (subdivided into mild ICP group (MICP group) and severe ICP group (SICP group)), and healthy pregnant women were chosen as the control group (CTR). The hematoxylin-eosin (HE) staining was used to observe the histologic changes of placenta. The SWATH analysis combined with liquid chromatography-tandem mass spectrometry (LC-MS) was used to screen the differentially expressed proteins (DEPs) in ICP and CTR groups, and bioinformatics analysis was used to find out the biological process of these differential proteins.

RESULTS

Proteomic studies showed there were 126 DEPs from pregnant women with ICP and healthy pregnant women. Most of the identified proteins were functionally related to humoral immune response, cell response to lipopolysaccharide, antioxidant activity and heme metabolism. A subsequent examination of placentas from patients with mild and severe ICP revealed 48 proteins that were differentially expressed. Through death domain receptors and fibrinogen complexes, these DEPs primarily regulate extrinsic apoptotic signaling pathways, blood coagulation, and fibrin clot formation. The differential expressions of HBD, HPX, PDE3A, and PRG4 were down-regulated by Western blot analysis, which was consistent with proteomics.

DISCUSSION

This preliminary study helps us to understand the changes in the placental proteome of ICP patients, and provides new insights into the pathophysiology of ICP.

Glial-Restricted Precursors stimulate endogenous cytogenesis and effectively recover emotional deficits in a model of cytogenesis ablation

Adult cytogenesis, the continuous generation of newly-born neurons (neurogenesis) and glial cells (gliogenesis) throughout life, is highly impaired in several neuropsychiatric disorders, such as Major Depressive Disorder (MDD), impacting negatively on cognitive and emotional domains. Despite playing a critical role in brain homeostasis, the importance of gliogenesis has been overlooked, both in healthy and diseased states. To examine the role of newly formed glia, we transplanted Glial Restricted Precursors (GRPs) into the adult hippocampal dentate gyrus (DG), or injected their secreted factors (secretome), into a previously validated transgenic GFAP-tk rat line, in which cytogenesis is transiently compromised. We explored the long-term effects of both treatments on physiological and behavioral outcomes. Grafted GRPs reversed anxiety-like and depressive-like deficits, while the secretome promoted recovery of only anxiety-like behavior. Furthermore, GRPs elicited a recovery of neurogenic and gliogenic levels in the ventral DG, highlighting the unique involvement of these cells in the regulation of brain cytogenesis. Both GRPs and their secretome induced significant alterations in the DG proteome, directly influencing proteins and pathways related to cytogenesis, regulation of neural plasticity and neuronal development. With this work, we demonstrate a valuable and specific contribution of glial progenitors to normalizing gliogenic levels, rescueing neurogenesis and, importantly, promoting recovery of emotional deficits characteristic of disorders such as MDD.

Chemical isotope labeling for quantitative proteomics

Advancements in liquid chromatography and mass spectrometry over the last decades have led to a significant development in mass spectrometry-based proteome quantification approaches. An increasingly attractive strategy is multiplex isotope labeling, which significantly improves the accuracy, precision and throughput of quantitative proteomics in the data-dependent acquisition mode. Isotope labeling-based approaches can be classified into MS1-based and MS2-based quantification. In this review, we give an overview of approaches based on chemical isotope labeling and discuss their principles, benefits, and limitations with the goal to give insights into fundamental questions and provide a useful reference for choosing a method for quantitative proteomics. As a perspective, we discuss the current possibilities and limitations of multiplex, isotope labeling approaches for the data-independent acquisition mode, which is increasing in popularity.

A Novel Blood Proteomic Signature for Prostate Cancer

Prostate cancer is the most common malignant tumour in men. Improved testing for diagnosis, risk prediction, and response to treatment would improve care. Here, we identified a proteomic signature of prostate cancer in peripheral blood using data-independent acquisition mass spectrometry combined with machine learning. A highly predictive signature was derived, which was associated with relevant pathways, including the coagulation, complement, and clotting cascades, as well as plasma lipoprotein particle remodeling. We further validated the identified biomarkers against a second cohort, identifying a panel of five key markers (GP5, SERPINA5, ECM1, IGHG1, and THBS1) which retained most of the diagnostic power of the overall dataset, achieving an AUC of 0.91. Taken together, this study provides a proteomic signature complementary to PSA for the diagnosis of patients with localised prostate cancer, with the further potential for assessing risk of future development of prostate cancer. Data are available via ProteomeXchange with identifier PXD025484.

