Mass spectrometry based biomarker discovery, verification, and validation--quality assurance and control of protein biomarker assays. Mol Oncol. 2014;8:840-858. [PMID: 24713096 DOI: 10.1016/j.molonc.2014.03.006]

A Proteomics Pipeline for Generating Clinical Grade Biomarker Candidates from Data-Independent Acquisition Mass Spectrometry (DIA-MS) Discovery

Clinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package that uses data-independent acquisition analysis from a discovery cohort to select precursors, peptides, and proteins that adhere to analytical criteria required for established targeted assays. TEAQ was applied to DIA-MS data from plasma samples acquired on a new high resolution accurate mass (HRAM) mass spectrometry platform where precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra-protein correlation based on 8- or 11-point loading curves at three throughputs. This data can be used as a general resource for developing other targeted assays. TEAQ analysis of data from a case and control cohort for inflammatory bowel disease (n=492) identified 1110 signature peptides for 326 quantifiable proteins from the 1179 identified proteins. Applying TEAQ analysis to discovery data will streamline targeted assay development and the transition to validation and clinical studies.

Synovial Fluid Markers and Extracellular Vesicles in Rheumatoid Arthritis

In recent years, numerous potential prognostic biomarkers for rheumatoid arthritis (RA) have been investigated. Despite these advancements, clinical practice primarily relies on autoantibody tests-for rheumatoid factor (RF) and anti-citrullinated protein antibody (anti-CCP)-alongside inflammatory markers, such as the erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP). Expanding the repertoire of diagnostic and therapeutic biomarkers is critical for improving clinical outcomes in RA. Emerging evidence highlights the significance of synovial fluid biomarkers, including aggrecan, matrix metalloproteinases, glucosyl-galactosyl-pyridinoline, hyaluronic acid, S100 proteins, calprotectin, and various cytokines, as well as immunological markers. Additionally, specific components of extracellular vesicles, such as non-coding RNAs, heat shock proteins, and lipids, are gaining attention. This review focuses on molecular markers found in synovial fluid and extracellular vesicles, excluding clinical and imaging biomarkers, and explores their potential applications in the diagnosis and management of RA.

Identification of protein biomarkers in wastewater linked to the incidence of COVID-19

Wastewater-based epidemiological (WBE) surveillance is a viable disease surveillance technique capable of monitoring the spread of infectious disease agents in sewershed communities. In addition to detecting viral genomes in wastewater, WBE surveillance can identify other endogenous biomarkers that are significantly elevated and excreted in the saliva, urine, and/or stool of infected individuals. Human protein biomarkers allow the realization of real-time WBE surveillance using highly sensitive biosensors. In this study, we analyzed endogenous protein biomarkers present in wastewater influent through liquid chromatography-tandem mass spectrophotometry and scaffold data-independent acquisition to identify candidate target protein biomarkers for WBE surveillance of SARS-CoV-2. We found that out of the 1382 proteins observed in the wastewater samples, 44 were human proteins associated with infectious diseases. These included immune response substances such as immunoglobulins, cytokine-chemokines, and complements, as well as proteins belonging to antimicrobial and antiviral groups. A significant correlation was observed between the intensity of human infectious disease-related protein biomarkers in wastewater and COVID-19 case numbers. Real-time WBE surveillance using biosensors targeting immune response proteins, such as antibodies or immunoglobulins, in wastewater holds promise for expediting the implementation of relevant policies for the effective prevention of infectious diseases in the near future.

Quality Assurance for Multiplex Quantitative Clinical Chemistry Proteomics in Large Clinical Trials

BACKGROUND

To evaluate the clinical performance and effectiveness of a multiplex apolipoprotein panel in the context of cardiovascular precision diagnostics, clinical samples of patients with recent acute coronary syndrome in the ODYSSEY OUTCOMES trial were measured by quantitative clinical chemistry proteomics (qCCP). The ISO15189-accredited laboratory setting, including the total testing process (TTP), served as a foundation for this study. Consequently, tailored quality assurance measures needed to be designed and implemented to suit the demands of a multiplex LC-MS/MS test.

METHODS

Nine serum apolipoproteins were measured in 23 376 samples with a laboratory-developed multiplex apolipoprotein test on 4 Agilent 6495 LC-MS/MS systems. A fit-for-purpose process was designed with tailored additions enhancing the accredited laboratory infrastructure and the TTP. Quality assurance was organized in 3 steps: system suitability testing (SST), internal quality control (IQC) evaluation with adjusted Westgard rules to fit a multiplex test, and interpeptide agreement analysis. Data was semi-automatically evaluated with a custom R script.

RESULTS

LC-MS/MS analyses were performed with the following between-run CVs: for apolipoprotein (Apo) (a) 6.2%, Apo A-I 2.3%, Apo A-II 2.1%, Apo A-IV 2.9%, Apo B 1.9%, Apo C-I 3.3%, Apo C-II 3.3%, Apo C-III 2.7%, and for Apo E 3.3% and an average interpeptide agreement Pearson r of 0.981.

CONCLUSIONS

This is the first study of its kind in which qCCP was performed at this scale. This research successfully demonstrates the feasibility of high-throughput LC-MS/MS applications in large clinical trials. ClinicalTrials.gov Registration Number: NCT01663402.

Progress in research of proteomics related to digestive system tumor markers

The incidence and mortality of digestive system tumors are high. Even though the number of methods for tumor diagnosis and treatment is increasing, most of these tumors still cannot be diagnosed early, and their prognosis is poor. The lack of effective biomarkers and therapeutic targets is the reason why they cannot be diagnosed early and treated effectively. With the continuous development of proteomics technology, proteomics has become increasingly valuable in exploring the mechanisms of tumorigenesis and searching for biomarkers and drug targets. This article reviews the application progress of proteomics technology in screening of biomarkers for digestive system tumors, with an aim to provide new ideas for early diagnosis, prognosis, and treatment of digestive system tumors.

Age-specific changes in the serum proteome of female anadromous, hilsa Tenualosa ilisha: a comparative analysis across developmental stages

Introduction

The proteome profile of the female Tenualosa ilisha (Hamilton, 1822), a species of great ecological and economic importance, across various age groups was investigated to comprehend the functional dynamics of the serum proteome for conservation and aquaculture, as well as sustain the population.

Methods

Advanced liquid chromatography-tandem mass spectrometry LC-MS/MS-based proteomic data were analysed and submitted to the ProteomeXchange Consortium via PRIDE (PRoteomics IDEntifications database). Bioinformatics analysis of serum proteome have been done and it showed different proteins associated with GO Gene Ontology () terms, and the genes associated with enriched KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways (such as phagosome, mTOR, Apelin signalling pathways, herpes simplex virus) implicated in immune responses.

Results

The expression levels of important immunological proteins, such as those involved in cellular defence and inflammatory responses, were significantly different age-dependently. In this study, we annotated 952, 494, 415, and 282 proteins in year classes IV, III, II, and I Hilsa, respectively, and analysed their Protein-Protein Interaction (PPI) networks based on their functional characteristics. From year classes I to IV, new proteins appeared and were more than three-fold. Notably, class I hilsa displayed a lower abundance of proteins than class IV hilsa.

Discussion

This is the first study, to the best of our knowledge, to report the analysis of the serum proteome of hilsa at different developmental stages, and the results can help improve the understanding of the mechanisms underlying the different changes in protein enrichment during migration in hilsa. This analysis also offers crucial insights into the immune system for hilsa conservation and management.

A Framework for Quality Control in Quantitative Proteomics

A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow, from planning to analysis. We share vignettes applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at the protein and peptide levels allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis (Skyline), longitudinal QC metrics (AutoQC), and server-based data deposition (PanoramaWeb). We propose that this integrated approach to QC is a useful starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible. Data are available on Panorama Public and ProteomeXchange under the identifier PXD051318.

