High-throughput antibody microarrays for quantitative proteomic analysis

The antibody microarray is an intrinsically robust and quantitative system that delivers high-throughput and parallel measurements on particular sets of known proteins. It has become an important proteomics research tool, complementary to the conventional unbiased separation-based and mass spectrometry-based approaches. This review summarizes the technical aspects of production and the application for quantitative proteomic analysis with an emphasis on disease proteomics, especially the identification of biomarkers. Quality control, data analysis methods and the challenges for quantitative assays are also discussed.     

CSF-PR 2.0: An Interactive Literature Guide to Quantitative Cerebrospinal Fluid Mass Spectrometry Data from Neurodegenerative Disorders

The rapidly growing number of biomedical studies supported by mass spectrometry based quantitative proteomics data has made it increasingly difficult to obtain an overview of the current status of the research field. A better way of organizing the biomedical proteomics information from these studies and making it available to the research community is therefore called for. In the presented work, we have investigated scientific publications describing the analysis of the cerebrospinal fluid proteome in relation to multiple sclerosis, Parkinson's disease and Alzheimer's disease. Based on a detailed set of filtering criteria we extracted 85 data sets containing quantitative information for close to 2000 proteins. This information was made available in CSF-PR 2.0 (http://probe.uib.no/csf-pr-2.0), which includes novel approaches for filtering, visualizing and comparing quantitative proteomics information in an interactive and user-friendly environment. CSF-PR 2.0 will be an invaluable resource for anyone interested in quantitative proteomics on cerebrospinal fluid.     

Driving biochemical discovery with quantitative proteomics

Proteomic analysis of biological samples plays an increasing role in modern research. Although the application of proteomics technologies varies across many disciplines, proteomics largely is a tool for discovery that then leads to novel hypotheses. In recent years, new methods and technologies have been developed and applied in many areas of proteomics, and there is a strong push towards using proteomics in a quantitative manner. Indeed, mass spectrometry-based, quantitative proteomics approaches have been applied to great success in a variety of biochemical studies. In particular, the use of quantitative proteomics provides new insights into protein complexes and post-translational modifications and leads to the generation of novel insights into these important biochemical systems.     

Quantitative plant proteomics

Quantitation is an inherent requirement in comparative proteomics and there is no exception to this for plant proteomics. Quantitative proteomics has high demands on the experimental workflow, requiring a thorough design and often a complex multi-step structure. It has to include sufficient numbers of biological and technical replicates and methods that are able to facilitate a quantitative signal read-out. Quantitative plant proteomics in particular poses many additional challenges but because of the nature of plants it also offers some potential advantages. In general, analysis of plants has been less prominent in proteomics. Low protein concentration, difficulties in protein extraction, genome multiploidy, high Rubisco abundance in green tissue, and an absence of well-annotated and completed genome sequences are some of the main challenges in plant proteomics. However, the latter is now changing with several genomes emerging for model plants and crops such as potato, tomato, soybean, rice, maize and barley. This review discusses the current status in quantitative plant proteomics (MS-based and non-MS-based) and its challenges and potentials. Both relative and absolute quantitation methods in plant proteomics from DIGE to MS-based analysis after isotope labeling and label-free quantitation are described and illustrated by published studies. In particular, we describe plant-specific quantitative methods such as metabolic labeling methods that can take full advantage of plant metabolism and culture practices, and discuss other potential advantages and challenges that may arise from the unique properties of plants.     

GProX, a user-friendly platform for bioinformatics analysis and visualization of quantitative proteomics data