Label-free proteome quantification and evaluation

The label-free quantification (LFQ) has emerged as an exceptional technique in proteomics owing to its broad proteome coverage, great dynamic ranges and enhanced analytical reproducibility. Due to the extreme difficulty lying in an in-depth quantification, the LFQ chains incorporating a variety of transformation, pretreatment and imputation methods are required and constructed. However, it remains challenging to determine the well-performing chain, owing to its strong dependence on the studied data and the diverse possibility of integrated chains. In this study, an R package EVALFQ was therefore constructed to enable a performance evaluation on >3000 LFQ chains. This package is unique in (a) automatically evaluating the performance using multiple criteria, (b) exploring the quantification accuracy based on spiking proteins and (c) discovering the well-performing chains by comprehensive assessment. All in all, because of its superiority in assessing from multiple perspectives and scanning among over 3000 chains, this package is expected to attract broad interests from the fields of proteomic quantification. The package is available at https://github.com/idrblab/EVALFQ.

NeuroLINCS Proteomics: Defining human-derived iPSC proteomes and protein signatures of pluripotency

The National Institute of Health (NIH) Library of integrated network-based cellular signatures (LINCS) program is premised on the generation of a publicly available data resource of cell-based biochemical responses or "signatures" to genetic or environmental perturbations. NeuroLINCS uses human inducible pluripotent stem cells (hiPSCs), derived from patients and healthy controls, and differentiated into motor neuron cell cultures. This multi-laboratory effort strives to establish i) robust multi-omic workflows for hiPSC and differentiated neuronal cultures, ii) public annotated data sets and iii) relevant and targetable biological pathways of spinal muscular atrophy (SMA) and amyotrophic lateral sclerosis (ALS). Here, we focus on the proteomics and the quality of the developed workflow of hiPSC lines from 6 individuals, though epigenomics and transcriptomics data are also publicly available. Known and commonly used markers representing 73 proteins were reproducibly quantified with consistent expression levels across all hiPSC lines. Data quality assessments, data levels and metadata of all 6 genetically diverse human iPSCs analysed by DIA-MS are parsable and available as a high-quality resource to the public.

A Novel Approach of SWATH-Based Metabolomics Analysis Using the Human Metabolome Database Spectral Library

Metabolomics is a potential approach to paving new avenues for clinical diagnosis, molecular medicine, and therapeutic drug monitoring and development. The conventional metabolomics analysis pipeline depends on the data-independent acquisition (DIA) technique. Although powerful, it still suffers from stochastic, non-reproducible ion selection across samples. Despite the presence of different metabolomics workbenches, metabolite identification remains a tedious and time-consuming task. Consequently, sequential windowed acquisition of all theoretical MS (SWATH) acquisition has attracted much attention to overcome this limitation. This article aims to develop a novel SWATH platform for data analysis with a generation of an accurate mass spectral library for metabolite identification using SWATH acquisition. The workflow was validated using inclusion/exclusion compound lists. The false-positive identification was 3.4% from the non-endogenous drugs with 96.6% specificity. The workflow has proven to overcome background noise despite the complexity of the SWATH sample. From the Human Metabolome Database (HMDB), 1282 compounds were tested in various biological samples to demonstrate the feasibility of the workflow. The current study identified 377 compounds in positive and 303 in negative modes with 392 unique non-redundant metabolites. Finally, a free software tool, SASA, was developed to analyze SWATH-acquired samples using the proposed pipeline.

diaPASEF Proteomics and Feature Selection for the Description of Sputum Proteome Profiles in a Cohort of Different Subtypes of Lung Cancer Patients and Controls