Development of a Fit-For-Purpose Multi-Marker Panel for Early Diagnosis of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) suffers from a lack of an effective diagnostic method, which hampers improvement in patient survival. Carbohydrate antigen 19-9 (CA19-9) is the only FDA-approved blood biomarker for PDAC, yet its clinical utility is limited due to suboptimal performance. Liquid chromatography-mass spectrometry (LC-MS) has emerged as a burgeoning technology in clinical proteomics for the discovery, verification, and validation of novel biomarkers. A plethora of protein biomarker candidates for PDAC have been identified using LC-MS, yet few has successfully transitioned into clinical practice. This translational standstill is owed partly to insufficient considerations of practical needs and perspectives of clinical implementation during biomarker development pipelines, such as demonstrating the analytical robustness of proposed biomarkers which is critical for transitioning from research-grade to clinical-grade assays. Moreover, the throughput and cost-effectiveness of proposed assays ought to be considered concomitantly from the early phases of the biomarker pipelines for enhancing widespread adoption in clinical settings. Here, we developed a fit-for-purpose multi-marker panel for PDAC diagnosis by consolidating analytically robust biomarkers as well as employing a relatively simple LC-MS protocol. In the discovery phase, we comprehensively surveyed putative PDAC biomarkers from both in-house data and prior studies. In the verification phase, we developed a multiple-reaction monitoring (MRM)-MS-based proteomic assay using surrogate peptides that passed stringent analytical validation tests. We adopted a high-throughput protocol including a short gradient (<10 min) and simple sample preparation (no depletion or enrichment steps). Additionally, we developed our assay using serum samples, which are usually the preferred biospecimen in clinical settings. We developed predictive models based on our final panel of 12 protein biomarkers combined with CA19-9, which showed improved diagnostic performance compared to using CA19-9 alone in discriminating PDAC from non-PDAC controls including healthy individuals and patients with benign pancreatic diseases. A large-scale clinical validation is underway to demonstrate the clinical validity of our novel panel.

A framework for quality control in quantitative proteomics

A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share vignettes applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis (Skyline), longitudinal QC metrics (AutoQC), and server-based data deposition (PanoramaWeb). We propose that this integrated approach to QC is a useful starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible. Data are available on Panorama Public and on ProteomeXchange under the identifier PXD051318.

Biomarker-Based Precision Therapy for Alzheimer's Disease: Multidimensional Evidence Leading a New Breakthrough in Personalized Medicine

(1) Background: Alzheimer's disease (AD) is a worldwide neurodegenerative disorder characterized by the buildup of abnormal proteins in the central nervous system and cognitive decline. Since no radical therapy exists, only symptomatic treatments alleviate symptoms temporarily. In this review, we will explore the latest advancements in precision medicine and biomarkers for AD, including their potential to revolutionize the way we diagnose and treat this devastating condition. (2) Methods: A literature search was performed combining the following Medical Subject Heading (MeSH) terms on PubMed: "Alzheimer's disease", "biomarkers", "APOE", "APP", "GWAS", "cerebrospinal fluid", "polygenic risk score", "Aβ42", "τP-181", " p-tau217", "ptau231", "proteomics", "total tau protein", and "precision medicine" using Boolean operators. (3) Results: Genome-wide association studies (GWAS) have identified numerous genetic variants associated with AD risk, while a transcriptomic analysis has revealed dysregulated gene expression patterns in the brains of individuals with AD. The proteomic and metabolomic profiling of biological fluids, such as blood, urine, and CSF, and neuroimaging biomarkers have also yielded potential biomarkers of AD that could be used for the early diagnosis and monitoring of disease progression. (4) Conclusion: By leveraging a combination of the above biomarkers, novel ultrasensitive immunoassays, mass spectrometry methods, and metabolomics, researchers are making significant strides towards personalized healthcare for individuals with AD.

Serum Proteomic Signatures in Cervical Cancer: Current Status and Future Directions

In 2020, the World Health Organization (WHO) reported 604,000 new diagnoses of cervical cancer (CC) worldwide, and over 300,000 CC-related fatalities. The vast majority of CC cases are caused by persistent human papillomavirus (HPV) infections. HPV-related CC incidence and mortality rates have declined worldwide because of increased HPV vaccination and CC screening with the Papanicolaou test (PAP test). Despite these significant improvements, developing countries face difficulty implementing these programs, while developed nations are challenged with identifying HPV-independent cases. Molecular and proteomic information obtained from blood or tumor samples have a strong potential to provide information on malignancy progression and response to therapy in CC. There is a large amount of published biomarker data related to CC available but the extensive validation required by the FDA approval for clinical use is lacking. The ability of researchers to use the big data obtained from clinical studies and to draw meaningful relationships from these data are two obstacles that must be overcome for implementation into clinical practice. We report on identified multimarker panels of serum proteomic studies in CC for the past 5 years, the potential for modern computational biology efforts, and the utilization of nationwide biobanks to bridge the gap between multivariate protein signature development and the prediction of clinically relevant CC patient outcomes.

Bringing proteomics to bear on male fertility: key lessons

INTRODUCTION

Male infertility is a major public health concern globally. Proteomics has revolutionized our comprehension of male fertility by identifying potential infertility biomarkers and reproductive defects. Studies comparing sperm proteome with other male reproductive tissues have the potential to refine fertility diagnostics and guide infertility treatment development.

AREAS COVERED

This review encapsulates literature using proteomic approaches to progress male reproductive biology. Our search methodology included systematic searches of databases such as PubMed, Scopus, and Web of Science for articles up to 2023. Keywords used included 'male fertility proteomics,' 'spermatozoa proteome,' 'testis proteomics,' 'epididymal proteomics,' and 'non-hormonal male contraception.' Inclusion criteria were robust experimental design, significant contributions to male fertility, and novel use of proteomic technologies.

EXPERT OPINION

Expert analysis shows a shift from traditional research to an integrative approach that clarifies male reproductive health's molecular intricacies. A gap exists between proteomic discoveries and clinical application. The expert opinions consolidated here not only navigate the current findings but also chart the future proteomic applications for scientific and clinical breakthroughs. We underscore the need for continued investment in proteomic research - both in the technological and collaborative arenas - to further unravel the secrets of male fertility, which will be central to resolving fertility issues in the coming era.

Shaping the future of oral cancer diagnosis: advances in salivary proteomics

INTRODUCTION

Saliva has gained increasing attention in the quest for disease biomarkers. Because it is a biological fluid that can be collected is an easy, painless, and safe way, it has been increasingly studied for the identification of oral cancer biomarkers. This is particularly important because oral cancer is often diagnosed at late stages with a poor prognosis.

AREAS COVERED

The review addresses the evolution of the experimental approaches used in salivary proteomics studies of oral cancer over the years and outlines advantages and pitfalls related to each one. In addition, examines the current landscape of oral cancer biomarker discovery and translation focusing on salivary proteomic studies. This discussion is based on an extensive literature search (PubMed, Scopus and Google Scholar).

EXPERT OPINION

The introduction of mass spectrometry has revolutionized the study of salivary proteomics. In the future, the focus will be on refining existing methods and introducing powerful experimental techniques such as mass spectrometry with selected reaction monitoring, which, despite their effectiveness, are still underutilized due to their high cost. In addition, conducting studies with larger cohorts and establishing standardized protocols for salivary proteomics are key challenges that need to be addressed in the coming years.

