Immunoassay and antibody microarray analysis of the HUPO Plasma Proteome Project reference specimens: Systematic variation between sample types and calibration of mass spectrometry data

In vitro to in vivo extrapolation to derive a metabolism factor for estimating the aggregate exposure to salicylic acid after dermal exposure of its esters

As part of the safety assessment of salicylate esters in cosmetics, we developed a metabolism factor based on in vitro to in vivo extrapolation (IVIVE) to provide a better estimation of the aggregate internal exposure to the common metabolite, salicylic acid. Optimal incubation conditions using human liver S9 were identified before measuring salicylic acid formation from 31 substances. Four control substances, not defined as salicylic esters but which could be mistaken as such due to their nomenclature, did not form salicylic acid. For the remaining substances, higher in vitro intrinsic clearance (CLint, in vitro) values generally correlated with lower LogP values. A "High-Throughput Pharmacokinetic" (HTPK) model was used to extrapolate CLint, in vitro values to human in vivo clearance and half-lives. The latter were used to calculate the percentage of substance metabolised to salicylic acid in 24 h in vivo following human exposure to the ester, i.e. the "metabolism factor". The IVIVE model correctly reproduced the observed elimination rate of 3 substances using in silico or in vitro input parameters. For other substances, in silico only-based predictions generally resulted in lower metabolism factors than when in vitro values for plasma binding and liver S9 CLint, in vitro were used. Therefore, in vitro data input provides the more conservative metabolism factors compared to those derived using on in silico input. In conclusion, these results indicate that not all substances contribute equally (or at all) to the systemic exposure to salicylic acid. Therefore, we propose a realistic metabolism correction factor by which the potential contribution of salicylate esters to the aggregate consumer exposure to salicylic acid from cosmetic use can be estimated.

Tutorial review for peptide assays: An ounce of pre-analytics is worth a pound of cure

The recent increase in peptidomimetic-based medications and the growing interest in peptide hormones has brought new attention to the quantification of peptides for diagnostic purposes. Indeed, the circulating concentrations of peptide hormones in the blood provide a snapshot of the state of the body and could eventually lead to detecting a particular health condition. Although extremely useful, the quantification of such molecules, preferably by liquid chromatography coupled to mass spectrometry, might be quite tricky. First, peptides are subjected to hydrolysis, oxidation, and other post-translational modifications, and, most importantly, they are substrates of specific and nonspecific proteases in biological matrixes. All these events might continue after sampling, changing the peptide hormone concentrations. Second, because they include positively and negatively charged groups and hydrophilic and hydrophobic residues, they interact with their environment; these interactions might lead to a local change in the measured concentrations. A phenomenon such as nonspecific adsorption to lab glassware or materials has often a tremendous effect on the concentration and needs to be controlled with particular care. Finally, the circulating levels of peptides might be low (pico- or femtomolar range), increasing the impact of the aforementioned effects and inducing the need for highly sensitive instruments and well-optimized methods. Thus, despite the extreme diversity of these peptides and their matrixes, there is a common challenge for all the assays: the need to keep concentrations unchanged from sampling to analysis. While significant efforts are often placed on optimizing the analysis, few studies consider in depth the impact of pre-analytical steps on the results. By working through practical examples, this solution-oriented tutorial review addresses typical pre-analytical challenges encountered during the development of a peptide assay from the standpoint of a clinical laboratory. We provide tips and tricks to avoid pitfalls as well as strategies to guide all new developments. Our ultimate goal is to increase pre-analytical awareness to ensure that newly developed peptide assays produce robust and accurate results.

Fabrication and development of a microfluidic paper-based immunosorbent assay platform (μPISA) for colorimetric detection of hepatitis C

Paper-based microfluidics is an emerging analysis tool used in various applications, especially in point-of-care (PoC) diagnostic applications, due to its advantages over other types of microfluidic devices in terms of simplicity in both production and operation, cost-effectiveness, rapid response time, low sample consumption, biocompatibility, and ease of disposal. Recently, various techniques have been developed and utilized for the fabrication of paper-based microfluidics, such as photolithography, micro-embossing, wax and PDMS printing, etc. In this study, we offer a fabrication methodology for a microfluidic paper-based immunosorbent assay (μPISA) platform and the detection of Hepatitis C Virus (HCV) was carried out to validate this platform. A laser ablation technique was utilized to form hydrophobic barriers easily and rapidly, which was the major advantage of the developed fabrication methodology. The characterization of the μPISA platform was performed in terms of micro-channel properties using bright-field (BF) microscopy, and surface properties using scanning electron microscopy (SEM). At the same time, sample volume and liquid handling capacity were analyzed quantitatively. Ablation speed (S) and laser power (P) were optimized, and it was shown that one combination (10P60S) provided minimal deviation in micro-channel dimensions and prevented deterioration of hydrophobic barriers. Also, the minimum hydrophobic barrier width, which prevents cross-barrier bleeding, was determined to be 255.92 ± 10.01 μm. Furthermore, colorimetric HCV NS3 detection was implemented to optimize and validate the μPISA platform. Here, HCV NS3 in both PBS and human blood plasma was successfully detected by the naked eye at concentrations as low as 1 ng mL^-1 and 10 ng mL^-1, respectively. Moreover, the limit of detection (LoD) values for HCV NS3 were acquired as 0.796 ng mL^-1 in PBS and 2.203 ng mL^-1 in human blood plasma with a turnaround time of 90 min. In comparison with conventional ELISA, highly sensitive and rapid HCV NS3 detection was accomplished colorimetrically on the developed μPISA platform.

