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In enzymatic 18O-labeling strategies for quantitative proteomics, the exchange of carboxyl oxygens at low pH is a common, undesired side reaction. We asked if acid-catalyzed back exchange could interfere with quantitation and whether the reaction itself could be used as an alternative method for introducing 18O label into peptides. Several synthetic amino acid sequences were dissolved in dilute acid containing 50% (v/v) H218O and incubated at room temperature. Aliquots were removed over a period of 3 weeks and analyzed by tandem mass spectrometry (MS/MS). 18O-incorporation ratios were determined by linear regression analysis that allowed for multiple stable isotope incorporations. At low pH, peptides exchanged their carboxyl oxygen atoms with the aqueous solvent. The isotope patterns gradually shifted to higher masses until they reached the expected binomial distribution at equilibrium after ~11 days. Reaction rates were residue and sequence specific. Due to its slow nature, the acid-based back exchange is expected to minimally interfere with enzymatic 18O-labeling studies provided that storage and analysis conditions minimize low pH exposure times. On its own, acid-catalyzed 18O labeling is a general tagging strategy that is an alternative to the chemical, metabolic and enzymatic isotope-labeling schemes currently used in quantitative proteomics.

There are numerous approaches to study the proteome in a quantitative manner. All rely heavily on optimized sample preparation and appropriate statistical analysis of resulting datasets. This session will cover the following aspects of quantitative proteomics approaches:

Quantitative profiling of the membrane proteome requires special considerations not addressed in typical mass-spectrometry analyses. Optimized sample preparation and separation strategies will be discussed in the context of enriched membrane fractions and a quantitative proteomics platform using stable isotopes.In shotgun proteomics, a complex protein mixture is first digested to peptides, which are then analyzed by a combination of nanoflow chromatography and tandem mass spectrometry. The effects of subtle changes in sample preparation and chromatographic conditions in the characterization of complex mixtures will be presented. A discovery-based mass spectrometry approach using a bench-top LTQ linear ion trap and in-house written software for label-free differential protein profiling will be presented. This approach is quite comprehensive and is compatible with even the most inexpensive mass spectrometers. For proteins not detected routinely using our discovery-based approaches, we have applied selected reaction monitoring using a TSQ Quantum Ultra. This approach has been used to identify and quantify proteins at the low ng/mL level in plasma without any prior fractionation. A software pipeline has been developed to go from hypothesized proteins of interest derived from the literature to predicted hSRM transitions, collision offsets, and predicted chromatographic retention times. The combination of both discovery- and hypothesis-driven proteomics using nanoflow separations and tandem mass spectrometry provides us with unparalleled sensitivity and dynamic range in characterizing complex mixtures.Spectrum counting is an appealing and relatively straightforward approach for quantitative proteomics. Since the spectrum count of a protein in a proteomic analysis is the total number of peptides, not just unique peptides detected and identified for a given protein, searching criteria and false-positive minimization is important. There are several different versions of spectral counting currently in use, but each approach has shared core characteristics. An additional important consideration for quantitative proteomic analysis is the use of replicates for statistical analysis and determining the proper statistical test to use based on the overall structure of the datasets. This presentation will describe the foundation of spectral counting and the modifications to this approach used by different researchers. In addition, selected examples of the biological implementation of these approaches will be described.

Differential 18O/16O stable isotope labeling of peptides that relies on enzyme-catalyzed oxygen exchange at their carboxyl termini in the presence of H218O has been widely used for relative quantitation of peptides/proteins. The role of tryptic proteolysis in bottom-up shotgun proteomics and low reagent costs, has made trypsin-catalyzed 18O post-digestion exchange a convenient and affordable stable isotope labeling approach. However, it is known that trypsin-catalyzed 18O exchange at the carboxyl terminus is in many instances inhomogeneous/incomplete. The extent of the 18O exchange/incorporation fluctuates from peptide to peptide mostly due to variable enzyme-substrate affinity. Thus, accurate calculation and interpretation of peptide ratios are analytically complicated and in some regard deficient. Therefore, a computational approach capable of improved measurement of actual 18O incorporation for each differentially labeled peptide pair is needed. In this regard, we have developed an algorithmic method that relies on the trapezoidal rule to integrate peak intensities of all detected isotopic species across a particular peptide ion over the retention time, which fits the isotopic manifold to Poisson distributions. Optimal values for manifold fitting were calculated and then 18O/16O ratios derived via evolutionary programming. The algorithm is tested using trypsin–catalyzed 18O post-digestion exchange to differentially label bovine serum albumin (BSA) at a priori determined ratios. Both, accuracy and precision are improved utilizing this rigorous mathematical approach. Utilizing this algorithmic technique, we demonstrate the effectiveness of this method to accurately calculate 18O/16O ratios for differentially labeled BSA peptides, by accounting for artifacts caused by a variable degree of post-digestion 18O exchange. We further demonstrate the effectiveness of this method to accurately calculate 18O/16O ratios in a large scale proteomic quantitation of detergent resistant membrane microdomains (DRMMs) isolated from cells expressing wild-type HIV-1 Gag and its non myristylated mutant.

