Quantitative Proteomics Based on Optimized Data-Independent Acquisition in Plasma Analysis

Proteomic Analyses of Plasma From Patients With Fracture-Related Infection Reveals Systemic Activation of the Complement and Coagulation Cascades

OBJECTIVES

The objective of this study was to compare plasma proteomes of patients with confirmed fracture-related infections (FRIs) matched to noninfected controls using liquid chromatography-mass spectrometry.

METHODS

DESIGN

This was a prospective case-control study.

SETTING

The study was conducted at a single, academic, Level 1 trauma center.

PATIENT SELECTION CRITERIA

Patients meeting confirmatory FRI criteria were matched to controls without infection based on fracture region, age, and time after surgery from June 2019 to January 2022. Tandem mass tag liquid chromatography-mass spectrometry analysis of patient plasma samples was performed.

OUTCOME MEASURES AND COMPARISONS

Protein abundance ratios in plasma for patients with FRI compared with those for matched controls without infection were calculated.

RESULTS

Twenty-seven patients meeting confirmatory FRI criteria were matched to 27 controls. Abundance ratios for more than 1000 proteins were measured in the 54 plasma samples. Seventy-three proteins were found to be increased or decreased in patients with FRI compared with those in matched controls (unadjusted t test P < 0.05). Thirty-two of these proteins were found in all 54 patient samples and underwent subsequent principal component analysis to reduce the dimensionality of the large proteomics dataset. A 3-component principal component analysis accounted for 45.7% of the variation in the dataset and had 88.9% specificity for the diagnosis of FRI. STRING protein-protein interaction network analysis of these 3 PCs revealed activation of the complement and coagulation cascades through the Reactome pathway database (false discovery rates <0.05).

CONCLUSIONS

Proteomic analyses of plasma from patients with FRI demonstrate systemic activation of the complement and coagulation cascades. Further investigation along these lines may help to better understand the systemic response to FRI and improve diagnostic strategies using proteomics.

LEVEL OF EVIDENCE

Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Mass spectrometry in the discovery of peptides involved in intercellular communication: From targeted to untargeted peptidomics approaches

Endogenous peptide hormones represent an essential class of biomolecules, which regulate cell-cell communications in diverse physiological processes of organisms. Mass spectrometry (MS) has been developed to be a powerful technology for identifying and quantifying peptides in a highly efficient manner. However, it is difficult to directly identify these peptide hormones due to their diverse characteristics, dynamic regulations, low abundance, and existence in a complicated biological matrix. Here, we summarize and discuss the roles of targeted and untargeted MS in discovering peptide hormones using bioassay-guided purification, bioinformatics screening, or the peptidomics-based approach. Although the peptidomics approach is expected to discover novel peptide hormones unbiasedly, only a limited number of successful cases have been reported. The critical challenges and corresponding measures for peptidomics from the steps of sample preparation, peptide extraction, and separation to the MS data acquisition and analysis are also discussed. We also identify emerging technologies and methods that can be integrated into the discovery platform toward the comprehensive study of endogenous peptide hormones.

Achieving quantitative reproducibility in label-free multisite DIA experiments through multirun alignment

DIA is a mainstream method for quantitative proteomics, but consistent quantification across multiple LC-MS/MS instruments remains a bottleneck in parallelizing data acquisition. One reason for this inconsistency and missing quantification is the retention time shift which current software does not adequately address for runs from multiple sites. We present multirun chromatogram alignment strategies to map peaks across columns, including the traditional reference-based Star method, and two novel approaches: MST and Progressive alignment. These reference-free strategies produce a quantitatively accurate data-matrix, even from heterogeneous multi-column studies. Progressive alignment also generates merged chromatograms from all runs which has not been previously achieved for LC-MS/MS data. First, we demonstrate the effectiveness of multirun alignment strategies on a gold-standard annotated dataset, resulting in a threefold reduction in quantitation error-rate compared to non-aligned DIA results. Subsequently, on a multi-species dataset that DIAlignR effectively controls the quantitative error rate, improves precision in protein measurements, and exhibits conservative peak alignment. We next show that the MST alignment reduces cross-site CV by 50% for highly abundant proteins when applied to a dataset from 11 different LC-MS/MS setups. Finally, the reanalysis of 949 plasma runs with multirun alignment revealed a more than 50% increase in insulin resistance (IR) and respiratory viral infection (RVI) proteins, identifying 11 and 13 proteins respectively, compared to prior analysis without it. The three strategies are implemented in our DIAlignR workflow (>2.3) and can be combined with linear, non-linear, or hybrid pairwise alignment.