Recent technological advances have made it possible to identify and quantify thousands of proteins in a single proteomics experiment. As a result of these developments, the analysis of data has become the bottleneck of proteomics experiment. To provide the proteomics community with a user-friendly platform for comprehensive analysis, inspection and visualization of quantitative proteomics data we developed the Graphical Proteomics Data Explorer (GProX)(1). The program requires no special bioinformatics training, as all functions of GProX are accessible within its graphical user-friendly interface which will be intuitive to most users. Basic features facilitate the uncomplicated management and organization of large data sets and complex experimental setups as well as the inspection and graphical plotting of quantitative data. These are complemented by readily available high-level analysis options such as database querying, clustering based on abundance ratios, feature enrichment tests for e.g. GO terms and pathway analysis tools. A number of plotting options for visualization of quantitative proteomics data is available and most analysis functions in GProX create customizable high quality graphical displays in both vector and bitmap formats. The generic import requirements allow data originating from essentially all mass spectrometry platforms, quantitation strategies and software to be analyzed in the program. GProX represents a powerful approach to proteomics data analysis providing proteomics experimenters with a toolbox for bioinformatics analysis of quantitative proteomics data. The program is released as open-source and can be freely downloaded from the project webpage at http://gprox.sourceforge.net.     

Evaluation of empirical rule of linearly correlated peptide selection (ERLPS) for proteotypic peptide‐based quantitative proteomics

Precise protein quantification is essential in comparative proteomics. Currently, quantification bias is inevitable when using proteotypic peptide‐based quantitative proteomics strategy for the differences in peptides measurability. To improve quantification accuracy, we proposed an “empirical rule for linearly correlated peptide selection (ERLPS)” in quantitative proteomics in our previous work. However, a systematic evaluation on general application of ERLPS in quantitative proteomics under diverse experimental conditions needs to be conducted. In this study, the practice workflow of ERLPS was explicitly illustrated; different experimental variables, such as, different MS systems, sample complexities, sample preparations, elution gradients, matrix effects, loading amounts, and other factors were comprehensively investigated to evaluate the applicability, reproducibility, and transferability of ERPLS. The results demonstrated that ERLPS was highly reproducible and transferable within appropriate loading amounts and linearly correlated response peptides should be selected for each specific experiment. ERLPS was used to proteome samples from yeast to mouse and human, and in quantitative methods from label‐free to O18/O16‐labeled and SILAC analysis, and enabled accurate measurements for all proteotypic peptide‐based quantitative proteomics over a large dynamic range.     

高精度相对和绝对定量的等量异位标签在定量蛋白质组学研究中的新进展

蛋白质组学逐渐从定性研究转向定量研究。在定量蛋白质组学技术中,相对和绝对定量的等量异位标签(Isobaric tags for relative and absolute quantitation,iTRAQ)是应用最广泛的技术之一,具有通量高、稳定性强及不受样品来源制约等优点,几乎可以对任意样品进行标记,而且可以同时对多达8个样品进行定量分析,有效地提高了通量。iTRAQ技术不断改进,其定量准确性显著提高,适用的平台越来越多,为微生物、动物、植物、生物医学领域蛋白质及其翻译后修饰组研究创造了条件。文中综述了高精度iTRAQ技术在定量蛋白质组学研究中的最新发展及其应用。     

MSnbase-an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation

MSnbase is an R/Bioconductor package for the analysis of quantitative proteomics experiments that use isobaric tagging. It provides an exploratory data analysis framework for reproducible research, allowing raw data import, quality control, visualization, data processing and quantitation. MSnbase allows direct integration of quantitative proteomics data with additional facilities for statistical analysis provided by the Bioconductor project. AVAILABILITY: MSnbase is implemented in R (version ≥ 2.13.0) and available at the Bioconductor web site (http://www.bioconductor.org/). Vignettes outlining typical workflows, input/output capabilities and detailing underlying infrastructure are included in the package.     

微生物蛋白质组学的定量分析

越来越多的微生物基因组序列数据为系统地研究基因的调节和功能创造了有利条件.由于蛋白质是具有生物功能的分子,蛋白质组学在微生物基因组的功能研究中异军突起、蓬勃发展.微生物蛋白质组学的基本原则是,用比较研究来阐明和理解不同微生物之间或不同生长条件下基因的表达水平.显而易见,定量分析技术是比较蛋白质组学中急需发展的核心技术.对蛋白质组学定量分析技术在微生物蛋白质组研究中的进展进行了综述.     