The high mortality, the presence of an initial asymptomatic stage and the fact that diagnosis in early stages reduces mortality justify the implementation of screening programs in the populations at risk of lung cancer. It is imperative to develop less aggressive methods that can complement existing diagnosis technologies. In this study, we aimed to identify lung cancer protein biomarkers and pathways affected in sputum samples, using the recently developed diaPASEF mass spectrometry (MS) acquisition mode. The sputum proteome of lung cancer cases and controls was analyzed through nano-HPLC-MS using the diaPASEF mode. For functional analysis, the results from differential expression analysis were further analyzed in the STRING platform, and feature selection was performed using sparse partial least squares discriminant analysis (sPLS-DA). Our results showed an activation of inflammation, with an alteration of pathways and processes related to acute-phase, complement, and immune responses. The resulting sPLS-DA model separated between case and control groups with high levels of sensitivity and specificity. In conclusion, we showed how new-generation proteomics can be used to detect potential biomarkers in sputum samples, and ultimately to discriminate patients from controls and even to help to differentiate between different cancer subtypes.

Thermopriming-associated proteome and sugar content responses in Pinus radiata embryogenic tissue

Improving the capacity of plants to face adverse environmental conditions requires a deep understanding of the molecular mechanisms governing stress response and adaptation. Proteomics, combined with metabolic analyses, offers a wide resource of information to be used in plant breeding programs. Previous studies have shown that somatic embryogenesis in Pinus spp. is a suitable tool not only to investigate stress response processes but also to modulate the behaviour of somatic plants. Based on this, the objective of this study was to analyse the protein and soluble sugar profiles of Pinus radiata embryonal masses after the application of high temperatures to unravel the mechanisms involved in thermopriming and memory acquisition at early stages of the somatic embryogenesis process. Results confirmed that heat provokes deep readjustments in the life cycle of proteins, together with a significant reduction in the carbon-flux of central-metabolism pathways. Heat-priming also promotes the accumulation of proteins involved in oxidative stress defence, in the synthesis of specific amino acids such as isoleucine, influences cell division, the organization of the cytoskeleton and cell-walls, and modifies the levels of free soluble sugars like glucose or fructose. All this seems to be regulated by proteins linked with epigenetic, transcriptional and post-transcriptional mechanisms.

Integrated Multilayer Omics Reveals the Genomic, Proteomic, and Metabolic Influences of Histidyl Dipeptides on the Heart

Background

Histidyl dipeptides such as carnosine are present in a micromolar to millimolar range in mammalian hearts. These dipeptides facilitate glycolysis by proton buffering. They form conjugates with reactive aldehydes, such as acrolein, and attenuate myocardial ischemia–reperfusion injury. Although these dipeptides exhibit multifunctional properties, a composite understanding of their role in the myocardium is lacking.

Methods and Results

To identify histidyl dipeptide–mediated responses in the heart, we used an integrated triomics approach, which involved genome‐wide RNA sequencing, global proteomics, and unbiased metabolomics to identify the effects of cardiospecific transgenic overexpression of the carnosine synthesizing enzyme, carnosine synthase (Carns), in mice. Our result showed that higher myocardial levels of histidyl dipeptides were associated with extensive changes in the levels of several microRNAs, which target the expression of contractile proteins, β‐fatty acid oxidation, and citric acid cycle (TCA) enzymes. Global proteomic analysis showed enrichment in the expression of contractile proteins, enzymes of β‐fatty acid oxidation, and the TCA in the Carns transgenic heart. Under aerobic conditions, the Carns transgenic hearts had lower levels of short‐ and long‐chain fatty acids as well as the TCA intermediate—succinic acid; whereas, under ischemic conditions, the accumulation of fatty acids and TCA intermediates was significantly attenuated. Integration of multiple data sets suggested that β‐fatty acid oxidation and TCA pathways exhibit correlative changes in the Carns transgenic hearts at all 3 levels.

Conclusions

Taken together, these findings reveal a central role of histidyl dipeptides in coordinated regulation of myocardial structure, function, and energetics.

Comprehensive spectral libraries for various rabbit eye tissue proteomes

Rabbits have been widely used for studying ocular physiology and pathology due to their relatively large eye size and similar structures with human eyes. Various rabbit ocular disease models, such as dry eye, age-related macular degeneration, and glaucoma, have been established. Despite the growing application of proteomics in vision research using rabbit ocular models, there is no spectral assay library for rabbit eye proteome publicly available. Here, we generated spectral assay libraries for rabbit eye compartments, including conjunctiva, cornea, iris, retina, sclera, vitreous humor, and tears using fractionated samples and ion mobility separation enabling deep proteome coverage. The rabbit eye spectral assay library includes 9,830 protein groups and 113,593 peptides. We present the data as a freely available community resource for proteomic studies in the vision field. Instrument data and spectral libraries are available via ProteomeXchange with identifier PXD031194.

