The effects of shared peptides on protein quantitation in label-free proteomics by LC/MS/MS

Assessment of differential protein abundance from the observed properties of detected peptides is an essential part of protein profiling based on shotgun proteomics. However, the abundance observed for shared peptides may be due to contributions from multiple proteins that are affected differently by a given treatment. Excluding shared peptides eliminates this ambiguity but may significantly decrease the number of proteins for which abundance estimates can be obtained. Peptide sharing within a family of biologically related proteins does not cause ambiguity if family members have a common response to treatment. On the basis of this concept, we have developed an approach for including shared peptides in the analysis of differential protein abundance in protein profiling. Data from a recent proteomics study of lung tissue from mice exposed to lipopolysaccharide, cigarette smoke, and a combination of these agents are used to illustrate our method. Starting from data where about half of the implicated database protein involved shared peptides, 82% of the affected proteins were grouped into families, based on FASTA annotation, with closure on peptide sharing. In many cases, a common abundance relative to control was sufficient to explain ion-current peak areas for peptides, both unique and shared, that identified biologically related proteins in a peptide-sharing closure group. On the basis of these results, we propose that peptide-sharing closure groups provide a way to include abundance data for shared peptides in quantitative protein profiling by high-throughput mass spectrometry.     

Relative quantification of proteins across the species boundary through the use of shared peptides

We show that shared peptides of proteins that are encoded in different species are suitable for cross-species relative protein quantification. A 14N-containing proteome from the thermoacidophilic archaeon Sulfolobus tokodaii was mixed with a 15N-labeled proteome from Sulfolobus solfataricus. Using three shared peptides per protein, the relative abundance of six orthologous proteins was calculated. Observed standard deviations were approximately 10%, indicating that the trypsin accessibility to cleavage sites was not altered in the orthologs. The abundance ratios of the and subunits of the Thermosome were 0.64 and 1.24 in Sulfolobus tokodaii compared to Sulfolobus solfataricus, suggesting a different stoichiometry of the complex in both species. In addition, an in silico study was performed on the occurrence of shared peptides. Inter- and intra-species peptide redundancy was investigated in the model organisms Homo sapiens, Mus musculus, Escherichia coli K12, Escherichia coli O157:H7, S. solfataricus, and S. tokodaii. M. musculus and H. sapiens share 30-50% of all peptides (6-15 residues). Moreover, approximately one-third of all proteins shared > or = 40% of their peptides with at least one other protein in the related species, thus offering strong potential for cross-species relative protein quantification. Conversely, approximately 40% of all peptides (6-15 residues) encoded in H. sapiens are encoded multiple times and therefore complicate identification and quantification.     

In-Gel Stable-Isotope Labeling (ISIL): a strategy for mass spectrometry-based relative quantification

Most proteomics approaches for relative quantification of protein expression use a combination of stable-isotope labeling and mass spectrometry. Traditionally, researchers have used difference gel electrophoresis (DIGE) from stained 1D and 2D gels for relative quantification. While differences in protein staining intensity can often be visualized, abundant proteins can obscure less abundant proteins, and quantification of post-translational modifications is difficult. A method is presented for quantifying changes in the abundance of a specific protein or changes in specific modifications of a protein using In-gel Stable-Isotope Labeling (ISIL). Proteins extracted from any source (tissue, cell line, immunoprecipitate, etc.), treated under two experimental conditions, are resolved in separate lanes by gel electrophoresis. The regions of interest (visualized by staining) are reacted separately with light versus heavy isotope-labeled reagents, and the gel slices are then mixed and digested with proteases. The resulting peptides are then analyzed by LC-MS to determine relative abundance of light/heavy isotope pairs and analyzed by LC-MS/MS for identification of sequence and modifications. The strategy compares well with other relative quantification strategies, and in silico calculations reveal its effectiveness as a global relative quantification strategy. An advantage of ISIL is that visualization of gel differences can be used as a first quantification step followed by accurate and sensitive protein level stable-isotope labeling and mass spectrometry-based relative quantification.     

