Mass spectrometric-based approaches in quantitative proteomics

Classically, experiments aimed at studying changes in protein expression have always followed a small set of proteins. This focused approach was necessary since tools to efficiently analyze large numbers of proteins were simply not available. Large-scale quantitative proteomics promises to produce reams of data that previously would have taken decades to measure with classical methods. Mass spectrometry is already a well-established protein identification tool and recent methodological developments indicate that it can also be successfully applied to extract quantitative data of protein abundance. From the first reports 4 years ago, numerous schemes to take advantage of stable isotope nuclei incorporation in proteins and peptides have been developed. Here we review the benefits and pitfalls of some of the most commonly used protocols, focusing on a procedure now being used extensively in our laboratory, stable isotope labeling with amino acids in cell culture (SILAC). The basic theory, application, and data analysis of a SILAC experiment are discussed. The emerging nature of these techniques and the rapid pace of technological development make forecasting the directions of the field difficult but we speculate that SILAC will soon be a key tool of quantitative proteomics.     

Recent advances in quantitative and chemical proteomics for autophagy studies

Macroautophagy/autophagy is an evolutionarily well-conserved cellular degradative process with important biological functions that is closely implicated in health and disease. In recent years, quantitative mass spectrometry-based proteomics and chemical proteomics have emerged as important tools for the study of autophagy, through large-scale unbiased analysis of the proteome or through highly specific and accurate analysis of individual proteins of interest. At present, a variety of approaches have been successfully applied, including (i) expression and interaction proteomics for the study of protein post-translational modifications, (ii) investigating spatio-temporal dynamics of protein synthesis and degradation, and (iii) direct determination of protein activity and profiling molecular targets in the autophagic process. In this review, we attempted to provide an overview of principles and techniques relevant to the application of quantitative and chemical proteomics methods to autophagy, and outline the current landscape as well as future outlook of these methods in autophagy research.     

Computational methods for the comparative quantification of proteins in label-free LCn-MS experiments

Liquid chromatography (LC) coupled to electrospray mass spectrometry (MS) is well established in high-throughput proteomics. The technology enables rapid identification of large numbers of proteins in a relatively short time. Comparative quantification of identified proteins from different samples is often regarded as the next step in proteomics experiments enabling the comparison of protein expression in different proteomes. Differential labeling of samples using stable isotope incorporation or conjugation is commonly used to compare protein levels between samples but these procedures are difficult to carry out in the laboratory and for large numbers of samples. Recently, comparative quantification of label-free LC(n)-MS proteomics data has emerged as an alternative approach. In this review, we discuss different computational approaches for extracting comparative quantitative information from label-free LC(n)-MS proteomics data. The procedure for computationally recovering the quantitative information is described. Furthermore, statistical tests used to evaluate the relevance of results will also be discussed.     

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.     

Subcellular fractionation methods and strategies for proteomics

Developments in subcellular fractionation strategies have provided the means to profile and analyze the protein composition of organelles and cellular structures by proteomics. Here, we review the application of classical (e.g. density gradient centrifugation) and emerging sophisticated techniques (fluorescent-assisted organelle sorting) in the fractionation, and statistical/bioinformatics tools for the prediction of protein localization in subcellular proteomics. We also review the validation methods currently used (such as microscopy, RNA interference and multiple reaction monitoring) and discuss the importance of verification of the results obtained in subcellular proteomics. Finally, the numerous challenges facing subcellular proteomics including the dynamics of organelles are being examined. However, complementary approaches such as modern statistics, bioinformatics and large-scale integrative analysis are beginning to emerge as powerful tools to proteomics for analyzing subcellular organelles and structures.     

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.     