Nanodiamond Solid-Phase Extraction and Triton X-114 Cloud Point Separation for Robust Fractionation and Shotgun Proteomics Analysis of the Human Serum Proteome

Human serum is one of the most attractive specimens in biomarker research. However, its overcomplicated properties have hindered the analysis of low-abundance proteins by conventional mass spectrometry techniques. This work proposes an innovative strategy for utilizing nanodiamonds (NDs) in combination with Triton X-114 protein extraction to fractionate the crude serum to six pH-tuned fractions, simplifying the overall proteome and facilitating protein profiling with high efficiency. A total of 663 proteins are identified and evenly distributed among the fractions along with 39 FDA-approved biomarkers─a remarkable increase from the 230 proteins found in unfractionated crude serum. In the low-abundance protein section, 88 proteins with 7 FDA-approved biomarkers are detected─a marked increase from the 15 proteins (2 biomarkers) observed in the untreated sample. Notably, fractions at pH 11, derived from the aqueous phase of detergent separation, suggest potential applications in rapid and robust serum proteome analysis. Notably, by outlining the excellent properties of NDs for proteomic research, this work suggests a promising extraction protocol utilizing the great compatibility of NDs with streamlined serum proteomics and identifies potential avenues for future developments. Finally, we believe that this work not just improves shotgun proteomics but also opens up studies on the interaction between NDs and the human proteome. Data are available via ProteomeXchange with the identifier PXD029710.

Simultaneous and quantitative monitoring transcription factors in human embryonic stem cell differentiation using mass spectrometry-based targeted proteomics

Human embryonic stem cells (hESCs) can be self-propagated indefinitely in culture while holding the capacity to generate almost all cell types. Although this powerful differentiation ability of hESCs has become a potential source of cell replacement therapies, application of stem cells in clinical practice relies heavily on the exquisite control of their developmental fate. In general, an essential first step in differentiation is to exit the pluripotent state, which is precariously balanced and depends on a variety of factors, mainly centering on the core transcriptional mechanism. To date, much evidence has indicated that transcription factors such as Sox2, Oct4, and Nanog control the self-renewal and pluripotency of hESCs. Their expression displays a restricted spatial-temporal pattern and their small changes in level can significantly affect directed differentiation and the cell type derived. So far, few assays have been developed to monitor this process. Herein, we provided a mass spectrometry (MS)-based approach for simultaneous and quantitative monitoring of these transcription factors, in an attempt to provide insight into their contributions in hESC differentiation.

The Effect of Molecular Weight on Passage of Proteins Through the Blood-Aqueous Barrier

Purpose

To determine the effect of molecular weight (MW) on the concentration of plasma-derived proteins in aqueous humor and to estimate the plasma-derived and eye-derived fractions for each protein.

Methods

Aqueous humor and plasma samples were obtained during cataract surgery on an institutional review board-approved protocol. Protein concentrations were determined by ELISA and quantitative antibody microarrays. A total of 93 proteins were studied, with most proteins analyzed using 27 to 116 aqueous and 6 to 30 plasma samples.

Results

Plasma proteins without evidence of intraocular expression by sequence tags were used to fit a logarithmic model relating aqueous-plasma ratio (AH:PL) to MW. The log(AH:PL) appears to be well predicted by the log(MW) (P < 0.0001), with smaller proteins such as cystatin C (13 kDa) having a higher AH:PL (1:6) than larger proteins such as albumin (66 kDa, 1:300) and complement component 5 (188 kDa, 1:2500). The logarithmic model was used to calculate the eye-derived intraocular fraction (IOF) for each protein. Based on the IOF, 66 proteins could be categorized as plasma-derived (IOF<20), whereas 10 proteins were primarily derived from eye tissue (IOF >80), and 17 proteins had contribution from both plasma and eye tissue (IOF 20-80).

Conclusions

Protein concentration of plasma-derived proteins in aqueous is nonlinearly dependent on MW in favor of smaller proteins. Our study demonstrates that for proper interpretation of results, proteomic studies evaluating changes in aqueous humor protein levels should take into account the plasma and eye-derived fractions.

A Comparative Proteomics Analysis of Five Body Fluids: Plasma, Urine, Cerebrospinal Fluid, Amniotic Fluid, and Saliva

PURPOSE

Body fluid is considered a rich source of disease biomarkers. Proteins in many body fluids have potential clinical applications for disease diagnostic and prognostic predictions.

EXPERIMENTAL DESIGN

To determine differences in the protein components and functional features of body fluids, a proteomic comparison of five body fluids (plasma, urine, cerebrospinal fluid, saliva, and amniotic fluid) was conducted by high-resolution mass spectrometry.

RESULTS

A total of 4717 nonredundant proteins were identified, and the concentrations of 3433 proteins were estimated by an intensity-based algorithm quantitation method. Among them, 564 proteins were shared among the five body fluids, with common functions in the coagulation/prothrombin system and inflammatory response. A total of 36.7% of the proteins were detected in only one body fluid and were closely related to their adjacent tissues by function. The functional analysis of the remaining 2986 proteins showed that similar functions might be shared among different body fluids, which highlighted intimate connection in the body.

CONCLUSIONS AND CLINICAL RELEVANCE

The quantitative comparative functional analysis indicated that body fluids might reflect the diverse functions of the whole body rather than the characteristics of their adjacent tissues. The above data might indicate the potential application of body fluids for biomarker discovery.

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.

Studies of Isolated Peripheral Blood Cells as a Model of Immune Dysfunction

Peripheral blood mononuclear cells (PBMCs) have been used as a surrogate model of immune function in studies of multiple medical areas, such as metabolic diseases and immune dysfunction. This chapter describes a standardized technique for blood draw and preparation of PBMCs from whole blood using density gradient centrifugation, followed by cell culture. The main focus is on collection of the PBMC culture media and extraction of cellular proteins in order to provide the materials for biomarker studies.