Discovery of protein biomarkers for venous thromboembolism in non-small cell lung cancer patients through data-independent acquisition mass spectrometry

Objective

Non-small cell lung cancer (NSCLC) patients present a high incidence of venous thromboembolism (VTE) with poor prognosis. It is crucial to identify and diagnose VTE early. The study aimed to identify potential protein biomarkers and mechanism of VTE in NSCLC patients via proteomics research.

Methods

Proteomic analysis of the human plasma was performed through data-independent acquisition mass spectrometry for 20 NSCLC patients with VTE, and 15 NSCLC patients without VTE. Significantly differentially expressed proteins were analyzed by multiple bioinformatics method for further biomarker analysis.

Results

A total of 280 differentially expressed proteins were identified in VTE and non-VTE patients, where 42 were upregulated and 238 were downregulated. These proteins were involved in acute-phase response, cytokine production, neutrophil migration and other biological processes related to VTE and inflammation. Five proteins including SAA1, S100A8, LBP, HP and LDHB had significant change between VTE and non-VTE patients, with the area under the curve (AUC) were 0.8067, 0.8308, 0.7767, 0.8021, 0.8533, respectively.

Conclusions

SAA1, S100A8, LBP, HP and LDHB may serve as potential plasma biomarkers for diagnosis VTE in NSCLC patients.

Optimizing data-independent acquisition (DIA) spectral library workflows for plasma proteomics studies

Traditional data-independent acquisition (DIA) workflows employ off-column fractionation with data-dependent acquisition (DDA) to generate spectral libraries for data extraction. Recent advances have led to the establishment of library-independent approaches for DIA analyses. The selection of a DIA workflow may affect the outcome of plasma proteomics studies. Here, we establish a gas-phase fractionation (GPF) workflow to create DIA libraries for DIA with parallel accumulation and serial fragmentation (diaPASEF). This workflow along with three other workflows, fractionated DDA libraries, fractionated DIA libraries, and predicted spectra libraries, were evaluated on 20 plasma samples from nonsmall cell lung cancer patients with low or high levels of IL-6. We sought to optimize protein identification and total experiment time. The novel GPF workflow for diaPASEF outperformed the traditional ddaPASEF workflow in the number of identified and quantified proteins. A library-independent workflow based on predicted spectra identified and quantified the most proteins in our experiment at the cost of computational power. Overall, the choice of DIA library workflow seemed to have a limited effect on the overall outcome of a plasma proteomics experiment, but it can affect the number of proteins identified and the total experiment time.

Data-independent acquisition-based proteomics analysis correlating type 2 diabetes mellitus with osteoarthritis in total knee arthroplasty patients

BACKGROUND

To explore the effects of type 2 diabetes mellitus (T2DM) on osteoarthritis (OA), 12 bone tissue samples were obtained surgically from the human total knee arthroplasty patients and analyzed by quantitative proteomics.

METHODS

Based on patient clinical histories, patient samples were assigned to diabetes mellitus osteoarthritis (DMOA) and OA groups. A data-independent acquisition method for data collection was used with proteomic data analysis to assess intergroup proteomic differences. Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genome pathway enrichment analysis were used to further find the correlation between T2DM and OA.

RESULTS

GO functional analysis found 153 differentially expressed proteins between DMOA and OA groups, of which 92 differentially expressed proteins were significantly up-regulated and 61 were significantly down-regulated. Kyoto Encyclopedia of Genes and Genome pathway analysis found 180 pathways, including 9 pathways significantly enriched. Further data analysis revealed that 6 signaling pathways were closely associated with T2DM and OA.

CONCLUSION

OA and DMOA onset and progression were closely related to synthesis and metabolism of extracellular matrix components (e.g., fibronectin, decorin, etc.). The effects of T2DM on OA occur though 2 major ways of oxidative stress and low-grade chronic inflammation, involving in 2 inhibited signaling pathways and 4 activated signaling pathways.