Label-free quantification in clinical proteomics

Nowadays, proteomic studies no longer focus only on identifying as many proteins as possible in a given sample, but aiming for an accurate quantification of them. Especially in clinical proteomics, the investigation of variable protein expression profiles can yield useful information on pathological pathways or biomarkers and drug targets related to a particular disease. Over the time, many quantitative proteomic approaches have been established allowing researchers in the field of proteomics to refer to a comprehensive toolbox of different methodologies. In this review we will give an overview of different methods of quantitative proteomics with focus on label-free proteomics and its use in clinical proteomics.     

Bioinformatics analysis of mass spectrometry-based proteomics data sets

Proteomics has made tremendous progress, attaining throughput and comprehensiveness so far only seen in genomics technologies. The consequent avalanche of proteome level data poses great analytical challenges for downstream interpretation. We review bioinformatic analysis of qualitative and quantitative proteomic data, focusing on current and emerging paradigms employed for functional analysis, data mining and knowledge discovery from high resolution quantitative mass spectrometric data. Many bioinformatics tools developed for microarrays can be reused in proteomics, however, the uniquely quantitative nature of proteomics data also offers entirely novel analysis possibilities, which directly suggest and illuminate biological mechanisms.     

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.     

Comparison of full versus partial metabolic labeling for quantitative proteomics analysis in Arabidopsis thaliana

In recent years a variety of quantitative proteomics techniques have been developed, allowing characterization of changes in protein abundance in a variety of organisms under various biological conditions. Because it allows excellent control for error at all steps in sample preparation and analysis, full metabolic labeling using (15)N has emerged as an important strategy for quantitative proteomics, having been applied in a variety of organisms from yeast to Arabidopsis and even rats. However, challenges associated with complete replacement of (14)N with (15)N can make its application in many complex eukaryotic systems impractical on a routine basis. Extending a concept proposed by Whitelegge et al. (Whitelegge, J. P., Katz, J. E., Pihakari, K. A., Hale, R., Aguilera, R., Gomez, S. M., Faull, K. F., Vavilin, D., and Vermaas, W. (2004) Subtle modification of isotope ratio proteomics; an integrated strategy for expression proteomics. Phytochemistry 65, 1507-1515), we investigate an alternative strategy for quantitative proteomics that relies upon the subtle changes in isotopic envelope shape that result from partial metabolic labeling to compare relative abundances of labeled and unlabeled peptides in complex mixtures. We present a novel algorithm for the automated quantitative analysis of partial incorporation samples via LC-MS. We then compare the performance of partial metabolic labeling with traditional full metabolic labeling for quantification of controlled mixtures of labeled and unlabeled Arabidopsis peptides. Finally we evaluate the performance of each technique for comparison of light- versus dark-grown Arabidopsis with respect to reproducibility and numbers of peptide and protein identifications under more realistic experimental conditions. Overall full metabolic labeling and partial metabolic labeling prove to be comparable with respect to dynamic range, accuracy, and reproducibility, although partial metabolic labeling consistently allows quantification of a higher percentage of peptide observations across the dynamic range. This difference is especially pronounced at extreme ratios. Ultimately both full metabolic labeling and partial metabolic labeling prove to be well suited for quantitative proteomics characterization.     

Quantitative proteomics analysis of adsorbed plasma proteins classifies nanoparticles with different surface properties and size

Nanoparticle biological activity, biocompatibility and fate can be directly affected by layers of readily adsorbed host proteins in biofluids. Here, we report a study on the interactions between human blood plasma proteins and nanoparticles with a controlled systematic variation of properties using (18)O-labeling and LC-MS-based quantitative proteomics. We developed a novel protocol to both simplify isolation of nanoparticle bound proteins and improve reproducibility. LC-MS analysis identified and quantified 88 human plasma proteins associated with polystyrene nanoparticles consisting of three different surface chemistries and two sizes, as well as, for four different exposure times (for a total of 24 different samples). Quantitative comparison of relative protein abundances was achieved by spiking an (18)O-labeled "universal" reference into each individually processed unlabeled sample as an internal standard, enabling simultaneous application of both label-free and isotopic labeling quantification across the entire sample set. Clustering analysis of the quantitative proteomics data resulted in distinctive patterns that classified the nanoparticles based on their surface properties and size. In addition, temporal data indicated that the formation of the stable protein corona was at equilibrium within 5 min. The comprehensive quantitative proteomics results obtained in this study provide rich data for computational modeling and have potential implications towards predicting nanoparticle biocompatibility.     