High throughput and accurate serum proteome profiling by integrated sample preparation technology and single-run data independent mass spectrometry analysis

Mass spectrometry (MS)-based serum proteome analysis is extremely challenging due to its high complexity and dynamic range of protein abundances. Developing high throughput and accurate serum proteomic profiling approach capable of analyzing large cohorts is urgently needed for biomarker discovery. Herein, we report a streamlined workflow for fast and accurate proteomic profiling from 1μL of blood serum. The workflow combined an integrated technique for highly sensitive and reproducible sample preparation and a new data-independent acquisition (DIA)-based MS method. Comparing with standard data dependent acquisition (DDA) approach, the optimized DIA method doubled the number of detected peptides and proteins with better reproducibility. Without protein immunodepletion and prefractionation, the single-run DIA analysis enables quantitative profiling of over 300 proteins with 50min gradient time. The quantified proteins span more than five orders of magnitude of abundance range and contain over 50 FDA-approved disease markers. The workflow allowed us to analyze 20 serum samples per day, with about 358 protein groups per sample being identified. A proof-of-concept study on renal cell carcinoma (RCC) serum samples confirmed the feasibility of the workflow for large scale serum proteomic profiling and disease-related biomarker discovery.

BIOLOGICAL SIGNIFICANCE

Blood serum or plasma is the predominant specimen for clinical proteomic studies while the analysis is extremely challenging for its high complexity. Many efforts had been made in the past for serum proteomics for maximizing protein identifications, whereas few have been concerned with throughput and reproducibility. Here, we establish a rapid, robust and high reproducible DIA-based workflow for streamlined serum proteomic profiling from 1μL serum. The workflow doesn't need protein depletion and pre-fractionation, while still being able to detect disease-relevant proteins accurately. The workflow is promising in clinical application, because the usage of small sample amounts makes blood testing much less invasive, the fully integrated sample preparation by the SISPROT technology greatly improve sample preparation throughput and reproducibility, and the scan feature of DIA method provides a way to convert nonrenewable clinical specimens into permanent digital proteome maps which could be easily reanalyzed.

The Development of Translational Biomarkers as a Tool for Improving the Understanding, Diagnosis and Treatment of Chronic Neuropathic Pain

Chronic neuropathic pain (CNP) is one of the most significant unmet clinical needs in modern medicine. Alongside the lack of effective treatments, there is a great deficit in the availability of objective diagnostic methods to reliably facilitate an accurate diagnosis. We therefore aimed to determine the feasibility of a simple diagnostic test by analysing differentially expressed genes in the blood of patients diagnosed with CNP of the lower back and compared to healthy human controls. Refinement of microarray expression data was performed using correlation analysis with 3900 human 2-colour microarray experiments. Selected genes were analysed in the dorsal horn of Sprague-Dawley rats after L5 spinal nerve ligation (SNL), using qRT-PCR and ddPCR, to determine possible associations with pathophysiological mechanisms underpinning CNP and whether they represent translational biomarkers of CNP. We found that of the 15 potential biomarkers identified, tissue inhibitor of matrix metalloproteinase-1 (TIMP1) gene expression was upregulated in chronic neuropathic lower back pain (CNBP) (p = 0.0049) which positively correlated (R = 0.68, p = ≤0.05) with increased plasma TIMP1 levels in this group (p = 0.0433). Moreover, plasma TIMP1 was also significantly upregulated in CNBP than chronic inflammatory lower back pain (p = 0.0272). In the SNL model, upregulation of the Timp1 gene was also observed (p = 0.0058) alongside a strong trend for the upregulation of melanocortin 1 receptor (p = 0.0847). Our data therefore highlights several genes that warrant further investigation, and of these, TIMP1 shows the greatest potential as an accessible and translational CNP biomarker.

Blood Sampling and Preparation Procedures for Proteomic Biomarker Studies of Psychiatric Disorders

A major challenge in proteomic biomarker discovery and validation for psychiatric diseases is the inherent biological complexity underlying these conditions. There are also many technical issues which hinder this process such as the lack of standardization in sampling, processing and storage of bio-samples in preclinical and clinical settings. This chapter describes a reproducible procedure for sampling blood serum and plasma that is specifically designed for maximizing data quality output in two-dimensional gel electrophoresis, multiplex immunoassay and mass spectrometry profiling studies.

Blood Bio-Sampling Procedures for Multiplex Biomarkers Studies

A major challenge in single or panel of biomarker discovery and validation is the inherent biological complexity underlying disease heterogeneity and inconsistent responses to treatment. Moreover, the lack of standardization in the sampling, processing, and storage of biological fluids such as plasma and serum disrupts the discovery and validation of blood-based biomarker tests in preclinical and clinical settings. This chapter presents a reproducible sample collection and handling procedure that aims to enhance analyte stability and ensure compatibility with the corresponding multiplex biomarker profiling platforms. The importance of defining bio-sample acquisition and processing, study design, and profiling platform guidelines for blood-based biomarker measurements is paramount for the success of personalized healthcare strategy and development of companion diagnostics.

Systems biology-opportunities and challenges: the application of proteomics to study the cardiovascular extracellular matrix

Systems biology approaches including proteomics are becoming more widely used in cardiovascular research. In this review article, we focus on the application of proteomics to the cardiac extracellular matrix (ECM). ECM remodelling is a hallmark of many cardiovascular diseases. Proteomic techniques using mass spectrometry (MS) provide a platform for the comprehensive analysis of ECM proteins without a priori assumptions. Proteomics overcomes various constraints inherent to conventional antibody detection. On the other hand, studies that use whole tissue lysates for proteomic analysis mask the identification of the less abundant ECM constituents. In this review, we first discuss decellularization-based methods that enrich for ECM proteins in cardiac tissue, and how targeted MS allows for accurate protein quantification. The second part of the review will focus on post-translational modifications including hydroxylation and glycosylation and on the release of matrix fragments with biological activity (matrikines), all of which can be interrogated by proteomic techniques.