Paradigm shift in biomarker translation: a pipeline to generate clinical grade biomarker candidates from DIA-MS discovery

Clinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package. This innovative tool uses the discovery cohort analysis to select precursors, peptides, and proteins that adhere to established targeted assay criteria. TEAQ was applied to Data-Independent Acquisition MS data from plasma samples acquired on an Orbitrap™ Astral™ MS. Identified precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra-protein correlation from 11-point loading curves under three throughputs, to develop a resource for clinical-grade targeted assays. From a clinical cohort of individuals with inflammatory bowel disease (n=492), TEAQ successfully identified 1116 signature peptides for 327 quantifiable proteins from 1180 identified proteins. Embedding stringent selection criteria adaptable to targeted assay development into the analysis of discovery data will streamline the transition to validation and clinical studies.

Multiple reaction monitoring assays for large-scale quantitation of proteins from 20 mouse organs and tissues

Mouse is the mammalian model of choice to study human health and disease due to its size, ease of breeding and the natural occurrence of conditions mimicking human pathology. Here we design and validate multiple reaction monitoring mass spectrometry (MRM-MS) assays for quantitation of 2118 unique proteins in 20 murine tissues and organs. We provide open access to technical aspects of these assays to enable their implementation in other laboratories, and demonstrate their suitability for proteomic profiling in mice by measuring normal protein abundances in tissues from three mouse strains: C57BL/6NCrl, NOD/SCID, and BALB/cAnNCrl. Sex- and strain-specific differences in protein abundances are identified and described, and the measured values are freely accessible via our MouseQuaPro database: http://mousequapro.proteincentre.com . Together, this large library of quantitative MRM-MS assays established in mice and the measured baseline protein abundances represent an important resource for research involving mouse models.

Systematic review and meta-analysis of human health-related protein markers for realizing real-time wastewater-based epidemiology

For effective implementation of the wastewater-based epidemiology (WBE) approach, real-time quantification of markers in wastewater is critical for data acquisition before data interpretation, dissemination, and decision-making. This can be achieved by using biosensor technology, but whether the quantification/detection limits of different types of biosensors comply with the concentration of WBE markers in wastewater is unclear. In the present study, we identified promising protein markers with relatively high concentrations in wastewater samples and analyzed biosensor technologies that are potentially available for real-time WBE. The concentrations of potential protein markers in stool and urine samples were obtained through systematic review and meta-analysis. We examined 231 peer-review papers to collect information regarding potential protein markers that can enable us to achieve real-time monitoring using biosensor technology. Fourteen markers in stool samples were identified at the ng/g level, presumably equivalent to ng/L of wastewater after dilution. Moreover, relatively high average concentrations of fecal inflammatory proteins were observed, e.g., fecal calprotectin, clusterin, and lactoferrin. Fecal calprotectin exhibited the highest average log concentration among the markers identified in stool samples with its mean value being 5.24 [95 % CI: 5.05, 5.42] ng/g. We identified 50 protein markers in urine samples at the ng/mL level. Uromodulin (4.48 [95 % CI: 4.20, 4.76] ng/mL) and plasmin (4.18 [95 % CI: 3.15, 5.21] ng/mL) had the top two highest log concentrations in urine samples. Furthermore, the quantification limit of some electrochemical- and optical-based biosensors was found to be around the femtogram/mL level, which is sufficiently low to detect protein markers in wastewater even after dilution in sewer pipes.

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.

Novel proteomic technologies to address gaps in pre-clinical ovarian cancer biomarker discovery efforts

INTRODUCTION

An estimated 20,000 women in the United States will receive a diagnosis of ovarian cancer in 2023. Late-stage diagnosis is associated with poor prognosis. There is a need for novel diagnostic biomarkers for ovarian cancer to improve early-stage detection and novel prognostic biomarkers to improve patient treatment.

AREAS COVERED

This review provides an overview of the clinicopathological features of ovarian cancer and the currently available biomarkers and treatment options. Two affinity-based platforms using proximity extension assays (Olink) and DNA aptamers (SomaLogic) are described in the context of highly reproducible and sensitive multiplexed assays for biomarker discovery. Recent developments in ion mobility spectrometry are presented as novel techniques to apply to the biomarker discovery pipeline. Examples are provided of how these aforementioned methods are being applied to biomarker discovery efforts in various diseases, including ovarian cancer.

EXPERT OPINION

Translating novel ovarian cancer biomarkers from candidates in the discovery phase to bona fide biomarkers with regulatory approval will have significant benefits for patients. Multiplexed affinity-based assay platforms and novel mass spectrometry methods are capable of quantifying low abundance proteins to aid biomarker discovery efforts by enabling the robust analytical interrogation of the ovarian cancer proteome.

DiffN Selection of Tandem Mass Spectrometry Precursors

Current data-dependent acquisition (DDA) approaches select precursor ions for tandem mass spectrometry (MS/MS) characterization based on their absolute intensity, known as a TopN approach. Low-abundance species may not be identified as biomarkers in a TopN approach. Herein, a new DDA approach is proposed, DiffN, which uses the relative differential intensity of ions between two samples to selectively target species undergoing the largest fold changes for MS/MS. Using a dual nano-electrospray (nESI) ionization source which allows samples contained in separate capillaries to be analyzed in parallel, the DiffN approach was developed and validated with well-defined lipid extracts. A dual nESI source and DiffN DDA approach was applied to quantify the differences in lipid abundance between two colorectal cancer cell lines. The SW480 and SW620 lines represent a matched pair from the same patient: the SW480 cells from a primary tumor and the SW620 cells from a metastatic lesion. A comparison of TopN and DiffN DDA approaches on these cancer cell samples highlights the ability of DiffN to increase the likelihood of biomarker discovery and the decreased probability of TopN to efficiently select lipid species that undergo large fold changes. The ability of the DiffN approach to efficiently select precursor ions of interest makes it a strong candidate for lipidomic analyses. This DiffN DDA approach may also apply to other molecule classes (e.g., other metabolites or proteins) that are amenable to shotgun analyses.

Affi-BAMS™: A Robust Targeted Proteomics Microarray Platform to Measure Histone Post-Translational Modifications

For targeted protein panels, the ability to specifically assay post-translational modifications (PTMs) in a quantitative, sensitive, and straightforward manner would substantially advance biological and pharmacological studies. The present study highlights the effectiveness of the Affi-BAMS™ epitope-directed affinity bead capture/MALDI MS platform for quantitatively defining complex PTM marks of H3 and H4 histones. Using H3 and H4 histone peptides and isotopically labelled derivatives, this affinity bead and MALDI MS platform achieves a range of >3 orders of magnitude with a technical precision CV of <5%. Using nuclear cellular lysates, Affi-BAMS PTM-peptide capture resolves heterogeneous histone N-terminal PTMs with as little as 100 µg of starting material. In an HDAC inhibitor and MCF7 cell line model, the ability to monitor dynamic histone H3 acetylation and methylation events is further demonstrated (including SILAC quantification). Affi-BAMS (and its capacity for the multiplexing of samples and target PTM-proteins) thus provides a uniquely efficient and effective approach for analyzing dynamic epigenetic histone marks, which is critical for the regulation of chromatin structure and gene expression.

Potential Biomarkers to Distinguish Type 1 Myocardial Infarction in Troponin-Elevated Diseases

Classifying myocardial infarction by subtype is crucial for appropriate patient management. Although troponin is currently the most commonly used biomarker, it is not a specific marker for myocardial infarction and cannot distinguish subtypes. Furthermore, previous studies have confirmed that proteins known as myocardial infarction markers could function to distinguish the type of myocardial infarction. Therefore, we identify a marker that can distinguish type 1 myocardial infarction from other diseases with elevated troponin. We used mass spectrometry to compare type 1 myocardial infarction with other conditions characterized by troponin elevation and identified new candidate markers for disease classification. We then verified these markers, along with those already known to be associated with cardiovascular disease and plaque rupture. We identified α-1 acid glycoprotein 2, corticosteroid-binding globulin, and serotransferrin as potential distinguishing markers. The presence of these markers and other parameters, such as chest pain, electrocardiogram, and troponin levels from the complementary diagnostic processes, could provide valuable information to specifically diagnose type 1 myocardial infarction.