Molecular mechanisms underlying the calcium-mediated uptake of hematite nanoparticles by the ciliate Tetrahymena thermophila

In aquatic ecosystems, the calcium (Ca) concentration varies greatly. It is well known that Ca affects the aggregation of nanoparticles (NPs) and thus their bioaccumulation. Nevertheless, Ca also plays critical roles in various biological processes, whose effects on NP accumulation in aquatic organisms remain unclear. In this study, the effects of Ca on the uptake of polyacrylate-coated hematite NPs (HemNPs) by the aquatic ciliate Tetrahymena thermophila were investigated. At all of the tested Ca concentrations, HemNPs were well dispersed in the experimental medium, excluding the possibility of Ca to influence HemNP bioaccumulation by aggregating the NPs. Instead, Ca was shown to induce the clathrin-mediated endocytosis and phagocytosis of HemNPs. Manipulation of the Ca speciation in the experimental medium as well as the influx and intracellular availability of Ca in T. thermophila indicated that HemNP uptake was regulated by the intracellular Ca level. The results of the proteomics analyses further showed that the binding of intracellular Ca to calmodulin altered the activity of proteins involved in clathrin-mediated endocytosis (calcineurin and dynamin) and phagocytosis (actin). Overall, the biologically inductive effects of Ca on NP accumulation in aquatic organisms should be considered when evaluating the environmental risks of NPs.

Etodolac improves collagen induced rheumatoid arthritis in rats by inhibiting synovial inflammation, fibrosis and hyperplasia

Synovial hyperplasia is the main cause of chronic rheumatoid arthritis (RA), but the mechanism of synovial hyperplasia is still unclear. Etodolac (ETD) is a selective COX-2 inhibitor for relieving pain and stiffness in RA, but the disease modifying effect is still lack of evidence. Proteomics method was used to study the differential proteome of synovial tissue in collagen induced arthritis (CIA) in rats. With the help of STRING analysis, the upregulated proteins enriched in the cluster of complement and coagulation cascades and platelet degranulation were highlighted, these proteins with fibrogenic factors Lum, CIV, CXI and Tgfbi participated in the synovial inflammation, fibrosis and hyperplasia in CIA. Based on KOG function class analysis, the proteins involved in the events of the central dogma was explored. They might be hyperplasia related proteins for most of them are related to the proliferation of cancer. ETD significantly attenuated synovial inflammation, fibrosis and hyperplasia in CIA rats by downregulating these proteins. Several proteins have not been observed in RA so far, such as Tmsb4x, Pura, Nfic, Ruvbl1, Snrpd3, U2af2, Srrm2, Srsf7, Elavl1, Hnrnph1, Wars, Yars, Bzw2, Mcts1, Eif4b, Ctsh, Lamp1, Dpp7, Ptges3, Cdc37 and Septin9, they might be potentials targets for RA. Blood biochemistry tests showed the safety of 7 months use of ETD on rats. In conclusion, present study displayed a comprehensive mechanism of synovial hyperplasia in CIA rats, on this basis, the clinical value of ETD in the treatment of RA was well confirmed.

Proteomics in the pharmaceutical and biotechnology industry: a look to the next decade

INTRODUCTION

Pioneering technologies such as proteomics have helped fuel the biotechnology and pharmaceutical industry with the discovery of novel targets and an intricate understanding of the activity of therapeutics and their various activities in vitro and in vivo. The field of proteomics is undergoing an inflection point, where new sensitive technologies are allowing intricate biological pathways to be better understood, and novel biochemical tools are pivoting us into a new era of chemical proteomics and biomarker discovery. In this review, we describe these areas of innovation, and discuss where the fields are headed in terms of fueling biotechnological and pharmacological research and discuss current gaps in the proteomic technology landscape.

AREAS COVERED

Single cell sequencing and single molecule sequencing. Chemoproteomics. Biological matrices and clinical samples including biomarkers. Computational tools including instrument control software, data analysis.

EXPERT OPINION

Proteomics will likely remain a key technology in the coming decade, but will have to evolve with respect to type and granularity of data, cost and throughput of data generation as well as integration with other technologies to fulfill its promise in drug discovery.

Recent Developments and Applications of Quantitative Proteomics Strategies for High-Throughput Biomolecular Analyses in Cancer Research

Innovations in medical technology and dedicated focus from the scientific community have inspired numerous treatment strategies for benign and invasive cancers. While these improvements often lend themselves to more positive prognoses and greater patient longevity, means for early detection and severity stratification have failed to keep pace. Detection and validation of cancer-specific biomarkers hinges on the ability to identify subtype-specific phenotypic and proteomic alterations and the systematic screening of diverse patient groups. For this reason, clinical and scientific research settings rely on high throughput and high sensitivity mass spectrometry methods to discover and quantify unique molecular perturbations in cancer patients. Discussed within is an overview of quantitative proteomics strategies and a summary of recent applications that enable revealing potential biomarkers and treatment targets in prostate, ovarian, breast, and pancreatic cancer in a high throughput manner.