Critical assessment of proteome‐wide label‐free absolute abundance estimation strategies

There is a great interest in reliable ways to obtain absolute protein abundances at a proteome‐wide scale. To this end, label‐free LC‐MS/MS quantification methods have been proposed where all identified proteins are assigned an estimated abundance. Several variants of this quantification approach have been presented, based on either the number of spectral counts per protein or MS1 peak intensities. Equipped with several datasets representing real biological environments, containing a high number of accurately quantified reference proteins, we evaluate five popular low‐cost and easily implemented quantification methods (Absolute Protein Expression, Exponentially Modified Protein Abundance Index, Intensity‐Based Absolute Quantification Index, Top3, and MeanInt). Our results demonstrate considerably improved abundance estimates upon implementing accurately quantified reference proteins; that is, using spiked in stable isotope labeled standard peptides or a standard protein mix, to generate a properly calibrated quantification model. We show that only the Top3 method is directly proportional to protein abundance over the full quantification range and is the preferred method in the absence of reference protein measurements. Additionally, we demonstrate that spectral count based quantification methods are associated with higher errors than MS1 peak intensity based methods. Furthermore, we investigate the impact of miscleaved, modified, and shared peptides as well as protein size and the number of employed reference proteins on quantification accuracy.     

Deconvolution of overlapping isotopic clusters improves quantification of stable isotope-labeled peptides

High-resolution mass spectrometry and the use of stable isotopes have greatly improved our ability to quantify proteomes. Typically, the relative abundance of peptides is estimated by identifying the isotopic clusters and by comparing the peak intensities of peptide pairs. However, when the mass shift between the labeled peptides is small, there can be the possibility for overlap of the isotopic clusters which will hamper quantification accuracy with a typical upwards bias for the heavier peptide. Here, we investigated the impact of the overlapping peak issue with respect to dimethyl based quantification and we confirmed there can be need for correction. In addition, we present a tool that can correct overlapping issues when they arise which is based on modeling isotopic distributions. We demonstrate that our approach leads to improved accuracy and precision of protein quantification.     

Relative Abundance of Integral Plasma Membrane Proteins in Arabidopsis Leaf and Root Tissue Determined by Metabolic Labeling and Mass Spectrometry

Metabolic labeling of proteins with a stable isotope (¹⁵N) in intact Arabidopsis plants was used for accurate determination by mass spectrometry of differences in protein abundance between plasma membranes isolated from leaves and roots. In total, 703 proteins were identified, of which 188 were predicted to be integral membrane proteins. Major classes were transporters, receptors, proteins involved in membrane trafficking and cell wall-related proteins. Forty-one of the integral proteins, including nine of the 13 isoforms of the PIP (plasma membrane intrinsic protein) aquaporin subfamily, could be identified by peptides unique to these proteins, which made it possible to determine their relative abundance in leaf and root tissue. In addition, peptides shared between isoforms gave information on the proportions of these isoforms. A comparison between our data for protein levels and corresponding data for mRNA levels in the widely used database Genevestigator showed an agreement for only about two thirds of the proteins. By contrast, localization data available in the literature for 21 of the 41 proteins show a much better agreement with our data, in particular data based on immunostaining of proteins and GUS-staining of promoter activity. Thus, although mRNA levels may provide a useful approximation for protein levels, detection and quantification of isoform-specific peptides by proteomics should generate the most reliable data for the proteome.     

Quantitative proteome analysis using differential stable isotopic labeling and microbore LC-MALDI MS and MS/MS

