IgY14 and SuperMix immunoaffinity separations coupled with liquid chromatography-mass spectrometry for human plasma proteomics biomarker discovery

Interest in the application of advanced proteomics technologies to human blood plasma- or serum-based clinical samples for the purpose of discovering disease biomarkers continues to grow; however, the enormous dynamic range of protein concentrations in these types of samples (often >10 orders of magnitude) represents a significant analytical challenge, particularly for detecting low-abundance candidate biomarkers. In response, immunoaffinity separation methods for depleting multiple high- and moderate-abundance proteins have become key tools for enriching low-abundance proteins and enhancing detection of these proteins in plasma proteomics. Herein, we describe IgY14 and tandem IgY14-Supermix separation methods for removing 14 high-abundance and up to 60 moderate-abundance proteins, respectively, from human blood plasma and highlight their utility when combined with liquid chromatography-tandem mass spectrometry for interrogating the human plasma proteome.     

Sampling and analytical strategies for biomarker discovery using mass spectrometry

There is an often unspoken truth behind the course of scientific investigation that involves not what is necessarily academically worthy of study, but rather what is scientifically worthy in the eyes of funding agencies. The perception of worthy research is, as cost is driven in the simplest sense in economics, often driven by demand. Presently, the demand for novel diagnostic and therapeutic protein biomarkers that possess high sensitivity and specificity is placing major impact on the field of proteomics. The focal discovery technology that is being relied on is mass spectrometry (MS), whereas the challenge of biomarker discovery often lies not in the application of MS but in the underlying proteome sampling and bioinformatic processing strategies. Although biomarker discovery research has been historically technology-driven, it is clear from the meager success in generating validated biomarkers that increasing attention must be placed at the pre-analytic stage, such as sample retrieval and preparation. As diseases vary, so do the combinations of sampling and sample analyses necessary to discover novel biomarkers. In this review, we highlight different strategies used toward biomarker discovery and discuss them in terms of their reliance on technology and methodology.     

Coherent pipeline for biomarker discovery using mass spectrometry and bioinformatics

Background  

Robust biomarkers are needed to improve microbial identification and diagnostics. Proteomics methods based on mass spectrometry can be used for the discovery of novel biomarkers through their high sensitivity and specificity. However, there has been a lack of a coherent pipeline connecting biomarker discovery with established approaches for evaluation and validation. We propose such a pipeline that uses in silico methods for refined biomarker discovery and confirmation.     

Proteomic analysis and discovery using affinity proteomics and mass spectrometry

Antibody-based microarrays are a rapidly evolving affinity-proteomic methodology that recently has shown great promise in clinical applications. The resolution of these proteomic analyses is, however, directly related to the number of data-points, i.e. antibodies, included on the array. Currently, this is a key bottleneck because of limited availability of numerous highly characterized antibodies. Here, we present a conceptually new method, denoted global proteome survey, opening up the possibility to probe any proteome in a species-independent manner while still using a limited set of antibodies. We use context-independent-motif-specific antibodies directed against short amino acid motifs, where each motif is present in up to a few hundred different proteins. First, the digested proteome is exposed to these antibodies, whereby motif-containing peptides are enriched, which then are detected and identified by mass spectrometry. In this study, we profiled extracts from human colon tissue, yeast cells lysate, and mouse liver tissue to demonstrate proof-of-concept.     

Human plasma N-glycoproteome analysis by immunoaffinity subtraction, hydrazide chemistry, and mass spectrometry

The enormous complexity, wide dynamic range of relative protein abundances of interest (over 10 orders of magnitude), and tremendous heterogeneity (due to post-translational modifications, such as glycosylation) of the human blood plasma proteome severely challenge the capabilities of existing analytical methodologies. Here, we describe an approach for broad analysis of human plasma N-glycoproteins using a combination of immunoaffinity subtraction and glycoprotein capture to reduce both the protein concentration range and the overall sample complexity. Six high-abundance plasma proteins were simultaneously removed using a pre-packed, immobilized antibody column. N-linked glycoproteins were then captured from the depleted plasma using hydrazide resin and enzymatically digested, and the bound N-linked glycopeptides were released using peptide-N-glycosidase F (PNGase F). Following strong cation exchange (SCX) fractionation, the deglycosylated peptides were analyzed by reversed-phase capillary liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Using stringent criteria, a total of 2053 different N-glycopeptides were confidently identified, covering 303 nonredundant N-glycoproteins. This enrichment strategy significantly improved detection and enabled identification of a number of low-abundance proteins, exemplified by interleukin-1 receptor antagonist protein (approximately 200 pg/mL), cathepsin L (approximately 1 ng/mL), and transforming growth factor beta 1 (approximately 2 ng/mL). A total of 639 N-glycosylation sites were identified, and the overall high accuracy of these glycosylation site assignments as assessed by accurate mass measurement using high-resolution liquid chromatography coupled to Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR) is initially demonstrated.     