Measurement(s)	database type spectral library
Technology Type(s)	ion mobility spectrometry-mass spectrometry
Sample Characteristic - Organism	Oryctolagus cuniculus
Sample Characteristic - Environment	eye
Sample Characteristic - Location	United States of America

Alteration of EIF2 Signaling, Glycolysis, and Dopamine Secretion in Form-Deprived Myopia in Response to 1% Atropine Treatment: Evidence From Interactive iTRAQ-MS and SWATH-MS Proteomics Using a Guinea Pig Model

Purpose: Atropine, a non-selective muscarinic antagonist, effectively slows down myopia progression in human adolescents and several animal models. However, the underlying molecular mechanism is unclear. The current study investigated retinal protein changes of form-deprived myopic (FDM) guinea pigs in response to topical administration of 1% atropine gel (10 g/L).

Methods: At the first stage, the differentially expressed proteins were screened using fractionated isobaric tags for a relative and absolute quantification (iTRAQ) approach, coupled with nano-liquid chromatography-tandem mass spectrometry (nano-LC-MS/MS) (n = 24, 48 eyes) using a sample pooling technique. At the second stage, retinal tissues from another cohort with the same treatment (n = 12, 24 eyes) with significant ocular changes were subjected to label-free sequential window acquisition of all theoretical mass spectra (SWATH-MS) proteomics for orthogonal protein target confirmation. The localization of Alpha-synuclein was verified using immunohistochemistry and confocal imaging.

Results: A total of 1,695 proteins (8,875 peptides) were identified with 479 regulated proteins (FC ≥ 1.5 or ≤0.67) found from FDM eyes and atropine-treated eyes receiving 4-weeks drug treatment using iTRAQ-MS proteomics. Combining the iTRAQ-MS and SWATH-MS datasets, a total of 29 confident proteins at 1% FDR were consistently quantified and matched, comprising 12 up-regulated and 17 down-regulated proteins which differed between FDM eyes and atropine treated eyes (iTRAQ: FC ≥ 1.5 or ≤0.67, SWATH: FC ≥ 1.4 or ≤0.71, p-value of ≤0.05). Bioinformatics analysis using IPA and STRING databases of these commonly regulated proteins revealed the involvement of the three commonly significant pathways: EIF2 signaling; glycolysis; and dopamine secretion. Additionally, the most significantly regulated proteins were closely connected to Alpha-synuclein (SNCA). Using immunostaining (n = 3), SNCA was further confirmed in the inner margin of the inner nuclear layer (INL) and spread throughout the inner plexiform layer (IPL) of the retina of guinea pigs.

Conclusion: The molecular evidence using next-generation proteomics (NGP) revealed that retinal EIF2 signaling, glycolysis, and dopamine secretion through SNCA are implicated in atropine treatment of myopia in the FDM-induced guinea pig model.

Human obese white adipose tissue sheds depot-specific extracellular vesicles and reveals candidate biomarkers for monitoring obesity and its comorbidities

Extracellular vesicles (EVs) have been recently postulated as key players in metabolic disorders emerging as an alternative way of paracrine/endocrine communication. However, the nature of EVs shed by adipose tissue (AT) and their role in obesity is still very limited. Here, we isolated human morbid obese visceral (VAT) and subcutaneous (SAT) whole AT shed EVs from donors submitted to bariatric surgery to characterize their protein cargo by qualitative and quantitative/SWATH mass spectrometry analysis. We identified 574 different proteins shed by morbid obese VAT and 401 proteins in those from SAT, establishing the first obese AT EV proteome reference map. Only 50% of identified proteins in VAT vesicles were common to those in SAT; additionally, EVs shed by obese VAT showed more AT and obesity-related adipokines than SAT. Functional classification shows that obese VAT vesicles exhibit an enrichment of proteins implicated in AT inflammation and insulin resistance such as TGFBI, CAVN1, CD14, mimecan, thrombospondin-1, FABP-4 or AHNAK. Selected candidate biomarkers from the quantitative-SWATH analysis were validated in EVs from independent morbid obese and from moderate obese to lean individuals showing that morbid obese VAT vesicles are characterized by a diminution of syntenin 1 and the elevation of TGFBI and mimecan. Interestingly, TGFBI and mimecan containing vesicles could be detected and quantified at circulating level in plasma. Thus, a significant elevation of -TGFBI-EVs was detected on those obese patients with a history of T2D compared to nondiabetic, and an augmentation of mimecan-EVs in obese plasma compared to those in healthy lean individuals. Thus, we conclude that obese AT release functional EVs carrying AT and obesity candidate biomarkers which vary regarding the AT of origin. Our findings suggest that circulating EV-TGFBI may facilitate monitoring T2D status in obese patients, and EV-mimecan may be useful to track adiposity, and more precisely, visceral obesity.

