Relative protein quantification by isobaric SILAC with immonium ion splitting (ISIS)

Metabolic labeling techniques have recently become popular tools for the quantitative profiling of proteomes. Classical stable isotope labeling with amino acids in cell cultures (SILAC) uses pairs of heavy/light isotopic forms of amino acids to introduce predictable mass differences in protein samples to be compared. After proteolysis, pairs of cognate precursor peptides can be correlated, and their intensities can be used for mass spectrometry-based relative protein quantification. We present an alternative SILAC approach by which two cell cultures are grown in media containing isobaric forms of amino acids, labeled either with 13C on the carbonyl (C-1) carbon or 15N on backbone nitrogen. Labeled peptides from both samples have the same nominal mass and nearly identical MS/MS spectra but generate upon fragmentation distinct immonium ions separated by 1 amu. When labeled protein samples are mixed, the intensities of these immonium ions can be used for the relative quantification of the parent proteins. We validated the labeling of cellular proteins with valine, isoleucine, and leucine with coverage of 97% of all tryptic peptides. We improved the sensitivity for the detection of the quantification ions on a pulsing instrument by using a specific fast scan event. The analysis of a protein mixture with a known heavy/light ratio showed reliable quantification. Finally the application of the technique to the analysis of two melanoma cell lines yielded quantitative data consistent with those obtained by a classical two-dimensional DIGE analysis of the same samples. Our method combines the features of the SILAC technique with the advantages of isobaric labeling schemes like iTRAQ. We discuss advantages and disadvantages of isobaric SILAC with immonium ion splitting as well as possible ways to improve it.     

Performance of isobaric and isotopic labeling in quantitative plant proteomics

Mass spectrometry has become indispensable for peptide and protein quantification in proteomics studies. When proteomics technologies are applied to understand the biology of plants, two-dimensional gel electrophoresis is still the prevalent method for protein fractionation, identification, and quantitation. In the present work, we have used LC-MS to compare an isotopic (ICPL) and isobaric (iTRAQ) chemical labeling technique to quantify proteins in the endosperm of Ricinus communis seeds at three developmental stages (IV, VI, and X). Endosperm proteins of each stage were trypsin-digested in-solution, and the same amount of peptides was labeled with ICPL and iTRAQ tags in two orders (forward and reverse). Each sample was submitted to nanoLC coupled to an LTQ-Orbitrap high-resolution mass spectrometer. Comparing labeling performance, iTRAQ was able to label 99.8% of all identified unique peptides, while 94.1% were labeled by ICPL. After statistical analysis, it was possible to quantify 309 (ICPL) and 321 (iTRAQ) proteins, from which 95 are specific to ICPL, 107 to iTRAQ, and 214 common to both labeling strategies. We noted that the iTRAQ quantification could be influenced by the tag. Even though the efficiency of the iTRAQ and ICPL in protein quantification depends on several parameters, both labeling methods were able to successfully quantify proteins present in the endosperm of castor bean during seed development and, when combined, increase the number of quantified proteins.     

IsobariQ: software for isobaric quantitative proteomics using IPTL, iTRAQ, and TMT

Isobaric peptide labeling plays an important role in relative quantitative comparisons of proteomes. Isobaric labeling techniques utilize MS/MS spectra for relative quantification, which can be either based on the relative intensities of reporter ions in the low mass region (iTRAQ and TMT) or on the relative intensities of quantification signatures throughout the spectrum due to isobaric peptide termini labeling (IPTL). Due to the increased quantitative information found in MS/MS fragment spectra generated by the recently developed IPTL approach, new software was required to extract the quantitative information. IsobariQ was specifically developed for this purpose; however, support for the reporter ion techniques iTRAQ and TMT is also included. In addition, to address recently emphasized issues about heterogeneity of variance in proteomics data sets, IsobariQ employs the statistical software package R and variance stabilizing normalization (VSN) algorithms available therein. Finally, the functionality of IsobariQ is validated with data sets of experiments using 6-plex TMT and IPTL. Notably, protein substrates resulting from cleavage by proteases can be identified as shown for caspase targets in apoptosis.     

iTRAQ labeling is superior to mTRAQ for quantitative global proteomics and phosphoproteomics