We demonstrate an approach for global quantitative analysis of protein mixtures using differential stable isotopic labeling of the enzyme-digested peptides combined with microbore liquid chromatography (LC) matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS). Microbore LC provides higher sample loading, compared to capillary LC, which facilitates the quantification of low abundance proteins in protein mixtures. In this work, microbore LC is combined with MALDI MS via a heated droplet interface. The compatibilities of two global peptide labeling methods (i.e., esterification to carboxylic groups and dimethylation to amine groups of peptides) with this LC-MALDI technique are evaluated. Using a quadrupole-time-of-flight mass spectrometer, MALDI spectra of the peptides in individual sample spots are obtained to determine the abundance ratio among pairs of differential isotopically labeled peptides. MS/MS spectra are subsequently obtained from the peptide pairs showing significant abundance differences to determine the sequences of selected peptides for protein identification. The peptide sequences determined from MS/MS database search are confirmed by using the overlaid fragment ion spectra generated from a pair of differentially labeled peptides. The effectiveness of this microbore LC-MALDI approach is demonstrated in the quantification and identification of peptides from a mixture of standard proteins as well as E. coli whole cell extract of known relative concentrations. It is shown that this approach provides a facile and economical means of comparing relative protein abundances from two proteome samples.     

The Effect of Peptide Identification Search Algorithms on MS2-Based Label-Free Protein Quantification

Abstract Several approaches exist for the quantification of proteins in complex samples processed by liquid chromatography-mass spectrometry followed by fragmentation analysis (MS2). One of these approaches is label-free MS2-based quantification, which takes advantage of the information computed from MS2 spectrum observations to estimate the abundance of a protein in a sample. As a first step in this approach, fragmentation spectra are typically matched to the peptides that generated them by a search algorithm. Because different search algorithms identify overlapping but non-identical sets of peptides, here we investigate whether these differences in peptide identification have an impact on the quantification of the proteins in the sample. We therefore evaluated the effect of using different search algorithms by examining the reproducibility of protein quantification in technical repeat measurements of the same sample. From our results, it is clear that a search engine effect does exist for MS2-based label-free protein quantification methods. As a general conclusion, it is recommended to address the overall possibility of search engine-induced bias in the protein quantification results of label-free MS2-based methods by performing the analysis with two or more distinct search engines.     

Multiplexed absolute quantification for proteomics using concatenated signature peptides encoded by QconCAT genes

An important area of proteomics involves the need for quantification, whether relative or absolute. Many methods now exist for relative quantification, but to support biomarker proteomics and systems biology, absolute quantification rather than relative quantification is required. Absolute quantification usually involves the concomitant mass spectrometric determination of signature proteotypic peptides and stable isotope-labeled analogs. However, the availability of standard labeled signature peptides in accurately known amounts is a limitation to the widespread adoption of this approach. We describe the design and synthesis of artificial QconCAT proteins that are concatamers of tryptic peptides for several proteins. This protocol details the methods for the design, expression, labeling, purification, characterization and use of the QconCATs in the absolute quantification of complex protein mixtures. The total time required to complete this protocol (from the receipt of the QconCAT expression plasmid to the absolute quantification of the set of proteins encoded by the QconCAT protein in an analyte sample) is approximately 29 d.     

Characterization and Quantification of Intact 26S Proteasome Proteins by Real-Time Measurement of Intrinsic Fluorescence Prior to Top-down Mass Spectrometry

Quantification of gas-phase intact protein ions by mass spectrometry (MS) is impeded by highly-variable ionization, ion transmission, and ion detection efficiencies. Therefore, quantification of proteins using MS-associated techniques is almost exclusively done after proteolysis where peptides serve as proxies for estimating protein abundance. Advances in instrumentation, protein separations, and informatics have made large-scale sequencing of intact proteins using top-down proteomics accessible to the proteomics community; yet quantification of proteins using a top-down workflow has largely been unaddressed. Here we describe a label-free approach to determine the abundance of intact proteins separated by nanoflow liquid chromatography prior to MS analysis by using solution-phase measurements of ultraviolet light-induced intrinsic fluorescence (UV-IF). UV-IF is measured directly at the electrospray interface just prior to the capillary exit where proteins containing at least one tryptophan residue are readily detected. UV-IF quantification was demonstrated using commercially available protein standards and provided more accurate and precise protein quantification than MS ion current. We evaluated the parallel use of UV-IF and top-down tandem MS for quantification and identification of protein subunits and associated proteins from an affinity-purified 26S proteasome sample from Arabidopsis thaliana. We identified 26 unique proteins and quantified 13 tryptophan-containing species. Our analyses discovered previously unidentified N-terminal processing of the β6 (PBF1) and β7 (PBG1) subunit - such processing of PBG1 may generate a heretofore unknown additional protease active site upon cleavage. In addition, our approach permitted the unambiguous identification and quantification both isoforms of the proteasome-associated protein DSS1.     