Urinary Metabolic Profiling of Liver Fluke-Induced Cholangiocarcinoma-A Follow-Up Study

Background/Aims

Global liquid chromatography mass spectrometry (LC-MS) profiling in a Thai population has previously identified a urinary metabolic signature in Opisthorchis viverrini-induced cholangiocarcinoma (CCA), primarily characterised by disturbance in acylcarnitine, bile acid, steroid, and purine metabolism. However, the detection of thousands of analytes by LC-MS in a biological sample in a single experiment potentially introduces false discovery errors. To verify these observed metabolic perturbations, a second validation dataset from the same population was profiled in a similar fashion.

Methods

Reverse-phase ultra-performance liquid-chromatography mass spectrometry was utilised to acquire the global spectral profile of 98 spot urine samples (from 46 healthy volunteers and 52 CCA patients) recruited from Khon Kaen, northeast Thailand (the highest incidence of CCA globally).

Results

Metabolites were differentially expressed in the urinary profiles from CCA patients. High urinary elimination of bile acids was affected by the presence of obstructive jaundice. The urine metabolome associated with non-jaundiced CCA patients showed a distinctive pattern, similar but not identical to published studies. A panel of 10 metabolites achieved a diagnostic accuracy of 93.4% and area under the curve value of 98.8% (CI = 96.3%-100%) for the presence of CCA.

Conclusions

Global characterisation of the CCA urinary metabolome identified several metabolites of biological interest in this validation study. Analyses of the diagnostic utility of the discriminant metabolites showed excellent diagnostic potential. Further larger scale studies are required to confirm these findings internationally, particularly in comparison to sporadic CCA, not associated with liver fluke infestation.

Development of multiple reaction monitoring (MRM) assays to identify Brucella abortus proteins in the serum of humans and livestock

In the present study, a targeted multiple reaction monitoring-mass spectrometry (MRM-MS) approach was developed to screen and identify protein biomarkers for brucellosis in humans and livestock. The selection of proteotypic peptides was carried out by generating in silico tryptic peptides of the Brucella proteome. Using bioinformatics analysis, 30 synthetic peptides corresponding to 10 immunodominant Brucella abortus proteins were generated. MRM-MS assays for the accurate detection of these peptides were optimized using 117 serum samples of human and livestock stratified as clinically confirmed (45), suspected (62), and control (10). Using high throughput MRM assays, transitions for four peptides were identified in several clinically confirmed and suspected human and livestock serum samples. Of these, peptide NAIYDVVTR corresponding to B. abortus proteins: BruAb2_0537 was consistently detected in the clinically confirmed serum samples of both humans and livestock with 100% specificity. To conclude, a high throughput MRM-MS-based protocol for detecting endogenous B. abortus peptides in serum samples of humans and livestock was developed. The developed protocol will help design sensitive assays to accurately diagnose brucellosis in humans and livestock. The data associated with this study are deposited in Panorama Public (https://panoramaweb.org/rNOZCy.url with ProteomeXchange ID: PXD034407).

Bioinformatics tools and data resources for assay development of fluid protein biomarkers

Fluid protein biomarkers are important tools in clinical research and health care to support diagnosis and to monitor patients. Especially within the field of dementia, novel biomarkers could address the current challenges of providing an early diagnosis and of selecting trial participants. While the great potential of fluid biomarkers is recognized, their implementation in routine clinical use has been slow. One major obstacle is the often unsuccessful translation of biomarker candidates from explorative high-throughput techniques to sensitive antibody-based immunoassays. In this review, we propose the incorporation of bioinformatics into the workflow of novel immunoassay development to overcome this bottleneck and thus facilitate the development of novel biomarkers towards clinical laboratory practice. Due to the rapid progress within the field of bioinformatics many freely available and easy-to-use tools and data resources exist which can aid the researcher at various stages. Current prediction methods and databases can support the selection of suitable biomarker candidates, as well as the choice of appropriate commercial affinity reagents. Additionally, we examine methods that can determine or predict the epitope - an antibody’s binding region on its antigen - and can help to make an informed choice on the immunogenic peptide used for novel antibody production. Selected use cases for biomarker candidates help illustrate the application and interpretation of the introduced tools.

LC-MS/MS Isomeric Profiling of N-Glycans Derived from Low-Abundant Serum Glycoproteins in Mild Cognitive Impairment Patients

Mild cognitive impairment (MCI) is an early stage of memory loss that affects cognitive abilities, such as language or virtual/spatial comprehension. This cognitive decline is mostly observed with the aging of individuals. Recently, MCI has been considered as a prodromal phase of Alzheimer's disease (AD), with a 10-15% conversion rate. However, the existing diagnostic methods fail to provide precise and well-timed diagnoses, and the pathophysiology of MCI is not fully understood. Alterations of serum N-glycan expression could represent essential contributors to the overall pathophysiology of neurodegenerative diseases and be used as a potential marker to assess MCI diagnosis using non-invasive procedures. Herein, we undertook an LC-MS/MS glycomics approach to determine and characterize potential N-glycan markers in depleted blood serum samples from MCI patients. For the first time, we profiled the isomeric glycome of the low abundant serum glycoproteins extracted from serum samples of control and MCI patients using an LC-MS/MS analytical strategy. Additionally, the MRM validation of the identified data showed five isomeric N-glycans with the ability to discriminate between healthy and MCI patients: the sialylated N-glycans GlcNAc5,Hex6,Neu5Ac3 and GlcNAc6,Hex7,Neu5Ac4 with single AUCs of 0.92 and 0.87, respectively, and a combined AUC of 0.96; and the sialylated-fucosylated N-glycans GlcNAc4,Hex5,Fuc,Neu5Ac, GlcNAc5,Hex6,Fuc,Neu5Ac2, and GlcNAc6,Hex7,Fuc,Neu5Ac3 with single AUCs of 0.94, 0.67, and 0.88, respectively, and a combined AUC of 0.98. According to the ingenuity pathway analysis (IPA) and in line with recent publications, the identified N-glycans may play an important role in neuroinflammation. It is a process that plays a fundamental role in neuroinflammation, an important process in the progression of neurodegenerative diseases.