Separation and detection methods for covalent drug-protein adducts

Covalent binding of reactive metabolites of drugs to proteins has been a predominant hypothesis for the mechanism of toxicity caused by numerous drugs. The development of efficient and sensitive analytical methods for the separation, identification, quantification of drug-protein adducts have important clinical and toxicological implications. In the last few decades, continuous progress in analytical methodology has been achieved with substantial increase in the number of new, more specific and more sensitive methods for drug-protein adducts. The methods used for drug-protein adduct studies include those for separation and for subsequent detection and identification. Various chromatographic (e.g., affinity chromatography, ion-exchange chromatography, and high-performance liquid chromatography) and electrophoretic techniques [e.g., sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), two-dimensional SDS-PAGE, and capillary electrophoresis], used alone or in combination, offer an opportunity to purify proteins adducted by reactive drug metabolites. Conventionally, mass spectrometric (MS), nuclear magnetic resonance, and immunological and radioisotope methods are used to detect and identify protein targets for reactive drug metabolites. However, these methods are labor-intensive, and have provided very limited sequence information on the target proteins adducted, and thus the identities of the protein targets are usually unknown. Moreover, the antibody-based methods are limited by the availability, quality, and specificity of antibodies to protein adducts, which greatly hindered the identification of specific protein targets of drugs and their clinical applications. Recently, the use of powerful MS technologies (e.g., matrix-assisted laser desorption/ionization time-of-flight) together with analytical proteomics have enabled one to separate, identify unknown protein adducts, and establish the sequence context of specific adducts by offering the opportunity to search for adducts in proteomes containing a large number of proteins with protein adducts and unmodified proteins. The present review highlights the separation and detection technologies for drug-protein adducts, with an emphasis on methodology, advantages and limitations to these techniques. Furthermore, a brief discussion of the application of these techniques to individual drugs and their target proteins will be outlined.     

Protein microarrays and their applications

In recent years, the importance of proteomic works, such as protein expression, detection and identification, has grown in the fields of proteomic and diagnostic research. This is because complete genome sequences of humans, and other organisms, progress as cellular processing and controlling are performed by proteins as well as DNA or RNA. However, conventional protein analyses are time-consuming; therefore, high throughput protein analysis methods, which allow fast, direct and quantitative detection, are needed. These are so-called protein microarrays or protein chips, which have been developed to fulfill the need for high-throughput protein analyses. Although protein arrays are still in their infancy, technical development in immobilizing proteins in their native conformation on arrays, and the development of more sensitive detection methods, will facilitate the rapid deployment of protein arrays as high-throughput protein assay tools in proteomics and diagnostics. This review summarizes the basic technologies that are needed in the fabrication of protein arrays and their recent applications.     

A proteomic analysis of maize chloroplast biogenesis

Proteomics studies to explore global patterns of protein expression in plant and green algal systems have proliferated within the past few years. Although most of these studies have involved mapping of the proteomes of various organs, tissues, cells, or organelles, comparative proteomics experiments have also led to the identification of proteins that change in abundance in various developmental or physiological contexts. Despite the growing use of proteomics in plant studies, questions of reproducibility have not generally been addressed, nor have quantitative methods been widely used, for example, to identify protein expression classes. In this report, we use the de-etiolation ("greening") of maize (Zea mays) chloroplasts as a model system to explore these questions, and we outline a reproducible protocol to identify changes in the plastid proteome that occur during the greening process using techniques of two-dimensional gel electrophoresis and mass spectrometry. We also evaluate hierarchical and nonhierarchical statistical methods to analyze the patterns of expression of 526 "high-quality," unique spots on the two-dimensional gels. We conclude that Adaptive Resonance Theory 2-a nonhierarchical, neural clustering technique that has not been previously applied to gene expression data-is a powerful technique for discriminating protein expression classes during greening. Our experiments provide a foundation for the use of proteomics in the design of experiments to address fundamental questions in plant physiology and molecular biology.     

Challenges in mass spectrometry-based proteomics

During the last decade, protein analysis and proteomics have been established as new tools for understanding various biological problems. As the identification of proteins after classical separation techniques, such as two-dimensional gel electrophoresis, have become standard methods, new challenges arise in the field of proteomics. The development of "functional proteomics" combines functional characterization, like regulation, localization and modification, with the identification of proteins for deeper insight into cellular functions. Therefore, different mass spectrometric techniques for the analysis of post-translational modifications, such as phosphorylation and glycosylation, have been established as well as isolation and separation methods for the analysis of highly complex samples, e.g. protein complexes or cell organelles. Furthermore, quantification of protein levels within cells is becoming a focus of interest as mass spectrometric methods for relative or even absolute quantification have currently not been available. Protein or genome databases have been an essential part of protein identification up to now. Thus, de novo sequencing offers new possibilities in protein analytical studies of organisms not yet completely sequenced. The intention of this review is to provide a short overview about the current capabilities of protein analysis when addressing various biological problems.     

