Design and initial characterization of the SC-200 proteomics standard mixture

High-throughput (HTP) proteomics studies generate large amounts of data. Interpretation of these data requires effective approaches to distinguish noise from biological signal, particularly as instrument and computational capacity increase and studies become more complex. Resolving this issue requires validated and reproducible methods and models, which in turn requires complex experimental and computational standards. The absence of appropriate standards and data sets for validating experimental and computational workflows hinders the development of HTP proteomics methods. Most protein standards are simple mixtures of proteins or peptides, or undercharacterized reference standards in which the identity and concentration of the constituent proteins is unknown. The Seattle Children's 200 (SC-200) proposed proteomics standard mixture is the next step toward developing realistic, fully characterized HTP proteomics standards. The SC-200 exhibits a unique modular design to extend its functionality, and consists of 200 proteins of known identities and molar concentrations from 6 microbial genomes, distributed into 10 molar concentration tiers spanning a 1,000-fold range. We describe the SC-200's design, potential uses, and initial characterization. We identified 84% of SC-200 proteins with an LTQ-Orbitrap and 65% with an LTQ-Velos (false discovery rate?=?1% for both). There were obvious trends in success rate, sequence coverage, and spectral counts with protein concentration; however, protein identification, sequence coverage, and spectral counts vary greatly within concentration levels.     

Proteome analysis of Escherichia coli using high-performance liquid chromatography and Fourier transform ion cyclotron resonance mass spectrometry

The basic problem of complexity poses a significant challenge for proteomic studies. To date two-dimensional gel electrophoresis (2-DE) followed by enzymatic in-gel digestion of the peptides, and subsequent identification by mass spectrometry (MS) is the most commonly used method to analyze complex protein mixtures. However, 2-DE is a slow and labor-intensive technique, which is not able to resolve all proteins of a proteome. To overcome these limitations gel-free approaches are developed based on high performance liquid chromatography (HPLC) and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). The high resolution and excellent mass accuracy of FT-ICR MS provides a basis for simultaneous analysis of numerous compounds. In the present study, a small protein subfraction of an Escherichia coli cell lysate was prepared by size-exclusion chromatography and proteins were analyzed using C4 reversed phase (RP)-HPLC for pre-separation followed by C18 RP nanoHPLC/nanoESI FT-ICR MS for analysis of the peptide mixtures after tryptic digestion of the protein fractions. We identified 231 proteins and thus demonstrated that a combination of two RP separation steps - one on the protein and one on the peptide level - in combination with high-resolution FT-ICR MS has the potential to become a powerful method for global proteomics studies.     

ICPLQuant – A software for non‐isobaric isotopic labeling proteomics

The main goal of many proteomics experiments is an accurate and rapid quantification and identification of regulated proteins in complex biological samples. The bottleneck in quantitative proteomics remains the availability of efficient software to evaluate and quantify the tremendous amount of mass spectral data acquired during a proteomics project. A new software suite, ICPLQuant, has been developed to accurately quantify isotope‐coded protein label (ICPL)‐labeled peptides on the MS level during LC‐MALDI and peptide mass fingerprint experiments. The tool is able to generate a list of differentially regulated peptide precursors for subsequent MS/MS experiments, minimizing time‐consuming acquisition and interpretation of MS/MS data. ICPLQuant is based on two independent units. Unit 1 performs ICPL multiplex detection and quantification and proposes peptides to be identified by MS/MS. Unit 2 combines MASCOT MS/MS protein identification with the quantitative data and produces a protein/peptide list with all the relevant information accessible for further data mining. The accuracy of quantification, selection of peptides for MS/MS‐identification and the automated output of a protein list of regulated proteins are demonstrated by the comparative analysis of four different mixtures of three proteins (Ovalbumin, Horseradish Peroxidase and Rabbit Albumin) spiked into the complex protein background of the DGPF Proteome Marker.     