Association of the serological status of rheumatoid arthritis patients with two circulating protein biomarkers: A useful tool for precision medicine strategies

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation of the joints and presence of systemic autoantibodies, with a great clinical and molecular heterogeneity. Rheumatoid Factor (RF) and anti-citrullinated protein antibodies (ACPA) are routinely used for the diagnosis of RA. However, additional serological markers are needed to improve the clinical management of this disease, allowing for better patient stratification and the desirable application of precision medicine strategies. In the present study, we investigated those systemic molecular changes that are associated with the RF and ACPA status of RA patients. To achieve this objective, we followed a proteomic biomarker pipeline from the discovery phase to validation. First, we performed an iTRAQ-based quantitative proteomic experiment on serum samples from the RA cohort of the Hospital of Santiago de Compostela (CHUS). In this discovery phase, serum samples from the CHUS cohort were pooled according to their RF/ACPA status. Shotgun analysis revealed that, in comparison with the double negative group (RF-/ACPA-), the abundance of 12 proteins was altered in the RF+/ACPA+ pool, 16 in the RF+/ACPA- pool and 10 in the RF-/ACPA+ pool. Vitamin D binding protein and haptoglobin were the unique proteins increased in all the comparisons. For the verification phase, 80 samples from the same cohort were analyzed individually. To this end, we developed a Multiple Reaction Monitoring (MRM) method that was employed in a comprehensive targeted analysis with the aim of verifying the results obtained in the discovery phase. Thirty-one peptides belonging to 12 proteins associated with RF and/or ACPA status were quantified by MRM. In a final validation phase, the serum levels of alpha-1-acid glycoprotein 1 (A1AG1), haptoglobin (HPT) and retinol-binding protein 4 (RET4) were measured by immunoassays in the RA cohort of the Hospital of A Coruña (HUAC). The increase of two of these putative biomarkers in the double seropositive group was validated in 260 patients from this cohort (p = 0.009 A1AG1; p = 0.003 HPT). The increased level of A1AG1 showed association with RF rather than ACPA (p = 0.023), whereas HPT showed association with ACPA rather than RF (p = 0.013). Altogether, this study has allowed a further classification of the RA seropositive patients into two novel clusters: RF+A1AG+ and ACPA+HPT+. The determination of A1AG1 and HPT in serum would provide novel information useful for RA patient stratification, which could facilitate the effective implementation of personalized medicine in routine clinical practice.

Mass spectroscopy-based proteomics and metabolomics analysis of triple-positive breast cancer cells treated with tamoxifen and/or trastuzumab

PURPOSE

HER2-enriched breast cancer with high levels of hormone receptor expression, known as "triple positive" breast cancer, may represent a new entity with a relatively favourable prognosis against which the combination of chemotherapy, HER-2 inhibition, and endocrine treatment may be considered overtreatment. We explored the effect of the anticancer drugs tamoxifen and trastuzumab, both separately and in combination, on the integrated proteomic and metabolic profile of "triple positive" breast cancer cells (BT-474).

METHOD

We employed ultra-high-performance liquid chromatography-quadrupole time of flight mass spectrometry using a Bruker timsTOF to investigate changes in BT-474 cell line treated with either tamoxifen, trastuzumab or a combination. Differentially abundant metabolites were identified using the Bruker Human Metabolome Database metabolite library and proteins using the Uniprot proteome for Homo sapiens using MetaboScape and MaxQuant, respectively, for identification and quantitation.

RESULTS

A total of 77 proteins and 85 metabolites were found to significantly differ in abundance in BT-474 treated cells with tamoxifen 5 μM/and or trastuzumab 2.5 μM. Findings suggest that by targeting important cellular signalling pathways which regulate cell growth, apoptosis, proliferation, and chemoresistance, these medicines have a considerable anti-growth effect in BT-474 cells. Pathways enriched for dysregulation include RNA splicing, neutrophil degranulation and activation, cellular redox homeostasis, mitochondrial transmembrane transport, ferroptosis and necroptosis, ABC transporters and central carbon metabolism.

CONCLUSION

Our findings in protein and metabolite level research revealed that anti-cancer drug therapy had a significant impact on the key signalling pathways and molecular processes in triple positive BT-474 cell lines.

Construction of 2DE Patterns of Plasma Proteins: Aspect of Potential Tumor Markers

The use of tumor markers aids in the early detection of cancer recurrence and prognosis. There is a hope that they might also be useful in screening tests for the early detection of cancer. Here, the question of finding ideal tumor markers, which should be sensitive, specific, and reliable, is an acute issue. Human plasma is one of the most popular samples as it is commonly collected in the clinic and provides noninvasive, rapid analysis for any type of disease including cancer. Many efforts have been applied in searching for “ideal” tumor markers, digging very deep into plasma proteomes. The situation in this area can be improved in two ways—by attempting to find an ideal single tumor marker or by generating panels of different markers. In both cases, proteomics certainly plays a major role. There is a line of evidence that the most abundant, so-called “classical plasma proteins”, may be used to generate a tumor biomarker profile. To be comprehensive these profiles should have information not only about protein levels but also proteoform distribution for each protein. Initially, the profile of these proteins in norm should be generated. In our work, we collected bibliographic information about the connection of cancers with levels of “classical plasma proteins”. Additionally, we presented the proteoform profiles (2DE patterns) of these proteins in norm generated by two-dimensional electrophoresis with mass spectrometry and immunodetection. As a next step, similar profiles representing protein perturbations in plasma produced in the case of different cancers will be generated. Additionally, based on this information, different test systems can be developed.

Mass spectrometry based biomarkers for early detection of HCC using a glycoproteomic approach

Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer-related mortality worldwide and 80%-90% of HCC develops in patients that have underlying cirrhosis. Better methods of surveillance are needed to increase early detection of HCC and the proportion of patients that can be offered curative therapies. Recent work in novel mass spec-based methods for glycomic and glycopeptide analysis for discovery and confirmation of markers for early detection of HCC versus cirrhosis is reviewed in this chapter. Results from recent work in these fields by several groups and the progress made in developing markers of early HCC which can outperform the current serum-based markers are described and discussed. Also, recent developments in isoform analysis of glycans and glycopeptides and in various mass spec fragmentation methods will be described and discussed.

Quantitative Plasma Proteomics to Identify Candidate Biomarkers of Relapse in Pediatric/Adolescent Hodgkin Lymphoma

Classical pediatric Hodgkin Lymphoma (HL) is a rare malignancy. Therapeutic regimens for its management may be optimized by establishing treatment response early on. The aim of this study was to identify plasma protein biomarkers enabling the prediction of relapse in pediatric/adolescent HL patients treated under the pediatric EuroNet-PHL-C2 trial. We used untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics at the time of diagnosis—before any therapy—as semiquantitative method to profile plasma proteins specifically associated with relapse in 42 children with nodular sclerosing HL. In both the exploratory and the validation cohorts, six proteins (apolipoprotein E, C4b-binding protein α chain, clusterin, fibrinogen γ chain, prothrombin, and vitronectin) were more abundant in the plasma of patients whose HL relapsed (|fold change| ≥ 1.2, p < 0.05, Student’s t-test). Predicting protein function with the Gene Ontology classification model, the proteins were included in four biological processes (p < 0.01). Using immunoblotting and Luminex assays, we validated two of these candidate biomarkers—C4b-binding protein α chain and clusterin—linked to innate immune response function (GO:0045087). This study identified C4b-binding protein α chain and clusterin as candidate early plasma biomarkers of HL relapse, and important for the purpose of shedding light on the molecular scenario associated with immune response in patients treated under the EuroNet-PHL-C2 trial.

Pro-inflammatory cytokines in aqueous humor from dogs with anterior uveitis and post-operative ocular hypertension following phacoemulsification, primary glaucoma, and normal healthy eyes

Background

The aim of this study was to evaluate the levels of pro-inflammatory cytokines in aqueous humor (AH) from dogs with anterior uveitis and post-operative ocular hypertension (POH) following phacoemulsification, in AH from dogs with primary glaucoma, and in normal healthy eyes with no signs of anterior uveitis or other ocular diseases.

Methods

An exploratory study including 21 samples of AH collected from 15 dogs; post-phacoemulsification with anterior uveitis and POH (‘POH group’, n = 10 samples), primary glaucoma (‘glaucoma group’, n = 6 samples), and normal (‘normal group’, n = 5 samples). Target mass spectrometry via multiple reaction monitoring (MRM-MS) with the Canine Cytokine SpikeMix™ as internal standard was used to measure the pro-inflammatory cytokine levels.