Toward Comprehensive Plasma Proteomics by Orthogonal Protease Digestion

Rapid and consistent protein identification across large clinical cohorts is an important goal for clinical proteomics. With the development of data-independent technologies (DIA/SWATH-MS), it is now possible to analyze hundreds of samples with great reproducibility and quantitative accuracy. However, this technology benefits from empirically derived spectral libraries that define the detectable set of peptides and proteins. Here, we apply a simple and accessible tip-based workflow for the generation of spectral libraries to provide a comprehensive overview on the plasma proteome in individuals with and without active tuberculosis (TB). To boost protein coverage, we utilized nonconventional proteases such as GluC and AspN together with the gold standard trypsin, identifying more than 30,000 peptides mapping to 3309 proteins. Application of this library to quantify plasma proteome differences in TB infection recovered more than 400 proteins in 50 min of MS acquisition, including diagnostic Mycobacterium tuberculosis (Mtb) proteins that have previously been detectable primarily by antibody-based assays and intracellular proteins not previously described to be in plasma.

Comparative proteomics analysis for identifying the lipid metabolism related pathways in patients with Klippel-Feil syndrome

Background

Klippel-Feil syndrome (KFS) represents the rare and complex deformity characterized by congenital defects in the formation or segmentation of the cervical vertebrae. There is a wide gap in understanding the detailed mechanisms of KFS because of its rarity, heterogeneity, small pedigrees, and the broad spectrum of anomalies.

Methods

We recruited eight patients of Chinese Han ethnicity with KFS, five patients with congenital scoliosis (CS) who presented with congenital fusion of the thoracic or lumbar spine and without known syndrome or cervical deformity, and seven healthy controls. Proteomic analysis by data-independent acquisition (DIA) was performed to identify the differential proteome among the three matched groups and the data were analyzed by bioinformatics tools including Gene Ontology (GO) categories and Ingenuity Pathway Analysis (IPA) database, to explore differentially abundant proteins (DAPs) and canonical pathways involved in the pathogenesis of KFS.

Results

A total of 49 DAPs were detected between KFS patients and the controls, and moreover, 192 DAPs were identified between patients with KFS and patients with CS. Fifteen DAPs that were common in both comparisons were considered as candidate biomarkers for KFS, including membrane primary amine oxidase, noelin, galectin-3-binding protein, cadherin-5, glyceraldehyde-3-phosphate dehydrogenase, peroxiredoxin-1, CD109 antigen, and eight immunoglobulins. Furthermore, the same significant canonical pathways of LXR/RXR activation and FXR/RXR activation were observed in both comparisons. Seven of DAPs were apolipoproteins related to these pathways that are involved in lipid metabolism.

Conclusions

This study provides the first proteomic profile for understanding the pathogenesis and identifying predictive biomarkers of KFS. We detected 15 DAPs that were common in both comparisons as candidate predictive biomarkers of KFS. The lipid metabolism-related canonical pathways of LXR/RXR and FXR/RXR activation together with seven differentially abundant apolipoproteins may play significant roles in the etiology of KFS and provide possible pathogenesis correlation between KFS and CS.

Data-independent acquisition proteomic analysis of biochemical factors in rice seedlings following treatment with chitosan oligosaccharides

Chitosan oligosaccharides (COS) can elicit plant immunity and defence responses in rice plants, but exactly how this promotes plant growth remains largely unknown. Herein, we explored the effects of 0.5 mg/L COS on plant growth promotion in rice seedlings by measuring root and stem length, investigating biochemical factors in whole plants via proteomic analysis, and confirming upregulated and downregulated genes by real-time quantitative PCR. Pathway enrichment results showed that COS promoted root and stem growth, and stimulated metabolic (biosynthetic and catabolic processes) and photosynthesis in rice plants during the seedling stage. Expression levels of genes related to chlorophyll a-b binding, RNA binding, catabolic processes and calcium ion binding were upregulated following COS treatment. Furthermore, comparative analysis indicated that numerous proteins involved in the biosynthesis, metabolic (catabolic) processes and photosynthesis pathways were upregulated. The findings indicate that COS may upregulate calcium ion binding, photosynthesis, RNA binding, and catabolism proteins associated with plant growth during the rice seedling stage.

Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer

Simple Summary

The traditional approach in identifying cancer related protein biomarkers has focused on evaluation of a single peptide/protein in tissue or circulation. At best, this approach has had limited success for clinical applications, since multiple pathological tumor pathways may be involved during initiation or progression of cancer which diminishes the significance of a single candidate protein/peptide. Emerging sensitive proteomic based technologies like liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics can provide a platform for evaluating serial serum or plasma samples to interrogate secreted products of tumor–host interactions, thereby revealing a more “complete” repertoire of biological variables encompassing heterogeneous tumor biology. However, several challenges need to be met for successful application of serum/plasma based proteomics. These include uniform pre-analyte processing of specimens, sensitive and specific proteomic analytical platforms and adequate attention to study design during discovery phase followed by validation of discovery-level signatures for prognostic, predictive, and diagnostic cancer biomarker applications.

Abstract

Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

Identifying the Potential Substrates of the Depalmitoylation Enzyme Acyl-protein Thioesterase 1

BACKGROUND

The homeostasis of palmitoylation and depalmitoylation is involved in various cellular processes, the disruption of which induces severe physiological consequences. Acyl-protein thioesterase (APT) and palmitoyl-protein thioesterases (PPT) catalyze the depalmitoylation process. The natural mutation in human PPT1 caused neurodegenerative disease, yet the understanding of APT1 remains to be elucidated. While the deletion of APT1 in mice turned out to be potentially embryonically lethal, the decoding of its function strictly relied on the identification of its substrates.

OBJECTIVE

To determine the potential substrates of APT1 by using the generated human APT1 knockout cell line.

METHODS

The combined techniques of palmitoyl-protein enrichment and massspectrometry were used to analyze the different proteins. Palmitoyl-proteins both in HEK293T and APT1-KO cells were extracted by resin-assisted capture (RAC) and data independent acquisition (DIA) quantitative method of proteomics for data collection.

RESULTS

In total, 382 proteins were identified. The gene ontology classification segregated these proteins into diverse biological pathways e.g. endoplasmic reticulum process and ubiquitin-mediated proteolysis. A few potential substrates were selected for verification; indeed, major proteins were palmitoylated. Importantly, their levels of palmitoylation were clearly changed in APT1-KO cells. Interestingly, the proliferation of APT1-KO cells escalated dramatically as compared to that of the WT cells, which could be rescued by APT1 overexpression.

CONCLUSION

Our study provides a large scale of potential substrates of APT1, thus facilitating the understanding of its intervened molecular functions.

Novel circulating protein biomarkers for thyroid cancer determined through data-independent acquisition mass spectrometry

Background

Distinguishing between different types of thyroid cancers (TC) remains challenging in clinical laboratories. As different tumor types require different clinical interventions, it is necessary to establish new methods for accurate diagnosis of TC.

Methods

Proteomic analysis of the human serum was performed through data-independent acquisition mass spectrometry for 29 patients with TC (stages I–IV): 13 cases of papillary TC (PTC), 10 cases of medullary TC (MTC), and six cases follicular TC (FTC). In addition, 15 patients with benign thyroid nodules (TNs) and 10 healthy controls (HCs) were included in this study. Subsequently, 17 differentially expressed proteins were identified in 291 patients with TC, including 247 with PTC, 38 with MTC, and six with FTC, and 69 patients with benign TNs and 176 with HC, using enzyme-linked immunosorbent assays.

Results

In total, 517 proteins were detected in the serum samples using an Orbitrap Q-Exactive-plus mass spectrometer. The amyloid beta A4 protein, apolipoprotein A-IV, gelsolin, contactin-1, gamma-glutamyl hydrolase, and complement factor H-related protein 1 (CFHR1) were selected for further analysis. The median serum CFHR1 levels were significantly higher in the MTC and FTC groups than in the PTC and control groups (P < 0.001). CFHR1 exhibited higher diagnostic performance in distinguishing patients with MTC from those with PTC (P < 0.001), with a sensitivity of 100.0%, specificity of 85.08%, area under the curve of 0.93, and detection cut-off of 0.92 ng/mL.

Conclusion

CFHR1 may serve as a novel biomarker to distinguish PTC from MTC with high sensitivity and specificity.

