Discovery- and target-based protein quantification using iTRAQ and pulsed Q collision induced dissociation (PQD)

Pulsed Q collision-induced dissociation (PQD) was developed in part to facilitate detection of low-mass reporter ions using labeling reagents (e.g. iTRAQ) on LTQ platforms. It has generally been recognized that the scan speed and sensitivity of an LTQ are superior than those of an Orbitrap using the higher-energy collisional dissociation (HCD). However, the use of PQD in quantitative proteomics is limited, primarily due to the meager reproducibility of reporter ion ratios. Optimizations of PQD for iTRAQ quantification using LTQ have been reported, but a universally applicable strategy for quantifying the less abundant proteins has not been fully established. Adjustments of the AGC target, μscan, or scan speed offer only incremental improvements in reproducibility. From our experience, however, satisfactory coefficients of variation (CVs) of reporter ion ratios were difficult to achieve using the discovery-based approach. As an alternative, we implemented a target-based approach that obviates data dependency to allow repetitive data acquisitions across chromatographic peaks. Such a strategy generates enough data points for more reliable quantification. Using cAMP treatment in S49 cell lysates and this target-based approach, we were able to validate differentially expressed proteins, which were initially identified as potential candidates using the discovery-based PQD. The target-based strategy also yielded results comparable to those obtained from HCD in an Orbitrap. Our findings should aid LTQ users who desire to explore iTRAQ quantitative proteomics but have limited access to the more costly Orbitrap or other instruments.     

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.     

iQuantitator: A tool for protein expression inference using iTRAQ

Background  

Isobaric Tags for Relative and Absolute Quantitation (iTRAQ™) [Applied Biosystems] have seen increased application in differential protein expression analysis. To facilitate the growing need to analyze iTRAQ data, especially for cases involving multiple iTRAQ experiments, we have developed a modeling approach, statistical methods, and tools for estimating the relative changes in protein expression under various treatments and experimental conditions.     

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.     

Optimization of iTRAQ labelling coupled to OFFGEL fractionation as a proteomic workflow to the analysis of microsomal proteins of Medicago truncatula roots

Background

Shotgun proteomics represents an attractive technical framework for the study of membrane proteins that are generally difficult to resolve using two-dimensional gel electrophoresis. The use of iTRAQ, a set of amine-specific isobaric tags, is currently the labelling method of choice allowing multiplexing of up to eight samples and the relative quantification of multiple peptides for each protein. Recently the hyphenation of different separation techniques with mass spectrometry was used in the analysis of iTRAQ labelled samples. OFFGEL electrophoresis has proved its effectiveness in isoelectric point-based peptide and protein separation in solution. Here we describe the first application of iTRAQ-OFFGEL-LC-MS/MS on microsomal proteins from plant material. The investigation of the iTRAQ labelling effect on peptide electrofocusing in OFFGEL fractionator was carried out on Medicago truncatula membrane protein digests.

Results

In-filter protein digestion, with easy recovery of a peptide fraction compatible with iTRAQ labelling, was successfully used in this study. The focusing quality in OFFGEL electrophoresis was maintained for iTRAQ labelled peptides with a higher than expected number of identified peptides in basic OFFGEL-fractions. We furthermore observed, by comparing the isoelectric point (pI) fractionation of unlabelled versus labelled samples, a non-negligible pI shifts mainly to higher values.

Conclusions

The present work describes a feasible and novel protocol for in-solution protein digestion in which the filter unit permits protein retention and buffer removal. The data demonstrates an impact of iTRAQ labelling on peptide electrofocusing behaviour in OFFGEL fractionation compared to their native counterpart by the induction of a substantial, generally basic pI shift. Explanations for the occasionally observed acidic shifts are likewise presented.     