High-throughput proteomics of nanogram-scale samples with Zeno SWATH MS

The possibility to record proteomes in high throughput and at high quality has opened new avenues for biomedical research, drug discovery, systems biology, and clinical translation. However, high-throughput proteomic experiments often require high sample amounts and can be less sensitive compared to conventional proteomic experiments. Here, we introduce and benchmark Zeno SWATH MS, a data-independent acquisition technique that employs a linear ion trap pulsing (Zeno trap pulsing) to increase the sensitivity in high-throughput proteomic experiments. We demonstrate that when combined with fast micro- or analytical flow-rate chromatography, Zeno SWATH MS increases protein identification with low sample amounts. For instance, using 20 min micro-flow-rate chromatography, Zeno SWATH MS identified more than 5000 proteins consistently, and with a coefficient of variation of 6%, from a 62.5 ng load of human cell line tryptic digest. Using 5 min analytical flow-rate chromatography (800 µl/min), Zeno SWATH MS identified 4907 proteins from a triplicate injection of 2 µg of a human cell lysate, or more than 3000 proteins from a 250 ng tryptic digest. Zeno SWATH MS hence facilitates sensitive high-throughput proteomic experiments with low sample amounts, mitigating the current bottlenecks of high-throughput proteomics.

A large-scale assay library for targeted protein quantification in renal cell carcinoma tissues

Renal cell carcinoma (RCC) represents 2.2% of all cancer incidences; however, prognostic or predictive RCC biomarkers at protein level are largely missing. To support proteomics research of localized and metastatic RCC, we introduce a new library of targeted mass spectrometry assays for accurate protein quantification in malignant and normal kidney tissue. Aliquots of 86 initially localized RCC, 75 metastatic RCC and 17 adjacent non-cancerous fresh frozen tissue lysates were trypsin digested, pooled, and fractionated using hydrophilic chromatography. The fractions were analyzed using LC-MS/MS on QExactive HF-X mass spectrometer in data-dependent acquisition (DDA) mode. A resulting spectral library contains 77,817 peptides representing 7960 protein groups (FDR = 1%). Further, we confirm applicability of this library on four RCC datasets measured in data-independent acquisition (DIA) mode, demonstrating a specific quantification of a substantially increased part of RCC proteome, depending on LC-MS/MS instrumentation. Impact of sample specificity of the library on the results of targeted DIA data extraction was demonstrated by parallel analyses of two datasets by two pan human libraries. The new RCC specific library has potential to contribute to better understanding the RCC development at molecular level, leading to new diagnostic and therapeutic targets.

Integrating SWATH-MS proteomics and transcriptome analysis to preliminarily identify three DEGs as biomarkers for proliferative diabetic retinopathy

PURPOSE

We intended to preliminarily find differentially expressed proteins that play crucial roles in proliferative diabetic retinopathy (PDR), and lay the foundation for subsequent further research on the mechanism.

EXPERIMENTAL DESIGN

Here, we developed a new strategy integrated the sequential windowed acquisition of all theoretical fragment ion (SWATH) mass spectra (MS) with multi-dataset joint analysis to screen for the PDR plasma biomarker. The annotation of the given gene list was performed with ClueGO function analysis. Additionally, the protein-protein interaction relationship was also revealed by the STRING database.