Shotgun identification of the structural proteome of shrimp white spot syndrome virus and iTRAQ differentiation of envelope and nucleocapsid subproteomes

White spot syndrome virus (WSSV) is a major pathogen that causes severe mortality and economic losses to shrimp cultivation worldwide. The genome of WSSV contains a 305-kb double-stranded circular DNA, which encodes 181 predicted ORFs. Previous gel-based proteomics studies on WSSV have identified 38 structural proteins. In this study, we applied shotgun proteomics using off-line coupling of an LC system with MALDI-TOF/TOF MS/MS as a complementary and comprehensive approach to investigate the WSSV proteome. This approach led to the identification of 45 viral proteins; 13 of them are reported for the first time. Seven viral proteins were found to have acetylated N termini. RT-PCR confirmed the mRNA expression of these 13 newly identified viral proteins. Furthermore iTRAQ (isobaric tags for relative and absolute quantification), a quantitative proteomics strategy, was used to distinguish envelope proteins and nucleocapsid proteins of WSSV. Based on iTRAQ ratios, we successfully identified 23 envelope proteins and six nucleocapsid proteins. Our results validated 15 structural proteins with previously known localization in the virion. Furthermore the localization of an additional 12 envelope proteins and two nucleocapsid proteins was determined. We demonstrated that iTRAQ is an effective approach for high throughput viral protein localization determination. Altogether WSSV is assembled by at least 58 structural proteins, including 13 proteins newly identified by shotgun proteomics and one identified by iTRAQ. The localization of 42 structural proteins was determined; 33 are envelope proteins, and nine are nucleocapsid proteins. A comprehensive identification of WSSV structural proteins and their localization should facilitate the studies of its assembly and mechanism of infection.     

Hexapeptide libraries for enhanced protein PTM identification and relative abundance profiling in whole human saliva

Dynamic range compression (DRC) by hexapeptide libraries increases MS/MS-based identification of lower-abundance proteins in complex mixtures. However, two unanswered questions impede fully realizing DRC's potential in shotgun proteomics. First, does DRC enhance identification of post-translationally modified proteins? Second, can DRC be incorporated into a workflow enabling relative protein abundance profiling? We sought to answer both questions analyzing human whole saliva. Addressing question one, we coupled DRC with covalent glycopeptide enrichment and MS/MS. With DRC we identified ～2 times more N-linked glycoproteins and their glycosylation sites than without DRC, dramatically increasing the known salivary glycoprotein catalog. Addressing question two, we compared differentially stable isotope-labeled saliva samples pooled from healthy and metastatic breast cancer women using a multidimensional peptide fractionation-based workflow, analyzing in parallel one sample portion with DRC and one portion without. Our workflow categorizes proteins with higher absolute abundance, whose relative abundance ratios are altered by DRC, from proteins of lower absolute abundance detected only after DRC. Within each of these salivary protein categories, we identified novel abundance changes putatively associated with breast cancer, demonstrating feasibility and benefits of DRC for relative abundance profiling. Collectively, our results bring us closer to realizing the full potential of DRC for proteomic studies.     

磷蛋白组的研究技术及其进展

真核细胞中蛋白质磷酸化是一个重要事件。真核细胞利用可逆的蛋白磷酸化来控制许多细胞过程包括信号转换、基因表达、细胞周期等。磷蛋白组的研究涉及磷蛋白的分离和鉴定 ,磷酸化残基定位和定量分析。由于蛋白质磷酸化是一个动态过程 ,在细胞中磷蛋白含量低 ,磷酸化位点可变 ,且磷酸肽的质谱信号常常会受到抑制 ,所以磷蛋白的分析存在更多的困难。本文介绍了国内外在磷酸蛋白的分离鉴定及定量分析方面的研究技术以及进展情况。目前 ,质谱仍然是核心的鉴定技术 ,寻找更好富集方法是最大的挑战。定量蛋白组学是对蛋白质的差异表达进行精确的定量分析。目前还不存在一种独立的方法可以完成磷蛋白的分离、鉴定 ,以及磷酸位点的定位和定量分析。随着样品分离技术和相关仪器的发展 ,磷酸蛋白快速、准确、全面分析鉴定将能够实现。     

Improvement of an in-gel tryptic digestion method for matrix-assisted laser desorption/ionization-time of flight mass spectrometry peptide mapping by use of volatile solubilizing agents