Evaluation of endogenous plasma peptide extraction methods for mass spectrometric biomarker discovery

Peptides have a role in the inflammatory response, tumor biology, and endocrine processes, presenting them as appealing biomarker candidates. However, peptide extraction efficacy for clinical profiling remains a pivotal technological challenge, as maximum coverage of the plasma peptidome is limited by a range of factors including the inherent complexity of human plasma and the lower concentration of peptides compared to abundant proteins. The aim of this study was to evaluate commonly employed peptide extraction methodologies in terms of total number of peptides detected and the mass range of peptides observed by MALDI. Despite showing coelution of proteins, solid-phase extraction (SPE) methods exhibited superior plasma peptide recovery than ultrafiltration, acetonitrile (ACN) precipitation, or size-exclusion chromatography methods under conditions employed in the study. Not surprisingly, in line with studies challenging the veracity of many peptide biomarker studies, the majority of identified peptides eluted from SPE methods corresponded to proteolytic truncations of the most abundant plasma proteins. The prefractionation of plasma with acetonitrile precipitation prior to SPE provided distinct ion signal profiles and is worthy of further study. In conclusion, this study favors the use of SPE in peptide extraction protocols for increased biomarker coverage and diversity from the plasma peptidome.     

Imaging mass spectrometry in biomarker discovery and validation

Biomarker discovery and validation involves the consideration of many issues and challenges in order to be effectively used for translation from bench to bedside. Imaging mass spectrometry (IMS) is a new technology to assess spatial molecular arrangements in tissue sections, going far beyond microscopy in providing hundreds of different molecular images from a single scan without the need of target-specific reagents. The possibility to correlate distribution maps of multiple analytes with histological and clinical features makes it an ideal tool to discover diagnostic and prognostic markers of diseases. Some recently published studies that show the usefulness and advantages of this technology in the field of cancer research are highlighted.     

Direct tandem mass spectrometry reveals limitations in protein profiling experiments for plasma biomarker discovery

The low molecular weight plasma proteome and its biological relevance are not well defined; therefore, experiments were conducted to directly sequence and identify peptides observed in plasma and serum protein profiles. Protein fractionation, matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) profiling, and liquid-chromatography coupled to MALDI tandem mass spectrometry (MS/MS) sequencing were used to analyze the low molecular weight proteome of heparinized plasma. Four fractionation techniques using functionally derivatized 96-well plates were used to extract peptides from plasma. Tandem TOF was successful for identifying peptides up to m/z 5500 with no prior knowledge of the sequence and was also used to verify the sequence assignments for larger ion signals. The peptides (n>250) sequenced in these profiles came from a surprisingly small number of proteins (n approximately 20), which were all common to plasma, including fibrinogen, complement components, antiproteases, and carrier proteins. The cleavage patterns were consistent with those of known plasma proteases, including initial cleavages by thrombin, plasmin and complement proteins, followed by aminopeptidase and carboxypeptidase activity. On the basis of these data, we discuss limitations in biomarker discovery in the low molecular weight plasma or serum proteome using crude fractionation coupled to MALDI-MS profiling.     

Targeted proteomics by selected reaction monitoring mass spectrometry: applications to systems biology and biomarker discovery

Mass Spectrometry-based proteomics is now considered a relatively established strategy for protein analysis, ranging from global expression profiling to the identification of protein complexes and specific post-translational modifications. Recently, Selected Reaction Monitoring Mass Spectrometry (SRM-MS) has become increasingly popular in proteome research for the targeted quantification of proteins and post-translational modifications. Using triple quadrupole instrumentation (QqQ), specific analyte molecules are targeted in a data-directed mode. Used routinely for the quantitative analysis of small molecular compounds for at least three decades, the technology is now experiencing broadened application in the proteomics community. In the current review, we will provide a detailed summary of current developments in targeted proteomics, including some of the recent applications to biological research and biomarker discovery.     

Nutrition proteomics and biomarker discovery

Baukje de Roos is a principal investigator at the University of Aberdeen, Rowett Institute of Nutrition and Health. She investigates mechanisms through which dietary fats and fatty acids, and also polyphenols, affect parameters involved in the development of heart disease in vivo. This is achieved not only by measuring their effect on conventional risk markers for heart disease but also by assessing their effect on new markers that are being developed through proteomic and mass spectrometry methods. She obtained her PhD in Human Nutrition at Wageningen University, The Netherlands, in January 2000, after which she was appointed as a post-doctoral research fellow at the Department of Vascular Biochemistry, Glasgow Royal Infirmary, in collaboration with GlaxoSmithKline. In June 2001 she joined the Rowett Research Institute in Aberdeen. She is currently working for the University of Aberdeen, where her research is funded by the Scottish Government Rural and Environment Research and Analysis Directorate (RERAD). She is an active member of the European Nutrigenomics Organisation (NuGO), an EU-funded Network of Excellence, which merges the nutrigenomics activities of its 23 partners across Europe.     