Plasma protein profiling in a stage defined pancreatic cancer cohort - Implications for early diagnosis

Pancreatic ductal adenocarcinoma (PDAC) is a disease where detection preceding clinical symptoms significantly increases the life expectancy of patients. In this study, a recombinant antibody microarray platform was used to analyze 213 Chinese plasma samples from PDAC patients and normal control (NC) individuals. The cohort was stratified according to disease stage, i.e. resectable disease (stage I/II), locally advanced (stage III) and metastatic disease (stage IV). Support vector machine analysis showed that all PDAC stages could be discriminated from controls and that the accuracy increased with disease progression, from stage I to IV. Patients with stage I/II PDAC could be discriminated from NC with high accuracy based on a plasma protein signature, indicating a possibility for early diagnosis and increased detection rate of surgically resectable tumors.

Prediction of colorectal cancer diagnosis based on circulating plasma proteins

Non-invasive detection of colorectal cancer with blood-based markers is a critical clinical need. Here we describe a phased mass spectrometry-based approach for the discovery, screening, and validation of circulating protein biomarkers with diagnostic value. Initially, we profiled human primary tumor tissue epithelia and characterized about 300 secreted and cell surface candidate glycoproteins. These candidates were then screened in patient systemic circulation to identify detectable candidates in blood plasma. An 88-plex targeting method was established to systematically monitor these proteins in two large and independent cohorts of plasma samples, which generated quantitative clinical datasets at an unprecedented scale. The data were deployed to develop and evaluate a five-protein biomarker signature for colorectal cancer detection.

Immunoglobulin detection in wild birds: effectiveness of three secondary anti-avian IgY antibodies in direct ELISAs in 41 avian species

Immunological reagents for wild, non-model species are limited or often non-existent for many species.In this study, we compare the reactivity of a new anti-passerine IgY secondary antibody with existing secondary antibodies developed for use with birds. Samples from 41 species from the following six avian orders were analysed: Anseriformes (1 family, 1 species), Columbiformes (1 family, 2 species), Galliformes (1 family, 1 species), Passeriformes (16 families, 34 species), Piciformes (1 family, 2 species) and Suliformes (1 family, 1 species). Direct ELISAs were performed to detect total IgY using goat anti-passerine IgY, goat anti-chicken IgY or goat anti-bird IgY secondary antibodies.The anti-passerine antibody exhibited significantly higher IgY reactivity compared to the anti-chicken and/or anti-bird antibodies in 80% of the passerine families tested. Birds in the order Piciformes (woodpeckers) and order Suliformes (cormorants) were poorly detected by all three secondary antibodies. A comparison of serum and plasma IgY levels was made within the same individuals for two passerine species (house finch and white-crowned sparrow), and serum exhibited significantly more IgY than the plasma for all three secondary antibodies. This result indicates that serum may be preferred to plasma when measuring total antibody levels in blood.This study indicates that the anti-passerine IgY secondary antibody can effectively be used in immunological assays to detect passerine IgY for species in most passerine families and is preferred over anti-chicken and anti-bird secondary antibodies for the majority of passerine species. This anti-passerine antibody will allow for more accurate detection and quantification of IgY in more wild bird species than was possible with previously available secondary antibodies.

Genomics, microRNA, epigenetics, and proteomics for future diagnosis, treatment and monitoring response in upper GI cancers

One major objective for our evolving understanding in the treatment of cancers will be to address how a combination of diagnosis and treatment strategies can be used to integrate patient and tumor variables with an outcome-oriented approach. Such an approach, in a multimodal therapy setting, could identify those patients (1) who should undergo a defined treatment (personalized therapy) (2) in whom modifications of the multimodal therapy due to observed responses might lead to an improvement of the response and/or prognosis (individualized therapy), (3) who might not benefit from a particular toxic treatment regimen, and (4) who could be identified early on and thereby be spared the morbidity associated with such treatments. These strategies could lead in the direction of precision medicine and there is hope of integrating translational molecular data to improve cancer classifications. In order to achieve these goals, it is necessary to understand the key issues in different aspects of biotechnology to anticipate future directions of personalized and individualized diagnosis and multimodal treatment strategies. Providing an overview of translational data in cancers proved to be a challenge as different methods and techniques used to obtain molecular data are used and studies are based on different tumor entities with different tumor biology and prognoses as well as vastly different therapeutic approaches. The pros and cons of the available methodologies and the potential response data in genomics, microRNA, epigenetics and proteomics with a focus on upper gastrointestinal cancers are considered herein to allow for an understanding of where these technologies stand with respect to cancer diagnosis, prognosis and treatment.

A standardized kit for automated quantitative assessment of candidate protein biomarkers in human plasma

Background: An increasingly popular mass spectrometry-based quantitative approach for health-related research in the biomedical field involves the use of stable isotope-labeled standards (SIS) and multiple/selected reaction monitoring (MRM/SRM). To improve inter-laboratory precision and enable more widespread use of this ‘absolute’ quantitative technique in disease-biomarker assessment studies, methods must be standardized. Results/methodology: Using this MRM-with-SIS-peptide approach, we developed an automated method (encompassing sample preparation, processing and analysis) for quantifying 76 candidate protein markers (spanning >4 orders of magnitude in concentration) in neat human plasma. Discussion/conclusion: The assembled biomarker assessment kit – the ‘BAK-76’ – contains the essential materials (SIS mixes), methods (for acquisition and analysis), and tools (Qualis-SIS software) for performing biomarker discovery or verification studies in a rapid and standardized manner.