基于质谱的选择反应监测技术相关策略和方法的研究进展

随着蛋白质组学研究的不断深入,基于质谱的选择反应监测技术(SRM)已经成为以发现生物标志物为代表的定向蛋白质组学研究的重要手段.SRM技术根据假设信息,特异性地获取符合假设条件的质谱信号,去除不符合条件的离子信号干扰,从而得到特定蛋白质的定量信息.SRM技术具有更高的灵敏度和精确性、更大的动态范围等优势.该技术可分为实验设计、数据获取和数据分析三个步骤.在这几个步骤中,最重要的是利用生物信息学手段总结当前实验数据的结果,并用机器学习方法和总结的经验规则进行SRM实验的母离子和子离子对的预测.针对数据质控和定量的生物信息学方法研究在提高SRM数据可靠性方面具有重要作用.此外,为方便SRM的研究,本文还收集、汇总了SRM技术相关的软件、工具和数据库资源.随着质谱仪器的不断发展,新的SRM实验策略以及分析方法、计算工具也应运而生.结合更优化的实验策略、方法,采用更精准的生物信息学算法和工具,SRM在未来蛋白质组学的发展中将发挥更加重要的作用.     

Biomarker discovery from pancreatic cancer secretome using a differential proteomic approach

Quantitative proteomics can be used as a screening tool for identification of differentially expressed proteins as potential biomarkers for cancers. Candidate biomarkers from such studies can subsequently be tested using other techniques for use in early detection of cancers. Here we demonstrate the use of stable isotope labeling with amino acids in cell culture (SILAC) method to compare the secreted proteins (secretome) from pancreatic cancer-derived cells with that from non-neoplastic pancreatic ductal cells. We identified 145 differentially secreted proteins (>1.5-fold change), several of which were previously reported as either up-regulated (e.g. cathepsin D, macrophage colony stimulation factor, and fibronectin receptor) or down-regulated (e.g. profilin 1 and IGFBP-7) proteins in pancreatic cancer, confirming the validity of our approach. In addition, we identified several proteins that have not been correlated previously with pancreatic cancer including perlecan (HSPG2), CD9 antigen, fibronectin receptor (integrin beta1), and a novel cytokine designated as predicted osteoblast protein (FAM3C). The differential expression of a subset of these novel proteins was validated by Western blot analysis. In addition, overexpression of several proteins not described previously to be elevated in human pancreatic cancer (CD9, perlecan, SDF4, apoE, and fibronectin receptor) was confirmed by immunohistochemical labeling using pancreatic cancer tissue microarrays suggesting that these could be further pursued as potential biomarkers. Lastly the protein expression data from SILAC were compared with mRNA expression data obtained using gene expression microarrays for the two cell lines (Panc1 and human pancreatic duct epithelial), and a correlation coefficient (r) of 0.28 was obtained, confirming previously reported poor associations between RNA and protein expression studies.     

Trifunctional chemical probes for the consolidated detection and identification of enzyme activities from complex proteomes

Chemical probes that covalently modify the active sites of enzymes in complex proteomes are useful tools for identifying enzyme activities associated with discrete (patho) physiological states. Researchers in proteomics typically use two types of activity-based probes to fulfill complementary objectives: fluorescent probes for rapid and sensitive target detection and biotinylated probes for target purification and identification. Accordingly we hypothesized that a strategy in which the target detection and target isolation steps of activity-based proteomic experiments were merged might accelerate the characterization of differentially expressed protein activities. Here we report the synthesis and application of trifunctional chemical proteomic probes in which elements for both target detection (e.g. rhodamine) and isolation (e.g. biotin) are appended to a sulfonate ester reactive group, permitting the consolidated visualization and affinity purification of labeled proteins by a combination of in-gel fluorescence and avidin chromatography procedures. A trifunctional phenyl sulfonate probe was used to identify several technically challenging protein targets, including the integral membrane enzyme 3beta-hydroxysteroid dehydrogenase/Delta5-isomerase and the cofactor-dependent enzymes platelet-type phosphofructokinase and type II tissue transglutaminase. The latter two enzyme activities were significantly up-regulated in the invasive estrogen receptor-negative (ER(-)) human breast cancer cell line MDA-MB-231 relative to the non-invasive ER(+) breast cancer lines MCF7 and T-47D. Collectively these studies demonstrate that chemical proteomic probes incorporating elements for both target detection and target isolation fortify the important link between the visualization of differentially expressed enzyme activities and their subsequent molecular identification, thereby augmenting the information content achieved in activity-based profiling experiments.     

