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.     

Multidimensional protein profiling technology and its application to human plasma proteome

In clinical and diagnostic proteomics, it is essential to develop a comprehensive and robust system for proteome analysis. Although multidimensional liquid chromatography/tandem mass spectrometry (LC/MS/MS) systems have been recently developed as powerful tools especially for identification of protein complexes, these systems still some drawbacks in their application to clinical research that requires an analysis of a large number of human samples. Therefore, in this study, we have constructed a technically simple and high throughput protein profiling system comprising a two-dimensional (2D)-LC/MS/MS system which integrates both a strong cation exchange (SCX) chromatography and a microLC/MS/MS system with micro-flowing reversed-phase chromatography. Using the microLC/MS/MS system as the second dimensional chromatography, SCX separation has been optimized as an off-line first dimensional peptide fractionation. To evaluate the performance of the constructed 2D-LC/MS/MS system, the results of detection and identification of proteins were compared using digests mixtures of 6 authentic proteins with those obtained using one-dimensional microLC/MS/MS system. The number of peptide fragments detected and the coverage of protein sequence were found to be more than double through the use of our newly built 2D-LC/MS/MS system. Furthermore, this multidimensional protein profiling system has been applied to plasma proteome in order to examine its feasibility for clinical proteomics. The experimental results revealed the identification of 174 proteins from one serum sample depleted HSA and IgG which corresponds to only 1 microL of plasma, and the total analysis run time was less than half a day, indicating a fairly high possibility of practicing clinical proteomics in a high throughput manner.     

Proteomic analysis of eccrine sweat: implications for the discovery of schizophrenia biomarker proteins

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) and multiple reaction monitoring mass spectrometry (MRM-MS) proteomics analyses were performed on eccrine sweat of healthy controls, and the results were compared with those from individuals diagnosed with schizophrenia (SZ). This is the first large scale study of the sweat proteome. First, we performed LC-MS/MS on pooled SZ samples and pooled control samples for global proteomics analysis. Results revealed a high abundance of diverse proteins and peptides in eccrine sweat. Most of the proteins identified from sweat samples were found to be different than the most abundant proteins from serum, which indicates that eccrine sweat is not simply a plasma transudate and may thereby be a source of unique disease-associated biomolecules. A second independent set of patient and control sweat samples were analyzed by LC-MS/MS and spectral counting to determine qualitative protein differential abundances between the control and disease groups. Differential abundances of selected proteins, initially determined by spectral counting, were verified by MRM-MS analyses. Seventeen proteins showed a differential abundance of approximately 2-fold or greater between the SZ pooled sample and the control pooled sample. This study demonstrates the utility of LC-MS/MS and MRM-MS as a viable strategy for the discovery and verification of potential sweat protein disease biomarkers.     

Clinical-scale high-throughput human plasma proteome analysis: lung adenocarcinoma

Clinical proteomics requires the stable and reproducible analysis of a large number of human samples. We report a high-throughput comprehensive protein profiling system comprising a fully automated, on-line, two-dimensional microflow liquid chromatography/tandem mass spectrometry (2-D microLC-MS/MS) system for use in clinical proteomics. A linear ion-trap mass spectrometer (ITMS) also known as a 2-D ITMS instrument, which is characterized by high scan speed, was incorporated into the microLC-MS/MS system in order to obtain highly improved sensitivity and resolution in MS/MS acquisition. This system was used to evaluate bovine serum albumin and human 26S proteasome. Application of these high-throughput microLC conditions and the 2-D ITMS resulted in a 10-fold increase in sensitivity in protein identification. Additionally, peptide fragments from the 26S proteasome were identified three-fold more efficiently than by the conventional 3-D ITMS instrument. In this study, the 2-D microLC-MS/MS system that uses linear 2-D ITMS has been applied for the plasma proteome analysis of a few samples from healthy individuals and lung adenocarcinoma patients. Using the 2-D and 1-D microLC-MS/MS analyses, approximately 250 and 100 different proteins were detected, respectively, in each HSA- and IgG-depleted sample, which corresponds to only 0.4 microL of blood plasma. Automatic operation enabled the completion of a single run of the entire 1-D and 2-D microLC-MS/MS analyses within 11 h. Investigation of the data extracted from the protein identification datasets of both healthy and adenocarcinoma groups revealed that several of the group-specific proteins could be candidate protein disease markers expressed in the human blood plasma. Consequently, it was demonstrated that this high-throughput microLC-MS/MS protein profiling system would be practically applicable to the discovery of protein disease markers, which is the primary objective in clinical plasma proteome projects.     