A comparison of the accuracy of iTRAQ quantification by nLC-ESI MSMS and nLC-MALDI MSMS methods

The accuracy of quantification obtained using the iTRAQ labelling methodology for measuring protein ratios more extreme than 1:1 was investigated. A comparison of nLC-ESI MSMS and nLC-MALDI MSMS analysis routes was performed. A fixed concentration of a standard six protein mix was spiked with two proteins at a range of concentrations. The two data analysis programmes, Mascot and ProteinPilot Paragon, were also compared. Whilst the lower ratios could be measured accurately, greater discrepancies were seen for the higher ratios, particularly by nLC-ESI MSMS. Filtering out the weaker reporter ion signals improved the accuracy of the ratios: this is likely due to several factors which are explored in more detail. Overall, analysis by nLC-MALDI MSMS followed by Mascot interpretation gave the most accurate results.     

On improved calibrations of unknowns in a system of quality-controlled assays

Two new estimators for calibrating unknowns from dose-response curves, in a system of quality-controlled assays, are examined. In contrast with the conventional estimator which uses only the results of the one assay in which the response of the unknown dose is measured, the new estimators also utilize the results of all other assays through the replications of the control samples in the system. The first estimator is based on maximizing the likelihood of the given system (with respect to the different dose-response parameters, the levels of the control samples and the levels of the unknowns) when response errors are normally distributed. The second estimator is a regression-like estimator obtained by subtracting from the conventional estimator its estimated regression on the deviation of the calibrated control levels in the given assay from their average values in the system. Evaluations of the reductions in bias and variance attained by the new estimators show when substantial reductions in mean square error can be expected. The new estimators are illustrated with a system of 22 hFSH radioimmunoassays.     

In-gel total protein quantification using a ninhydrin-based method

Precise in-gel quantification of total protein amount of bands or spots in gels is the basis of subsequent biochemical, molecular biological and immunological analyses. Though several methods have been designed to evaluate relative amounts of proteins, these methods are of limited reliability because (semi-) quantifications depend on the amount of protein migrating into the gel and different proteins may lead to different absorptions/intensities of stained bands or spots. In the present study, we described a method to quantify both, hydrophilic and hydrophobic proteins using in-gel digestion with proteinase K, subsequent extraction and acid hydrolysis followed by the use of the ninhydrin reaction. The protocol is accurate and compatible with mass spectrometric characterization of proteins. Reproducible in-gel protein quantification was performed from SDS-PAGE and IEF/SDS-PAGE gels using bovine serum albumin as a standard protein. Bacteriorhodopsin separated on SDS-PAGE gel was quantified in addition in order to show that the method is also suitable for quantification of hydrophobic protein. This protocol for reliable in-gel protein quantification, which not only provides “arbitrary units of optical density”, can also be completed in a minimum of 4 days or maximum 1 week depending on the type of electrophoresis with the disadvantage of being time consuming.     

IQcat: multiplexed protein quantification by isoelectric QconCAT

Expression of isotopically labeled peptide standards as artificial concatamers (QconCATs) allows for the multiplex quantification of proteins in unlabeled samples by mass spectrometry. We have developed a generalizable QconCAT design strategy, which we term IQcat, wherein concatenated peptides are binned by pI to facilitate MS-sample enrichment by isoelectric focusing. Our method utilizes a rapid (～2 weeks), inexpensive and scalable purification of arg/lys labeled IQcat standards in the Escherichia coli auxotroph AT713. With this pipeline, we assess the fidelity of IQcat-based absolute quantification for ten yeast proteins over a broad concentration range in a single information-rich isoelectric fraction. The technique is further employed for a quantitative study of androgen-dependent protein expression in cultured prostate cancer cells.     

Differentially isotope-coded N-terminal protein sulphonation: combining protein identification and quantification