Informatics platform for global proteomic profiling and biomarker discovery using liquid chromatography-tandem mass spectrometry

We have developed an integrated suite of algorithms, statistical methods, and computer applications to support large-scale LC-MS-based gel-free shotgun profiling of complex protein mixtures using basic experimental procedures. The programs automatically detect and quantify large numbers of peptide peaks in feature-rich ion mass chromatograms, compensate for spurious fluctuations in peptide signal intensities and retention times, and reliably match related peaks across many different datasets. Application of this toolkit markedly facilitates pattern recognition and biomarker discovery in global comparative proteomic studies, simplifying mechanistic investigation of physiological responses and the detection of proteomic signatures of disease.     

Quantitative proteomics using mass spectrometry

The use of stable isotopes as internal standards in mass spectrometry has opened a new era for quantitative proteomics. Depending on the point at which the label is introduced, most procedures can be classified as in vivo labeling, in vitro pre-digestion labeling or in vitro post-digestion labeling. In vivo labeling has been used for cells that can be grown in culture and has the advantage of being more accurate. The pre-digestion and post-digestion labeling procedures are suitable for all types of sample including human body fluids and biopsies. Several new mass spectrometric strategies mark significant achievements in determining relative protein concentrations and in quantifying post-translational modifications. However, further technology developments are needed for understanding the complexity of a dynamic system like the proteome.     

MALDI mass spectrometry in prostate cancer biomarker discovery

Matrix-assisted laser desorption/ionisation (MALDI) mass spectrometry (MS) is a highly versatile and sensitive analytical technique, which is known for its soft ionisation of biomolecules such as peptides and proteins. Generally, MALDI MS analysis requires little sample preparation, and in some cases like MS profiling it can be automated through the use of robotic liquid-handling systems. For more than a decade now, MALDI MS has been extensively utilised in the search for biomarkers that could aid clinicians in diagnosis, prognosis, and treatment decision making. This review examines the various MALDI-based MS techniques like MS imaging, MS profiling and proteomics in-depth analysis where MALDI MS follows fractionation and separation methods such as gel electrophoresis, and how these have contributed to prostate cancer biomarker research. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.     

High-resolution mass spectrometry proteomics for the identification of candidate plasma protein biomarkers for chronic obstructive pulmonary disease

Although cigarette smoking is recognized as the most important cause of chronic obstructive pulmonary disease (COPD), the pathophysiological mechanisms underlying the lung function decline are not well understood. Using off-line strong cation exchange fractionation with RP-LC-ESI-MS/MS and robust database searching, 1758 tryptic peptides were identified in plasma samples from cigarette smokers. Using two statistical approaches, 30 peptides were identified to be associated with the annualized rate of lung function decline over 5 years among smokers with COPD characterized as having rapid (n?=?18) or slow (n?=?18) decline and 18 smokers without COPD. The identified peptides belong to proteins that are involved in the complement or coagulation systems or have antiprotease or metabolic functions. This research demonstrates the utility of proteomic profiling to improve the understanding of molecular mechanisms involved in cigarette smoking-related COPD by identifying plasma proteins that correlate with decline in lung function.     

A robust biomarker discovery pipeline for high-performance mass spectrometry data

A high-throughput software pipeline for analyzing high-performance mass spectral data sets has been developed to facilitate rapid and accurate biomarker determination. The software exploits the mass precision and resolution of high-performance instrumentation, bypasses peak-finding steps, and instead uses discrete m/z data points to identify putative biomarkers. The technique is insensitive to peak shape, and works on overlapping and non-Gaussian peaks which can confound peak-finding algorithms. Methods are presented to assess data set quality and the suitability of groups of m/z values that map to peaks as potential biomarkers. The algorithm is demonstrated with serum mass spectra from patients with and without ovarian cancer. Biomarker candidates are identified and ranked by their ability to discriminate between cancer and noncancer conditions. Their discriminating power is tested by classifying unknowns using a simple distance calculation, and a sensitivity of 95.6% and a specificity of 97.1% are obtained. In contrast, the sensitivity of the ovarian cancer blood marker CA125 is approximately 50% for stage I/II and approximately 80% for stage III/IV cancers. While the generalizability of these markers is currently unknown, we have demonstrated the ability of our analytical package to extract biomarker candidates from high-performance mass spectral data.     