The combination of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), in-gel enzymatic digestion of proteins separated by two-dimensional gel electrophoresis and searches of molecular weight in peptide-mass databases is a powerful and well established method for protein identification in proteomics analysis. For successful protein identification by MALDI-TOF mass spectrometry of peptide mixtures, critical parameters include highly specific enzymatic cleavage, high mass accuracy and sufficient numbers and sequence coverage of the peptides which can be analyzed. For in-gel digestion with trypsin, the method employed should be compatible both with enzymatic cleavage and subsequent MALDI-TOF MS analysis. We report here an improved method for preparation of peptides for MALDI-TOF MS mass fingerprinting by using volatile solubilizing agents during the in-gel digestion procedure. Our study clearly demonstrates that modification of the in-gel digestion protocols by addition of dimethyl formamide (DMF) or a mixture of DMF/N,N-dimethyl acetamide at various concentrations can significantly increase the recovery of peptides. These higher yields of peptides resulted in more effective protein identification.     

Mascot file parsing and quantification (MFPaQ), a new software to parse, validate, and quantify proteomics data generated by ICAT and SILAC mass spectrometric analyses: application to the proteomics study of membrane proteins from primary human endothelial cells

Proteomics strategies based on nanoflow (nano-) LC-MS/MS allow the identification of hundreds to thousands of proteins in complex mixtures. When combined with protein isotopic labeling, quantitative comparison of the proteome from different samples can be achieved using these approaches. However, bioinformatics analysis of the data remains a bottleneck in large scale quantitative proteomics studies. Here we present a new software named Mascot File Parsing and Quantification (MFPaQ) that easily processes the results of the Mascot search engine and performs protein quantification in the case of isotopic labeling experiments using either the ICAT or SILAC (stable isotope labeling with amino acids in cell culture) method. This new tool provides a convenient interface to retrieve Mascot protein lists; sort them according to Mascot scoring or to user-defined criteria based on the number, the score, and the rank of identified peptides; and to validate the results. Moreover the software extracts quantitative data from raw files obtained by nano-LC-MS/MS, calculates peptide ratios, and generates a non-redundant list of proteins identified in a multisearch experiment with their calculated averaged and normalized ratio. Here we apply this software to the proteomics analysis of membrane proteins from primary human endothelial cells (ECs), a cell type involved in many physiological and pathological processes including chronic inflammatory diseases such as rheumatoid arthritis. We analyzed the EC membrane proteome and set up methods for quantitative analysis of this proteome by ICAT labeling. EC microsomal proteins were fractionated and analyzed by nano-LC-MS/MS, and database searches were performed with Mascot. Data validation and clustering of proteins were performed with MFPaQ, which allowed identification of more than 600 unique proteins. The software was also successfully used in a quantitative differential proteomics analysis of the EC membrane proteome after stimulation with a combination of proinflammatory mediators (tumor necrosis factor-alpha, interferon-gamma, and lymphotoxin alpha/beta) that resulted in the identification of a full spectrum of EC membrane proteins regulated by inflammation.     

Quantitative profiling of proteins in complex mixtures using liquid chromatography and mass spectrometry

The objective of this study was to determine if liquid chromatography mass spectrometry (LC/MS) data of tryptic digests of proteins can be used for quantitation. In theory, the peak area of peptides should correlate to their concentration; hence, the peak areas of peptides from one protein should correlate to the concentration of that particular protein. To evaluate this hypothesis, different amounts of tryptic digests of myoglobin were analyzed by LC/MS in a wide range between 10 fmol and 100 pmol. The results show that the peak areas from liquid chromatography mass spectrometry correlate linearly to the concentration of the protein (r2 = 0.991). The method was further evaluated by adding two different concentrations of horse myoglobin to human serum. The results confirm that the quantitation method can also be used for quantitative profiling of proteins in complex mixtures such as human sera. Expected and calculated protein ratios differ by no more than 16%. We describe a new method combining protein identification with accurate profiling of individual proteins. This approach should provide a widely applicable means to compare global protein expression in biological samples.     

