Assessment of reproducibility in depletion and enrichment workflows for plasma proteomics using label-free quantitative data-independent LC-MS

Establishing Quality Control Metrics for Large-Scale Plasma Proteomic Sample Preparation

Large-scale plasma proteomics studies have been transformed due to the multiplexing and automation of sample preparation workflows. However, these workflows can suffer from reproducibility issues, a lack of standardized quality control (QC) metrics, and the assessment of variation before liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. The incorporation of robust QC metrics in sample preparation workflows ensures better reproducibility, lower assay variation, and better-informed decisions for troubleshooting. Our laboratory conducted a plasma proteomics study of a cohort of patient samples (N = 808) using tandem mass tag (TMT) 16-plex batches (N = 58). The proteomic workflow consisted of protein depletion, protein digestion, TMT labeling, and fractionation. Five QC sample types (QCstd, QCdig, QCpool, QCTMT, and QCBSA) were created to measure the performance of sample preparation prior to the final LC-MS/MS analysis. We measured <10% CV for individual sample preparation steps in the proteomic workflow based on data from various QC sample steps. The establishment of robust measures for QC of sample preparation steps allowed for greater confidence in prepared samples for subsequent LC-MS/MS analysis. This study also provides recommendations for standardized QC metrics that can assist with future large-scale cohort sample preparation workflows.

A novel deep proteomic approach in human skeletal muscle unveils distinct molecular signatures affected by aging and resistance training

The skeletal muscle proteome alterations to aging and resistance training have been reported in prior studies. However, conventional proteomics in skeletal muscle typically yields wide protein abundance ranges that mask the detection of lowly expressed proteins. Thus, we adopted a novel deep proteomics approach whereby myofibril (MyoF) and non-MyoF fractions were separately subjected to protein corona nanoparticle complex formation prior to digestion and Liquid Chromatography Mass Spectrometry (LC-MS). Specifically, we investigated MyoF and non-MyoF proteomic profiles of the vastus lateralis muscle of younger (Y, 22±2 years old; n=5) and middle-aged participants (MA, 56±8 years old; n=6). Additionally, MA muscle was analyzed following eight weeks of resistance training (RT, 2d/week). Across all participants, the number of non-MyoF proteins detected averaged to be 5,645±266 (range: 4,888-5,987) and the number of MyoF proteins detected averaged to be 2,611±326 (range: 1,944-3,101). Differences in the non-MyoF (8.4%) and MyoF (2.5%) proteomes were evident between age cohorts, and most differentially expressed non-MyoF proteins (447/543) were more enriched in MA versus Y. Biological processes in the non-MyoF fraction were predicted to be operative in MA versus Y including increased cellular stress, mRNA splicing, translation elongation, and ubiquitin-mediated proteolysis. RT in MA participants only altered ~0.3% of MyoF and ~1.0% of non-MyoF proteomes. In summary, aging and RT predominantly affect non-contractile proteins in skeletal muscle. Additionally, marginal proteome adaptations with RT suggest more rigorous training may stimulate more robust effects or that RT, regardless of age, subtly alters basal state skeletal muscle protein abundances.

A novel deep proteomic approach in human skeletal muscle unveils distinct molecular signatures affected by aging and resistance training

