A combined dataset of human cerebrospinal fluid proteins identified by multi-dimensional chromatography and tandem mass spectrometry

Human cerebrospinal fluid (CSF) is an important source for studying protein biomarkers of age-related neurodegenerative diseases. Before characterizing biomarkers unique to each disease, it is necessary to categorize CSF proteins systematically and extensively. However, the enormous complexity, great dynamic range of protein concentrations, and tremendous protein heterogeneity due to post-translational modification of CSF create significant challenges to the existing proteomics technologies for an in-depth, nonbiased profiling of the human CSF proteome. To circumvent these difficulties, in the last few years, we have utilized several different separation methodologies and mass spectrometric platforms that greatly enhanced the identification coverage and the depth of protein profiling of CSF to characterize CSF proteome. In total, 2594 proteins were identified in well-characterized pooled human CSF samples using stringent proteomics criteria. This report summarizes our efforts to comprehensively characterize the human CSF proteome to date.     

Challenges in translating plasma proteomics from bench to bedside: update from the NHLBI Clinical Proteomics Programs

The emerging scientific field of proteomics encompasses the identification, characterization, and quantification of the protein content or proteome of whole cells, tissues, or body fluids. The potential for proteomic technologies to identify and quantify novel proteins in the plasma that can function as biomarkers of the presence or severity of clinical disease states holds great promise for clinical use. However, there are many challenges in translating plasma proteomics from bench to bedside, and relatively few plasma biomarkers have successfully transitioned from proteomic discovery to routine clinical use. Key barriers to this translation include the need for "orthogonal" biomarkers (i.e., uncorrelated with existing markers), the complexity of the proteome in biological samples, the presence of high abundance proteins such as albumin in biological samples that hinder detection of low abundance proteins, false positive associations that occur with analysis of high dimensional datasets, and the limited understanding of the effects of growth, development, and age on the normal plasma proteome. Strategies to overcome these challenges are discussed.     

Advances and challenges in liquid chromatography-mass spectrometry-based proteomics profiling for clinical applications

Recent advances in proteomics technologies provide tremendous opportunities for biomarker-related clinical applications; however, the distinctive characteristics of human biofluids such as the high dynamic range in protein abundances and extreme complexity of the proteomes present tremendous challenges. In this review we summarize recent advances in LC-MS-based proteomics profiling and its applications in clinical proteomics as well as discuss the major challenges associated with implementing these technologies for more effective candidate biomarker discovery. Developments in immunoaffinity depletion and various fractionation approaches in combination with substantial improvements in LC-MS platforms have enabled the plasma proteome to be profiled with considerably greater dynamic range of coverage, allowing many proteins at low ng/ml levels to be confidently identified. Despite these significant advances and efforts, major challenges associated with the dynamic range of measurements and extent of proteome coverage, confidence of peptide/protein identifications, quantitation accuracy, analysis throughput, and the robustness of present instrumentation must be addressed before a proteomics profiling platform suitable for efficient clinical applications can be routinely implemented.     

Clinical cancer proteomics: promises and pitfalls

Proteome analysis promises to be valuable for the identification of tissue and serum biomarkers associated with human malignancies. In addition, proteome technologies offer the opportunity to analyze protein expression profiles and to analyze the activity of signaling pathways. Many published proteomic studies of human tumor tissue are associated with weaknesses in tumor representativity, sample contamination by nontumor cells and serum proteins. Studies often include a moderate number of tumors which may not be representative of clinical materials. It is therefore very important that biomarkers identified by proteomics are validated in representative tumor materials by other techniques, such as immunohistochemistry. Proteome technologies can be used to identify disease markers in human serum. Tumor derived proteins are present at nanomolar to picomolar concentrations in cancer patient sera, 10(6)-10(9)-fold lower than albumin, and will give rise to correspondingly smaller spots/peaks in protein separations. This leads to the need to prefractionate serum samples before analysis. Despite various pitfalls, proteomic analysis is a promising approach to the identification of biomarkers, and for generation of protein expression profiles that can be analyzed by artificial learning methods for improved diagnosis of human malignancy. Recent advances in the field of proteomic analysis of human tumors are summarized in the present review.     

