Population proteomics: addressing protein diversity in humans

In the past several years, proteomics and its subdiscipline clinical proteomics have been engaged in the discovery of the next generation protein of biomarkers. As the effort and the intensive debate it has sparked continue, it is becoming apparent that a paradigm shift is needed in proteomics in order to truly comprehend the complexity of the human proteome and assess its subtle variations among individuals. This review introduces the concept of population proteomics as a future direction in proteomics research. Population proteomics is the study of protein diversity in human populations. High-throughput, top-down mass spectrometric approaches are employed to investigate, define and understand protein diversity and modulations across and within populations. Population proteomics is a discovery-oriented endeavor with a goal of establishing the incidence of protein structural variations and quantitative regulation of these modifications. Assessing human protein variations among and within populations is viewed as a paramount undertaking that can facilitate clinical proteomics’ effort in discovery and validation of protein features that can be used as markers for early diagnosis of disease, monitoring of disease progression and assessment of therapy. This review outlines the growing need for analyzing individuals’ proteomes and describes the approaches that are likely to be applied in such a population proteomics endeavor.     

Population proteomics: addressing protein diversity in humans

In the past several years, proteomics and its subdiscipline clinical proteomics have been engaged in the discovery of the next generation protein of biomarkers. As the effort and the intensive debate it has sparked continue, it is becoming apparent that a paradigm shift is needed in proteomics in order to truly comprehend the complexity of the human proteome and assess its subtle variations among individuals. This review introduces the concept of population proteomics as a future direction in proteomics research. Population proteomics is the study of protein diversity in human populations. High-throughput, top-down mass spectrometric approaches are employed to investigate, define and understand protein diversity and modulations across and within populations. Population proteomics is a discovery-oriented endeavor with a goal of establishing the incidence of protein structural variations and quantitative regulation of these modifications. Assessing human protein variations among and within populations is viewed as a paramount undertaking that can facilitate clinical proteomics' effort in discovery and validation of protein features that can be used as markers for early diagnosis of disease, monitoring of disease progression and assessment of therapy. This review outlines the growing need for analyzing individuals' proteomes and describes the approaches that are likely to be applied in such a population proteomics endeavor.     

Mass spectrometry-based immunoassays for the next phase of clinical applications

Recent applications of affinity mass spectrometry into clinical laboratories brought a renewed interest in immunoaffinity mass spectrometry as a more specific affinity method capable of selectively targeting and studying protein biomarkers. In mass spectrometry-based immunoassays, proteins are affinity retrieved from biological samples via surface-immobilized antibodies, and are then detected via mass spectrometric analysis. The assays benefit from dual specificity, which is brought about by the affinity of the antibody and the protein mass readout. The mass spectrometry aspect of the assays enables single-step detection of protein isoforms and their individual quantification. This review offers a comprehensive review of mass spectrometry-based immunoassays, from historical perspectives in the development of the immunoaffinity mass spectrometry, to current applications of the assays in clinical and population proteomic endeavors. Described in more detail are two types of mass spectrometry-based immunoassays, one of which incorporates surface plasmon resonance detection for protein quantification. All mass spectrometry-based immunoassays offer high-throughput targeted protein investigation, with clear implications in clinical research, encompassing biomarker discovery and validation, and in diagnostic settings as the next-generation immunoassays.     

Mass spectrometry-based immunoassays for the next phase of clinical applications

Recent applications of affinity mass spectrometry into clinical laboratories brought a renewed interest in immunoaffinity mass spectrometry as a more specific affinity method capable of selectively targeting and studying protein biomarkers. In mass spectrometry-based immunoassays, proteins are affinity retrieved from biological samples via surface-immobilized antibodies, and are then detected via mass spectrometric analysis. The assays benefit from dual specificity, which is brought about by the affinity of the antibody and the protein mass readout. The mass spectrometry aspect of the assays enables single-step detection of protein isoforms and their individual quantification. This review offers a comprehensive review of mass spectrometry-based immunoassays, from historical perspectives in the development of the immunoaffinity mass spectrometry, to current applications of the assays in clinical and population proteomic endeavors. Described in more detail are two types of mass spectrometry-based immunoassays, one of which incorporates surface plasmon resonance detection for protein quantification. All mass spectrometry-based immunoassays offer high-throughput targeted protein investigation, with clear implications in clinical research, encompassing biomarker discovery and validation, and in diagnostic settings as the next-generation immunoassays.     