Multidimensional chromatography coupled to electrospray ionization time-of-flight mass spectrometry as an alternative to two-dimensional gels for the identification and analysis of complex mixtures of intact proteins

The limitations of 2-D gels for global proteomics have encouraged the development of alternative approaches for identifying proteins in complicated mixtures, and determining their modification state. In this work, we describe the application of multidimensional liquid chromatography (SCX-RPLC) coupled with electrospray time-of-flight mass spectrometry and off-line fraction collection to analyze complex intact protein mixtures. Methods were developed using both standard proteins and an enriched yeast ribosomal fraction sample containing approximately 100 proteins, which permitted assessment of the effectiveness of the individual separation dimensions, as well as investigation of the interplay between separation capacity and electrospray MS performance.     

Label-free quantitative analysis of one-dimensional PAGE LC/MS/MS proteome: application on angiotensin II-stimulated smooth muscle cells secretome

A widely used method for protein identification couples prefractionation of protein samples by one-dimensional (1D) PAGE with LC/MS/MS. We developed a new label-free quantitative algorithm by combining measurements of spectral counting, ion intensity, and peak area on 1D PAGE-based proteomics. This algorithm has several improvements over other label-free quantitative algorithms: (i) Errors in peak detection are reduced because the retention time is based on each LC/MS/MS run and actual precursor m/z. (ii) Detection sensitivity is increased because protein quantification is based on the combination of peptide count, ion intensity, and peak area. (iii) Peak intensity and peak area are calculated in each LC/MS/MS run for all slices from 1D PAGE for every single identified protein and visualized as a Western blot image. The sensitivity and accuracy of this algorithm were demonstrated by using standard curves (17.4 fmol to 8.7 pmol), complex protein mixtures (30 fmol to 1.16 pmol) of known composition, and spiked protein (34.8 fmol to 17.4 pmol) in complex proteins. We studied the feasibility of this approach using the secretome of angiotensin II (Ang II)-stimulated vascular smooth muscle cells (VSMCs). From the VSMC-conditioned medium, 629 proteins were identified including 212 putative secreted proteins. 26 proteins were differently expressed in control and Ang II-stimulated VSMCs, including 18 proteins not previously reported. Proteins related to cell growth (CYR61, protein NOV, and clusterin) were increased, whereas growth arrest-specific 6 (GAS6) and growth/differentiation factor 6 were decreased by Ang II stimulation. Ang II-stimulated changes of plasminogen activator inhibitor-1, GAS6, cathepsin B, and periostin were validated by Western blot. In conclusion, a novel label-free quantitative analysis of 1D PAGE-LC/MS/MS-based proteomics has been successfully applied to the identification of new potential mediators of Ang II action and may provide an alternative to traditional protein staining methods.     

EBP, a program for protein identification using multiple tandem mass spectrometry datasets

MS/MS combined with database search methods can identify the proteins present in complex mixtures. High throughput methods that infer probable peptide sequences from enzymatically digested protein samples create a challenge in how best to aggregate the evidence for candidate proteins. Typically the results of multiple technical and/or biological replicate experiments must be combined to maximize sensitivity. We present a statistical method for estimating probabilities of protein expression that integrates peptide sequence identifications from multiple search algorithms and replicate experimental runs. The method was applied to create a repository of 797 non-homologous zebrafish (Danio rerio) proteins, at an empirically validated false identification rate under 1%, as a resource for the development of targeted quantitative proteomics assays. We have implemented this statistical method as an analytic module that can be integrated with an existing suite of open-source proteomics software.     

Comparison of 4-plex to 8-plex iTRAQ quantitative measurements of proteins in human plasma samples

Methods for isobaric tagging of peptides, iTRAQ or TMT, are commonly used platforms in mass spectrometry based quantitative proteomics. These two methods are very often used to quantitate proteins in complex samples, e.g., serum/plasma or CSF supporting biomarker discovery studies. The success of these studies depends on multiple factors, including the accuracy of ratios of reporter ions reflecting quantitative changes of proteins. Because reporter ions are generated during peptide fragmentation, the differences of chemical structure of iTRAQ balance groups may have an effect on how efficiently these groups are fragmented and thus how differences in protein expression will be measured. Because 4-plex and 8-plex iTRAQ reagents do have different structures of balanced groups, it has been postulated that indeed differences in protein identification and quantitation exist between these two reagents. In this study we controlled the ratios of tagged samples and compared quantitation of proteins using 4-plex versus 8-plex reagents in the context of a highly complex sample of human plasma using ABSciex 4800 MALDI-TOF/TOF mass spectrometer and ProteinPilot 4.0 software. We observed that 8-plex tagging provides more consistent ratios than 4-plex without compromising protein identification, thus allowing investigation of eight experimental conditions in one analytical experiment.     