Site-specific O-Glycosylation Analysis of Human Blood Plasma Proteins

Site-specific glycosylation analysis is key to investigate structure-function relationships of glycoproteins, e.g. in the context of antigenicity and disease progression. The analysis, though, is quite challenging and time consuming, in particular for O-glycosylated proteins. In consequence, despite their clinical and biopharmaceutical importance, many human blood plasma glycoproteins have not been characterized comprehensively with respect to their O-glycosylation. Here, we report on the site-specific O-glycosylation analysis of human blood plasma glycoproteins. To this end pooled human blood plasma of healthy donors was proteolytically digested using a broad-specific enzyme (Proteinase K), followed by a precipitation step, as well as a glycopeptide enrichment and fractionation step via hydrophilic interaction liquid chromatography, the latter being optimized for intact O-glycopeptides carrying short mucin-type core-1 and -2 O-glycans, which represent the vast majority of O-glycans on human blood plasma proteins. Enriched O-glycopeptide fractions were subjected to mass spectrometric analysis using reversed-phase liquid chromatography coupled online to an ion trap mass spectrometer operated in positive-ion mode. Peptide identity and glycan composition were derived from low-energy collision-induced dissociation fragment spectra acquired in multistage mode. To pinpoint the O-glycosylation sites glycopeptides were fragmented using electron transfer dissociation. Spectra were annotated by database searches as well as manually. Overall, 31 O-glycosylation sites and regions belonging to 22 proteins were identified, the majority being acute-phase proteins. Strikingly, also 11 novel O-glycosylation sites and regions were identified. In total 23 O-glycosylation sites could be pinpointed. Interestingly, the use of Proteinase K proved to be particularly beneficial in this context. The identified O-glycan compositions most probably correspond to mono- and disialylated core-1 mucin-type O-glycans (T-antigen). The developed workflow allows the identification and characterization of the major population of the human blood plasma O-glycoproteome and our results provide new insights, which can help to unravel structure-function relationships. The data were deposited to ProteomeXchange PXD003270.

Human plasma protein N-glycosylation

Glycosylation is the most abundant and complex protein modification, and can have a profound structural and functional effect on the conjugate. The oligosaccharide fraction is recognized to be involved in multiple biological processes, and to affect proteins physical properties, and has consequentially been labeled a critical quality attribute of biopharmaceuticals. Additionally, due to recent advances in analytical methods and analysis software, glycosylation is targeted in the search for disease biomarkers for early diagnosis and patient stratification. Biofluids such as saliva, serum or plasma are of great use in this regard, as they are easily accessible and can provide relevant glycosylation information. Thus, as the assessment of protein glycosylation is becoming a major element in clinical and biopharmaceutical research, this review aims to convey the current state of knowledge on the N-glycosylation of the major plasma glycoproteins alpha-1-acid glycoprotein, alpha-1-antitrypsin, alpha-1B-glycoprotein, alpha-2-HS-glycoprotein, alpha-2-macroglobulin, antithrombin-III, apolipoprotein B-100, apolipoprotein D, apolipoprotein F, beta-2-glycoprotein 1, ceruloplasmin, fibrinogen, immunoglobulin (Ig) A, IgG, IgM, haptoglobin, hemopexin, histidine-rich glycoprotein, kininogen-1, serotransferrin, vitronectin, and zinc-alpha-2-glycoprotein. In addition, the less abundant immunoglobulins D and E are included because of their major relevance in immunology and biopharmaceutical research. Where available, the glycosylation is described in a site-specific manner. In the discussion, we put the glycosylation of individual proteins into perspective and speculate how the individual proteins may contribute to a total plasma N-glycosylation profile determined at the released glycan level.

A Multicenter Trial Defining a Serum Protein Signature Associated with Pancreatic Ductal Adenocarcinoma

Background. Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with rapid tumor progression and poor prognosis. This study was motivated by the lack of sensitive and specific PDAC biomarkers and aimed to identify a diagnostic, serum protein signature for PDAC. Methods. To mimic a real life test situation, a multicenter trial comprising a serum sample cohort, including 338 patients with either PDAC or other pancreatic diseases (OPD) and controls with nonpancreatic conditions (NPC), was analyzed on 293-plex recombinant antibody microarrays targeting immunoregulatory and cancer-associated antigens. Results. Serum samples collected from different hospitals were analyzed and showed that (i) sampling from five different hospitals could not be identified as a preanalytical variable and (ii) a multiplexed biomarker signature could be identified, utilizing up to 10 serum markers that could discriminate PDAC from controls, with sensitivities and specificities in the 91–100% range. The first protein profiles associated with the location of the primary tumor in the pancreas could also be identified. Conclusions. The results demonstrate that robust enough serum signatures could be identified in a multicenter trial, potentially contributing to the development of a multiplexed biomarker immunoassay for improved PDAC diagnosis.

New insights in molecular mechanisms involved in chronic kidney disease using high-resolution plasma proteome analysis

BACKGROUND

The reduced glomerular filtration rate in the advanced stages of chronic kidney disease (CKD) leads to plasma accumulation of uraemic retention solutes including proteins. It has been hypothesized that these changes may, at least in part, be responsible for CKD-associated morbidity and mortality. However, most studies focused on the role of individual proteins, while a holistic, large-scale, integrative approach may generate significant additional insight.

METHODS

In a discovery study, we analysed the plasma proteome of patients with stage 2-3 CKD (n = 14) and stage 5 CKD with haemodialysis (HD) (n = 15), using high-resolution LC-MS/MS analysis. Selected results were validated in a cohort of 40 patients with different CKD stages with or without HD, using ELISA.

RESULTS

Of a total of 2054 detected proteins, 127 displayed lower, while 206 displayed higher abundance in the plasma of patients on HD. Molecular pathway analysis confirmed the modification of known processes involved in CKD complications, including decreased haemostasis and increased inflammation, complement activation and vascular damage. In addition, we identified the plasma increase during CKD progression of lysozyme C and leucine-rich alpha-2 glycoprotein, two proteins related to vascular damage and heart failure. High level of leucine-rich alpha-2 glycoprotein was associated with higher mortality in stage 5 CKD patients on HD.

CONCLUSIONS

This study provides for the first time a comprehensive assessment of CKD plasma proteome, contributing to new knowledge and potential markers of CKD. These results will serve as a basis for future studies investigating the relevance of these molecules in CKD associated morbidity and mortality.