RESULTS

In SWATH-MS assays, we identified 23 upregulated and 13 downregulated proteins in PDR plasma. In the mRNA database analysis, 375 genes were identified as differentially expressed genes in GSE102485. Only three genes (FCGR3A, DPEP2, and ADGRF5) were characterized as upregulated in both the dataset and the SWATH-MS list. The area under the ROC curve (AUC) of FCGR3A, DPEP2, and ADGRF5 in distinguishing PDR from others was 0.739, 0.770, and 0.739.

CONCLUSIONS AND CLINICAL RELEVANCE

We provide a novel strategy for biomarker screening and identified plasma FCGR3A, DPEP2, and ADGRF5 as potential biomarkers for patients with PDR. Identifying the key molecules of the disease is essential for the development of new therapeutic molecules and new uses of existing drugs.

Proteomes Are of Proteoforms: Embracing the Complexity

Proteomes are complex-much more so than genomes or transcriptomes. Thus, simplifying their analysis does not simplify the issue. Proteomes are of proteoforms, not canonical proteins. While having a catalogue of amino acid sequences provides invaluable information, this is the Proteome-lite. To dissect biological mechanisms and identify critical biomarkers/drug targets, we must assess the myriad of proteoforms that arise at any point before, after, and between translation and transcription (e.g., isoforms, splice variants, and post-translational modifications [PTM]), as well as newly defined species. There are numerous analytical methods currently used to address proteome depth and here we critically evaluate these in terms of the current 'state-of-the-field'. We thus discuss both pros and cons of available approaches and where improvements or refinements are needed to quantitatively characterize proteomes. To enable a next-generation approach, we suggest that advances lie in transdisciplinarity via integration of current proteomic methods to yield a unified discipline that capitalizes on the strongest qualities of each. Such a necessary (if not revolutionary) shift cannot be accomplished by a continued primary focus on proteo-genomics/-transcriptomics. We must embrace the complexity. Yes, these are the hard questions, and this will not be easy…but where is the fun in easy?

ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies

Label-free quantification (LFQ) with a specific and sequentially integrated workflow of acquisition technique, quantification tool and processing method has emerged as the popular technique employed in metaproteomic research to provide a comprehensive landscape of the adaptive response of microbes to external stimuli and their interactions with other organisms or host cells. The performance of a specific LFQ workflow is highly dependent on the studied data. Hence, it is essential to discover the most appropriate one for a specific data set. However, it is challenging to perform such discovery due to the large number of possible workflows and the multifaceted nature of the evaluation criteria. Herein, a web server ANPELA (https://idrblab.org/anpela/) was developed and validated as the first tool enabling performance assessment of whole LFQ workflow (collective assessment by five well-established criteria with distinct underlying theories), and it enabled the identification of the optimal LFQ workflow(s) by a comprehensive performance ranking. ANPELA not only automatically detects the diverse formats of data generated by all quantification tools but also provides the most complete set of processing methods among the available web servers and stand-alone tools. Systematic validation using metaproteomic benchmarks revealed ANPELA's capabilities in 1 discovering well-performing workflow(s), (2) enabling assessment from multiple perspectives and (3) validating LFQ accuracy using spiked proteins. ANPELA has a unique ability to evaluate the performance of whole LFQ workflow and enables the discovery of the optimal LFQs by the comprehensive performance ranking of all 560 workflows. Therefore, it has great potential for applications in metaproteomic and other studies requiring LFQ techniques, as many features are shared among proteomic studies.

Robust optimization of SWATH-MS workflow for human blood serum proteome analysis using a quality by design approach

Background

It is not enough to optimize proteomics assays. It is critical those assays are robust to operating conditions. Without robust assays, proteomic biomarkers are unlikely to translate readily into the clinic. This study outlines a structured approach to the identification of a robust operating window for proteomics assays and applies that method to Sequential Window Acquisition of all Theoretical Spectra Mass Spectroscopy (SWATH-MS).

Methods

We used a sequential quality by design approach exploiting a fractional screening design to first identify critical SWATH-MS parameters, then using response surface methods to identify a robust operating window with good reproducibility, before validating those settings in a separate validation study.

Results

The screening experiment identified two critical SWATH-MS parameters. We modelled the number of proteins and reproducibility as a function of those parameters identifying an operating window permitting robust maximization of the number of proteins quantified in human serum. In a separate validation study, these settings were shown to give good proteome-wide coverage and high quantification reproducibility.