A Dual Workflow to Improve the Proteomic Coverage in Plasma Using Data-Independent Acquisition-MS

Plasma is one of the most important and common matrices for clinical chemistry and proteomic analyses. Data-independent acquisition (DIA) mass spectrometry has enabled the simultaneous quantitative analysis of hundreds of proteins in plasma samples in support population and disease studies. Depletion of the highest abundant proteins is a common tool to increase plasma proteome coverage, but this strategy can result in the nonspecific depletion of protein subsets with which proteins targeted for depletion interact, adversely affecting their analysis. Our work using an antibody-based depletion column revealed significant complementarity not only in the identification of the proteins derived from depleted and undepleted plasma, but importantly also in the extent to which different proteins can be reproducibly quantified in each fraction. We systematically defined four major quantitative parameters of increasing stringency in both the depleted plasma fraction and in undepleted plasma for 757 observed plasma proteins: Linearity cutoff r² > 0.8; lower limit of quantification (LLOQ); measurement range; limit of detection (LOD). We applied the results of our study to build a web-based tool, PlasmaPilot, that can serve as a protocol decision tree to determine whether the analysis of a specific protein warrants IgY14 mediated depletion.

Plasma/serum proteomics: depletion strategies for reducing high-abundance proteins for biomarker discovery

Plasma and serum are widely used for proteomics-based biomarker discovery. However, analysis of these biofluids is highly challenging due to the complexity and wide dynamic range of their proteomes. Notably, highly abundant proteins tend to obscure the detection of potential biomarkers that are usually of lower concentrations. Among the strategies to resolve this problem are: depletion of high-abundance proteins, enrichment of low abundant proteins of interest and prefractionation. In this review, we focus on current and emerging depletion techniques used to enhance the detection and identification of the less abundant proteins in plasma and serum. We discuss the applications and contributions of these methods to proteomics analysis of plasma and serum alongside their limitations and future perspectives.

Bioinformatics approaches for improving seminal plasma proteome analysis

Reproduction efficiency of male animals is one of the key factors influencing the sustainability of livestock. Mass spectrometry (MS) based proteomics has become an important tool for studying seminal plasma proteomes. In this review, we summarize bioinformatics analysis strategies for current proteomics approaches, for identifying novel biomarkers of reproductive robustness.

DIAlignR Provides Precise Retention Time Alignment Across Distant Runs in DIA and Targeted Proteomics

Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectra (SWATH-MS) is widely used for proteomics analysis given its high throughput and reproducibility, but ensuring consistent quantification of analytes across large-scale studies of heterogeneous samples such as human plasma remains challenging. Heterogeneity in large-scale studies can be caused by large time intervals between data acquisition, acquisition by different operators or instruments, and intermittent repair or replacement of parts, such as the liquid chromatography column, all of which affect retention time (RT) reproducibility and, successively, performance of SWATH-MS data analysis. Here, we present a novel algorithm for RT alignment of SWATH-MS data based on direct alignment of raw MS2 chromatograms using a hybrid dynamic programming approach. The algorithm does not impose a chronological order of elution and allows for alignment of elution-order-swapped peaks. Furthermore, allowing RT mapping in a certain window around a coarse global fit makes it robust against noise. On a manually validated dataset, this strategy outperformed the current state-of-the-art approaches. In addition, on real-world clinical data, our approach outperformed global alignment methods by mapping 98% of peaks compared with 67% cumulatively. DIAlignR reduced alignment error up to 30-fold for extremely distant runs. The robustness of technical parameters used in this pairwise alignment strategy is also demonstrated. The source code is released under the BSD license at https://github.com/Roestlab/DIAlignR.

Protein Biomarker Discovery in Non-depleted Serum by Spectral Library-Based Data-Independent Acquisition Mass Spectrometry

In discovery proteomics experiments, tandem mass spectrometry and data-dependent acquisition (DDA) are classically used to identify and quantify peptides and proteins through database searching. This strategy suffers from known limitations such as under-sampling and lack of reproducibility of precursor ion selection in complex proteomics samples, leading to somewhat inconsistent analytical results across large datasets. Data-independent acquisition (DIA) based on fragmentation of all the precursors detected in predetermined isolation windows can potentially overcome this limitation. DIA promises reproducible peptide and protein quantification with deeper proteome coverage and fewer missing values than DDA strategies. This approach is particularly attractive in the field of clinical biomarker discovery, where large numbers of samples must be analyzed. Here, we describe a DIA workflow for non-depleted serum analysis including a straightforward approach through which to construct a dedicated spectral library, and indications on how to optimize chromatographic and mass spectrometry analytical methods to produce high-quality DIA data and results.