Advances in top-down proteomics for disease biomarker discovery

Top-down mass spectrometry strategies allow identification and characterization of proteins and protein networks by direct fragmentation. These analytical processes involve a panel of fragmentation mechanisms, some of which preserve protein post-translational modifications. Thus top-down is of special interest in clinical biochemistry to probe modified proteins as potential disease biomarkers. This review describes separating methods, mass spectrometry instrumentation, bioinformatics, and theoretical aspects of fragmentation mechanisms used for top-down analysis. The biological interest of this strategy is extensively reported regarding the characterization of post-translational modifications in biochemical pathways and the discovery of biomarkers. One has to bear in mind that quantitative aspects that are beyond the focus of this review are also of critical important for biomarker discovery. The constant evolution of technologies makes top-down strategies crucial players in clinical and basic proteomics.     

Biomarker discovery for arsenic exposure using functional data. Analysis and feature learning of mass spectrometry proteomic data

Plasma biomarkers of exposure to environmental contaminants play an important role in early detection of disease. The emerging field of proteomics presents an attractive opportunity for candidate biomarker discovery, as it simultaneously measures and analyzes a large number of proteins. This article presents a case study for measuring arsenic concentrations in a population residing in an As-endemic region of Bangladesh using plasma protein expressions measured by SELDI-TOF mass spectrometry. We analyze the data using a unified statistical method based on functional learning to preprocess mass spectra and extract mass spectrometry (MS) features and to associate the selected MS features with arsenic exposure measurements. The task is challenging due to several factors, the high dimensionality of mass spectrometry data, complicated error structures, and a multiple comparison problem. We use nonparametric functional regression techniques for MS modeling, peak detection based on the significant zero-downcrossing method, and peak alignment using a warping algorithm. Our results show significant associations of arsenic exposure to either under- or overexpressions of 20 proteins.     

Halobacterium salinarum NRC-1 PeptideAtlas: toward strategies for targeted proteomics and improved proteome coverage

The relatively small numbers of proteins and fewer possible post-translational modifications in microbes provide a unique opportunity to comprehensively characterize their dynamic proteomes. We have constructed a PeptideAtlas (PA) covering 62.7% of the predicted proteome of the extremely halophilic archaeon Halobacterium salinarum NRC-1 by compiling approximately 636 000 tandem mass spectra from 497 mass spectrometry runs in 88 experiments. Analysis of the PA with respect to biophysical properties of constituent peptides, functional properties of parent proteins of detected peptides, and performance of different mass spectrometry approaches has highlighted plausible strategies for improving proteome coverage and selecting signature peptides for targeted proteomics. Notably, discovery of a significant correlation between absolute abundances of mRNAs and proteins has helped identify low abundance of proteins as the major limitation in peptide detection. Furthermore, we have discovered that iTRAQ labeling for quantitative proteomic analysis introduces a significant bias in peptide detection by mass spectrometry. Therefore, despite identifying at least one proteotypic peptide for almost all proteins in the PA, a context-dependent selection of proteotypic peptides appears to be the most effective approach for targeted proteomics.     