Conclusions

Using design of experiments permits identification of a robust operating window for SWATH-MS. The method gives a good understanding of proteomics assays and greater data-driven confidence in SWATH-MS performance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-021-09323-z.

Differential Retinal Protein Expression in Primary and Secondary Retinal Ganglion Cell Degeneration Identified by Integrated SWATH and Target-Based Proteomics

To investigate the retinal proteins associated with primary and secondary retinal ganglion cell (RGC) degeneration and explore their molecular pathways, SWATH label-free and target-based mass spectrometry was employed to identify the proteomes in various retinal locations in response to localized optic nerve injury. Unilateral partial optic nerve transection (pONT) was performed on adult Wistar rats and their retinas were harvested 2 weeks later. To confirm the separation of primary and secondary RGC degeneration, immunohistochemistry of RNA binding protein with multiple splicing (RBPMS) and glial fibrillary acidic protein (GFAP) was performed on retinal whole-mounts. Retinal proteomes in the temporal and nasal quadrants were evaluated with high resolution hybrid quadrupole time-of-flight mass spectrometry (QTOF-MS), and SWATH-based acquisition, and their expression was compared to the corresponding retinal quadrant in contralateral control eyes and further validated by multiple reaction monitoring mass spectrometry (MRM-MS). A total of 3641 proteins (FDR < 1%) were identified using QTOF-MS. The raw data are available via ProteomeXchange with the identifier PXD026783. Bioinformatics data analysis showed that there were 37 upregulated and 25 downregulated proteins in the temporal quadrant, whereas 20 and five proteins were upregulated and downregulated, respectively, in the nasal quadrant, respectively (n = 4, p < 0.05; fold change ≥ 1.4-fold or ≤0.7). Six proteins were regulated in both the temporal and the nasal quadrants, including CLU, GFAP, GNG5, IRF2BPL, L1CAM, and CPLX1. Linear regression analysis indicated a strong association between the data obtained by means of SWATH-MS and MRM-MS (temporal, R² = 0.97; nasal, R² = 0.96). Gene ontology analysis revealed statistically significant changes in the biological processes and cellular components of primary RGC degeneration. The majority of the significant changes in structural, signaling, and cell death proteins were associated with the loss of RGCs in the area of primary RGC degeneration. The combined use of SWATH-MS and MRM-MS methods detects and quantifies regional changes of retinal protein expressions after localized injury. Future investigation with this integrated approach will significantly increase the understanding of diverse processes of progressive RGC degeneration from a proteomic prospective.

Sweat metabolome and proteome: Recent trends in analytical advances and potential biological functions

Metabolome and proteome profiling of biofluids, e.g., urine, plasma, has generated vast and ever-increasing amounts of knowledge over the last few decades. Paradoxically, omics analyses of sweat, one of the most readily available human biofluids, have lagged behind. This review capitalizes on the current knowledge and state of the art analytical advances of sweat metabolomics and proteomics. Moreover, current applications of sweat omics such as the discovery of disease biomarkers and monitoring athletic performance are also presented in this review. Another area of emerging knowledge that has been highlighted herein lies in the role of skin host-microbiome interactions in shaping the sweat metabolite-protein profiles. Discussion of future research directions describes the need to have a better grasp of sweat chemicals and to better understand how they function as aided by advances in omics tools. Overall, the role of sweat as an information-rich biofluid that could complement the exploration of the skin metabolome/proteome is emphasized.

A Proteomics-Based Analysis of Blood Biomarkers for the Diagnosis of COPD Acute Exacerbation

Purpose

The identification of blood biomarkers to diagnose acute exacerbation of chronic obstructive pulmonary disease (AECOPD) will have clinical utility. Here, we used a proteomics-based approach to identify biomarkers capable of identifying AECOPD.

Patients and Methods

This prospective, single-center pilot study enrolled 12 patients who came to Asan Medical Center (South Korea) via the outpatient clinic or emergency department with symptoms of AECOPD and were follow-up in the outpatient clinic during convalescence between 2015 and 2017. Paired blood samples collected from each patient during the treatment naïve AECOPD and convalescence stages were analyzed. A sequential window acquisition of all theoretical fragmentation spectra-mass spectrometry (SWATH-MS)-based proteome analysis was performed and a subset of the data were verified by ELISA.