Labeling of primary amines on peptides with reagents containing stable isotopes is a commonly used technique in quantitative mass spectrometry. Isobaric labeling techniques such as iTRAQ™ or TMT™ allow for relative quantification of peptides based on ratios of reporter ions in the low m/z region of spectra produced by precursor ion fragmentation. In contrast, nonisobaric labeling with mTRAQ™ yields precursors with different masses that can be directly quantified in MS1 spectra. In this study, we compare iTRAQ- and mTRAQ-based quantification of peptides and phosphopeptides derived from EGF-stimulated HeLa cells. Both labels have identical chemical structures, therefore precursor ion- and fragment ion-based quantification can be directly compared. Our results indicate that iTRAQ labeling has an additive effect on precursor intensities, whereas mTRAQ labeling leads to more redundant MS2 scanning events caused by triggering on the same peptide with different mTRAQ labels. We found that iTRAQ labeling quantified nearly threefold more phosphopeptides (12,129 versus 4,448) and nearly twofold more proteins (2,699 versus 1,597) than mTRAQ labeling. Although most key proteins in the EGFR signaling network were quantified with both techniques, iTRAQ labeling allowed quantification of twice as many kinases. Accuracy of reporter ion quantification by iTRAQ is adversely affected by peptides that are cofragmented in the same precursor isolation window, dampening observed ratios toward unity. However, because of tighter overall iTRAQ ratio distributions, the percentage of statistically significantly regulated phosphopeptides and proteins detected by iTRAQ and mTRAQ was similar. We observed a linear correlation of logarithmic iTRAQ to mTRAQ ratios over two orders of magnitude, indicating a possibility to correct iTRAQ ratios by an average compression factor. Spike-in experiments using peptides of defined ratios in a background of nonregulated peptides show that iTRAQ quantification is less accurate but not as variable as mTRAQ quantification.Stable isotope labeling techniques have become very popular in recent years to perform quantitative mass spectrometry experiments with high precision and accuracy. In contrast to label-free approaches, multiplexed isotopically labeled samples can be simultaneously analyzed resulting in increased reproducibility and accuracy for quantification of peptides and proteins from different biological states. Isotopic labeling strategies can be grouped into two major categories: isobaric labels and nonisobaric labels. In the former category are iTRAQ¹ (isobaric tags for relative and absolute quantification (1)) and TMT (tandem mass tags (2)) mass tags. In the nonisobaric labeling category are methods such as mTRAQ (mass differential tags for relative and absolute quantification), stable isotope labeling by amino acids in cell culture (SILAC (3)), and reductive dimethylation (4). Isobaric labeling techniques allow relative quantification of peptides based on ratios of low m/z reporter ions produced by fragmentation of the precursor ion, whereas nonisobaric labeling yields precursors with different masses that can be directly quantified from MS1 intensity. iTRAQ and mTRAQ reagents provide a great opportunity to directly compare capabilities of reporter and precursor ion quantification since both labels have identical chemical structures and differ only in their composition and number of ¹³C, ¹⁵N, and ¹⁸O atoms. In fact, iTRAQ-117 and mTRAQ-Δ4 are identical mass tags with a total mass of 145 Da (Fig. 1A). To achieve 4-plex quantification capabilities for iTRAQ labels, the composition of stable isotopes is arranged in a way to obtain the reporter ion/balancing group pairs 114/31, 115/30, 116/29, and 117/28 (1). Three nonisobaric mTRAQ labels were generated by adding or removing four neutrons to the mTRAQ-Δ4 label resulting in mTRAQ-Δ8 and mTRAQ-Δ0, respectively. Both iTRAQ and mTRAQ reagents are available as N-hydroxy-succinimide esters to facilitate primary amine labeling of peptides.Open in a separate windowFig. 1.A, Labeling strategy for comparative evaluation of iTRAQ and mTRAQ tags. Peptides were labeled with the indicated iTRAQ and mTRAQ reagents for combined phosphoproteome and proteome analysis. B, Selection of optimal instrument methods for analysis of iTRAQ- and mTRAQ-labeled peptides. Unfractionated proteome samples (1 ug) and phosphoproteome samples (enriched from 250 μg peptides) were analyzed for iTRAQ samples with a CID/HCD-Top8 method, whereas for mTRAQ we compared CID-Top16 acquisition to HCD-Top8. Note that duty cycle times were for all instrument methods ∼3.1 s.One potential advantage of an iTRAQ labeling strategy is its additive effect on precursor intensities when samples are multiplexed, resulting in increased sensitivity. However, iTRAQ ratios have been demonstrated to be prone to compression. This occurs when other nonregulated background peptides are co-isolated and cofragmented in the same isolation window of the peptide of interest and contribute fractional intensity to the reporter ions in MS2-scans (5–7). Because most peptides in an experiment are present at 1:1:1:1 ratios between multiplexed samples, all ratios in the experiment tend to be dampened toward unity when cofragmentation occurs. This inaccuracy led to the development of mTRAQ labels to facilitate accurate precursor-based quantification of proteins initially identified in iTRAQ discovery experiments with targeted assays, such as multiple reaction monitoring (MRM) (8). Although iTRAQ has been widely used in discovery-based proteomics studies, mTRAQ has only appeared in a small number of studies thus far (8).In this study we investigated the advantages and disadvantages of iTRAQ and mTRAQ labeling for proteome-wide analysis of protein phosphorylation and expression changes. We selected epidermal growth factor (EGF)-stimulated HeLa cells as a model system for our comparative evaluation of iTRAQ and mTRAQ labeling, as both changes in the phosphoproteome (9) as well as the proteome (10) are well described for EGF stimulation. We show that iTRAQ labeling yields superior results to mTRAQ in terms of numbers of quantified phosphopeptides, proteins and regulated components. By means of spike-in experiments with GluC generated peptides of known ratios we find that iTRAQ quantification is more precise but less accurate than mTRAQ due to ratio compression. We identify a linear relationship of observed versus expected logarithmic GluC generated peptide ratios as well as for logarithmic iTRAQ and mTRAQ ratios of the phosphoproteome and proteome analysis. This indicates a uniform degree of ratio compression over two orders of magnitude throughout iTRAQ data sets and explains why iTRAQ ratio compression does not compromise the ability to detect regulated elements in these experiments.     