Results

The MRM-MS method measured 15 pro-inflammatory cytokines. Tumor-necrosis-factor-alpha (TNFα) and interleukin-18 (IL-18) levels in AH were different between all three groups (glaucoma>POH>normal) (p = .05, p = .02, respectively). Additionally, IL-6 was higher in the ‘POH group’ compared to the ‘glaucoma group’ (p = .04) and IL-4 was higher in the ‘POH group’ compared to the ‘normal group’ (p = .04). Intraocular pressure (IOP) was positively associated with increased AH levels of IL-18 (Spearman correlation = .64, p = .03).

Conclusions

MRM-MS using the Canine Cytokine SpikeMix™ as an internal standard was established as a method to detect pro-inflammatory cytokine levels in canine AH. The study demonstrated increased levels of IL-4, IL-6, IL-18, and TNFα in AH from canines with POH following phacoemulsification. Primary glaucomatous eyes had the highest levels of IL-18 and TNFα which may indicate that inflammation plays a role in the pathogenesis of primary glaucoma in dogs.

Modeling and integration of N-glycan biomarkers in a comprehensive biomarker data model

Molecular biomarkers measure discrete components of biological processes that can contribute to disorders when impaired. Great interest exists in discovering early cancer biomarkers to improve outcomes. Biomarkers represented in a standardized data model, integrated with multi-omics data, may improve understanding and use of novel biomarkers such as glycans and glycoconjugates. Among altered components in tumorigenesis, N-glycans exhibit substantial biomarker potential, when analyzed with their protein carriers. However, such data are distributed across publications and databases of diverse formats, which hampers their use in research and clinical application. Mass spectrometry measures of fifty N-glycans, on seven serum proteins in liver disease, were integrated (as a panel) into a cancer biomarker data model, providing a unique identifier, standard nomenclature, links to glycan resources, and accession and ontology annotations to standard protein, gene, disease, and biomarker information. Data provenance was documented with a standardized FDA-supported BioCompute Object. Using the biomarker data model allows capture of granular information, such as glycans with different levels of abundance in cirrhosis, hepatocellular carcinoma, and transplant groups. Such representation in a standardized data model harmonizes glycomics data in a unified framework, making glycan-protein biomarker data exploration more available to investigators and to other data resources. The biomarker data model we describe can be used by researchers to describe their novel glycan and glycoconjugate biomarkers, can integrate N-glycan biomarker data with multi-source biomedical data, and can foster discovery and insight within a unified data framework for glycan biomarker representation thereby making the data FAIR (Findable, Accessible, Interoperable, Reusable) (https://www.go-fair.org/fair-principles/).

On the Compatibility of Fish Meal Replacements in Aquafeeds for Rainbow Trout. A Combined Metabolomic, Proteomic and Histological Study

The sustainable development of modern aquaculture must rely on a significant reduction of the fish meal (FM) used in aquafeed formulations. However, FM substitution with alternative ingredients in diets for carnivorous fish species often showed reduced nutrient absorption, significantly perturbed metabolisms, and histological changes at both hepatic and intestinal levels. In the present study, rainbow trout (Oncorhynchus mykiss) were fed three different experimental aquafeeds. A control diet with higher FM content (27.3%) than two test formulations in which FM was substituted with two more sustainable and promising alternatives: insect meal (Hermetia illucens larvae = 10.1%, FM = 11.6%) and poultry by-products meal (PBM = 14.8%; FM = 11.7%). Combined metabolomics and proteomics analyses of fish liver, together with histological examination of liver and intestine demonstrated that a well-balanced formulation of nutrients in the three diets allowed high metabolic compatibility of either substitution, paving the way for a deeper understanding of the impact of novel raw materials for the fish feed industry. Results show that the main metabolic pathways of nutrient absorption and catabolism were essentially unaltered by alternative feed ingredients, and also histological alterations were negligible. It is demonstrated that the substitution of FM with sustainable alternatives does not have a negative impact on fish metabolism, as long as the nutritional requirements of rainbow trout are fulfilled.

Dystrophin Protein Quantification as a Duchenne Muscular Dystrophy Diagnostic Biomarker in Dried Blood Spots Using Multiple Reaction Monitoring Tandem Mass Spectrometry: A Preliminary Study

Duchenne muscular dystrophy (DMD) is an X-linked recessive disorder characterized by progressive muscle loss, leading to difficulties in movement. Mutations in the DMD gene that code for the protein dystrophin are responsible for the development of DMD disorder, where the synthesis of this protein is completely halted. Therefore, circulating dystrophin protein could be a promising biomarker of DMD disease. Current methods for diagnosing DMD have sensitivity, specificity, and reproducibility limitations. Herein, a quantitative liquid chromatography-tandem spectrometry (LC-MS/MS) technique in multiple reaction monitoring (MRM) mode was designed and validated for accurate dystrophin protein measurement in a dried blood spot (DBS). The method was successfully validated on the basis of international guidelines regarding calibration curves, precision, and accuracy. In addition, patients and healthy controls were used to test the amount of dystrophin protein circulating in DBS samples as a potential biomarker for DMD disorders. DMD patients were found to have considerably lower levels than controls. To the best of our knowledge, this is the first study to report dystrophin levels in DBS through LC-MS/MS as a diagnostic marker for DMD to the proposed MRM method, providing a highly specific and sensitive approach to dystrophin quantification in a DBS that can be applied in DMD screening.

Tear and ocular surface disease biomarkers: A diagnostic and clinical perspective for ocular allergies and dry eye disease

Validated biomarkers to be used as biological tools for managing ocular surface diseases (OSDs) are still an unmet need in daily clinical practice. Many studies have contributed to the already extensive list of candidate biomarkers for these disorders. Dry eye (DE) and ocular allergy (OA) are complex and multifactorial diseases, often coexisting and with overlapping symptoms. The purpose of this review is to present a comprehensive updated revision of the most relevant biomarkers of DE and OA, with an emphasis on quantitative analyses and correlations with clinical parameter data. Analysis of biomarkers common for these pathologies has highlighted an important physiological process. Namely, the interleukin proteins (IL-1α, IL-1β and IL-17), tumour necrotic factor (TNFα) and interferon gamma (IFNγ; Th1-Th7 pathway) and IL-4, IL-5 and IL-13 (Th2 pathway) seem to represent similar inflammatory mechanisms. Moreover, changes in the levels of mucins (MUC1, MUC2, MUC4, MUC5 and MUC16) are common alterations in the tear film mucous layer. We also examine the current state of medical devices and the main limitations to their use in clinical practice. Translational research in biomarkers for clinical practice depends on a feasible transition from the laboratory to the point-of-care. This requires large-scale, coordinated clinical validation campaigns to select the biomarkers with the highest specificity and sensitivity and significant correlation with clinical parameters. Moreover, technical limitations of multiplexed quantitation systems must be overcome to detect and measure the levels of several biomarkers in very small samples. To ensure the future of biomarker research, significant progress is necessary in a number of fields. There is an urgent need for global unification of clinical classification and diagnostics criteria. Widespread integration of proteomic and transcriptomic data is paramount for performing meaningful analyses using appropriate bioinformatics tools and artificial intelligence systems.

Stable Zinc(II) Coordination Polymer as a Rapid and Highly Sensitive Fluorescence Sensor for the Discriminative Sensing of Biomarker 2-(2-Methoxyethoxy) Acetic Acid

A novel two-dimensional bilayer Zn-based luminescent coordination polymer (LCP) [Zn₂(μ₂-OH)(4-dptp)(3,4',5-bpt)] (LCP 1) [4-dptp = N³,N⁴-bis(pyridin-4-ylmethyl)thiophene-3,4-dicarboxamide and 3,4',5-H₃bpt = biphenyl-3,4',5-tricarboxylic acid] was successfully prepared under hydrothermal conditions and characterized by single-crystal X-ray diffraction, IR spectroscopy, powder X-ray diffraction, and luminescence spectroscopy. LCP 1 displayed excellent fluorescence-quenching efficiency toward a biomarker 2-(2-methoxyethoxy) acetic acid (MEAA) with a high K_sv (5.153 × 104 M^-1), a low limit of detection (0.244 μM), and a rapid response time (28 s). Additionally, LCP 1 can repeatedly detect MEAA at least eight times with excellent stability. The sensing mechanism was also carefully investigated through UV-vis absorption spectroscopy, density functional theory calculations, and fluorescence lifetime analysis.