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.     

Identification and relative quantification of membrane proteins by surface biotinylation and two-dimensional peptide mapping

Membrane proteins play a central role in biological processes, but their separation and quantification using two-dimensional gel electrophoresis is often limited by their poor solubility and relatively low abundance. We now present a method for the simultaneous recovery, separation, identification, and relative quantification of membrane proteins, following their selective covalent modification with a cleavable biotin derivative. After cell lysis, biotinylated proteins are purified on streptavidin-coated resin and proteolytically digested. The resulting peptides are analyzed by high-pressure liquid chromatography and mass spectrometry, thus yielding a two-dimensional peptide map. Matrix assisted laser desorption/ionization-time of flight signal intensity of peptides, in the presence of internal standards, is used to quantify the relative abundance of membrane proteins from cells treated in different experimental conditions. As experimental examples, we present (i) an analysis of a BSA-spiked human embryonic kidney membrane protein extract, and (ii) an analysis of membrane proteins of human umbilical vein endothelial cells cultured in normoxic and hypoxic conditions. This last study allowed the recovery of the vascular endothelial-cadherin/actin/catenin complex, revealing an increased accumulation of beta-catenin at 2% O(2) concentration.     

Comparative evaluation of label-free SINQ normalized spectral index quantitation in the central proteomics facilities pipeline

Normalized spectral index quantification was recently presented as an accurate method of label‐free quantitation, which improved spectral counting by incorporating the intensities of peptide MS/MS fragment ions into the calculation of protein abundance. We present SINQ, a tool implementing this method within the framework of existing analysis software, our freely available central proteomics facilities pipeline (CPFP). We demonstrate, using data sets of protein standards acquired on a variety of mass spectrometers, that SINQ can rapidly provide useful estimates of the absolute quantity of proteins present in a medium‐complexity sample. In addition, relative quantitation of standard proteins spiked into a complex lysate background and run without pre‐fractionation produces accurate results at amounts above 1 fmol on column. We compare quantitation performance to various precursor intensity‐ and identification‐based methods, including the normalized spectral abundance factor (NSAF), exponentially modified protein abundance index (emPAI), MaxQuant, and Progenesis LC‐MS. We anticipate that the SINQ tool will be a useful asset for core facilities and individual laboratories that wish to produce quantitative MS data, but lack the necessary manpower to routinely support more complicated software workflows. SINQ is freely available to obtain and use as part of the central proteomics facilities pipeline, which is released under an open‐source license.     

Estimation of absolute protein quantities of unlabeled samples by selected reaction monitoring mass spectrometry

For many research questions in modern molecular and systems biology, information about absolute protein quantities is imperative. This information includes, for example, kinetic modeling of processes, protein turnover determinations, stoichiometric investigations of protein complexes, or quantitative comparisons of different proteins within one sample or across samples. To date, the vast majority of proteomic studies are limited to providing relative quantitative comparisons of protein levels between limited numbers of samples. Here we describe and demonstrate the utility of a targeting MS technique for the estimation of absolute protein abundance in unlabeled and nonfractionated cell lysates. The method is based on selected reaction monitoring (SRM) mass spectrometry and the "best flyer" hypothesis, which assumes that the specific MS signal intensity of the most intense tryptic peptides per protein is approximately constant throughout a whole proteome. SRM-targeted best flyer peptides were selected for each protein from the peptide precursor ion signal intensities from directed MS data. The most intense transitions per peptide were selected from full MS/MS scans of crude synthetic analogs. We used Monte Carlo cross-validation to systematically investigate the accuracy of the technique as a function of the number of measured best flyer peptides and the number of SRM transitions per peptide. We found that a linear model based on the two most intense transitions of the three best flying peptides per proteins (TopPep3/TopTra2) generated optimal results with a cross-correlated mean fold error of 1.8 and a squared Pearson coefficient R(2) of 0.88. Applying the optimized model to lysates of the microbe Leptospira interrogans, we detected significant protein abundance changes of 39 target proteins upon antibiotic treatment, which correlate well with literature values. The described method is generally applicable and exploits the inherent performance advantages of SRM, such as high sensitivity, selectivity, reproducibility, and dynamic range, and estimates absolute protein concentrations of selected proteins at minimized costs.     