Results

The SWATH-MS analysis identified 226 plasma proteins across all samples examined. The median coefficient of variation for triplicate technical replicates of each sample was 1.13 ± 1.38%, indicating high precision of the technique. Fold-change and paired t-test analyses revealed that 14 proteins were present at higher levels in the AECOPD samples than in the convalescence samples. A gene ontology analysis revealed that these proteins are involved in the acute-phase response. A total of 15 proteins were present at higher levels during the recovery (convalescence) stage than during the acute exacerbation phase, and gene ontology analysis revealed that these proteins are related to lipid metabolism and transport. Verification of the SWATH-MS data was performed using ELISAs for three proteins that were up-regulated in AECOPD, namely, LBP, ORM2, and SERPINA3. Among them, SERPINA3 (p = 0.005) was up-regulated significantly in AECOPD compared with the convalescence state.

Conclusion

Potential plasma biomarkers of AECOPD were discovered using the SWATH-MS proteomics method, and functional molecular associations were investigated. SERPINA3 could be a promising diagnostic biomarker for the early identification and tracking of AECOPD.

Chronic pain susceptibility is associated with anhedonic behavior and alterations in the accumbal ubiquitin-proteasome system

ABSTRACT

It remains unknown why on similar acute/subacute painful conditions, pain persists in some individuals while in others it resolves. Genetic factors, mood, and functional alterations, particularly involving the mesolimbic network, seem to be key. To explore potential susceptibility or resistance factors, we screened a large population of rats with a peripheral neuropathy and we isolated a small subset (<15%) that presented high thresholds (HTs) to mechanical allodynia (reduced pain manifestation). The phenotype was sustained over 12 weeks and was associated with higher hedonic behavior when compared with low-threshold (LT) subjects. The nucleus accumbens of HT and LT animals were isolated for proteomic analysis by Sequential Window Acquisition of All Theoretical Mass Spectra. Two hundred seventy-nine proteins displayed different expression between LT and HT animals or subjects. Among several protein families, the proteasome pathway repeatedly emerged in gene ontology enrichment and KEGG analyses. Several alpha and beta 20S proteasome subunits were increased in LT animals when compared with HT animals (eg, PSMα1, PSMα2, and PSMβ5). On the contrary, UBA6, an upstream ubiquitin-activating enzyme, was decreased in LT animals. Altogether these observations are consistent with an overactivation of the accumbal proteasome pathway in animals that manifest pain and depressive-like behaviors after a neuropathic injury. All the proteomic data are available through ProteomeXchange with identifier PXD022478.

Fast in-line bottom-up analysis of monoclonal antibodies: Toward an electrophoretic fingerprinting approach

For their characterization and quality control, monoclonal antibodies are frequently analyzed at the bottom-up level to generate specific fingerprints that can be used to tackle post-translational modifications or ensure production consistency between lots. To circumvent time-consuming and labor-intensive off-line sample preparation steps, the implementation of integrated methodologies from sample preparation to separation and detection is highly valuable. In this perspective, capillary zone electrophoresis appears as a choice technique since the capillary can subsequently be used as a vessel for sample preparation and electrophoretic discrimination/detection of the reaction products. Here, a fast in-line methodology for the routine quality control of mAbs at the bottom-up level is reported. Simultaneous denaturation and reduction (pretreatment step) were conducted with RapiGest® surfactant and dithiothreitol before in-line tryptic digestion. Reactant mixing was realized by transverse diffusion of laminar flow profile under controlled temperature. In-line digestion was carried out with a resistant trypsin to autolysis. The main parameters affecting the digestion efficiency (trypsin concentration and incubation conditions) were optimized to generate mAb electrophoretic profiles free from trypsin interferences. An acidic MS-compatible BGE was used to obtain high resolution separation of released peptides and in-line surfactant cleavage. The whole methodology was performed in less than two hours with good repeatability of migration times (RSD = 0.91%, n = 5) and corrected peak areas (RSD = 9.6%, n = 5). CE-fingerprints were successfully established for different mAbs and an antibody-drug conjugate.