We examined the myofibrillar (MyoF) and non-myofibrillar (non-MyoF) proteomic profiles of the vastus lateralis (VL) muscle of younger (Y, 22±2 years old; n=5) and middle-aged participants (MA, 56±8 years old; n=6), and MA following eight weeks of knee extensor resistance training (RT, 2d/week). Shotgun/bottom-up proteomics in skeletal muscle typically yields wide protein abundance ranges that mask lowly expressed proteins. Thus, we adopted a novel approach whereby the MyoF and non-MyoF fractions were separately subjected to protein corona nanoparticle complex formation prior to digestion and Liquid Chromatography Mass Spectrometry (LC-MS) analysis. A total of 10,866 proteins (4,421 MyoF and 6,445 non-MyoF) were identified. Across all participants, the number of non-MyoF proteins detected averaged to be 5,645±266 (range: 4,888-5,987) and the number of MyoF proteins detected averaged to be 2,611±326 (range: 1,944-3,101). Differences in the non-MyoF (8.4%) and MyoF (2.5%) proteome were evident between age cohorts. Further, most of these age-related non-MyoF proteins (447/543) were more enriched in MA versus Y. Several biological processes in the non-MyoF fraction were predicted to be operative in MA versus Y including (but not limited to) increased cellular stress, mRNA splicing, translation elongation, and ubiquitin-mediated proteolysis. Non-MyoF proteins associated with splicing and proteostasis were further interrogated, and in agreement with bioinformatics, alternative protein variants, spliceosome-associated proteins (snRNPs), and proteolysis-related targets were more abundant in MA versus Y. RT in MA non-significantly increased VL muscle cross-sectional area (+6.5%, p=0.066) and significantly increased knee extensor strength (+8.7%, p=0.048). However, RT modestly altered the MyoF (~0.3%, 11 upregulated and two downregulated proteins) and non-MyoF proteomes (~1.0%, 56 upregulated and eight downregulated proteins, p<0.01). Further, RT did not affect predicted biological processes in either fraction. Although participant numbers were limited, these preliminary results using a novel deep proteomic approach in skeletal muscle suggest that aging and RT predominantly affects protein abundances in the non-contractile protein pool. However, the marginal proteome adaptations occurring with RT suggest either: a) this may be an aging-associated phenomenon, b) more rigorous RT may stimulate more robust effects, or c) RT, regardless of age, subtly affects skeletal muscle protein abundances in the basal state.

Proteomics Studies in Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis

Gestational Diabetes Mellitus (GDM) is the most common metabolic complication during pregnancy and is associated with serious maternal and fetal complications such as pre-eclampsia and stillbirth. Further, women with GDM have approximately 10 times higher risk of diabetes later in life. Children born to mothers with GDM also face a higher risk of childhood obesity and diabetes later in life. Early prediction/diagnosis of GDM leads to early interventions such as diet and lifestyle, which could mitigate the maternal and fetal complications associated with GDM. However, no biomarkers identified to date have been proven to be effective in the prediction/diagnosis of GDM. Proteomic approaches based on mass spectrometry have been applied in various fields of biomedical research to identify novel biomarkers. Although a number of proteomic studies in GDM now exist, a lack of a comprehensive and up-to-date meta-analysis makes it difficult for researchers to interpret the data in the existing literature. Thus, we undertook a systematic review and meta-analysis on proteomic studies and GDM. We searched MEDLINE, EMBASE, Web of Science and Scopus from inception to January 2022. We searched Medline, Embase, CINHAL and the Cochrane Library, which were searched from inception to February 2021. We included cohort, case-control and observational studies reporting original data investigating the development of GDM compared to a control group. Two independent reviewers selected eligible studies for meta-analysis. Data collection and analyses were performed by two independent reviewers. The PROSPERO registration number is CRD42020185951. Of 120 articles retrieved, 24 studies met the eligibility criteria, comparing a total of 1779 pregnant women (904 GDM and 875 controls). A total of 262 GDM candidate biomarkers (CBs) were identified, with 49 CBs reported in at least two studies. We found 22 highly replicable CBs that were significantly different (nine CBs were upregulated and 12 CBs downregulated) between women with GDM and controls across various proteomic platforms, sample types, blood fractions and time of blood collection and continents. We performed further analyses on blood (plasma/serum) CBs in early pregnancy (first and/or early second trimester) and included studies with more than nine samples (nine studies in total). We found that 11 CBs were significantly upregulated, and 13 CBs significantly downregulated in women with GDM compared to controls. Subsequent pathway analysis using Database for Annotation, Visualization and Integrated Discovery (DAVID) bioinformatics resources found that these CBs were most strongly linked to pathways related to complement and coagulation cascades. Our findings provide important insights and form a strong foundation for future validation studies to establish reliable biomarkers for GDM.