Combining bioinformatics and MS-based proteomics: clinical implications

Clinical proteomics research aims at i) discovery of protein biomarkers for screening, diagnosis and prognosis of disease, ii) discovery of protein therapeutic targets for improvement of disease prevention, treatment and follow-up, and iii) development of mass spectrometry (MS)-based assays that could be implemented in clinical chemistry, microbiology or hematology laboratories. MS has been increasingly applied in clinical proteomics studies for the identification and quantification of proteins. Bioinformatics plays a key role in the exploitation of MS data in several aspects such as the generation and curation of protein sequence databases, the development of appropriate software for MS data treatment and integration with other omics data and the establishment of adequate standard files for data sharing. In this article, we discuss the main MS approaches and bioinformatics solutions that are currently applied to accomplish the objectives of clinical proteomic research.     

Bioinformatics in proteomics

Several genome sequencing projects have recently been completed and the majority of human coding regions have been sequenced. In the next step many of the further studies will concentrate on proteins. Proteomics methods are essential for studying protein expression, activity, regulation and modifications. Bioinformatics is an integral part of proteomics research. The recent developments and applications in proteomics are discussed including mass spectrometry data analysis and interpretation, analysis and storage of the gel images to databases, gel comparison, and advanced methods to study e.g. protein co-expression, protein-protein interactions, as well as metabolic and cellular pathways. The significance of informatics in proteomics will gradually increase because of the advent of high-throughput methods relying on powerful data analysis.     

Investigation of plasma biomarkers in HIV-1/HCV mono- and coinfected individuals by multiplex iTRAQ quantitative proteomics

The analysis of plasma samples from HIV-1/HCV mono- and coinfected individuals by quantitative proteomics is an efficient strategy to investigate changes in protein abundances and to characterize the proteins that are the effectors of cellular functions involved in viral pathogenesis. In this study, the infected and healthy plasma samples (in triplicate) were treated with ProteoMiner beads to equalize protein concentrations and subjected to 4-plex iTRAQ labeling and liquid chromatography/mass spectrometry (LC-MS/MS) analysis. A total of 70 proteins were identified with high confidence in the triplicate analysis of plasma proteins and 65% of the proteins were found to be common among the three replicates. Apolipoproteins and complement proteins are the two major classes of proteins that exhibited differential regulation. The results of quantitative analysis revealed that APOA2, APOC2, APOE, C3, HRG proteins were upregulated in the plasma of all the three HIV-1 mono-, HCV mono-, and coinfected patient samples compared to healthy control samples. Ingenuity pathway analysis (IPA) of the upregulated proteins revealed that they are implicated in the hepatic lipid metabolism, inflammation, and acute-phase response signaling pathways. Thus, we identified several differentially regulated proteins in HIV-1/HCV mono and coinfected plasma samples that may be potential biomarkers for liver disease.     

蛋白质芯片SELDI-TOFMS技术的研究进展及其在临床中的应用

蛋白质芯片为新一代的蛋白质组研究技术,由美国Ciphergen生物系统公司引进,表面增强激光解吸电离-飞行时间质谱(SELDI-TOFMS)提供一个高通量和高灵敏度的检测平台。投放至今虽短短10来年,但卓越的成果已广为医学科学界重视,尤其在恶性肿瘤的早期诊断、监控和预后研究上。蛋白质是细胞内执行生物功能的最终分子,蛋白质组学研究让人类更深入了解疾病和生命的本源,不断发现的特异性肿瘤标志物更为攻克癌症带来新希望。这里除对表面增强激光解吸电离_飞行时间质谱作较详尽的介绍外,更重点阐述其近年来蛋白质芯片近期的研究进展和在临床中的应用,并就其优劣和发展前景作出评估。     

The knowledge-integrated network biomarkers discovery for major adverse cardiac events

The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein-protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein-protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction.     

Optimizing Size Exclusion Chromatography for Extracellular Vesicle Enrichment and Proteomic Analysis from Clinically Relevant Samples

The field of extracellular vesicle (EV) research has rapidly expanded in recent years, with particular interest in their potential as circulating biomarkers. Proteomic analysis of EVs from clinical samples is complicated by the low abundance of EV proteins relative to highly abundant circulating proteins such as albumin and apolipoproteins. To overcome this, size exclusion chromatography (SEC) has been proposed as a method to enrich EVs whilst depleting protein contaminants; however, the optimal SEC parameters for EV proteomics have not been thoroughly investigated. Here, quantitative evaluation and optimization of SEC are reported for separating EVs from contaminating proteins. Using a synthetic model system followed by cell line‐derived EVs, it is found that a 10 mL Sepharose 4B column in PBS produces optimal resolution of EVs from background protein. By spiking‐in cancer cell‐derived EVs to healthy plasma, it is shown that some cancer EV‐associated proteins are detectable by nano‐LC‐MS/MS when as little as 1% of the total plasma EV number are derived from a cancer cell line. These results suggest that an optimized SEC and nanoLC‐MS/MS workflow may be sufficiently sensitive for disease EV protein biomarker discovery from patient‐derived clinical samples.     