iTRAQ reagent-based quantitative proteomic analysis on a linear ion trap mass spectrometer

For proteomic analysis using tandem mass spectrometry, linear ion trap instruments provide unsurpassed sensitivity but unreliably detect low mass peptide fragments, precluding their use with iTRAQ reagent-labeled samples. Although the popular LTQ linear ion trap supports analyzing iTRAQ reagent-labeled peptides via pulsed Q dissociation, PQD, its effectiveness remains questionable. Using a standard mixture, we found careful tuning of relative collision energy necessary for fragmenting iTRAQ reagent-labeled peptides, and increasing microscan acquisition and repeat count improves quantification but identifies somewhat fewer peptides. We developed software to calculate abundance ratios via summing reporter ion intensities across spectra matching to each protein, thereby providing maximized accuracy. Testing found that results closely corresponded between analysis using optimized LTQ-PQD settings plus our software and using a Qstar instrument. Thus, we demonstrate the effectiveness of LTQ-PQD analyzing iTRAQ reagent-labeled peptides, and provide guidelines for successful quantitative proteomic studies.     

Robust and sensitive iTRAQ quantification on an LTQ Orbitrap mass spectrometer

Isobaric stable isotope tagging reagents such as tandem mass tags or isobaric tags for relative and absolute quantification enable multiplexed quantification of peptides via reporter ion signals in the low mass range of tandem mass spectra. Until recently, the poor recovery of low mass fragments observed in tandem mass spectra acquired on ion trap mass spectrometers precluded the use of these reagents on this widely available instrument platform. The Pulsed Q Dissociation (PQD) technique allows negotiating this limitation but suffers from poor fragmentation efficiency, which has raised doubts in the community as to its practical utility. Here we show that by carefully optimizing instrument parameters such as collision energy, activation Q, delay time, ion isolation width, number of microscans, and number of trapped ions, low m/z fragment ion intensities can be generated that enable accurate peptide quantification at the 100 amol level. Side by side comparison of PQD on an LTQ Orbitrap with CID on a five-year old Q-Tof Ultima using complex protein digests shows that whereas precision of quantification of 10-15% can be achieved by both approaches, PQD quantifies twice as many proteins. PQD on an LTQ Orbitrap also outperforms "higher energy collision induced dissociation" on the same instrument using the recently introduced octapole collision cell in terms of lower limit of quantification. Finally, we demonstrate the significant analytical potential of iTRAQ quantification using PQD on an LTQ Orbitrap by quantitatively measuring the kinase interaction profile of the small molecule drug imatinib in K-562 cells. This article gives practical guidance for the implementation of PQD, discusses its merits, and for the first time, compares its performance to higher energy collision-induced dissociation.     