The Interleukin-1 (IL-1) Superfamily Cytokines and Their Single Nucleotide Polymorphisms (SNPs)

Interleukins (ILs)—which are important members of cytokines—consist of a vast group of molecules, including a wide range of immune mediators that contribute to the immunological responses of many cells and tissues. ILs are immune-glycoproteins, which directly contribute to the growth, activation, adhesion, differentiation, migration, proliferation, and maturation of immune cells; and subsequently, they are involved in the pro and anti-inflammatory responses of the body, by their interaction with a wide range of receptors. Due to the importance of immune system in different organisms, the genes belonging to immune elements, such as ILs, have been studied vigorously. The results of recent investigations showed that the genes pertaining to the immune system undergo progressive evolution with a constant rate. The occurrence of any mutation or polymorphism in IL genes may result in substantial changes in their biology and function and may be associated with a wide range of diseases and disorders. Among these abnormalities, single nucleotide polymorphisms (SNPs) can represent as important disruptive factors. The present review aims at concisely summarizing the current knowledge available on the occurrence, properties, role, and biological consequences of SNPs within the IL-1 family members.

Effect of serum sample storage temperature on metabolomic and proteomic biomarkers

Prospective biomarker studies can be used to identify biomarkers predictive of disease onset. However, if serum biomarkers are measured years after their collection, the storage conditions might affect analyte concentrations. Few data exists concerning which metabolites and proteins are affected by storage at − 20 °C vs − 80 °C. Our objectives were to document analytes affected by storage of serum samples at − 20 °C vs − 80 °C, and to identify those indicative of the storage temperature. We utilized liquid chromatography tandem mass spectrometry and Luminex to quantify 300 analytes from serum samples of 16 Finnish individuals with type 1 diabetes, with split-aliquot samples stored at − 80 °C and − 20 °C for a median of 4.2 years. Results were validated in 315 Finnish and 916 Scottish individuals with type 1 diabetes, stored at − 20 °C and at − 80 °C, respectively. After quality control, we analysed 193 metabolites and proteins of which 120 were apparently unaffected and 15 clearly susceptible to storage at − 20 °C vs − 80 °C. Further, we identified serum glutamate/glutamine ratio greater than 0.20 as a biomarker of storage at − 20 °C vs − 80 °C. The results provide a catalogue of analytes unaffected and affected by storage at − 20 °C vs − 80 °C and biomarkers indicative of sub-optimal storage.

Mass Spectrometry-Based Proteomics Research to Fight COVID-19: An Expert Review on Hopes and Challenges

The COVID-19 pandemic caused by the severe acute respiratory syndrome (SARS)-CoV-2 infection is a systemic disease and a major planetary health burden. While SARS-CoV-2 impacts host biology extensively, our knowledge of these alterations from a systems perspective remains incomplete. Moreover, there is currently only a limited description of this systemic disease. For precision diagnosis and treatment of SARS-CoV-2, multiomics technologies and systems science research offer significant prospects. This expert review offers a critical analysis of the prospects and challenges of the emerging mass spectrometry-based proteomics approaches to the study of COVID-19 as seen through a systems medicine lens. We also discuss the ways in which proteomics is poised to offer hope for diagnostics and therapeutics innovation on SARS-CoV-2 infection as the disease transitions from a pandemic to an endemic disease, and thus further challenging the health systems and services worldwide in the coming decade. Proteomics is an important high-throughput technology platform to achieve a functional overview of the ways in which COVID-19 changes host biology, and hence, can help identify possible points of entry for innovation in medicines and vaccines, among others.

Multiple heart-cutting two dimensional liquid chromatography and isotope dilution tandem mass spectrometry for the absolute quantification of proteins in human serum

We evaluate here the combination of two-dimensional liquid chromatography (2D-LC) in the multiple heart cutting mode and isotope dilution tandem mass spectrometry for the direct analysis of tryptic digests of serum samples. As a proof of concept, we attempt the quantification of proteotypic peptides of Apolipoprotein AIV (APOA4), Complement C3 (C3) and Vitronectin (VTN) which have been previously identified as potential candidate biomarkers of glaucoma. Using this 2D-LC strategy, analyte enrichment steps are avoided and the sample preparation involved after enzymatic digestion amounted to a simple centrifugation, evaporation of the supernatant and reconstitution in the 1D mobile phase. A mobile phase not compatible with the ESI source (10 mM KH₂PO₄ at pH 2.7) was used in the first dimension as it provided a satisfactory chromatographic resolution of the peptides and a high buffering capacity avoiding changes in retention times when analyzing complex matrices like human serum. We also demonstrate that using coeluting labelled analogues of the target peptides, protein concentrations were not affected by slight retention time shifts affecting the amount of target peptides transferred to the second dimension. Satisfactory results were obtained when analyzing fortified serum samples (recoveries from 98 to 113%). Precisions in the range of 1-9% RSD were obtained when replicating the analysis of a pooled serum sample. The comparative analysis of serum samples from n = 94 control subjects and n = 91 patients diagnosed with primary open-angle glaucoma did not show significant differences in the APOA4, VTN and C3 concentrations in contrast with previous studies using immunoassays.

A highly multiplexed quantitative phosphosite assay for biology and preclinical studies

Reliable methods to quantify dynamic signaling changes across diverse pathways are needed to better understand the effects of disease and drug treatment in cells and tissues but are presently lacking. Here, we present SigPath, a targeted mass spectrometry (MS) assay that measures 284 phosphosites in 200 phosphoproteins of biological interest. SigPath probes a broad swath of signaling biology with high throughput and quantitative precision. We applied the assay to investigate changes in phospho-signaling in drug-treated cancer cell lines, breast cancer preclinical models, and human medulloblastoma tumors. In addition to validating previous findings, SigPath detected and quantified a large number of differentially regulated phosphosites newly associated with disease models and human tumors at baseline or with drug perturbation. Our results highlight the potential of SigPath to monitor phosphoproteomic signaling events and to nominate mechanistic hypotheses regarding oncogenesis, response, and resistance to therapy.

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?

Candidate Glaucoma Biomarkers: From Proteins to Metabolites, and the Pitfalls to Clinical Applications

Glaucoma is an insidious group of eye diseases causing degeneration of the optic nerve, progressive loss of vision, and irreversible blindness. The number of people affected by glaucoma is estimated at 80 million in 2021, with 3.5% prevalence in people aged 40-80. The main biomarker and risk factor for the onset and progression of glaucoma is the elevation of intraocular pressure. However, when glaucoma is diagnosed, the level of retinal ganglion cell death usually amounts to 30-40%; hence, the urgent need for its early diagnosis. Molecular biomarkers of glaucoma, from proteins to metabolites, may be helpful as indicators of pathogenic processes observed during the disease's onset. The discovery of human glaucoma biomarkers is hampered by major limitations, including whether medications are influencing the expression of molecules in bodily fluids, or whether tests to validate glaucoma biomarker candidates should include human subjects with different types and stages of the disease, as well as patients with other ocular and neurodegenerative diseases. Moreover, the proper selection of the biofluid or tissue, as well as the analytical platform, should be mandatory. In this review, we have summarized current knowledge concerning proteomics- and metabolomics-based glaucoma biomarkers, with specificity to human eye tissue and fluid, as well the analytical approach and the main results obtained. The complex data published to date, which include at least 458 different molecules altered in human glaucoma, merit a new, integrative approach allowing for future diagnostic tests based on the absolute quantification of local and/or systemic biomarkers of glaucoma.

Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation

Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.

Quantification of SARS-CoV-2 spike and nucleocapsid proteins using isotope dilution tandem mass spectrometry

The emergence and subsequent global outbreak of the novel coronavirus SARS-CoV-2 prompted our laboratory to launch efforts to develop methods for SARS-CoV-2 antigen detection and quantification. We present an isotope dilution mass spectrometry method (IDMS) for rapid and accurate quantification of the primary antigens, spike and nucleocapsid proteins. This IDMS method utilizes liquid chromatography-tandem mass spectrometry (LC-MS/MS) to analyze sample tryptic digests for detection and quantification of selected conserved peptides of SARS-CoV-2 spike and nucleocapsid proteins. The IDMS method has the necessary attributes to be successfully utilized for accurate quantification in SARS-CoV-2 protein-based vaccines and as targets of rapid diagnostic tests. Absolute quantification was achieved by quantifying and averaging 5 peptides for spike protein (3 peptides in the S1 subunit and 2 peptides in the S2 subunit) and 4 peptides for nucleocapsid protein. The overall relative standard deviation of the method was 3.67% for spike protein and 5.11% for nucleocapsid protein. IDMS offers speed (5 h total analysis time), sensitivity (LOQ; 10 fmol/µL) and precision for quantification of SARS-CoV-2 spike and nucleocapsid proteins.

What is proteomics?

Proteomics is the study of a large number of proteins in biological systems. We aim to introduce the complex field to paediatricians and present some recent examples of applications to paediatric problems. Various approaches have been used to study proteomes. The current mainstay is tandem mass spectrometry of enzymatically digested proteins ('bottom-up proteomics'), and we describe the experimental and computational approach further. Proteomics can offer advantages over transcriptomics by giving direct information about proteins rather than RNA; however, typically data are obtained at lower depth and the confident identification of mass spectra can be challenging. Proteomics frequently complements transcriptomics and other -omics. Used effectively, proteomics offers promise to help answer important clinical and biological questions.

Plasma Protein Biomarkers Associated with Higher Ovarian Cancer Risk in BRCA1/2 Carriers

Simple Summary

Most hereditary ovarian cancer is associated with BRCA1/2 variants, and risk-reducing salpingo-oophorectomy during the follow-up monitoring of ovarian cancer development in heathy women with the BRCA1/2 variant reduces ovarian cancer incidence. The aim of this study was to identify plasma protein biomarkers that can indicate an increased risk of developing ovarian cancer using a proteomic approach based on a population of genetic variants. Two identified biomarkers among differentially expressed proteins, SPARC and THBS1, had lower plasma concentrations in healthy BRCA1/2 variant carriers than in ovarian cancer patients with the BRCA1/2 variant; concentration of two proteins increased at the onset of ovarian cancer. These protein markers from non-invasive liquid biopsy sampling could be used to help women with the BRCA1/2 variant determine whether to undergo an oophorectomy that could potentially affect the quality of life.

Abstract

Ovarian cancer (OC) is the most lethal gynecologic malignancy and in-time diagnosis is limited because of the absence of effective biomarkers. Germline BRCA1/2 genetic alterations are risk factors for hereditary OC; risk-reducing salpingo-oophorectomy (RRSO) is pursued for disease prevention. However, not all healthy carriers develop the disease. Therefore, identifying predictive markers in the BRCA1/2 carrier population could help improve the identification of candidates for preventive RRSO. In this study, plasma samples from 20 OC patients (10 patients with BRCA1/2 wild type (_wt) and 10 with the BRCA1/2 variant (_var)) and 20 normal subjects (10 subjects with BRCA1/2_wt and 10 with BRCA1/2_var) were analyzed for potential biomarkers of hereditary OC. We applied a bottom-up proteomics approach, using nano-flow LC-MS to analyze depleted plasma proteome quantitatively, and potential plasma protein markers specific to the BRCA1/2 variant were identified from a comparative statistical analysis of the four groups. We obtained 1505 protein candidates from the 40 subjects, and SPARC and THBS1 were verified by enzyme-linked immunosorbent assay. Plasma SPARC and THBS1 concentrations in healthy BRCA1/2 carriers were found to be lower than in OC patients with BRCA1/2_var. If plasma SPARC concentrations increase over 337.35 ng/mL or plasma THBS1 concentrations increase over 65.28 μg/mL in a healthy BRCA1/2 carrier, oophorectomy may be suggested.

Feature Selection Methods for Protein Biomarker Discovery from Proteomics or Multiomics Data

Untargeted mass spectrometry (MS)-based proteomics provides a powerful platform for protein biomarker discovery, but clinical translation depends on the selection of a small number of proteins for downstream verification and validation. Due to the small sample size of typical discovery studies, protein markers identified from discovery data may not be generalizable to independent datasets. In addition, a good protein marker identified using a discovery platform may be difficult to implement in verification and validation platforms. Moreover, although multiomics characterization is being increasingly used in discovery cohort studies, there is no existing method for multiomics-facilitated protein biomarker selection. Here, we present ProMS, a computational algorithm for protein marker selection. The algorithm is based on the hypothesis that a phenotype is characterized by a few underlying biological functions, each manifested by a group of coexpressed proteins. A weighted k-medoids clustering algorithm is applied to all univariately informative proteins to identify both coexpressed protein clusters and a representative protein for each cluster as markers. In two clinically important classification problems, ProMS shows superior performance compared with existing feature selection methods. ProMS can be extended to the multiomics setting (ProMS_mo) through a constrained weighted k-medoids clustering algorithm, and the protein panels selected by ProMS_mo show improved performance on independent test data compared with ProMS. In addition to superior performance, ProMS and ProMS_mo also have two unique strengths. First, the feature clusters enable functional interpretation of the selected protein markers. Second, the feature clusters provide an opportunity to select replacement protein markers, facilitating a robust transition to the verification and validation platforms. In summary, this study provides a unified and effective computational framework for selecting protein biomarkers using proteomics or multiomics data. The software implementation is publicly available at https://github.com/bzhanglab/proms.

Utilization of chromogenic enzyme substrates for signal amplification in multiplexed detection of biomolecules using surface mass spectrometry

MicroRNAs (miRNAs) are important post-transcriptional gene regulators and can serve as potential biomarkers for many diseases. Most of the current miRNA detection techniques require purification from biological samples, amplification, labeling, or tagging, which makes quantitative analysis of clinically relevant samples challenging. Here we present a new strategy for the detection of miRNAs with uniformity over a large area based on signal amplification using enzymatic reactions and measurements using time-of-flight secondary ion mass spectrometry (ToF-SIMS), a sensitive surface analysis tool. This technique has high sequence specificity through hybridization with a hairpin DNA probe and allows the identification of single-base mismatches that are difficult to distinguish by conventional mass spectrometry. We successfully detected target miRNAs in biological samples without purification, amplification, or labeling of target molecules. In addition, by adopting a well-known chromogenic enzymatic reaction from the field of biotechnology, we extended the use of enzyme-amplified signal enhancement ToF (EASE-ToF) to protein detection. Our strategy has advantages with respect to scope, quantification, and throughput over the currently available methods, and is amenable to multiplexing based on the outstanding molecular specificity of mass spectrometry (MS). Therefore, our technique not only has the potential for use in clinical diagnosis, but also provides evidence that MS can serve as a useful readout for biosensing to perform multiplexed analysis extending beyond the limitations of existing technology.