Population proteomics: the concept, attributes, and potential for cancer biomarker research

This review outlines the concept of population proteomics and its implication in the discovery and validation of cancer-specific protein modulations. Population proteomics is an applied subdiscipline of proteomics engaging in the investigation of human proteins across and within populations to define and better understand protein diversity. Population proteomics focuses on interrogation of specific proteins from large number of individuals, utilizing top-down, targeted affinity mass spectrometry approaches to probe protein modifications. Deglycosylation, sequence truncations, side-chain residue modifications, and other modifications have been reported for myriad of proteins, yet little is know about their incidence rate in the general population. Such information can be gathered via population proteomics and would greatly aid the biomarker discovery efforts. Discovery of novel protein modifications is also expected from such large scale population proteomics, expanding the protein knowledge database. In regard to cancer protein biomarkers, their validation via population proteomics-based approaches is advantageous as mass spectrometry detection is used both in the discovery and validation process, which is essential for the detection of those structurally modified protein biomarkers.     

Structural proteomics by NMR spectroscopy

Structural proteomics is one of the powerful research areas in the postgenomic era, elucidating structure-function relationships of uncharacterized gene products based on the 3D protein structure. It proposes biochemical and cellular functions of unannotated proteins and thereby identifies potential drug design and protein engineering targets. Recently, a number of pioneering groups in structural proteomics research have achieved proof of structural proteomic theory by predicting the 3D structures of hypothetical proteins that successfully identified the biological functions of those proteins. The pioneering groups made use of a number of techniques, including NMR spectroscopy, which has been applied successfully to structural proteomics studies over the past 10 years. In addition, advances in hardware design, data acquisition methods, sample preparation and automation of data analysis have been developed and successfully applied to high-throughput structure determination techniques. These efforts ensure that NMR spectroscopy will become an important methodology for performing structural proteomics research on a genomic scale. NMR-based structural proteomics together with x-ray crystallography will provide a comprehensive structural database to predict the basic biological functions of hypothetical proteins identified by the genome projects.     

Protein identification assisted by the prediction of retention time in liquid chromatography/tandem mass spectrometry

Two-dimensional liquid chromatography (2D-LC) coupled on-line with electrospray ionization tandem mass spectrometry (2D-LC-ESI-MS/MS) is a new platform for analysis and identification of proteome. Peptides are separated by 2D-LC and then performed MS/MS analysis by tandem MS/MS. The MS/MS data are searched against database for protein identification. In one 2D-LC-ESI-MS/MS run, we obtained not only the structural information of peptides directly from MS/MS, but also the retention time of peptides eluted from LC. Information on the chromatographic behavior of peptides can assist protein identification in the new platform for proteomics. The retention time of the matching peptides of the identified protein was predicted by the hydrophobic contribute of each amino acid on reversed-phase liquid chromatography (RPLC). By using this strategy proteins were identified by four types of information: peptide mass fingerprinting (PMF), sequence query, and MS/MS ions searched and the predicted retention time. This additional information obtained from LC could assist protein identification with no extra experimental cost.     

Clinical proteomics: present and future prospects

Advances in proteomics technology offer great promise in the understanding and treatment of the molecular basis of disease. The past decade of proteomics research, the study of dynamic protein expression, post-translational modifications, cellular and sub-cellular protein distribution, and protein-protein interactions, has culminated in the identification of many disease-related biomarkers and potential new drug targets. While proteomics remains the tool of choice for discovery research, new innovations in proteomic technology now offer the potential for proteomic profiling to become standard practice in the clinical laboratory. Indeed, protein profiles can serve as powerful diagnostic markers, and can predict treatment outcome in many diseases, in particular cancer. A number of technical obstacles remain before routine proteomic analysis can be achieved in the clinic; however the standardisation of methodologies and dissemination of proteomic data into publicly available databases is starting to overcome these hurdles. At present the most promising application for proteomics is in the screening of specific subsets of protein biomarkers for certain diseases, rather than large scale full protein profiling. Armed with these technologies the impending era of individualised patient-tailored therapy is imminent. This review summarises the advances in proteomics that has propelled us to this exciting age of clinical proteomics, and highlights the future work that is required for this to become a reality.     