Mass spectrometry for the study of protein-protein interactions

The identification of subpicomolar amounts of protein by mass spectrometry (MS) coupled with two-dimensional methods to separate complex protein mixtures is fueling the field of proteomics, and making feasible the notion of cataloging and comparing all of the expressed proteins in a biological sample. Functional proteomics is a complementary effort aimed at the characterization of functional features of proteins, such as their interactions with other proteins. Proteins comprise modular domains, many of which are noncatalytic modules that direct protein-protein interactions. Capturing proteins of interest and their interacting proteins by using high-affinity antibodies presents a simple method to prepare relatively simple protein mixtures easily resolved in one-dimensional formats. Individual or mixtures of proteins identified as stained bands in polyacrylamide gels are subjected to in situ digestion with the protease trypsin, and the extracted peptide fragments are analyzed by MS. The quality, quantity, and complexity of the tryptic digest, the species origin of the proteins, and the quality of the corresponding databases of genomic and protein information greatly influence the subsequent MS analysis in terms of degree of difficulty and methodological approach required to make an unambiguous protein identification. In this article we report the isolation of associated proteins from a complex cell-derived lysate by using an epitope-directed antibody. The protein pICLn engineered to carry an epitope tag was purified from cultured human embryonic kidney cells, and found to associate with a variety of proteins including the spliceosomal proteins smE and smG. By application of this general approach, the systematic identification of protein complexes and assignment of protein function are possible.     

Mixed-effects statistical model for comparative LC-MS proteomics studies

Comparing a protein's concentrations across two or more treatments is the focus of many proteomics studies. A frequent source of measurements for these comparisons is a mass spectrometry (MS) analysis of a protein's peptide ions separated by liquid chromatography (LC) following its enzymatic digestion. Alas, LC-MS identification and quantification of equimolar peptides can vary significantly due to their unequal digestion, separation, and ionization. This unequal measurability of peptides, the largest source of LC-MS nuisance variation, stymies confident comparison of a protein's concentration across treatments. Our objective is to introduce a mixed-effects statistical model for comparative LC-MS proteomics studies. We describe LC-MS peptide abundance with a linear model featuring pivotal terms that account for unequal peptide LC-MS measurability. We advance fitting this model to an often incomplete LC-MS data set with REstricted Maximum Likelihood (REML) estimation, producing estimates of model goodness-of-fit, treatment effects, standard errors, confidence intervals, and protein relative concentrations. We illustrate the model with an experiment featuring a known dilution series of a filamentous ascomycete fungus Trichoderma reesei protein mixture. For 781 of the 1546 T. reesei proteins with sufficient data coverage, the fitted mixed-effects models capably described the LC-MS measurements. The LC-MS measurability terms effectively accounted for this major source of uncertainty. Ninety percent of the relative concentration estimates were within 0.5-fold of the true relative concentrations. Akin to the common ratio method, this model also produced biased estimates, albeit less biased. Bias decreased significantly, both absolutely and relative to the ratio method, as the number of observed peptides per protein increased. Mixed-effects statistical modeling offers a flexible, well-established methodology for comparative proteomics studies integrating common experimental designs with LC-MS sample processing plans. It favorably accounts for the unequal LC-MS measurability of peptides and produces informative quantitative comparisons of a protein's concentration across treatments with objective measures of uncertainties.     

Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition

Relative quantification methods have dominated the quantitative proteomics field. There is a need, however, to conduct absolute quantification studies to accurately model and understand the complex molecular biology that results in proteome variability among biological samples. A new method of absolute quantification of proteins is described. This method is based on the discovery of an unexpected relationship between MS signal response and protein concentration: the average MS signal response for the three most intense tryptic peptides per mole of protein is constant within a coefficient of variation of less than +/-10%. Given an internal standard, this relationship is used to calculate a universal signal response factor. The universal signal response factor (counts/mol) was shown to be the same for all proteins tested in this study. A controlled set of six exogenous proteins of varying concentrations was studied in the absence and presence of human serum. The absolute quantity of the standard proteins was determined with a relative error of less than +/-15%. The average MS signal responses of the three most intense peptides from each protein were plotted against their calculated protein concentrations, and this plot resulted in a linear relationship with an R(2) value of 0.9939. The analyses were applied to determine the absolute concentration of 11 common serum proteins, and these concentrations were then compared with known values available in the literature. Additionally within an unfractionated Escherichia coli lysate, a subset of identified proteins known to exist as functional complexes was studied. The calculated absolute quantities were used to accurately determine their stoichiometry.     

Improving large-scale proteomics by clustering of mass spectrometry data

Tandem mass spectrometry (MS/MS), coupled with liquid chromatography (LC), is a powerful tool for the analysis and comparison of complex protein and peptide mixtures. However, the extremely large amounts of data that result from the process are very complex and difficult to analyze. We show how the clustering of similar spectra from multiple LC-MS/MS runs can help in data management and improve the analysis of complex peptide mixtures. The major effect of spectrum clustering is the reduction of the huge amounts of data to a manageable size. As a result, analysis time is shorter and more data can be stored for further analysis. Furthermore, spectrum quality improvement allows the identification of more peptides with greater confidence, the comparison of complex peptide mixtures is facilitated, and the entire proteomics project is presented in concise form. Pep-Miner is an advanced software tool that implements these clustering-based applications. It proved useful in several comparative proteomics projects involving lung cancer cells and various other cell types. In one of these projects, Pep-Miner reduced 517 000 spectra to 20 900 clusters and identified 2518 peptides derived from 830 proteins. Clustering and identification lasted less than two hours on an IBM Thinkpad T23 computer (laptop). Pep-Miner's unique properties make it a very useful tool for large-scale shotgun proteomics projects.     

A protein processing filter method for bacterial identification by mass spectrometry-based proteomics

A "one-pot" alternative method for processing proteins and isolating peptide mixtures from bacterial samples is presented for liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis and data reduction. The conventional in-solution digestion of the protein contents of bacteria is compared to a small disposable filter unit placed inside a centrifuge vial for processing and digestion of bacterial proteins. Each processing stage allows filtration of excess reactants and unwanted byproduct while retaining the proteins. Upon addition of trypsin, the peptide mixture solution is passed through the filter while retaining the trypsin enzyme. The peptide mixture is then analyzed by LC-MS/MS with an in-house BACid algorithm for a comparison of the experimental unique peptides to a constructed proteome database of bacterial genus, specie, and strain entries. The concentration of bacteria was varied from 10 × 10(7) to 3.3 × 10(3) cfu/mL for analysis of the effect of concentration on the ability of the sample processing, LC-MS/MS, and data analysis methods to identify bacteria. The protein processing method and dilution procedure result in reliable identification of pure suspensions and mixtures at high and low bacterial concentrations.     

Interaction Proteomics

The term proteome is traditionally associated with the identification of a large number of proteins within complex mixtures originating from a given organelle, cell or even organism. Current proteome investigations are basically focused on two major areas, expression proteomics and functional proteomics. Both approaches rely on the fractionation of protein mixtures essentially by two-dimensional polyacrylamide gel electrophoresis (2D-gel) and the identification of individual protein bands by mass spectrometric techniques (2D-MS). Functional proteomics approaches are basically addressing two main targets, the elucidation of the biological function of unknown proteins and the definition of cellular mechanisms at the molecular level. In the cell many processes are governed not only by the relative abundance of proteins but also by rapid and transient regulation of activity, association and localization of proteins and protein complexes. The association of an unknown protein with partners belonging to a specific protein complex involved in a particular process would then be strongly suggestive of its biological function. The identification of interacting proteins in stable complexes in a cellular system is essentially achieved by affinity-based procedures. Different strategies relying on this simple concept have been developed and a brief overview of the main approaches presently used in functional proteomics studies is described.