Statistical considerations of optimal study design for human plasma proteomics and biomarker discovery

A mass spectrometry-based plasma biomarker discovery workflow was developed to facilitate biomarker discovery. Plasma from either healthy volunteers or patients with pancreatic cancer was 8-plex iTRAQ labeled, fractionated by 2-dimensional reversed phase chromatography and subjected to MALDI ToF/ToF mass spectrometry. Data were processed using a q-value based statistical approach to maximize protein quantification and identification. Technical (between duplicate samples) and biological variance (between and within individuals) were calculated and power analysis was thereby enabled. An a priori power analysis was carried out using samples from healthy volunteers to define sample sizes required for robust biomarker identification. The result was subsequently validated with a post hoc power analysis using a real clinical setting involving pancreatic cancer patients. This demonstrated that six samples per group (e.g., pre- vs post-treatment) may provide sufficient statistical power for most proteins with changes>2 fold. A reference standard allowed direct comparison of protein expression changes between multiple experiments. Analysis of patient plasma prior to treatment identified 29 proteins with significant changes within individual patient. Changes in Peroxiredoxin II levels were confirmed by Western blot. This q-value based statistical approach in combination with reference standard samples can be applied with confidence in the design and execution of clinical studies for predictive, prognostic, and/or pharmacodynamic biomarker discovery. The power analysis provides information required prior to study initiation.     

Advances in clinical cancer proteomics: SELDI-ToF-mass spectrometry and biomarker discovery.

For most cancers, survival rates depend on the early detection of the disease. So far, no biomarkers exist to cope with this difficult task. New proteomic technologies have brought the hope of discovering novel early cancer-specific biomarkers in complex biological samples and/or of the setting up of new clinically relevant test systems. Novel mass spectrometry-(MS) based technologies in particular, such as surface-enhanced laser desorption/ionisation time of flight (SELDI-ToF-MS), have shown promising results in the recent literature. Here, proteomic profiles of control and disease states are compared to find biomarkers for diagnosis. This paper aims to address the authors' own work and that of other groups in clinical cancer proteomics based on SELDI-ToF-MS. Shortcomings and hopes for the future are discussed.     

Improved candidate biomarker detection based on mass spectrometry data using the Hilbert-Huang transform

Mass spectrometry biomarker discovery may assist patient's diagnosis in time and realize the characteristics of new diseases. Our previous work built a preprocess method called HHTmass which is capable of removing noise, but HHTmass only a proof of principle to be peak detectable and did not tested for peak reappearance rate and used on medical data. We developed a modified version of biomarker discovery method called Enhance HHTMass (E-HHTMass) for MALDI-TOF and SELDI-TOF mass spectrometry data which improved old HHTMass method by removing the interpolation and the biomarker discovery process. E-HHTMass integrates the preprocessing and classification functions to identify significant peaks. The results show that most known biomarker can be found and high peak appearance rate achieved comparing to MSCAP and old HHTMass2. E-HHTMass is able to adapt to spectra with a small increasing interval. In addition, new peaks are detected which can be potential biomarker after further validation.     

Global and targeted quantitative proteomics for biomarker discovery

The extraordinary developments made in proteomic technologies in the past decade have enabled investigators to consider designing studies to search for diagnostic and therapeutic biomarkers by scanning complex proteome samples using unbiased methods. The major technology driving these studies is mass spectrometry (MS). The basic premises of most biomarker discovery studies is to use the high data-gathering capabilities of MS to compare biological samples obtained from healthy and disease-afflicted patients and identify proteins that are differentially abundant between the two specimen. To meet the need to compare the abundance of proteins in different samples, a number of quantitative approaches have been developed. In this article, many of these will be described with an emphasis on their advantageous and disadvantageous for the discovery of clinically useful biomarkers.     

In vitro biomarker discovery for atherosclerosis by proteomics

The purpose of this study was to identify in vitro and then prioritize a tractable set of protein biomarker candidates of atherosclerosis that may eventually be developed to measure the extent, progression, regression, and stability of atherosclerotic lesions. A study was conducted using an in vitro"foam cell" model based on the stimulation of differentiated THP1 cells with oxidized low-density lipoprotein (oxidized LDL) as compared with low-density lipoprotein (LDL). Analysis of the proteins contained in the cell supernatant using proteome scanning technology identified 59 proteins as being increased, 57 with no statistically measurable difference, and 17 decreasing in abundance following treatment with oxidized LDL, as compared with LDL. From the up-regulated list, proteins were prioritized based on their analytical confidence as well as their relevance to atherosclerosis pathways. Within the group of increased abundance, seven families of proteins were of particular interest: fatty acid-binding proteins, chitinase-like enzymes, cyclophilins, cathepsins, proteoglycans, urokinase-type plasminogen activator receptor, and a macrophage scavenger receptor.