Characterization and non-parametric modeling of the developing serum proteome during infancy and early childhood

Children develop rapidly during the first years of life, and understanding the sources and associated levels of variation in the serum proteome is important when using serum proteins as markers for childhood diseases. The aim of this study was to establish a reference model for the evolution of a healthy serum proteome during early childhood. Label-free quantitative proteomics analyses were performed for 103 longitudinal serum samples collected from 15 children at birth and between the ages of 3–36 months. A flexible Gaussian process-based probabilistic modelling framework was developed to evaluate the effects of different variables, including age, living environment and individual variation, on the longitudinal expression profiles of 266 reliably identified and quantified serum proteins. Age was the most dominant factor influencing approximately half of the studied proteins, and the most prominent age-associated changes were observed already during the first year of life. High inter-individual variability was also observed for multiple proteins. These data provide important details on the maturing serum proteome during early life, and evaluate how patterns detected in cord blood are conserved in the first years of life. Additionally, our novel modelling approach provides a statistical framework to detect associations between covariates and non-linear time series data.

Needle lost in the haystack: multiple reaction monitoring fails to detect Treponema pallidum candidate protein biomarkers in plasma and urine samples from individuals with syphilis

Background: Current syphilis diagnostic strategies are lacking a sensitive manner of directly detecting Treponema pallidum antigens. A diagnostic test that could directly detect T. pallidum antigens in individuals with syphilis would be of considerable clinical utility, especially for the diagnosis of reinfections and for post-treatment serological follow-up.

Methods: In this study, 11 candidate T. pallidum biomarker proteins were chosen according to their physiochemical characteristics, T. pallidum specificity and predicted abundance. Thirty isotopically labelled proteotypic surrogate peptides (hPTPs) were synthesized and incorporated into a scheduled multiple reaction monitoring assay. Protein extracts from undepleted/unenriched plasma (N = 18) and urine (N = 4) samples from 18 individuals with syphilis in various clinical stages were tryptically digested, spiked with the hPTP mixture and analysed with a triple quadruple mass spectrometer.

Results: No endogenous PTPs corresponding to the eleven candidate biomarkers were detected in any samples analysed. To estimate the Limit of Detection (LOD) of a comparably sensitive mass spectrometer (LTQ-Orbitrap), two dilution series of rabbit cultured purified T. pallidum were prepared in PBS. Polyclonal anti- T. pallidum antibodies coupled to magnetic Dynabeads were used to enrich one sample series; no LOD improvement was found compared to the unenriched series. The estimated LOD of MS instruments is 300 T. pallidum/ml in PBS.

Conclusions: Biomarker protein detection likely failed due to the low (femtomoles/liter) predicted concentration of T. pallidum proteins. Alternative sample preparation strategies may improve the detectability of T. pallidum proteins in biofluids.

The Human Plasma Proteome Draft of 2017: Building on the Human Plasma PeptideAtlas from Mass Spectrometry and Complementary Assays

Human blood plasma provides a highly accessible window to the proteome of any individual in health and disease. Since its inception in 2002, the Human Proteome Organization's Human Plasma Proteome Project (HPPP) has been promoting advances in the study and understanding of the full protein complement of human plasma and on determining the abundance and modifications of its components. In 2017, we review the history of the HPPP and the advances of human plasma proteomics in general, including several recent achievements. We then present the latest 2017-04 build of Human Plasma PeptideAtlas, which yields ∼43 million peptide-spectrum matches and 122,730 distinct peptide sequences from 178 individual experiments at a 1% protein-level FDR globally across all experiments. Applying the latest Human Proteome Project Data Interpretation Guidelines, we catalog 3509 proteins that have at least two non-nested uniquely mapping peptides of nine amino acids or more and >1300 additional proteins with ambiguous evidence. We apply the same two-peptide guideline to historical PeptideAtlas builds going back to 2006 and examine the progress made in the past ten years in plasma proteome coverage. We also compare the distribution of proteins in historical PeptideAtlas builds in various RNA abundance and cellular localization categories. We then discuss advances in plasma proteomics based on targeted mass spectrometry as well as affinity assays, which during early 2017 target ∼2000 proteins. Finally, we describe considerations about sample handling and study design, concluding with an outlook for future advances in deciphering the human plasma proteome.

Data-Independent Acquisition-Based Quantitative Proteomic Analysis Reveals Potential Biomarkers of Kidney Cancer

PURPOSE

Renal cell carcinoma (RCC) is a malignant and metastatic cancer with 95% mortality, and clear cell RCC (ccRCC) is the most observed among the five major subtypes of RCC. Specific biomarkers that can distinguish cancer tissues from adjacent normal tissues should be developed to diagnose this disease in early stages and conduct a reliable prognostic evaluation.