Most proteomic labelling technologies intend to improve protein quantification and/or facilitate (de novo) peptide sequencing. We present here a novel stable-isotope labelling method to simultaneously identify and quantify protein components in complex mixtures by specifically derivatizing the N-terminus of proteins with 4-sulphophenyl isothiocyanate (SPITC). Our approach combines protein identification with quantification through differential isotope-coded labelling at the protein N-terminus prior to digestion. The isotope spacing of 6 Da (unlabelled vs. six-fold 13C-labelled tag) between derivatized peptide pairs enables the detection on different MS platforms (MALDI and ESI). Optimisation of the reaction conditions using SPITC was performed on three model proteins. Improved detection of the N-terminally derivatized peptide compared to the native analogue was observed in negative-ion MALDI-MS. Simpler fragmentation patterns compared to native peptides facilitated protein identification. The 13C-labelled SPITC resulted in convenient peptide pair spacing without isotopic overlap and hence facilitated relative quantification by MALDI-TOF/TOF and LC-ESI-MS/MS. The combination of facilitated identification and quantification achieved by differentially isotope-coded N-terminal protein tagging with light/heavy SPITC represents, to our knowledge, a new approach to quantitative proteomics.     

MapQuant: open-source software for large-scale protein quantification

Whole-cell protein quantification using MS has proven to be a challenging task. Detection efficiency varies significantly from peptide to peptide, molecular identities are not evident a priori, and peptides are dispersed unevenly throughout the multidimensional data space. To overcome these challenges we developed an open-source software package, MapQuant, to quantify comprehensively organic species detected in large MS datasets. MapQuant treats an LC/MS experiment as an image and utilizes standard image processing techniques to perform noise filtering, watershed segmentation, peak finding, peak fitting, peak clustering, charge-state determination and carbon-content estimation. MapQuant reports abundance values that respond linearly with the amount of sample analyzed on both low- and high-resolution instruments (over a 1000-fold dynamic range). Background noise added to a sample, either as a medium-complexity peptide mixture or as a high-complexity trypsinized proteome, exerts negligible effects on the abundance values reported by MapQuant and with coefficients of variance comparable to other methods. Finally, MapQuant's ability to define accurate mass and retention time features of isotopic clusters on a high-resolution mass spectrometer can increase protein sequence coverage by assigning sequence identities to observed isotopic clusters without corresponding MS/MS data.     

Prevalidation of potential protein biomarkers in toxicology using iTRAQ reagent technology

Today, toxicoproteomics still relies mainly on 2-DE followed by MS for detection and identification of proteins, which might characterize a certain state of disease, indicate toxicity or even predict carcinogenicity. We utilized the classical 2-DE/MS approach for the evaluation of early protein biomarkers which are predictive for chemically induced hepatocarcinogenesis in rats. We were able to identify statistically significantly deregulated proteins in N-nitrosomorpholine exposed rat liver tissue. Based on literature data, biological relevance in the early molecular process of hepatocarcinogenicity could be suggested for most of these potential biomarkers. However, in order to ensure reliable results and to create the prerequisites necessary for integration in routine toxicology studies in the future, these protein expression patterns need to be prevalidated using independent technology platforms. In the current study, we evaluated the usefulness of iTRAQ reagent technology (Applied Biosystems, Framingham, USA), a recently introduced MS-based protein quantitation method, for verification of the 2-DE/MS biomarkers. In summary, the regulation of 26 2-DE/MS derived protein biomarkers could be verified. Proteins like HSP 90-beta, annexin A5, ketohexokinase, N-hydroxyarylamine sulfotransferase, ornithine aminotransferase, and adenosine kinase showed highly comparable fold changes using both proteomic quantitation strategies. In addition, iTRAQ analysis delivered further potential biomarkers with biological relevance to the processes of hepatocarcinogenicity: e.g. placental form of glutathione S-transferase (GST-P), carbonic anhydrase, and aflatoxin B1 aldehyde reductase. Our results show both the usefulness of iTRAQ reagent technology for biomarker prevalidation as well as for identification of further potential marker proteins, which are indicative for liver hepatocarcinogenicity.     