A critical appraisal of techniques, software packages, and standards for quantitative proteomic analysis

Abstract New methods for performing quantitative proteome analyses based on differential labeling protocols or label-free techniques are reported in the literature on an almost monthly basis. In parallel, a correspondingly vast number of software tools for the analysis of quantitative proteomics data has also been described in the literature and produced by private companies. In this article we focus on the review of some of the most popular techniques in the field and present a critical appraisal of several software packages available to process and analyze the data produced. We also describe the importance of community standards to support the wide range of software, which may assist researchers in the analysis of data using different platforms and protocols. It is intended that this review will serve bench scientists both as a useful reference and a guide to the selection and use of different pipelines to perform quantitative proteomics data analysis. We have produced a web-based tool ( http://www.proteosuite.org/?q=other_resources ) to help researchers find appropriate software for their local instrumentation, available file formats, and quantitative methodology.     

The methods of quantitative proteomics

In modern science proteomic analysis is inseparable from other fields of systemic biology. Possessing huge resources quantitative proteomics operates colossal information on molecular mechanisms of life. Advances in proteomics help researchers to solve complex problems of cell signaling, posttranslational modification, structure and funciotnal homology of proteins, molecular diagnostics etc. More than 40 various methods have been developed in proteomics for quantitative analysis of proteins. Although each method is unique and has certain advantages and disadvantages all these use various isotope labels (tags). In this review we will consider the most popular and effective methods employing both chemical modifications of proteins and also metabolic and enzymatic methods of isotope labeling.     

High-performance proteomics as a tool in biomarker discovery

Biomarkers allowing early detection of disease or therapy control have a huge influence in curing a disease. A wide variety of methods were applied to find new biomarkers. In contrast to methods focused on DNA or mRNA techniques, approaches considering proteins as potential biomarker candidates have the advantage that proteins are more diverse than DNA or RNA and are more reflective of a biological system. Here, we present an approach for the identification of new biomarkers relying on our experience from the past 10 years of proteomics, outlining a concept of "high-performance proteomics" This approach is based on quantitative proteome analysis using a sufficient number of clinical samples and statistical validation of proteomics data by independent methods, such as Western blot analysis or immunohistochemistry.     

2D electrophoresis-based expression proteomics: a microbiologist's perspective

Quantitative proteomics based on 2D electrophoresis (2-DE) coupled with peptide mass fingerprinting is still one of the most widely used quantitative proteomics approaches in microbiology research. Our view on the exploitation of this global expression analysis technique and its contribution and potential to push forward the field of molecular microbial physiology towards a molecular systems microbiology perspective is discussed in this article. The advances registered in 2-DE-based quantitative proteomic analysis leading to increased protein resolution, sensitivity and accuracy, and the promising use of 2-DE to gain insights into post-translational modifications at a proteome-wide level (considering all the proteins/protein forms expressed by the genome) are focused on. Given the progress made in this field, it is foreseen that the 2-DE-based approach to quantitative proteomics will continue to be a fundamental tool for microbiologists working at a genome-wide scale. Guidelines are also provided for the exploitation of expression proteomics data, based on useful computational tools, and for the integration of these data with other genome-wide expression information. The advantages and limitations of a complete 2-DE-based expression proteomics analysis, envisaging the quantification of the global changes occurring in the proteome of a given cell depending on environmental or genetic manipulations, are discussed from the microbiologist's perspective. Particular focus is given to the emerging field of toxicoproteomics, a new systems toxicity approach that offers a powerful tool to directly monitor the earliest stages of the toxicological response by identifying critical proteins and pathways that are affected by, and respond to, a chemical stress. The experimental design and the bioinformatics analysis of data used in our laboratory to gain mechanistic insights through expression proteomics into the responses of the eukaryotic model Saccharomyces cerevisiae or of Pseudomonas strains to environmental toxicants are presented as case studies.