Preanalytical Variables Affecting the Integrity of Human Biospecimens in Biobanking

BACKGROUND

Most errors in a clinical chemistry laboratory are due to preanalytical errors. Preanalytical variability of biospecimens can have significant effects on downstream analyses, and controlling such variables is therefore fundamental for the future use of biospecimens in personalized medicine for diagnostic or prognostic purposes.

CONTENT

The focus of this review is to examine the preanalytical variables that affect human biospecimen integrity in biobanking, with a special focus on blood, saliva, and urine. Cost efficiency is discussed in relation to these issues.

SUMMARY

The quality of a study will depend on the integrity of the biospecimens. Preanalytical preparations should be planned with consideration of the effect on downstream analyses. Currently such preanalytical variables are not routinely documented in the biospecimen research literature. Future studies using biobanked biospecimens should describe in detail the preanalytical handling of biospecimens and analyze and interpret the results with regard to the effects of these variables.

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.

The pre-analytical processing of blood samples for detecting biomarkers on protein microarrays

Specimen collection method and quality insurance are pivotal in biomarker discovery. Pre-analytical variables concerning blood collection and sample handling might affect analytical results and should be standardised prior application. In this study, we examine pre-analytical characteristics of blood samples using protein microarray. The influences of 1) standby times until centrifugation (1 h, 4 h, 24 h and 48 h), 2) four blood collection methods, and 3) IgG purified from those samples on differentially reactive antigens between samples ("DIRAGs") were investigated. Spearman correlation analyses of intra-individual arrays demonstrated remarkable differences (0.75-0.98 vs. 0.5-0.75) of antibody reactivities within and between serum and plasma samples. Class comparison showed that reactive antigen profiles were best preserved using IgG purified samples of serum tubes without separation gel as after 24h 83% of the 1h baseline DIRAGs were re-found. Testing dilution series with protein microarrays and Luminex xMap® Technology, we found linear correlations (R(2) = 0.94-0.99) between IgG concentration and read-out when using purified IgG instead of serum. Therefore, we highly recommend standardising pre-analytics and proposing the use of purified IgG for autoantibody immune-profiling with protein microarrays to reduce potential unspecific binding of matrix proteins abundant in serum and plasma samples.

SIGNIFICANCE

Although purified IgG cannot completely compensate the influence of pre-analytics, in highly parallel immune-profiling IgG enables reduction of unspecific effects, which occur when using serum or plasma for analysis on protein microarrays. Reproducibility problems due to pre-analytical processing of blood samples might explain discrepant results reported in various biomarker studies.

Preanalysis storage conditions influence the measurement of brain-derived neurotrophic factor levels in peripheral blood

BACKGROUND

Brain-derived neurotrophic factor (BDNF) is a neurotrophin that plays a pivotal role in regulating neuronal function throughout life, and this factor is regarded as a potential biomarker of mental disorders. However, previous studies have suggested that plasma BDNF levels are more variable than serum BDNF levels.

METHODS

We determined the influence of time and temperature on the measurement of peripheral blood BDNF levels. Blood samples were aliquoted into four types of tubes, including tubes containing heparin, ethylenediaminetetraacetic acid (EDTA), and citrate for plasma, and anticoagulant-free tubes for serum. The samples were stored at 4 or 25°C for 0, 1, 2, 4, 6, 24 or 48 h, and the plasma and serum BDNF levels were measured using enzyme-linked immunosorbent assay.

RESULTS

There were interindividual and interanticoagulant compound variability in the plasma BDNF levels. The measured plasma BDNF levels increased over time, whereas the serum BDNF levels remained unchanged. Furthermore, the BDNF levels detected in plasma stored in heparin tubes at 4°C and those for samples stored in EDTA tubes at 25°C were much higher than those of the other samples.

CONCLUSION

This study indicates that measurements of plasma BDNF levels are dependent not only on the anticoagulant compounds but also on the storage time and temperature conditions used after blood sampling.

Colorectal cancer biomarker discovery and validation using LC-MS/MS-based proteomics in blood: truth or dare?

Globally, colorectal cancer (CRC) is the third most common malignant neoplasm. However, highly sensitive, specific, noninvasive tests that allow CRC diagnosis at an early stage are still needed. As circulatory blood reflects the physiological status of an individual and/or the disease status for several disorders, efforts have been undertaken to identify candidate diagnostic CRC markers in plasma and serum. In this review, the challenges, bottlenecks and promising properties of mass spectrometry (MS)-based proteomics in blood are discussed. More specifically, important aspects in clinical design, sample retrieval, sample preparation, and MS analysis are presented. The recent developments in targeted MS approaches in plasma or serum are highlighted as well.

Cytokines as biomarkers in rheumatoid arthritis

RA is a complex disease that develops as a series of events often referred to as disease continuum. RA would benefit from novel biomarker development for diagnosis where new biomarkers are still needed (even if progresses have been made with the inclusion of ACPA into the ACR/EULAR 2010 diagnostic criteria) and for prognostic notably in at risk of evolution patients with autoantibody-positive arthralgia. Risk biomarkers for rapid evolution or cardiovascular complications are also highly desirable. Monitoring biomarkers would be useful in predicting relapse. Finally, predictive biomarkers for therapy outcome would allow tailoring therapy to the individual. Increasing numbers of cytokines have been involved in RA pathology. Many have the potential as biomarkers in RA especially as their clinical utility is already established in other diseases and could be easily transferable to rheumatology. We will review the current knowledge's relation to cytokine used as biomarker in RA. However, given the complexity and heterogeneous nature of RA, it is unlikely that a single cytokine may provide sufficient discrimination; therefore multiple biomarker signatures may represent more realistic approach for the future of personalised medicine in RA.