Amine-reactive isobaric tagging reagents: requirements for absolute quantification of proteins and peptides

Amine-reactive isobaric tagging reagents such as iTRAQ (isobaric tags for relative and absolute quantitation) have recently become increasing popular for relative protein quantification, cell expression profiling, and biomarker discovery. This is due mainly to the possibility of simultaneously identifying and quantifying multiple samples. The principles of iTRAQ may also be applied to absolute protein quantification with the use of synthetic peptides as standards. The prerequisites that must be fulfilled to perform absolute quantification of proteins by iTRAQ have been investigated and are described here. Three samples of somatropin were quantified using iTRAQ and synthetic peptides as standards, corresponding to a portion of the protein sequence. The results were compared with those obtained by quantification of the same protein solutions using double exact matching isotope dilution mass spectrometry (IDMS). To obtain reliable results, the appropriate standard peptides needed to be selected carefully and enzymatic digestion needed to be optimized to ensure complete release of the peptides from the protein. The kinetics and efficiency of the iTRAQ derivatization reaction of the standard peptides and digested proteins with isobaric tagging reagents were studied using a mixture of seven synthetic peptides and their corresponding labeled peptides. The implications of incomplete derivatization are also presented.     

Defining,Comparing, and Improving iTRAQ Quantification in Mass Spectrometry Proteomics Data

The purpose of this study was to generate a basis for the decision of what protein quantities are reliable and find a way for accurate and precise protein quantification. To investigate this we have used thousands of peptide measurements to estimate variance and bias for quantification by iTRAQ (isobaric tags for relative and absolute quantification) mass spectrometry in complex human samples. A549 cell lysate was mixed in the proportions 2:2:1:1:2:2:1:1, fractionated by high resolution isoelectric focusing and liquid chromatography and analyzed by three mass spectrometry platforms; LTQ Orbitrap Velos, 4800 MALDI-TOF/TOF and 6530 Q-TOF. We have investigated how variance and bias in the iTRAQ reporter ions data are affected by common experimental variables such as sample amount, sample fractionation, fragmentation energy, and instrument platform. Based on this, we have suggested a concept for experimental design and a methodology for protein quantification. By using duplicate samples in each run, each experiment is validated based on its internal experimental variation. The duplicates are used for calculating peptide weights, unique to the experiment, which is used in the protein quantification. By weighting the peptides depending on reporter ion intensity, we can decrease the relative error in quantification at the protein level and assign a total weight to each protein that reflects the protein quantitation confidence. We also demonstrate the usability of this methodology in a cancer cell line experiment as well as in a clinical data set of lung cancer tissue samples. In conclusion, we have in this study developed a methodology for improved protein quantification in shotgun proteomics and introduced a way to assess quantification for proteins with few peptides. The experimental design and developed algorithms decreased the relative protein quantification error in the analysis of complex biological samples.Recent developments in methods and instruments for mass spectrometry enable quantitative proteomics analysis of complex samples with good coverage (1–4). Several techniques for quantification by mass spectrometry exist, both using isotopic labeling and label free methods (5, 6). Quantification by isotopic labeling can be done on precursor ion level or by quantifying isobaric label fragments in fragment spectra. Isotope-coded affinity tag (7), isobaric tags for relative and absolute quantification (iTRAQ)¹ (8), and stable isotope labeling by amino acids in cell culture (SILAC) (9) are among the most commonly used labeling methods based on stable isotopes. iTRAQ allows for simultaneous relative quantification of up to eight samples within a single run. Quantification by mass spectrometry is however a challenge, and several factors contribute to the uncertainty in the quantitative estimate; differences in labeling efficiency, protein digestion, precursor mixing, ion suppression, peak detection, data preprocessing, and data analysis (10). The quality of quantitation methods can be measured in terms of precision and accuracy. Precision is affected by random errors, that is, random fluctuations around the true value (variance). Lack of accuracy is caused by systematic errors, that is, differences between true and observed values (bias).Several studies have shown that iTRAQ labeling is associated with bias; fold changes are compressed toward one (11–14). It has been suggested that this underestimation of fold change is caused by co-eluting peptides with similar m/z values that are isolated together, creating mixed iTRAQ intensities in complex samples (14). Concerning precision, iTRAQ data has been reported to exhibit variance heterogeneity. The coefficient of variance (CV) of the signal depends on the intensity, with larger CV for low intensity peaks (11, 12, 15, 16). Measurements of iTRAQ intensities for quantification are made in the MS/MS spectra of the peptides, and thereafter combined to calculate a summarized relative protein quantity. There are several different approaches for combining the iTRAQ peptide data to compute a reliable protein ratio. Methods to improve the protein quantification by addressing the variance heterogeneity have been based on excluding low intensity peptide data (17, 18), weighting the peptide data according to intensity (18–21) or stabilizing the variance (12).Quantitative studies of complex human samples are subject to even more challenges related to large biological variation, large and unknown complexity of the human proteome and a large concentration range of proteins. This in turn results in many peptides and a large variety of peptides that can cause interference and related problems in the mass spectrometry analysis. In, for example, biomarker discovery research the goal is to measure quantitative changes or differences in protein levels between two or more clinical conditions. It is therefore crucial to achieve as accurate and precise quantitative information from the data as possible as well as to correctly estimate the limitations of the quantification. Setting adequate standards for quantitative proteomics analysis is hence essential for being able to detect relevant changes in protein abundance, select important proteins, and further use those proteins to interpret the biological and clinical meaning (10, 22). Selecting a protein as significant and taking it to further validation in other clinical material using complementary techniques is time consuming and costly (23). For successful use of iTRAQ labeling in biomarker discovery, and to avoid false discoveries, it is hence essential to assess the accuracy and precision of the methodology.A common approach to study variance and bias in mass spectrometry based protein quantification is to spike a set of standard proteins into a sample and then measure the CV and bias of the intensities of those peptides. Spike-in of proteins has the benefit of looking at a small controlled set of peptides and how they behave in the studied system. This strategy has been used in several of the previously mentioned papers that address iTRAQ quantification (11–14). However, the number of data points studied may be unlikely to represent the complexity of a real biological sample, which often contains thousands of proteins (24). In the current study, all peptides detected in a complex human cell line sample (A549) are used to get an estimate of the quantitative accuracy and precision. This experimental setup is hence more similar to a real biomarker discovery study with high complex human proteome samples. The quality of the protein quantifications is compared among several different mass spectrometers in this work; also the influence of different loaded peptide amounts and the use of different methods for sample separation are examined. Factors such as variance and bias of peptide quantification by iTRAQ are systematically evaluated in those high complex samples. Further, methods for improving the protein quantification are investigated; by filtering on the peptide level to remove low quality intensities and by weighting the peptide values to account for the higher risk of errors at low intensities (20).We have described the factors contributing to bias and variance in protein quantification by iTRAQ labeling. This has generated guidelines for how to estimate the accuracy of protein quantities, which will be an essential tool in both biomarker discovery and studies of biological systems. Based on the results, we suggest an experimental design where each labeling set (e.g., iTRAQ) includes duplicate samples, and we describe how these duplicates are used for calculating peptide weights that can be used in addressing the accuracy of protein quantities. This novel approach is shown to improve protein quantification by iTRAQ in six data sets of A431 cell line samples treated with drug and a clinical data set of lung cancer tissue samples.     