Age and sex effects across the blood proteome after ionizing radiation exposure can bias biomarker screening and risk assessment

Molecular biomarkers of ionizing radiation (IR) exposure are a promising new tool in various disciplines: they can give necessary information for adaptive treatment planning in cancer radiotherapy, enable risk projection for radiation-induced survivorship diseases, or facilitate triage and intervention in radiation hazard events. However, radiation biomarker discovery has not yet resolved the most basic features of personalized medicine: age and sex. To overcome this critical bias in biomarker identification, we quantitated age and sex effects and assessed their relevance in the radiation response across the blood proteome. We used high-throughput mass spectrometry on blood plasma collected 24 h after 0.5 Gy total body irradiation (15 MV nominal photon energy) from male and female C57BL/6 N mice at juvenile (7-weeks-old) or adult (18-weeks-old) age. We also assessed sex and strain effects using juvenile male and female BALB/c nude mice. We showed that age and sex created significant effects in the proteomic response regarding both extent and functional quality of IR-induced responses. Furthermore, we found that age and sex effects appeared non-linear and were often end-point specific. Overall, age contributed more to differences in the proteomic response than sex, most notably in immune responses, oxidative stress, and apoptotic cell death. Interestingly, sex effects were pronounced for DNA damage and repair pathways and associated cellular outcome (pro-survival vs. pro-apoptotic). Only one protein (AHSP) was identified as a potential general biomarker candidate across age and sex, while GMNN, REG3B, and SNCA indicated some response similarity across age. This low yield advocated that unisex or uniage biomarker screening approaches are not feasible. In conclusion, age- and sex-specific screening approaches should be implemented as standard protocol to ensure robustness and diagnostic power of biomarker candidates. Bias-free molecular biomarkers are a necessary progression towards personalized medicine and integral for advanced adaptive cancer radiotherapy and risk assessment.

Data-Independent Acquisition Enables Robust Quantification of 400 Proteins in Non-Depleted Canine Plasma

Mass spectrometry-based plasma proteomics offers a major advance for biomarker discovery in the veterinary field, which has traditionally been limited to quantification of a small number of proteins using biochemical assays. The development of foundational data and tools related to sequential window acquisition of all theoretical mass spectra (SWATH)-mass spectrometry has allowed for quantitative profiling of a significant number of plasma proteins in humans and several animal species. Enabling SWATH in dogs enhances human biomedical research as a model species, and significantly improves diagnostic and disease monitoring capability. In this study, a comprehensive peptide spectral library specific to canine plasma proteome was developed and evaluated using SWATH for protein quantification in non-depleted dog plasma. Specifically, plasma samples were subjected to various orthogonal fractionation and digestion techniques, and peptide fragmentation data corresponding to over 420 proteins was collected. Subsequently, a SWATH-based assay was introduced that leveraged the developed resource and that enabled reproducible quantification of 400 proteins in non-depleted plasma samples corresponding to various disease conditions. The ability to profile the abundance of such a significant number of plasma proteins using a single method in dogs has the potential to accelerate biomarker discovery studies in this species.

Systemic Effects of Hemorrhagic Snake Venom Metalloproteinases: Untargeted Peptidomics to Explore the Pathodegradome of Plasma Proteins

Hemorrhage induced by snake venom metalloproteinases (SVMPs) is a complex phenomenon that involves capillary disruption and blood extravasation. HF3 (hemorrhagic factor 3) is an extremely hemorrhagic SVMP of Bothrops jararaca venom. Studies using proteomic approaches revealed targets of HF3 among intracellular and extracellular proteins. However, the role of the cleavage of plasma proteins in the context of the hemorrhage remains not fully understood. The main goal of this study was to analyze the degradome of HF3 in human plasma. For this purpose, approaches for the depletion of the most abundant proteins, and for the enrichment of low abundant proteins of human plasma, were used to minimize the dynamic range of protein concentration, in order to assess the proteolytic activity of HF3 on a wide spectrum of proteins, and to detect the degradation products using mass spectrometry-based untargeted peptidomics. The results revealed the hydrolysis products generated by HF3 and allowed the identification of cleavage sites. A total of 61 plasma proteins were identified as cleaved by HF3. Some of these proteins corroborate previous studies, and others are new HF3 targets, including proteins of the coagulation cascade, of the complement system, proteins acting on the modulation of inflammation, and plasma proteinase inhibitors. Overall, the data indicate that HF3 escapes inhibition and sculpts the plasma proteome by degrading key proteins and generating peptides that may act synergistically in the hemorrhagic process.