Current methods for global proteome identification

In a time frame of a few decades, protein identification went from laborious single protein identification to automated identification of entire proteomes. This shift was enabled by the emergence of peptide-centric, gel-free analyses, in particular the so-called shotgun approaches, which not only rely on extensive experiments, but also on cutting-edge data processing methods. The present review therefore provides an overview of a shotgun proteomics identification workflow, listing the state-of-the-art methods involved and software that implement these. The authors focus on freely available tools where possible. Finally, data analysis in the context of emerging across-omics studies will also be discussed briefly, where proteomics goes beyond merely delivering a list of protein accession numbers.     

Establishing Informative Prior for Gene Expression Variance from Public Databases

Identifying differential expressed genes across various conditions or genotypes is the most typical approach to studying the regulation of gene expression. An estimate of gene-specific variance is often needed for the assessment of statistical significance in most differential expression (DE) detection methods, including linear models (e.g., for transformed and normalized microarray data) and generalized linear models (e.g., for count data in RNAseq). Due to a common limit in sample size, the variance estimate is often unstable in small experiments. Shrinkage estimates using empirical Bayes methods have proven useful in improving the variance estimate, hence improving the detection of DE. The most widely used empirical Bayes methods borrow information across genes within the same experiments. In these methods, genes are considered exchangeable or exchangeable conditioning on expression level. We propose, with the increasing accumulation of expression data, borrowing information from historical data on the same gene can provide better estimate of gene-specific variance, thus further improve DE detection. Specifically, we show that the variation of gene expression is truly gene-specific and reproducible between different experiments. We present a new method to establish informative gene-specific prior on the variance of expression using existing public data, and illustrate how to shrink the variance estimate and detect DE. We demonstrate improvement in DE detection under our strategy compared to leading DE detection methods.     

差异蛋白质组学的研究进展

差异蛋白质组是蛋白质组学研究的一个主要内容,其核心在于寻找某种特定臣寸素引起样本之间蛋白质组的差异,揭示并验证蛋白质组在生理或病理过程中的变化。进一步对蛋白质组差异信息分析后,理论上可以推断造成这种变化的原因。因此,对于临床上肿瘤预诊、药物靶标寻找、细胞调控分子的鉴别等有着极大的实际意义。差异蛋白质组研究要求可靠性和可重复性。因此,对于样本处理要求较高,激光微切割技术和高丰度蛋白去除技术的应用优化了样本处理方法。目前差异蛋白质组的主要研究方法仍是2-DE分离和MS鉴定联合应用,基于2-DE的2-DDIGE方法弥补了2-DE的弱点,更适用于差异蛋白质组研究。除2-DE技术外的其他几种技术手段,如多维液相色谱分离技术、ICAT技术、蛋白芯片技术等差异蛋白质组学研究技术可以作为2-DE技术的补充,甚至或替代技术。     

Recent developments in structural proteomics for protein structure determination

The major challenges in structural proteomics include identifying all the proteins on the genome-wide scale, determining their structure-function relationships, and outlining the precise three-dimensional structures of the proteins. Protein structures are typically determined by experimental approaches such as X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy. However, the knowledge of three-dimensional space by these techniques is still limited. Thus, computational methods such as comparative and de novo approaches and molecular dynamic simulations are intensively used as alternative tools to predict the three-dimensional structures and dynamic behavior of proteins. This review summarizes recent developments in structural proteomics for protein structure determination; including instrumental methods such as X-ray crystallography and NMR spectroscopy, and computational methods such as comparative and de novo structure prediction and molecular dynamics simulations.