ELISA microarray technology as a high-throughput system for cancer biomarker validation

A large gap currently exists between the ability to discover potential biomarkers and the ability to assess the real value of these proteins for cancer screening. One major challenge in biomarker validation is the inherent variability in biomarker levels. This variability stems from the diversity across the human population and the considerable molecular heterogeneity between individual tumors, even those that originate from a single tissue. An additional challenge with cancer screening is that most cancers are rare in the general population, meaning that assay specificity must be very high. Otherwise, the number of false positives will be much greater than the number of true positives. Due to these challenges associated with biomarker validation, it is necessary to analyze thousands of samples in order to obtain a clear idea of the utility of a screening assay. Enzyme-linked immunosorbent assay (ELISA) microarray technology can simultaneously quantify levels of multiple proteins and, thus, has the potential to accelerate validation of protein biomarkers for clinical use. This review will discuss current ELISA microarray technology and potential advances that could help to achieve the reproducibility and throughput that are required to evaluate cancer biomarkers.

High-throughput proteomics using antibody microarrays: an update

Antibody-based microarrays are a rapidly emerging technology that has advanced from the first proof-of-concept studies to demanding serum protein profiling applications during recent years, displaying great promise within disease proteomics. Miniaturized micro- and nanoarrays can be fabricated with an almost infinite number of antibodies carrying the desired specificities. While consuming only minute amounts of reagents, multiplexed and ultrasensitive assays can be performed targeting high- as well as low-abundance analytes in complex nonfractionated proteomes. The microarray images generated can then be converted into protein expression profiles or protein atlases, revealing a detailed composition of the sample. The technology will provide unique opportunities for fields such as disease diagnostics, biomarker discovery, patient stratification, predicting disease recurrence and drug target discovery. This review describes an update of high-throughput proteomics, using antibody-based microarrays, focusing on key technological advances and novel applications that have emerged over the last 3 years.

Quantifying antibody binding on protein microarrays using microarray nonlinear calibration

We present a microarray nonlinear calibration (MiNC) method for quantifying antibody binding to the surface of protein microarrays that significantly increases the linear dynamic range and reduces assay variation compared with traditional approaches. A serological analysis of guinea pig Mycobacterium tuberculosis models showed that a larger number of putative antigen targets were identified with MiNC, which is consistent with the improved assay performance of protein microarrays. MiNC has the potential to be employed in biomedical research using multiplex antibody assays that need quantitation, including the discovery of antibody biomarkers, clinical diagnostics with multi-antibody signatures, and construction of immune mathematical models.

Statistical design for biospecimen cohort size in proteomics-based biomarker discovery and verification studies

Protein biomarkers are needed to deepen our understanding of cancer biology and to improve our ability to diagnose, monitor, and treat cancers. Important analytical and clinical hurdles must be overcome to allow the most promising protein biomarker candidates to advance into clinical validation studies. Although contemporary proteomics technologies support the measurement of large numbers of proteins in individual clinical specimens, sample throughput remains comparatively low. This problem is amplified in typical clinical proteomics research studies, which routinely suffer from a lack of proper experimental design, resulting in analysis of too few biospecimens to achieve adequate statistical power at each stage of a biomarker pipeline. To address this critical shortcoming, a joint workshop was held by the National Cancer Institute (NCI), National Heart, Lung, and Blood Institute (NHLBI), and American Association for Clinical Chemistry (AACC) with participation from the U.S. Food and Drug Administration (FDA). An important output from the workshop was a statistical framework for the design of biomarker discovery and verification studies. Herein, we describe the use of quantitative clinical judgments to set statistical criteria for clinical relevance and the development of an approach to calculate biospecimen sample size for proteomic studies in discovery and verification stages prior to clinical validation stage. This represents a first step toward building a consensus on quantitative criteria for statistical design of proteomics biomarker discovery and verification research.

Analysis of the plasma proteome in COPD: Novel low abundance proteins reflect the severity of lung remodeling

The search for COPD biomarkers has largely employed a targeted approach that focuses on plasma proteins involved in the systemic inflammatory response and in lung injury and repair. This proof of concept study was designed to test the idea that an open, unbiased, in-depth proteomics approach could identify novel, low abundance plasma proteins i.e., ng/mL concentration, which could serve as potential biomarkers. Differentially expressed proteins were identified in a discovery group with severe COPD (FEV1 <45% predicted; n = 10). Subjects with normal lung function matched for age, sex, ethnicity and smoking history served as controls (n = 10). Pooled plasma from each group was exhaustively immunodepleted of abundant proteins, d separated by 1-D gel electrophoresis and extensively fractionated prior to LC-tandem mass spectroscopy (GeLC-MS). Thirty one differentially expressed proteins were identified in the discovery group including markers of lung defense against oxidant stress, alveolar macrophage activation, and lung tissue injury and repair. Four of the 31 proteins (i.e., GRP78, soluble CD163, IL1AP and MSPT9) were measured in a separate verification group of 80 subjects with varying COPD severity by immunoassay. All 4 were significantly altered in COPD and 2 (GRP78 and soluble CD163) correlated with both FEV1 and the extent of emphysema. In-depth, plasma proteomic analysis identified a group of novel, differentially expressed, low abundance proteins that reflect known pathogenic mechanisms and the severity of lung remodeling in COPD. These proteins may also prove useful as COPD biomarkers.

N-glycoprotein SRMAtlas: a resource of mass spectrometric assays for N-glycosites enabling consistent and multiplexed protein quantification for clinical applications

Protein biomarkers have the potential to transform medicine as they are clinically used to diagnose diseases, stratify patients, and follow disease states. Even though a large number of potential biomarkers have been proposed over the past few years, almost none of them have been implemented so far in the clinic. One of the reasons for this limited success is the lack of technologies to validate proposed biomarker candidates in larger patient cohorts. This limitation could be alleviated by the use of antibody-independent validation methods such as selected reaction monitoring (SRM). Similar to measurements based on affinity reagents, SRM-based targeted mass spectrometry also requires the generation of definitive assays for each targeted analyte. Here, we present a library of SRM assays for 5568 N-glycosites enabling the multiplexed evaluation of clinically relevant N-glycoproteins as biomarker candidates. We demonstrate that this resource can be utilized to select SRM assay sets for cancer-associated N-glycoproteins for their subsequent multiplexed and consistent quantification in 120 human plasma samples. We show that N-glycoproteins spanning 5 orders of magnitude in abundance can be quantified and that previously reported abundance differences in various cancer types can be recapitulated. Together, the established N-glycoprotein SRMAtlas resource facilitates parallel, efficient, consistent, and sensitive evaluation of proposed biomarker candidates in large clinical sample cohorts.