Use of proteomics for the identification of novel drug targets in brain diseases

In spite of the rapid advances in the development of the new proteomic technologies, there are, to date, relatively fewer studies aiming to explore the neuronal proteome. One of the reasons is the complexity of the brain, which presents high cellular heterogeneity and a unique subcellular compartmentalization. Therefore, tissue fractionation of the brain to enrich proteins of interest will reduce the complexity of the proteomics approach leading to the production of manageable and meaningful results. In this review, general considerations and strategies of proteomics, the advantages and challenges to exploring the neuronal proteome are described and summarized. In addition, this article presents an overview of recent advances of proteomic technologies and shows that proteomics can serve as a valuable tool to globally explore the changes in brain proteome during various disease states. Understanding the molecular basis of brain function will be extremely useful in identifying novel targets for the treatment of brain diseases.     

Innovative proteomic approaches for cancer biomarker discovery

Substantial technological advances in proteomics and related computational science have been made in the past few years. These advances overcome in part the complexity and heterogeneity of the human proteome, permitting quantitative analysis and identification of protein changes associated with tumor development. Here, we discuss some of these advances that are uncovering new cancer biomarkers that have potential to detect cancer at early and curable stages and address remaining challenges.     

Suction blister fluid as potential body fluid for biomarker proteins

Early diagnosis is important for effective disease management. Measurement of biomarkers present at the local level of the skin could be advantageous in facilitating the diagnostic process. The analysis of the proteome of suction blister fluid, representative for the interstitial fluid of the skin, is therefore a desirable first step in the search for potential biomarkers involved in biological pathways of particular diseases. Here, we describe a global analysis of the suction blister fluid proteome as potential body fluid for biomarker proteins. The suction blister fluid proteome was compared with a serum proteome analyzed using identical protocols. By using stringent criteria allowing less than 1% false positive identifications, we were able to detect, using identical experimental conditions and amount of starting material, 401 proteins in suction blister fluid and 240 proteins in serum. As a major result of our analysis we construct a prejudiced list of 34 proteins, relatively highly and uniquely detected in suction blister fluid as compared to serum, with established and putative characteristics as biomarkers. We conclude that suction blister fluid might potentially serve as a good alternative biomarker body fluid for diseases that involve the skin.     

Practical points in urinary proteomics

During the proteomic era, one of the most rapidly growing areas in biomedical research is biomarker discovery, particularly using proteomic technologies. Urinary proteomics has become one of the most attractive subdisciplines in clinical proteomics, as the urine is an ideal source for the discovery of noninvasive biomarkers for human diseases. However, there are several barriers to the success of the field and urinary proteome analysis is not a simple task because the urine has low protein concentration, high levels of salts or other interfering compounds, and more importantly, high degree of variations (both intra-individual and inter-individual variabilities). This article provides step-by-step practical points to perform urinary proteome analysis, covering detailed information for study design, sample collection, sample storage, sample preparation, proteomic analysis, and data interpretation. The discussion herein should stimulate further discussion and refinement to develop guidelines and standardizations for urinary proteome study.     

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.     