Sensitive single-molecule protein quantification and protein complex detection in a microarray format

Single-molecule protein analysis provides sensitive protein quantitation with a digital read-out and is promising for studying biological systems and detecting biomarkers clinically. However, current single-molecule platforms rely on the quantification of one protein at a time. Conventional antibody microarrays are scalable to detect many proteins simultaneously, but they rely on less sensitive and less quantitative quantification by the ensemble averaging of fluorescent molecules. Here, we demonstrate a single-molecule protein assay in a microarray format enabled by an ultra-low background surface and single-molecule imaging. The digital read-out provides a highly sensitive, low femtomolar limit of detection and four orders of magnitude of dynamic range through the use of hybrid digital-analog quantification. From crude cell lysate, we measured levels of p53 and MDM2 in parallel, proving the concept of a digital antibody microarray for use in proteomic profiling. We also applied the single-molecule microarray to detect the p53-MDM2 protein complex in cell lysate. Our study is promising for development and application of single-molecule protein methods because it represents a technological bridge between single-plex and highly multiplex studies.     

Lowry method of protein quantification: Evidence for photosensitivity

Despite reports of its susceptibility to various interfering factors, the Folin Phenol protein quantification method of O. H. Lowry, N. J. Rosebrough, A. L. Farr, and R. J. Randall (1951, J. Biol. Chem. 193, 265–275) remains the most convenient and accurate method for routine protein determinations. Our findings indicate that the Lowry assay is also photosensitive which can result in a discrepancy of up to 10% in estimated protein concentrations, unless appropriate precautions are taken.     

Isobaric protein and peptide quantification: perspectives and issues

An important challenge for proteomics is the ability to compare protein levels across biological samples. Since their introduction, isotopic and isobaric peptide labeling have played an important role in relative quantitative comparisons of proteomes. One important drawback of most of the isotopic-labeling techniques is an increase in sample complexity. This problem was successfully addressed with the construction of isobaric labeling strategies, such as isobaric tag for relative and absolute quantification (iTRAQ), tandem mass tagging, the cleavable isobaric affinity tag, dimethylated leucines and isobaric peptide termini labeling. Furthermore, numerous applications for multiplexing using iTRAQ and tandem mass tagging have been reported.     

Isobaric protein and peptide quantification: perspectives and issues

An important challenge for proteomics is the ability to compare protein levels across biological samples. Since their introduction, isotopic and isobaric peptide labeling have played an important role in relative quantitative comparisons of proteomes. One important drawback of most of the isotopic-labeling techniques is an increase in sample complexity. This problem was successfully addressed with the construction of isobaric labeling strategies, such as isobaric tag for relative and absolute quantification (iTRAQ), tandem mass tagging, the cleavable isobaric affinity tag, dimethylated leucines and isobaric peptide termini labeling. Furthermore, numerous applications for multiplexing using iTRAQ and tandem mass tagging have been reported.     

Scientific workflow optimization for improved peptide and protein identification

Background

Peptide-spectrum matching is a common step in most data processing workflows for mass spectrometry-based proteomics. Many algorithms and software packages, both free and commercial, have been developed to address this task. However, these algorithms typically require the user to select instrument- and sample-dependent parameters, such as mass measurement error tolerances and number of missed enzymatic cleavages. In order to select the best algorithm and parameter set for a particular dataset, in-depth knowledge about the data as well as the algorithms themselves is needed. Most researchers therefore tend to use default parameters, which are not necessarily optimal.

Results

We have applied a new optimization framework for the Taverna scientific workflow management system (http://ms-utils.org/Taverna_Optimization.pdf) to find the best combination of parameters for a given scientific workflow to perform peptide-spectrum matching. The optimizations themselves are non-trivial, as demonstrated by several phenomena that can be observed when allowing for larger mass measurement errors in sequence database searches. On-the-fly parameter optimization embedded in scientific workflow management systems enables experts and non-experts alike to extract the maximum amount of information from the data. The same workflows could be used for exploring the parameter space and compare algorithms, not only for peptide-spectrum matching, but also for other tasks, such as retention time prediction.

Conclusion

Using the optimization framework, we were able to learn about how the data was acquired as well as the explored algorithms. We observed a phenomenon identifying many ammonia-loss b-ion spectra as peptides with N-terminal pyroglutamate and a large precursor mass measurement error. These insights could only be gained with the extension of the common range for the mass measurement error tolerance parameters explored by the optimization framework.