Protein labeling by iTRAQ: a new tool for quantitative mass spectrometry in proteome research

A novel, MS-based approach for the relative quantification of proteins, relying on the derivatization of primary amino groups in intact proteins using isobaric tag for relative and absolute quantitation (iTRAQ) is presented. Due to the isobaric mass design of the iTRAQ reagents, differentially labeled proteins do not differ in mass; accordingly, their corresponding proteolytic peptides appear as single peaks in MS scans. Because quantitative information is provided by isotope-encoded reporter ions that can only be observed in MS/MS spectra, we analyzed the fragmentation behavior of ESI and MALDI ions of peptides generated from iTRAQ-labeled proteins using a TOF/TOF and/or a QTOF instrument. We observed efficient liberation of reporter ions for singly protonated peptides at low-energy collision conditions. In contrast, increased collision energies were required to liberate the iTRAQ label from lysine side chains of doubly charged peptides and, thus, to observe reporter ions suitable for relative quantification of proteins with high accuracy. We then developed a quantitative strategy that comprises labeling of intact proteins by iTRAQ followed by gel electrophoresis and peptide MS/MS analyses. As proof of principle, mixtures of five different proteins in various concentration ratios were quantified, demonstrating the general applicability of the approach presented here to quantitative MS-based proteomics.     

Balancing robust quantification and identification for iTRAQ: Application of UHR‐ToF MS

iTRAQ reagents allow the simultaneous multiplex identification and quantification of a large number of proteins. Success depends on effective peptide fragmentation in order to generate both peptide sequence ions (higher mass region, 150–2200 m/z) and reporter ions (low mass region, 113–121 m/z) for protein identification and relative quantification, respectively. After collision‐induced dissociation, the key requirements to achieve a good balance between the high and low m/z ions are effective ion transmission and detection across the MS/MS mass range, since the ion transmission of the higher m/z range competes with that of the low m/z range. This study describes an analytical strategy for the implementation of iTRAQ on maXis UHR‐Qq‐ToF instruments, and discusses the impact of adjusting the MS/MS ion transmission parameters on the quality of the overall data sets. A technical discussion highlights a number of maXis‐specific parameters, their impact of quantification and identification, and their cross‐interactions.     