基于质谱的蛋白质生物标志物发现中的特征选择与机器学习方法研究进展

随着质谱技术的进步以及生物信息学与统计学算法的发展,以疾病研究为主要目的之一的人类蛋白质组计划正快速推进。蛋白质生物标志物在疾病早期诊断和临床治疗等方面有着非常重要的意义,其发现策略和方法的研究已成为一个重要的热点领域。特征选择与机器学习对于解决蛋白质组数据"高维度"及"稀疏性"问题有较好的效果,因而逐渐被广泛地应用于发现蛋白质生物标志物的研究中。文中主要阐述蛋白质生物标志物的发现策略以及其中特征选择与机器学习方法的原理、应用实例和适用范围,并讨论深度学习方法在本领域的应用前景及局限性,以期为相关研究提供参考。     

A proteomic analysis of human bile

We have carried out a comprehensive characterization of human bile to define the bile proteome. Our approach involved fractionation of bile by one-dimensional gel electrophoresis and lectin affinity chromatography followed by liquid chromatography tandem mass spectrometry. Overall, we identified 87 unique proteins, including several novel proteins as well as known proteins whose functions are unknown. A large majority of the identified proteins have not been previously described in bile. Using lectin affinity chromatography and enzymatically labeling of asparagine residues carrying glycan moieties by (18)O, we have identified a total of 33 glycosylation sites. The strategy described in this study should be generally applicable for a detailed proteomic analysis of most body fluids. In combination with "tagging" approaches for differential proteomics, our method could be used for identification of cancer biomarkers from any body fluid.     

Proteomics approaches to biomarker detection.

The development of mass spectrometry (MS) technologies has brought the ability to gather massive amounts of data characterising the proteomes of complex mixtures. A major focus in proteomics is to leverage this data-gathering capability to conduct comparative analyses of biofluids from healthy and disease-affected patients for the identification of highly specific biomarkers and/or the development of MS-based diagnostic platforms. Much effort has gone into optimising the biofluid proteome coverage that can be obtained using these technologies, leaving proteomics poised to make an important impact in disease diagnostics in the future.     

Affinity prefractionation for MS‐based plasma proteomics

The plasma proteome has proven to be one of the most challenging proteomes to profile using currently available proteomics technologies. A plethora of methodologies have been used to profile human plasma in order to discover potential biomarkers for disease and for therapy optimization. Affinity‐based prefractionation coupled to MS has been shown to be one of the most successful ways to dig deeper into the plasma proteome. Depletion of high abundant plasma proteins is becoming an initial method of choice in any plasma profiling project. However, several other affinity‐based enrichment methods have been published in recent years. Here we review both protein and peptide affinity prefractionation methods coupled with MS‐based proteomics. Analysis of the proportion of cellular and extracellular annotated proteins of publicly available MS plasma proteomics data is performed to estimate the analytical depth of various prefractionation methods.     

Combined analysis of transcriptome and proteome data as a tool for the identification of candidate biomarkers in renal cell carcinoma

Results obtained from expression profilings of renal cell carcinoma using different “ome”‐based approaches and comprehensive data analysis demonstrated that proteome‐based technologies and cDNA microarray analyses complement each other during the discovery phase for disease‐related candidate biomarkers. The integration of the respective data revealed the uniqueness and complementarities of the different technologies. While comparative cDNA microarray analyses though restricted to up‐regulated targets largely revealed genes involved in controlling gene/protein expression (19%) and signal transduction processes (13%), proteomics/PROTEOMEX‐defined candidate biomarkers include enzymes of the cellular metabolism (36%), transport proteins (12%), and cell motility/structural molecules (10%). Candidate biomarkers defined by proteomics and PROTEOMEX are frequently shared, whereas the sharing rate between cDNA microarray and proteome‐based profilings is limited. Putative candidate biomarkers provide insights into their cellular (dys)function and their diagnostic/prognostic value but still warrant further validation in larger patient numbers. Based on the fact that merely three candidate biomarkers were shared by all applied technologies, namely annexin A4, tubulin α‐1A chain, and ubiquitin carboxyl‐terminal hydrolase L1, the analysis at a single hierarchical level of biological regulation seems to provide only limited results thus emphasizing the importance and benefit of performing rather combinatorial screenings which can complement the standard clinical predictors.     