Proteomics in idiopathic pulmonary fibrosis: the quest for biomarkers

Idiopathic pulmonary fibrosis (IPF) is a debilitating chronic progressive and fibrosing lung disease that culminates in the destruction of alveolar integrity and dismal prognosis. Its etiology is unknown and pathophysiology remains unclear. While great advances have been made in elucidating the pathogenesis mechanism, considerable gaps related to information on pathogenetic pathways and key protein targets involved in the clinical course of the disease exist. These issues need to be addressed for better clinical management of this highly challenging disease. Omics approach has revolutionized the entire area of disease understanding and holds promise in its translation to clinical biomarker discovery. This review outlines the contribution of proteomics towards identification of important biomarkers in IPF in terms of their clinical utility, i.e. prognosis, differential diagnosis, disease progression and treatment monitoring. The major dysregulated pathways associated with IPF are also discussed. Based on numerous proteomics studies on human and animal models, it is proposed that IPF pathogenesis involves complex interactions of several pathways such as oxidative stress, endoplasmic reticulum stress, unfolded protein response, coagulation system, inflammation, abnormal wounding, fibroblast proliferation, fibrogenesis and deposition of extracellular matrix. These pathways and their key path-changing mediators need further validation in large well-planned multi-centric trials at various geographical locations for successful development of clinical biomarkers of this confounding disease.

Comprehensive plasma proteomic profiling reveals biomarkers for active tuberculosis

BACKGROUNDTuberculosis (TB) kills more people than any other infection, and new diagnostic tests to identify active cases are required. We aimed to discover and verify novel markers for TB in nondepleted plasma.METHODSWe applied an optimized quantitative proteomics discovery methodology based on multidimensional and orthogonal liquid chromatographic separation combined with high-resolution mass spectrometry to study nondepleted plasma of 11 patients with active TB compared with 10 healthy controls. Prioritized candidates were verified in independent UK (n = 118) and South African cohorts (n = 203).RESULTSWe generated the most comprehensive TB plasma proteome to date, profiling 5022 proteins spanning 11 orders-of-magnitude concentration range with diverse biochemical and molecular properties. We analyzed the predominantly low-molecular weight subproteome, identifying 46 proteins with significantly increased and 90 with decreased abundance (peptide FDR ≤ 1%, q ≤ 0.05). Verification was performed for novel candidate biomarkers (CFHR5, ILF2) in 2 independent cohorts. Receiver operating characteristics analyses using a 5-protein panel (CFHR5, LRG1, CRP, LBP, and SAA1) exhibited discriminatory power in distinguishing TB from other respiratory diseases (AUC = 0.81).CONCLUSIONWe report the most comprehensive TB plasma proteome to date, identifying novel markers with verification in 2 independent cohorts, leading to a 5-protein biosignature with potential to improve TB diagnosis. With further development, these biomarkers have potential as a diagnostic triage test.FUNDINGColciencias, Medical Research Council, Innovate UK, NIHR, Academy of Medical Sciences, Program for Advanced Research Capacities for AIDS, Wellcome Centre for Infectious Diseases Research.

Plasma proteomic approach in patients with heart failure: insights into pathogenesis of disease progression and potential novel treatment targets

AIMS

To provide insights into pathogenesis of disease progression and potential novel treatment targets for patients with heart failure by investigation of the plasma proteome using network analysis.