Development of candidate biomarkers for pancreatic ductal adenocarcinoma using multiple reaction monitoring

Pancreatic ductal adenocarcinoma (PDAC) is the fourth most frequent cause of cancer mortality in the United States. Because CA 19-9 increases not only in PDAC, but also in benign conditions, there is urgent need for an additional PDAC biomarker. Isotope tags for relative and absolute quantification (iTRAQ) were performed using 6 pairs of PDAC and normal tissues from the same patients, to obtain preliminary PDAC-specific proteins; and verification was performed by multiple reactions monitoring (MRM), using 30 PDAC and 20 normal serum, targeting high-abundant serum proteins without any pre-preparation. As a result, 17 candidate proteins from tissue iTRAQ were verified as potential markers (AUC values > 0.7). Multivariate analysis (MA) demonstrated that a 6-marker panel, consisting of alpha-1 antitrypsin, haptoglobin beta chain, hemopexin, transferrin, zinc alpha-2 glycoprotein, and apolipoprotein A4 from the MRM result, had comparable discriminatory power versus CA 19-9. Our study demonstrated that a combination of iTRAQ on PDAC tissue and verification MRM-MA on individual serum was an efficient method for the development of PDAC multimarkers.     

Isobaric tags for relative and absolute quantitation (iTRAQ) reproducibility: Implication of multiple injections

We analyzed 10 isobaric tags for relative and absolute quantitation (iTRAQ) experiments using three different model organisms across the domains of life: Saccharomyces cerevisiae KAY446, Sulfolobussolfataricus P2, and Synechocystis sp. PCC6803. A double database search strategy was employed to minimize the rate of false positives to less than 3% for all organisms. The reliability of proteins with single-peptide identification was also assessed using the search strategy, coupled with multiple analyses of samples into LC-MS/MS. The outcomes of the three LC-MS/MS analyses provided higher proteome coverage with an average increment in total proteins identified of 6%, 33%, and 50% found in S. cerevisiae, S. solfataricus, and Synechocystis sp., respectively. The iTRAQ quantification values were found to be highly reproducible across the injections, with an average coefficient of variation (CV) of 0.09 (scattering from 0.14 to 0.04) calculated based on log mean average ratio for all three organisms. Hence, we recommend multiple analyses of iTRAQ samples for greater proteome coverage and precise quantification.     

Global topology analysis of pancreatic zymogen granule membrane proteins

The zymogen granule is the specialized organelle in pancreatic acinar cells for digestive enzyme storage and regulated secretion and is a classic model for studying secretory granule function. Our long term goal is to develop a comprehensive architectural model for zymogen granule membrane (ZGM) proteins that would direct new hypotheses for subsequent functional studies. Our initial proteomics analysis focused on identification of proteins from purified ZGM (Chen, X., Walker, A. K., Strahler, J. R., Simon, E. S., Tomanicek-Volk, S. L., Nelson, B. B., Hurley, M. C., Ernst, S. A., Williams, J. A., and Andrews, P. C. (2006) Organellar proteomics: analysis of pancreatic zymogen granule membranes. Mol. Cell. Proteomics 5, 306-312). In the current study, a new global topology analysis of ZGM proteins is described that applies isotope enrichment methods to a protease protection protocol. Our results showed that tryptic peptides of ZGM proteins were separated into two distinct clusters according to their isobaric tag for relative and absolute quantification (iTRAQ) ratios for proteinase K-treated versus control zymogen granules. The low iTRAQ ratio cluster included cytoplasm-orientated membrane and membrane-associated proteins including myosin V, vesicle-associated membrane proteins, syntaxins, and all the Rab proteins. The second cluster having unchanged ratios included predominantly luminal proteins. Because quantification is at the peptide level, this technique is also capable of mapping both cytoplasm- and lumen-orientated domains from the same transmembrane protein. To more accurately assign the topology, we developed a statistical mixture model to provide probabilities for identified peptides to be cytoplasmic or luminal based on their iTRAQ ratios. By implementing this approach to global topology analysis of ZGM proteins, we report here an experimentally constrained, comprehensive topology model of identified zymogen granule membrane proteins. This model contributes to a firm foundation for developing a higher order architecture model of the ZGM and for future functional studies of individual ZGM proteins.     