YPED:An Integrated Bioinformatics Suite and Database for Mass Spectrometry-based Proteomics Research

We report a significantly-enhanced bioinformatics suite and database for proteomics research called Yale Protein Expression Database(YPED) that is used by investigators at more than 300 institutions worldwide. YPED meets the data management, archival, and analysis needs of a high-throughput mass spectrometry-based proteomics research ranging from a singlelaboratory, group of laboratories within and beyond an institution, to the entire proteomics community. The current version is a significant improvement over the first version in that it contains new modules for liquid chromatography–tandem mass spectrometry(LC–MS/MS) database search results, label and label-free quantitative proteomic analysis, and several scoring outputs for phosphopeptide site localization. In addition, we have added both peptide and protein comparative analysis tools to enable pairwise analysis of distinct peptides/proteins in each sample and of overlapping peptides/proteins between all samples in multiple datasets. We have also implemented a targeted proteomics module for automated multiple reaction monitoring(MRM)/selective reaction monitoring(SRM) assay development. We have linked YPED's database search results and both label-based and label-free fold-change analysis to the Skyline Panorama repository for online spectra visualization. In addition, we have built enhanced functionality to curate peptide identifications into an MS/MS peptide spectral library for all of our protein database search identification results.     

A stable panel comprising 18 urinary proteins in the human healthy population

Just as biomarkers specific for diseases, biomarkers indicative of healthy conditions are valuable for the early diagnosis, monitoring, and prognosis of diseases. Our study focused on discovering via proteomics a stable panel of urinary proteins in the human healthy population. Urine samples were collected three times during 4 months from 100 male and 100 female healthy donors and analyzed through four different fractionation techniques (i.e. in-gel, 2D-LC, OFFGEL, and mRP) coupled with HPLC-Chip-MS/MS. Thus, 1641 urinary proteins were identified with a high confidence, among which 70 exhibiting an intergender/day variation <0.25 were selected and matched with the previously published five largest urinary proteomes to get 56 candidate proteins. Next, a panel comprising 18 intact urinary proteins was constructed by comparing the urinary proteomes via SDS-PAGE and 2DE. Finally, such 18 urinary proteins were validated via enzyme-linked immunosorbent assay in eight healthy individuals. Most of these proteins had been related to multiple rather than to single diseases. Therefore, we surmise that this protein set could be used as a biomarker to assess the human health status. Further determinations of the normal fluctuations of the single urinary proteins in this series using samples from large numbers of healthy individuals are required prior to any application in clinical settings.     

Molecular profiling of the human nasal epithelium: A proteomics approach

A comprehensive proteomic profiling of nasal epithelium (NE) is described. This study relies on simple subcellular fractionation used to obtain soluble- and membrane-enriched fractions followed by 2-dimensional liquid chromatography (2D-LC) separation and tandem mass spectrometry (MS/MS). The cells were collected using a brushing technique applied on NE of clinically evaluated volunteers. Subsequently, the soluble- and the membrane-protein enriched fractions were prepared and analyzed in parallel using 2D-LC-MS/MS. In a set of 1482 identified proteins, 947 (63.9%) proteins were found to be associated to membrane fraction. Grand average hydropathy value index (GRAVY) analysis, the transmembrane protein mapping and annotations of primary location deposited in the Human Protein Reference Database (HPRD) confirmed an enrichment of hydrophobic proteins on this dataset. Ingenuity Pathway Analysis (IPA) of soluble fraction revealed an enrichment of molecular and cellular functions associated with cell death, protein folding and drug metabolism while in membrane fraction showed an enrichment of functions associated with molecular transport, protein trafficking and cell-to-cell signaling and interaction. The IPA showed similar enrichment of functions associated with cellular growth and proliferation in both soluble and membrane subproteomes. This finding was in agreement with protein content analysis using exponentially modified protein abundance index (emPAI). A comparison of our data with previously published studies focusing on respiratory tract epithelium revealed similarities related to identification of proteins associated with physical barrier function and immunological defence. In summary, we extended the NE molecular profile by identifying and characterizing proteins associated to pivotal functions of a respiratory epithelium, including the control of fluid volume and ionic composition at the airways' surface, physical barrier maintenance, detoxification and immunological defence. The extent of similarities supports the applicability of a less invasive analysis of NE to assess prognosis and treatment response of lung diseases such as asthma, cystic fibrosis and chronic obstructive pulmonary disease.