METHODS AND RESULTS

The plasma proteome of 50 patients with heart failure who died or were rehospitalised were compared with 50 patients with heart failure, matched for age and sex, who did not have an event. Peptides were analysed on two-dimensional liquid chromatography coupled to tandem mass spectrometry (2D LC ESI-MS/MS) in high definition mode (HDMSE). We identified and quantified 3001 proteins, of which 51 were significantly up-regulated and 46 down-regulated with more than two-fold expression changes in those who experienced death or rehospitalisation. Gene ontology enrichment analysis and protein-protein interaction networks of significant differentially expressed proteins discovered the central role of metabolic processes in clinical outcomes of patients with heart failure. The findings revealed that a cluster of proteins related to glutathione metabolism, arginine and proline metabolism, and pyruvate metabolism in the pathogenesis of poor outcome in patients with heart failure who died or were rehospitalised.

CONCLUSIONS

Our findings show that in patients with heart failure who died or were rehospitalised, the glutathione, arginine and proline, and pyruvate pathways were activated. These pathways might be potential targets for therapies to improve poor outcomes in patients with heart failure.

Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data

The proteomic analysis of human blood and blood-derived products (e.g., plasma) offers an attractive avenue to translate research progress from the laboratory into the clinic. However, due to its unique protein composition, performing proteomics assays with plasma is challenging. Plasma proteomics has regained interest due to recent technological advances, but challenges imposed by both complications inherent to studying human biology (e.g., interindividual variability) and analysis of biospecimens (e.g., sample variability), as well as technological limitations remain. As part of the Human Proteome Project (HPP), the Human Plasma Proteome Project (HPPP) brings together key aspects of the plasma proteomics pipeline. Here, we provide considerations and recommendations concerning study design, plasma collection, quality metrics, plasma processing workflows, mass spectrometry (MS) data acquisition, data processing, and bioinformatic analysis. With exciting opportunities in studying human health and disease though this plasma proteomics pipeline, a more informed analysis of human plasma will accelerate interest while enhancing possibilities for the incorporation of proteomics-scaled assays into clinical practice.

Mass Spectrometry-Based Biomarkers in Drug Development

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

MS-Based Proteomic Analysis of Serum and Plasma: Problem of High Abundant Components and Lights and Shadows of Albumin Removal

Blood serum or plasma proteome is a gold mine of disease biomarkers. However, complexity and a huge dynamic range of their components, combined with multiple mechanisms of degradation and posttranslational modifications, further complicated by the presence of lipids, salts, and other metabolites, represent a real challenge for analytical sensitivity, resolution, and reproducibility. This problem exists particularly in the case of potential disease-specific markers, most typically represented by low-abundant proteins (LAPs), whose detection is usually impaired by the dominance of albumins, immunoglobulins, and other high-abundant serum/plasma proteins (HAPs). Hence, analysis of biomarker candidates in serum/plasma samples frequently requires separation of their components, usually including depletion of albumin in a fraction of interest. Such "preprocessing" of serum/plasma specimens is critical in proteomic analysis based on mass spectrometry. This approach is very potent; nevertheless a wide range of protein concentrations in serum/plasma represents a particular challenge, since high-abundant proteins (mostly albumin) dominate in a sample subjected to mass spectrometry and suppress peptide ions originating from low-abundant proteins, thus limiting probability and reliability of their detection. An emerging approach in serum-/plasma-based biomarker-oriented studies is the proteome component of exosomes - nanovesicles secreted by cells and involved in multiple aspects of intercellular communication. However, the presence of albumin, frequent contaminant of exosomes isolated from human serum/plasma, represents a real challenge also in this type of study. A similar problem is encountered in proteomic studies based on exosomes obtained in in vitro experiments where culture media are normally supplemented with fetal bovine serum containing growth factors and hormones. In this case exosomes are frequently contaminated with bovine serum albumin and other bovine serum proteins which should be removed before proteomic analysis of exosome cargo.