Comparative study of three proteomic quantitative methods, DIGE, cICAT, and iTRAQ, using 2D gel- or LC-MALDI TOF/TOF

A comparative study on the three quantitative methods frequently used in proteomics, 2D DIGE (difference gel electrophoresis), cICAT (cleavable isotope-coded affinity tags) and iTRAQ (isobaric tags for relative and absolute quantification), was carried out. DIGE and cICAT are familiar techniques used in gel- and LC-based quantitative proteomics, respectively. iTRAQ is a new LC-based technique which is gradually gaining in popularity. A systematic comparison among these quantitative methods has not been reported. In this study, we conducted well-designed comparisons using a six-protein mixture, a reconstituted protein mixture (BSA spiked into human plasma devoid of six abundant proteins), and complex HCT-116 cell lysates as the samples. All three techniques yielded quantitative results with reasonable accuracy when the six-protein or the reconstituted protein mixture was used. In DIGE, accurate quantification was sometimes compromised due to comigration or partial comigration of proteins. The iTRAQ method is more susceptible to errors in precursor ion isolation, which could be manifested with increasing sample complexity. The quantification sensitivity of each method was estimated by the number of peptides detected for each protein. In this regard, the global-tagging iTRAQ technique was more sensitive than the cysteine-specific cICAT method, which in turn was as sensitive as, if not more sensitive than, the DIGE technique. Protein profiling on HCT-116 and HCT-116 p53 -/- cell lysates displayed limited overlapping among proteins identified by the three methods, suggesting the complementary nature of these methods.     

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.     

Multiple reaction monitoring of mTRAQ-labeled peptides enables absolute quantification of endogenous levels of a potential cancer marker in cancerous and normal endometrial tissues

While iTRAQ analyses have proved invaluable for the discovery of potential cancer markers, two outstanding issues that remained were its ineffectiveness to consistently detect specific proteins of interest in a complex sample and to determine the absolute abundance of those proteins. These have been addressed by availability of the mTRAQ reagents (Applied Biosystems, Inc., Foster City, CA) a nonisobaric variant of iTRAQ. We have applied this newly emerging technique to quantify one of our potential markers for endometrial cancer, viz. pyruvate kinase M1/M2. The mTRAQ methodolgy relies on multiple reaction monitoring (MRM) to target tryptic peptides from the protein of interest, thus, ensuring maximal opportunity for detection, while the nonisobaric tags enable specific quantification of each version of the labeled peptides through unique MRM transitions conferred by the labels. Known amounts of synthetic peptides tagged with one of the two available mTRAQ labels, when used as quantification standards in a mixture with the oppositely labeled tryptically digested sample, permit determination of the absolute amounts of the corresponding protein in the sample. The ability to label the sample and reference peptides with either one of the two possible combinations is an inherent advantage of this method, as it provides a means for verification of the reported ratios. In this study, we determined that the amount of pyruvate kinase present in the homogenate from a biopsied EmCa tissue sample was 85 nmol/g of total proteins, while the equivalent concentration in the nonmalignant controls was 21-26 nmol/g of total proteins. This approximately 4-fold higher amount of pyruvate kinase in the cancer sample was further confirmed not only by a direct comparison between the cancer sample and one of the nonmalignant controls, but also independently by an enzyme-linked immunosorbant assay (ELISA). Additionally, the 4-fold higher level of pyruvate kinase amount in the cancer homogenate reported in this study is considerably higher than the 2-fold higher ratio reported across 20 cancer samples in the discovery phase with the iTRAQ technique, suggesting that there exists a possibility that the dynamic range of ratios determined by the iTRAQ technique may have been compressed.     

A technically detailed and pragmatic protocol for quantitative serum proteomics using iTRAQ

Blood is recognised as a highly important source of disease-related biomarkers, and proteomic approaches for identifying novel blood-borne biomarkers are in demand. The complexity and dynamic protein concentration range of plasma/serum however complicates the analysis process. A number of strategies for simplification of blood prior to proteomic analysis have been developed. In addition, methods for quantifying the levels of proteins in samples, such as isobaric tags for relative and absolute quantification (iTRAQ) are emerging. However, the successful application of these procedures is not always straightforward and technical hurdles must be overcome. Here we provide a technically detailed working protocol for iTRAQ-based quantification of serum proteins following immunodepletion of high abundance proteins. To improve the number of proteins identified and quantified we have introduced several modifications to the standard iTRAQ protocol. We report identifications of 217 proteins (5773 peptides) with a false discovery rate of 1% or 254 proteins with 95% confidence, respectively. Relative quantification data were obtained for 234 (95% confidence) serum proteins, including species present in the concentration range of tissue leakage factors. The samples described here relate to pancreatic cancer; however the protocol can be applied to serum from other control or disease types.     