Redox proteomics: from residue modifications to putative biomarker identification by gel- and LC-MS-based approaches

Quantitative determination of reactive oxygen species and reactive nitrogen species in body fluids, tissues or cells has always been problematic due to their high chemical reactivity and the resulting short half-life. This high reactivity may involve reversible and/or irreversible protein modifications, in particular the covalent oxidative modification of specific amino acid residues. Thus, the occurrence of reactive oxygen species and reactive nitrogen species can be monitored indirectly from the identification of specific protein-chemical footprints. In combination with classical gel-based proteomics or liquid chromatography labeling or label-free techniques, mass spectrometry has emerged as a powerful tool to identify these protein modifications in biological samples. In this review, we present the main methodological approaches for gel-based proteomics and quantitative mass spectrometry applied to oxidative protein modifications, mainly Cys. Representative examples from their application in identifying respective biomarkers in diseases related to oxidative stress are also presented.     

Divide and conquer: subproteomic approaches toward gastric cancer biomarker and drug target discovery

The discovery of biomarkers for early detection and treatment for gastric cancer are two important gaps that proteomics have the potential to fill. Advancements in mass spectrometry, sample preparation and separation strategies are crucial to proteomics-based discoveries and subsequent translations from bench to bedside. A great number of studies exploiting various subproteomic approaches have emerged for higher-resolution analysis (compared with shotgun proteomics) that permit interrogation of different post-translational and subcellular compartmentalized forms of the same proteins as determinants of disease phenotypes. This is a unique and key strength of proteomics over genomics. In this review, the salient features, competitive edges and pitfalls of various subproteomic approaches are discussed. We also highlight valuable insights from several subproteomic studies that have increased our understanding of the molecular etiology of gastric cancer and the findings that led to the discovery of potential biomarkers/drug targets that were otherwise not revealed by conventional shotgun expression proteomics.     

Modification-specific proteomics: characterization of post-translational modifications by mass spectrometry

Post-translational modifications generate tremendous diversity, complexity and heterogeneity of gene products, and their determination is one of the main challenges in proteomics research. Recent developments in mass spectrometry based approaches for systematic, qualitative and quantitative determination of modified proteins promise to bring new insights on the dynamics and spatio-temporal control of protein activities by post-translational modifications, and reveal their roles in biological processes and pathogenic conditions. Combinations of affinity-based enrichment and extraction methods, multidimensional separation technologies and mass spectrometry are particularly attractive for systematic investigation of post-translationally modified proteins in proteomics.     

DeltAMT: a statistical algorithm for fast detection of protein modifications from LC-MS/MS data

Identification of proteins and their modifications via liquid chromatography-tandem mass spectrometry is an important task for the field of proteomics. However, because of the complexity of tandem mass spectra, the majority of the spectra cannot be identified. The presence of unanticipated protein modifications is among the major reasons for the low spectral identification rate. The conventional database search approach to protein identification has inherent difficulties in comprehensive detection of protein modifications. In recent years, increasing efforts have been devoted to developing unrestrictive approaches to modification identification, but they often suffer from their lack of speed. This paper presents a statistical algorithm named DeltAMT (Delta Accurate Mass and Time) for fast detection of abundant protein modifications from tandem mass spectra with high-accuracy precursor masses. The algorithm is based on the fact that the modified and unmodified versions of a peptide are usually present simultaneously in a sample and their spectra are correlated with each other in precursor masses and retention times. By representing each pair of spectra as a delta mass and time vector, bivariate Gaussian mixture models are used to detect modification-related spectral pairs. Unlike previous approaches to unrestrictive modification identification that mainly rely upon the fragment information and the mass dimension in liquid chromatography-tandem mass spectrometry, the proposed algorithm makes the most of precursor information. Thus, it is highly efficient while being accurate and sensitive. On two published data sets, the algorithm effectively detected various modifications and other interesting events, yielding deep insights into the data. Based on these discoveries, the spectral identification rates were significantly increased and many modified peptides were identified.     