Abundant plasma protein depletion using ammonium sulfate precipitation and Protein A affinity chromatography

Plasma is a highly valuable resource for biomarker research since it is easy obtainable and contains a high amount of information on patient health status. Although advancements in the field of proteomics enabled analysis of the plasma proteome, identification of low abundant proteins remains challenging due to high complexity and large dynamic range. In order to reduce the dynamic range of protein concentrations, a tandem depletion technique consisting of ammonium sulfate precipitation and Protein A affinity chromatography was developed. Using this method, 50% of albumin, together with other high abundant proteins such as alpha-1-antitrypsin, was depleted from the plasma sample at 20% to 40% ammonium sulfate saturation levels. In combination with immunoglobulin removal using a Protein A column, this technique delivered up to 40 new low- to medium abundance protein identifications when performing a shotgun mass spectrometry analysis. Compared to non-depleted plasma, 270 additional protein spots were observed during 2D-PAGE analysis. These results illustrate that this tandem depletion method is equivalent to commercial kits which are based on immune-affinity chromatography. Moreover, this method using Protein A immunoglobulin depletion was shown to be highly reproducible and a minimal amount of non-target proteins was depleted. The combination of ammonium sulfate precipitation and Protein A affinity chromatography offers a low cost, efficient, straightforward and reproducible alternative to commercial kits, with proteins remaining in native conformation, allowing protein activity and protein interaction studies.

Clinical Proteomics: Liquid Chromatography-Mass Spectrometry (LC-MS) Purification Systems

Liquid chromatography-mass spectrometry (LC-MS) has become a routine powerful technology in clinical proteomic studies for protein identification, protein characterization and the discovery of biomarkers. In this chapter, we describe two protocol methods to analyze clinical patient samples using a resin based depletion column followed by either protein In-gel enzymatic digestion or protein in-solution enzymatic digestion and then analysis by one-dimensional reverse-phase chromatography or two-dimensional strong cation exchange (SCX)-reverse-phase chromatography (RPC).

Mining the human plasma proteome with three-dimensional strategies by high-resolution Quadrupole Orbitrap Mass Spectrometry

Human plasma is a readily available clinical sample that reflects the status of the body in normal physiological and disease states. Although the wide dynamic range and immense complexity of plasma proteins are obstacles, comprehensive proteomic analysis of human plasma is necessary for biomarker discovery and further verification. Various methods such as immunodepletion, protein equalization and hyper fractionation have been applied to reduce the influence of high-abundance proteins (HAPs) and to reduce the high level of complexity. However, the depth at which the human plasma proteome has been explored in a relatively short time frame has been limited, which impedes the transfer of proteomic techniques to clinical research. Development of an optimal strategy is expected to improve the efficiency of human plasma proteome profiling. Here, five three-dimensional strategies combining HAP depletion (the 1st dimension) and protein fractionation (the 2nd dimension), followed by LC-MS/MS analysis (the 3rd dimension) were developed and compared for human plasma proteome profiling. Pros and cons of the five strategies are discussed for two issues: HAP depletion and complexity reduction. Strategies A and B used proteome equalization and tandem Seppro IgY14 immunodepletion, respectively, as the first dimension. Proteome equalization (strategy A) was biased toward the enrichment of basic and low-molecular weight proteins and had limited ability to enrich low-abundance proteins. By tandem removal of HAPs (strategy B), the efficiency of HAP depletion was significantly increased, whereas more off-target proteins were subtracted simultaneously. In the comparison of complexity reduction, strategy D involved a deglycosylation step before high-pH RPLC separation. However, the increase in sequence coverage did not increase the protein number as expected. Strategy E introduced SDS-PAGE separation of proteins, and the results showed oversampling of HAPs and identification of fewer proteins. Strategy C combined single Seppro IgY14 immunodepletion, high-pH RPLC fractionation and LC-MS/MS analysis. It generated the largest dataset, containing 1544 plasma protein groups and 258 newly identified proteins in a 30-h-machine-time analysis, making it the optimum three-dimensional strategy in our study. Further analysis of the integrated data from the five strategies showed identical distribution patterns in terms of sequence features and GO functional analysis with the 1929-plasma-protein dataset, further supporting the reliability of our plasma protein identifications. The characterization of 20 cytokines in the concentration range from sub-nanograms/milliliter to micrograms/milliliter demonstrated the sensitivity of the current strategies.