Proteomic identification of immunoproteasome accumulation in formalin-fixed rodent spinal cords with experimental autoimmune encephalomyelitis

Clinically relevant formalin-fixed and paraffin-embedded (FFPE) tissues have not been widely used in neuroproteomic studies because many proteins are presumed to be degraded during tissue preservation. Recent improvements in proteomics technologies, from the 2D gel analysis of intact proteins to the "shotgun" quantification of peptides and the use of isobaric tags for absolute and relative quantification (iTRAQ) method, have made the analysis of FFPE tissues possible. In recent years, iTRAQ has been one of the main methods of choice for high throughput quantitative proteomics analysis, which enables simultaneous comparison of up to eight samples in one experiment. Our objective was to assess the relative merits of iTRAQ analysis of fresh frozen versus FFPE nervous tissues by comparing experimental autoimmune encephalomyelitis (EAE)-induced proteomic changes in FFPE rat spinal cords and frozen tissues. EAE-induced proteomic changes in FFPE tissues were positively correlated with those found in the frozen tissues, albeit with ～50% less proteome coverage. Subsequent validation of the enrichment of immunoproteasome (IP) activator 1 in EAE spinal cords led us to evaluate other proteasome and IP-specific proteins. We discovered that many IP-specific (as opposed to constitutive) proteasomal proteins were enriched in EAE rat spinal cords, and EAE-induced IP accumulation also occurred in the spinal cords of an independent mouse EAE model in a disability score-dependent manner. Therefore, we conclude that it is feasible to generate useful information from iTRAQ-based neuroproteomics analysis of archived FFPE tissues for studying neurological disease tissues.     

Qualitative and quantitative comparison of brand name and generic protein pharmaceuticals using isotope tags for relative and absolute quantification and matrix-assisted laser desorption/ionization tandem time-of-flight mass spectrometry

The capability of iTRAQ (isotope tags for relative and absolute quantification) reagents coupled with matrix-assisted laser desorption/ionization tandem time-of-flight mass spectrometry (MALDI-TOF/TOF-MS) as a qualitative and quantitative technique for the analysis of complicated protein pharmaceutical mixtures was evaluated. Mixtures of Somavert and Miacalcin with a small amount of bovine serum albumin (BSA) as an impurity were analyzed. Both Somavert and Miacalcin were qualitatively identified, and BSA was detected at levels as low as 0.8 mol%. Genotropin and Somavert were compared in a single experiment, and all of the distinct amino acid residues from the two proteins were readily identified. Four somatropin drug products (Genotropin, Norditropin, Jintropin, and Omnitrope) were compared using the iTRAQ/MALDI-MS method to determine the similarity between their primary structures and quantify the amount of protein in each product. All four product samples were well labeled and successfully compared when a filtration cleanup step preceded iTRAQ labeling. The quantitative accuracy of the iTRAQ method was evaluated. In all cases, the accuracy of experimentally determined protein ratios was higher than 90%, and the relative standard deviation (RSD) was less than 10%. The iTRAQ and global internal standard technology (GIST) methods were compared, and the iTRAQ method provided both higher sequence coverage and enhanced signal intensity.     

MS-specific noise model reveals the potential of iTRAQ in quantitative proteomics

MOTIVATION: Mass spectrometry (MS) data are impaired by noise similar to many other analytical methods. Therefore, proteomics requires statistical approaches to determine the reliability of regulatory information if protein quantification is based on ion intensities observed in MS. RESULTS: We suggest a procedure to model instrument and workflow-specific noise behaviour of iTRAQ reporter ions that can provide regulatory information during automated peptide sequencing by LC-MS/MS. The established mathematical model representatively predicts possible variations of iTRAQ reporter ions in an MS data-dependent manner. The model can be utilized to calculate the robustness of regulatory information systematically at the peptide level in so-called bottom-up proteome approaches. It allows to determine the best fitting regulation factor and in addition to calculate the probability of alternative regulations. The result can be visualized as likelihood curves summarizing both the quantity and quality of regulatory information. Likelihood curves basically can be calculated from all peptides belonging to different regions of proteins if they are detected in LC-MS/MS experiments. Therefore, this approach renders excellent opportunities to detect and statistically validate dynamic post-translational modifications usually affecting only particular regions of the whole protein. The detection of known phosphorylation events at protein kinases served as a first proof of concept in this study and underscores the potential for noise models in quantitative proteomics.