Comparison of label-free methods for quantifying human proteins by shotgun proteomics

Measurements of mass spectral peak intensities and spectral counts are promising methods for quantifying protein abundance changes in shotgun proteomic analyses. We describe Serac, software developed to evaluate the ability of each method to quantify relative changes in protein abundance. Dynamic range and linearity using a three-dimensional ion trap were tested using standard proteins spiked into a complex sample. Linearity and good agreement between observed versus expected protein ratios were obtained after normalization and background subtraction of peak area intensity measurements and correction of spectral counts to eliminate discontinuity in ratio estimates. Peak intensity values useful for protein quantitation ranged from 10(7) to 10(11) counts with no obvious saturation effect, and proteins in replicate samples showed variations of less than 2-fold within the 95% range (+/-2sigma) when >or=3 peptides/protein were shared between samples. Protein ratios were determined with high confidence from spectral counts when maximum spectral counts were >or=4 spectra/protein, and replicates showed equivalent measurements well within 95% confidence limits. In further tests, complex samples were separated by gel exclusion chromatography, quantifying changes in protein abundance between different fractions. Linear behavior of peak area intensity measurements was obtained for peptides from proteins in different fractions. Protein ratios determined by spectral counting agreed well with those determined from peak area intensity measurements, and both agreed with independent measurements based on gel staining intensities. Overall spectral counting proved to be a more sensitive method for detecting proteins that undergo changes in abundance, whereas peak area intensity measurements yielded more accurate estimates of protein ratios. Finally these methods were used to analyze differential changes in protein expression in human erythroleukemia K562 cells stimulated under conditions that promote cell differentiation by mitogen-activated protein kinase pathway activation. Protein changes identified with p<0.1 showed good correlations with parallel measurements of changes in mRNA expression.     

In-gel stable isotope labeling for relative quantification using mass spectrometry

Although differences in protein staining intensity can often be visualized by difference gel electrophoresis, abundant proteins can obscure less abundant proteins, and quantification of post-translational modifications is difficult. We present a protocol for quantifying changes in the abundance of a specific protein or changes in specific modifications of a protein using in-gel stable isotope labeling. In this protocol protein extracts from any source treated under two experimental conditions are resolved in two separate lanes by gel electrophoresis. Parallel gel regions of interest are reacted separately with either light or heavy isotope-labeled reagents, and the gel slices are then combined and digested with proteases. The resulting peptides are then analyzed by liquid chromatography/mass spectrometry (LC/MS) to determine relative abundance of light- and heavy-isotope lysine-containing peptide pairs and analyzed by LC/MS/MS for identification of sequence and modifications. This protocol should take approximately 24-26 h to complete, including the incubation time for proteolytic digestion. Additional time will be needed for data analysis and interpretation.     

MRCQuant- an accurate LC-MS relative isotopic quantification algorithm on TOF instruments

Background  

Relative isotope abundance quantification, which can be used for peptide identification and differential peptide quantification, plays an important role in liquid chromatography-mass spectrometry (LC-MS)-based proteomics. However, several major issues exist in the relative isotopic quantification of peptides on time-of-flight (TOF) instruments: LC peak boundary detection, thermal noise suppression, interference removal and mass drift correction. We propose to use the Maximum Ratio Combining (MRC) method to extract MS signal templates for interference detection/removal and LC peak boundary detection. In our method, MRCQuant, MS templates are extracted directly from experimental values, and the mass drift in each LC-MS run is automatically captured and compensated. We compared the quantification accuracy of MRCQuant to that of another representative LC-MS quantification algorithm (msInspect) using datasets downloaded from a public data repository.     

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.