Evaluating the effects of preanalytical variables on the stability of the human plasma proteome

High quality clinical biospecimens are vital for biomarker discovery, verification, and validation. Variations in blood processing and handling can affect protein abundances and assay reliability. Using an untargeted LC-MS approach, we systematically measured the impact of preanalytical variables on the plasma proteome. Time prior to processing was the only variable that affected the plasma protein levels. LC-MS quantification showed that preprocessing times <6h had minimal effects on the immunodepleted plasma proteome, but by 4 days significant changes were apparent. Elevated levels of many proteins were observed, suggesting that in addition to proteolytic degradation during the preanalytical phase, changes in protein structure are also important considerations for protocols using antibody depletion. As to processing variables, a comparison of single- vs double-spun plasma showed minimal differences. After processing, the impact ⩽3 freeze-thaw cycles was negligible regardless of whether freshly collected samples were processed in short succession or the cycles occurred during 14-17 years of frozen storage (-80 °C). Thus, clinical workflows that necessitate modest delays in blood processing times or employ different centrifugation steps can yield valuable samples for biomarker discovery and verification studies.

Simultaneous Enrichment of Plasma Soluble and Extracellular Vesicular Glycoproteins Using Prolonged Ultracentrifugation-Electrostatic Repulsion-hydrophilic Interaction Chromatography (PUC-ERLIC) Approach

Plasma glycoproteins and extracellular vesicles represent excellent sources of disease biomarkers, but laboratory detection of these circulating structures are limited by their relatively low abundance in complex biological fluids. Although intensive research has led to the development of effective methods for the enrichment and isolation of either plasma glycoproteins or extracellular vesicles from clinical materials, at present it is not possible to enrich both structures simultaneously from individual patient sample, a method that affords the identification of biomarker combinations from both entities for the prediction of clinical outcomes will be clinically useful. We have therefore developed an enrichment method for use in mass spectrometry-based proteomic profiling that couples prolonged ultracentrifugation with electrostatic repulsion-hydrophilic interaction chromatography, to facilitate the recovery of both glycoproteins and extracellular vesicles from nondepleted human plasma. Following prolonged ultracentrifugation, plasma glycoproteins and extracellular vesicles were concentrated as a yellow suspension, and simultaneous analyses of low abundant secretory and vesicular glycoproteins was achieved in a single LC-MS/MS run. Using this systematic prolonged ultracentrifugation-electrostatic repulsion-hydrophilic interaction chromatography approach, we identified a total of 127 plasma glycoproteins at a high level of confidence (FDR ≤ 1%), including 48 glycoproteins with concentrations ranging from pg to ng/ml. The novel enrichment method we report should facilitate future human plasma-based proteome and glycoproteome that will identify novel biomarkers, or combinations of secreted and vesicle-derived biomarkers, that can be used to predict clinical outcomes in human patients.

Mixed-bed affinity chromatography: principles and methods

Mixed-bed chromatography is far from being a well-established technology within the panoply of bioseparation tools. Composed of an assembly of distinct sorbents that are mixed in a single bed, they have been mostly developed in the last decade for the reduction of dynamic concentration range where they allowed discovering many low-copy proteins within very complex proteomes. Other interesting preparative applications of mixed-bed chromatography have since been developed. In this chapter the basic concepts first and then detailed application recipes are described for (1) the reduction of protein dynamic concentration range, (2) the removal of impurity traces at the last stage of a biopurification process, and (3) the selection and use of sorbents as mixed bed in protein purification.

Biomarkers in Alzheimer's disease analysis by mass spectrometry-based proteomics

Alzheimer’s disease (AD) is a common chronic and destructive disease. The early diagnosis of AD is difficult, thus the need for clinically applicable biomarkers development is growing rapidly. There are many methods to biomarker discovery and identification. In this review, we aim to summarize Mass spectrometry (MS)-based proteomics studies on AD and discuss thoroughly the methods to identify candidate biomarkers in cerebrospinal fluid (CSF) and blood. This review will also discuss the potential research areas on biomarkers.

Mass spectrometry-based biomarkers in drug development

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