Remarkable temporal stability of high-abundance human plasma proteins assessed by targeted mass spectrometry

PURPOSE

To examine temporal fluctuations in selected plasma protein levels over a period of nearly 4 months and assess biological variation of these proteins across a healthy population cohort.

EXPERIMENTAL DESIGN

Plasma was collected from ten healthy volunteers over two time-courses: (i) weekly for 4 weeks; (ii) bimonthly over 4 months and depleted of albumin and IgG. SRM MS was used to determine the relative quantitation of 31 plasma proteins commonly observed in biomarker studies over these time courses. Estimates of between-subject and within-subject biological variances determined by SRM were calculated for each protein.

RESULTS

Replicate analysis demonstrated the high precision of SRM assays of plasma proteins. Statistical analysis indicated that none of the measured proteins exhibited significant temporal fluctuations over either time course. Overall, time-based intraindividual quantitative variation of plasma protein levels was considerably lower than biological variation occurring between individual volunteers.

CONCLUSIONS AND CLINICAL RELEVANCE

This study is the first to show robust temporal stability of the plasma proteome in healthy individuals using SRM-based peptide quantitation. This is important as it provides a strong basis for reliable detection of disease/treatment-related changes of these plasma proteins and others using SRM.

Biological and methodical challenges of blood-based proteomics in the field of neurological research

Biomarker discovery is an application of major importance in today's proteomic research. There is an urgent need for suitable biomarkers to improve diagnostic tools and treatment in various neurological diseases, such as neurodegenerative disorders. Recent years have witnessed an enormous interest in proteomics, which is currently seen as an invaluable tool to shed more light on complex interacting signalling pathways and molecular networks involved in several neuropathological conditions. However, while first results of proteomic research studies have sparked much public attention, the momentum of further proteomic biomarker research in neurological disorders may suffer by its very complex methodology which is sensitive to various sources of artefacts. A major source of variability is proteome perturbation caused by sample handling/preservation (preanalytical phase) and processing/measurement (analytical phase). The aim of the present review is to summarize the current literature focusing on the crucial role played by preanalytical and analytical factors that affect the quality of samples and the reliability of the data produced in blood-based proteomic biomarker research in neurology, which may apply to Alzheimer's disease (AD) as well as other neurological disorders. Procedures for sample preparation and protocols for the analysis of serum and plasma samples will be delineated. Finally, the potential usefulness of bioinformatics--allowing for the assembly, store, and processing of data--as well as its contribution to the execution of proteomic studies will be critically discussed.

Quantitative mass spectrometry analysis using PAcIFIC for the identification of plasma diagnostic biomarkers for abdominal aortic aneurysm

BACKGROUND

Abdominal aortic aneurysm (AAA) is characterized by increased aortic vessel wall diameter (>1.5 times normal) and loss of parallelism. This disease is responsible for 1-4% mortality occurring on rupture in males older than 65 years. Due to its asymptomatic nature, proteomic techniques were used to search for diagnostic biomarkers that might allow surgical intervention under nonlife threatening conditions.

METHODOLOGY/PRINCIPAL FINDINGS

Pooled human plasma samples of 17 AAA and 17 control patients were depleted of the most abundant proteins and compared using a data-independent shotgun proteomic strategy, Precursor Acquisition Independent From Ion Count (PAcIFIC), combined with spectral counting and isobaric tandem mass tags. Both quantitative methods collectively identified 80 proteins as statistically differentially abundant between AAA and control patients. Among differentially abundant proteins, a subgroup of 19 was selected according to Gene Ontology classification and implication in AAA for verification by Western blot (WB) in the same 34 individual plasma samples that comprised the pools. From the 19 proteins, 12 were detected by WB. Five of them were verified to be differentially up-regulated in individual plasma of AAA patients: adiponectin, extracellular superoxide dismutase, protein AMBP, kallistatin and carboxypeptidase B2.

CONCLUSIONS/SIGNIFICANCE

Plasma depletion of high abundance proteins combined with quantitative PAcIFIC analysis offered an efficient and sensitive tool for the screening of new potential biomarkers of AAA. However, WB analysis to verify the 19 PAcIFIC identified proteins of interest proved inconclusive save for five proteins. We discuss these five in terms of their potential relevance as biological markers for use in AAA screening of population at risk.

Sensitive single-molecule protein quantification and protein complex detection in a microarray format

Single-molecule protein analysis provides sensitive protein quantitation with a digital read-out and is promising for studying biological systems and detecting biomarkers clinically. However, current single-molecule platforms rely on the quantification of one protein at a time. Conventional antibody microarrays are scalable to detect many proteins simultaneously, but they rely on less sensitive and less quantitative quantification by the ensemble averaging of fluorescent molecules. Here, we demonstrate a single-molecule protein assay in a microarray format enabled by an ultra-low background surface and single-molecule imaging. The digital read-out provides a highly sensitive, low femtomolar limit of detection and four orders of magnitude of dynamic range through the use of hybrid digital-analog quantification. From crude cell lysate, we measured levels of p53 and MDM2 in parallel, proving the concept of a digital antibody microarray for use in proteomic profiling. We also applied the single-molecule microarray to detect the p53-MDM2 protein complex in cell lysate. Our study is promising for development and application of single-molecule protein methods because it represents a technological bridge between single-plex and highly multiplex studies.