Population proteomics: investigation of protein diversity in human populations

Outlined in this review is the concept of population proteomics, its aspects, enabling approaches, and significance in understanding proteins' roles in physiological processes and diseases. Population proteomics addresses the need for individual assessment of proteins across large populations to delineate the existence of structural variations, determine their frequency, and explore the association of the modifications with specific diseases. Besides the basic concepts and underlying reasons for such protein diversity studies, also reviewed here are the results of two fundamental studies that investigated human plasma protein diversity across the healthy population in the United States. Such studies of protein diversity are needed to map all the post-expression protein modifications and determine the wild-type protein profiles, similar to the human diversity studies at the genome level that have helped redefine the "normal" human genome.     

Targeted proteomics: a bridge between discovery and validation

New technologies in mass spectrometry are beginning to mature and show unique advantages for the identification and quantitation of proteins. In recent years, one of the significant goals of clinical proteomics has been to identify biomarkers that can be used for clinical diagnosis. As technology has progressed, the list of potential biomarkers has grown. However, the verification and validation of these potential biomarkers is increasingly challenging and require high-throughput quantitative assays, targeting specific candidates. Targeted proteomics bridges the gap between biomarker discovery and the development of clinically applicable biomarker assays.     

Current progress in protein profiling of complex samples

Accurate cancer biomarkers are needed for early detection, disease classification, prediction of therapeutic response and monitoring treatment. While there appears to be no shortage of candidate biomarker proteins, a major bottleneck in the biomarker pipeline continues to be their verification by enzyme linked immunosorbent assays. Multiple reaction monitoring (MRM), also known as selected reaction monitoring, is a targeted mass spectrometry approach to protein quantitation and is emerging to bridge the gap between biomarker discovery and clinical validation. Highly multiplexed MRM assays are readily configured and enable simultaneous verification of large numbers of candidates facilitating the development of biomarker panels which can increase specificity. This review focuses on recent applications of MRM to the analysis of plasma and serum from cancer patients for biomarker verification. The current status of this approach is discussed along with future directions for targeted mass spectrometry in clinical biomarker validation.     

Proteomics of colorectal cancer: Overview of discovery studies and identification of commonly identified cancer-associated proteins and candidate CRC serum markers

Colorectal cancer (CRC) is a common cause of cancer-related mortality in the developed world. Improved methods for early detection and disease management are urgently needed. Many efforts in the past 5 years have been devoted to protein biomarker discovery for early detection of CRC. Here, we discuss identity-based studies employing tandem mass spectrometry that analyzed clinical material as well as model systems. Through meta-analysis we provide a list of CRC-associated tissue proteins discovered in multiple studies, with the greater majority being 2D gel-based discoveries coupled to MS/MS. So far only a limited number of CRC-associated proteins have been validated in serum for non-invasive testing for CRC. This list includes several intracellular and nuclear proteins that a priori would not have been considered candidate biomarkers based on their predicted subcellular localization. Finally, we highlight promising new directions that combine targeted analyses of subcellular proteomes, like the cell surface, secretome, exosome, and nuclear matrix, with nanoLC-MS/MS-based proteomics. We anticipate that in the near future, these novel mass spectrometry-based in-depth approaches will uncover many novel, specific CRC marker candidates in clinical tissues and that their targeted validation with multi-reaction monitoring MS will speed up development of non-invasive tests in feces and serum/plasma.     

Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer

Despite their potential to impact diagnosis and treatment of cancer, few protein biomarkers are in clinical use. Biomarker discovery is plagued with difficulties ranging from technological (inability to globally interrogate proteomes) to biological (genetic and environmental differences among patients and their tumors). We urgently need paradigms for biomarker discovery. To minimize biological variation and facilitate testing of proteomic approaches, we employed a mouse model of breast cancer. Specifically, we performed LC-MS/MS of tumor and normal mammary tissue from a conditional HER2/Neu-driven mouse model of breast cancer, identifying 6758 peptides representing >700 proteins. We developed a novel statistical approach (SASPECT) for prioritizing proteins differentially represented in LC-MS/MS datasets and identified proteins over- or under-represented in tumors. Using a combination of antibody-based approaches and multiple reaction monitoring-mass spectrometry (MRM-MS), we confirmed the overproduction of multiple proteins at the tissue level, identified fibulin-2 as a plasma biomarker, and extensively characterized osteopontin as a plasma biomarker capable of early disease detection in the mouse. Our results show that a staged pipeline employing shotgun-based comparative proteomics for biomarker discovery and multiple reaction monitoring for confirmation of biomarker candidates is capable of finding novel tissue and plasma biomarkers in a mouse model of breast cancer. Furthermore, the approach can be extended to find biomarkers relevant to human disease.     

Mapping protein post-translational modifications with mass spectrometry

Post-translational modifications of proteins control many biological processes, and examining their diversity is critical for understanding mechanisms of cell regulation. Mass spectrometry is a fundamental tool for detecting and mapping covalent modifications and quantifying their changes. Modern approaches have made large-scale experiments possible, screening complex mixtures of proteins for alterations in chemical modifications. By profiling protein chemistries, biologists can gain deeper insight into biological control. The aim of this review is introduce biologists to current strategies in mass spectrometry-based proteomics that are used to characterize protein post-translational modifications, noting strengths and shortcomings of various approaches.     

Discovery of regulatory molecular events and biomarkers using 2D capillary chromatography and mass spectrometry

An important component of proteomic research is the high-throughput discovery of novel proteins and protein–protein interactions that control molecular events that contribute to critical cellular functions and human disease. The interactions of proteins are essential for cellular functions. Identifying perturbation of normal cellular protein interactions is vital for understanding the disease process and intervening to control the disease. A second area of proteomics research is the discovery of proteins that will serve as biomarkers for the early detection, diagnosis and drug treatment response for specific diseases. These studies have been referred to as clinical proteomics. To discover biomarkers, proteomics research employs the quantitative comparison of peptide and protein expression in body fluids and tissues from diseased individuals (case) versus normal individuals (control). Methods that couple 2D capillary liquid chromatography (LC) and tandem mass spectrometry (MS/MS) analysis have greatly facilitated this discovery science. Coupling 2D-LC/MS/MS analysis with automated genome-assisted spectra interpretation allows a direct, high-throughput and high-sensitivity identification of thousands of individual proteins from complex biological samples. The systematic comparison of experimental conditions and controls allows protein function or disease states to be modeled. This review discusses the different purification and quantification strategies that have been developed and used in combination with 2D-LC/MS/MS and computational analysis to examine regulatory protein networks and clinical samples.     

Discovery of regulatory molecular events and biomarkers using 2D capillary chromatography and mass spectrometry

An important component of proteomic research is the high-throughput discovery of novel proteins and protein-protein interactions that control molecular events that contribute to critical cellular functions and human disease. The interactions of proteins are essential for cellular functions. Identifying perturbation of normal cellular protein interactions is vital for understanding the disease process and intervening to control the disease. A second area of proteomics research is the discovery of proteins that will serve as biomarkers for the early detection, diagnosis and drug treatment response for specific diseases. These studies have been referred to as clinical proteomics. To discover biomarkers, proteomics research employs the quantitative comparison of peptide and protein expression in body fluids and tissues from diseased individuals (case) versus normal individuals (control). Methods that couple 2D capillary liquid chromatography (LC) and tandem mass spectrometry (MS/MS) analysis have greatly facilitated this discovery science. Coupling 2D-LC/MS/MS analysis with automated genome-assisted spectra interpretation allows a direct, high-throughput and high-sensitivity identification of thousands of individual proteins from complex biological samples. The systematic comparison of experimental conditions and controls allows protein function or disease states to be modeled. This review discusses the different purification and quantification strategies that have been developed and used in combination with 2D-LC/MS/MS and computational analysis to examine regulatory protein networks and clinical samples.