EXPERIMENTAL DESIGN

Data-independent acquisition (DIA) strategy has been widely employed in proteomic analysis because of various advantages, including enhanced protein coverage and reliable data acquisition. In this study, a DIA workflow is constructed on a quadrupole-Orbitrap LC-MS platform to reveal dysregulated proteins between ccRCC and adjacent normal tissues.

RESULTS

More than 4000 proteins are identified, 436 of these proteins are dysregulated in ccRCC tissues. Bioinformatic analysis reveals that multiple pathways and Gene Ontology items are strongly associated with ccRCC. The expression levels of L-lactate dehydrogenase A chain, annexin A4, nicotinamide N-methyltransferase, and perilipin-2 examined through RT-qPCR, Western blot, and immunohistochemistry confirm the validity of the proteomic analysis results.

CONCLUSIONS AND CLINICAL RELEVANCE

The proposed DIA workflow yields optimum time efficiency and data reliability and provides a good choice for proteomic analysis in biological and clinical studies, and these dysregulated proteins might be potential biomarkers for ccRCC diagnosis.

New Insight of Common Regulatory Pathways in Human Trabecular Meshwork Cells in Response to Dexamethasone and Prednisolone Using an Integrated Quantitative Proteomics: SWATH and MRM-HR Mass Spectrometry

The molecular pathophysiology of corticosteroid-induced ocular hypertension (CIH) is not well understood. To determine the biological mechanisms of CIH, this study investigated protein expression profiles of human trabecular meshwork (hTM) cells in response to dexamethasone and prednisolone treatment. Both discovery-based sequential windowed data independent acquisition of the total high-resolution mass spectra (SWATH-MS) and targeted based high resolution multiple reaction monitoring (MRM-HR) confirmation were applied using a hybrid quadrupole-time-of-flight mass spectrometer. A comprehensive list of 1759 proteins (1% FDR) was generated from the hTM. Quantitative proteomics revealed 20 differentially expressed proteins (p-value ≤ 0.05 and fold-change ≥ 1.5 or ≤ 0.67) commonly induced by prednisolone and dexamethasone, both at 300 nM. These included connective tissue growth factor (CTGF) and thrombospondin-1 (THBS1), two proteins previously implicated in ocular hypertension, glaucoma, and the transforming growth factor-β pathway. Their gene expressions in response to corticosteroids were further confirmed using reverse-transcription polymerase chain reaction. Together with other novel proteins identified in the data sets, additional pathways implicated by these regulated proteins were the phosphatidylinositol 3-kinase (PI3K)-protein kinase B (Akt) signaling pathway, integrin cell surface interaction, extracellular matrix (ECM) proteoglycans, and ECM-receptor interaction. Our results indicated that an integrated platform of SWATH-MS and MRM-HR allows high throughput identification and confirmation of novel and known corticosteroid-regulated proteins in trabecular meshwork cells, demonstrating the power of this technique in extending the current understanding of the pathogenesis of CIH.

Spectral library-based glycopeptide analysis-detection of circulating galectin-3 binding protein in pancreatic cancer

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease characterized by its late diagnosis, poor prognosis and rapid development of drug resistance. Using the data-independent acquisition (DIA) technique, the authors applied a spectral library-based proteomic approach to analyze N-glycosylated peptides in human plasma, in the context of pancreatic cancer study.

EXPERIMENTAL DESIGN

The authors extended the application of DIA to the quantification of N-glycosylated peptides enriched from plasma specimens from a clinically well-defined cohort that consists of patients with early stage PDAC, chronic pancreatitis and healthy subjects.

RESULTS

The analytical platform was evaluated in light of its robustness for quantitative analysis of large-scale clinical specimens. The authors analysis indicated that the level of N-glycosylated peptides derived from galectin-3 binding proteins (LGALS3BP) were frequently elevated in plasma from PDAC patients, concurrent with the altered N-glycosylation of LGALS3BP observed in the tumor tissue.

CONCLUSION AND CLINICAL RELEVANCE

The glycosylation form of LGALS3BP influences its function in the galectin network, which profoundly involves in cancer progression, immune response and drug resistance. As one of the major binding ligands of galectin network, discovery of site specific N-glycosylation changes of LGALS3BP in association of PDAC may provide useful clues to facilitate cancer detection or phenotype stratification.