In-depth analysis of the human tear proteome

The tears, a critical body fluid of the surface of the eye, contain an unknown number of molecules including proteins/peptides, lipids, small molecule metabolites, and electrolytes. There have been continued efforts for exploring the human tear proteome to develop biomarkers of disease. In this study, we used the high speed TripleTOF 5600 system as the platform to analyze the human tear proteome from healthy subjects (3 females and 1 male, average age: 36±14). We have identified 1543 proteins in the tears with less than 1% false discovery rate, which represents the largest number of human tear proteins reported to date. The data set was analyzed for gene ontology (GO) and compared with the human plasma proteome, NEIBank lacrimal gland gene dataset and NEIBank cornea gene dataset. This comprehensive tear protein list may serve as a reference list of human tear proteome for biomarker research of ocular diseases or establishment of MRM (Multiple Reaction Monitoring) assays for targeted analysis. Tear fluid is a useful and an accessible source not only for evaluating ocular surface tissues (cornea and conjunctiva), inflammation, lacrimal gland function and a number of disease conditions, such as dry eye as well as response to treatment.     

Proteomic profiling of pancreatic cancer for biomarker discovery

Pancreatic cancer is a uniformly lethal disease that is difficult to diagnose at early stage and even more difficult to cure. In recent years, there has been a substantial interest in applying proteomics technologies to identify protein biomarkers for early detection of cancer. Quantitative proteomic profiling of body fluids, tissues, or other biological samples to identify differentially expressed proteins represents a very promising approach for improving the outcome of this disease. Proteins associated with pancreatic cancer identified through proteomic profiling technologies could be useful as biomarkers for the early diagnosis, therapeutic targets, and disease response markers. In this article, we discuss recent progress and challenges for applying quantitative proteomics technologies for biomarker discovery in pancreatic cancer.     

Avian proteomics: advances, challenges and new technologies

Proteomics is defined as an analysis of the full complement of proteins of a cell or tissue under given conditions. Avian proteomics, or more specifically chicken proteomics, has focussed on the study of individual tissues and organs of interest to specific researchers. Researchers have looked at skeletal muscle and growth, and embryonic development and have performed initial studies in avian disease. Traditional proteomics involves identifying and cataloguing proteins in a cell and identifying relative changes in populations between two or more states, be that physiological or disease-induced states. Recent advances in proteomic technologies have included absolute quantification, proteome simplification and the ability to determine the turnover of individual proteins in a global context. This review discusses the current developments in this relatively new field, new technologies and how they may be applied to biological questions, and the challenges faced by researchers in this ever-expanding and exciting field.     

Human saliva proteome analysis and disease biomarker discovery

Human saliva is an attractive body fluid for disease diagnosis and prognosis because saliva testing is simple, safe, low-cost and noninvasive. Comprehensive analysis and identification of the proteomic content in human whole and ductal saliva will not only contribute to the understanding of oral health and disease pathogenesis, but also form a foundation for the discovery of saliva protein biomarkers for human disease detection. In this article, we have summarized the proteomic technologies for comprehensive identification of proteins in human whole and ductal saliva. We have also discussed potential quantitative proteomic approaches to the discovery of saliva protein biomarkers for human oral and systemic diseases. With the fast development of mass spectrometry and proteomic technologies, we are enthusiastic that saliva protein biomarkers will be developed for clinical diagnosis and prognosis of human diseases in the future.     

Urinary proteomics: a tool to discover biomarkers of kidney diseases

There is intense interest in applying proteomics to urine analysis in order to promote a better understanding of kidney disease processes, develop new biomarkers for diagnosis and detect early factors that contribute to end-stage renal diseases. This interest creates numerous opportunities as well as challenges. To fulfill this task, proteomics requires, in its different stages of realization, various technological platforms with high sensitivity, high throughput and large automation ability. In this review, we will give an overview of promising proteomic methods that can be used for analyzing urinary proteome and detecting biomarkers for different kidney diseases. Furthermore, we will focus on the current status and future directions in investigating kidney diseases using urinary proteomics.