To address the challenges associated with differential expression proteomics, label-free mass spectrometric protein quantification methods have been developed as alternatives to array-based, gel-based, and stable isotope tag or label-based approaches. In this paper, we focus on the issues associated with label-free methods that rely on quantitation based on peptide ion peak area measurement. These issues include chromatographic alignment, peptide qualification for quantitation, and normalization. In addressing these issues, we present various approaches, assembled in a recently developed label-free quantitative mass spectrometry platform, that overcome these difficulties and enable comprehensive, accurate, and reproducible protein quantitation in highly complex protein mixtures from experiments with many sample groups. As examples of the utility of this approach, we present a variety of cases where the platform was applied successfully to assess differential protein expression or abundance in body fluids, in vitro nanotoxicology models, tissue proteomics in genetic knock-in mice, and cell membrane proteomics.

In order to study the differential protein expression in complex biological samples, strategies for rapid, highly reproducible and accurate quantification are necessary. Isotope labeling and fluorescent labeling techniques have been widely used in quantitative proteomics research. However, researchers are increasingly turning to label-free shotgun proteomics techniques for faster, cleaner, and simpler results. Mass spectrometry-based label-free quantitative proteomics falls into two general categories. In the first are the measurements of changes in chromatographic ion intensity such as peptide peak areas or peak heights. The second is based on the spectral counting of identified proteins. In this paper, we will discuss the technologies of these label-free quantitative methods, statistics, available computational software, and their applications in complex proteomics studies.

A proteomics-based method using stable isotope labeling to assess the relative abundance of peptides or proteins is described. Bradykinin and carbonic anhydrase were labeled with sulfosuccinimidyl-2-(biotinamido) ethyl-1,3-dithiopropionate, a membrane impermeant reagent that is reactive with primary amines. Specificity of the label to primary amines was demonstrated using tandem mass spectrometry. Also, relative quantitation was achieved by secondary labeling with natural isotopic abundance and stable isotope-labeled methyl iodide. We believe this to be an effective stable isotope-labeling method for quantitative proteomics.

Stable isotope labeling (SIL) methods coupled with nanoscale liquid chromatography and high resolution tandem mass spectrometry are increasingly useful for elucidation of the proteome-wide differences between multiple biological samples. Development of more effective programs for the sensitive identification of peptide pairs and accurate measurement of the relative peptide/protein abundance are essential for quantitative proteomic analysis. We developed and evaluated the performance of a new program, termed UNiquant, for analyzing quantitative proteomics data using stable isotope labeling. UNiquant was compared with two other programs, MaxQuant and Mascot Distiller, using SILAC-labeled complex proteome mixtures having either known or unknown heavy/light ratios. For the SILAC-labeled Jeko-1 cell proteome digests with known heavy/light ratios (H/L = 1:1, 1:5, and 1:10), UNiquant quantified a similar number of peptide pairs as MaxQuant for the H/L = 1:1 and 1:5 mixtures. In addition, UNiquant quantified significantly more peptides than MaxQuant and Mascot Distiller in the H/L = 1:10 mixtures. UNiquant accurately measured relative peptide/protein abundance without the need for post-measurement normalization of peptide ratios, which is required by the other programs.

Many diseases and disorders are characterized by quantitative and/or qualitative changes in complex carbohydrates. Mass spectrometry methods show promise in monitoring and detecting these important biological changes. Here we report a new glycomics method, termed Glycan Reductive Isotope Labeling (GRIL), where free glycans are derivatized by reductive amination with the differentially coded stable isotope tags [12C6]-aniline and [13C6]-aniline. These dual-labeled aniline-tagged glycans can be recovered by reversed-phase chromatography and quantified based on UV-absorbance and relative ion abundances. Unlike previously reported isotopically coded reagents for glycans, GRIL does not contain deuterium, which can be chromatographically resolved. Our method shows no chromatographic resolution of differentially labeled glycans. Mixtures of differentially tagged glycans can be directly compared and quantified using mass spectrometric techniques. We demonstrate the use of GRIL to determine relative differences in glycan amount and composition. We analyze free glycans and glycans enzymatically or chemically released from a variety of standard glycoproteins, as well as human and mouse serum glycoproteins using this method. This technique allows for linear, relative quantitation of glycans over a 10-fold concentration range and can accurately quantify sub-picomole levels of released glycans, providing a needed advancement in the field of Glycomics.

Differential analysis of whole cell proteomes by mass spectrometry has largely been applied using various forms of stable isotope labeling. While metabolic stable isotope labeling has been the method of choice, it is often not possible to apply such an approach. Four different label free ways of calculating expression ratios in a classic “two-state” experiment are compared: signal intensity at the peptide level, signal intensity at the protein level, spectral counting at the peptide level, and spectral counting at the protein level. The quantitative data were mined from a dataset of 1245 qualitatively identified proteins, about 56% of the protein encoding open reading frames from Porphyromonas gingivalis, a Gram-negative intracellular pathogen being studied under extracellular and intracellular conditions. Two different control populations were compared against P. gingivalis internalized within a model human target cell line. The q-value statistic, a measure of false discovery rate previously applied to transcription microarrays, was applied to proteomics data. For spectral counting, the most logically consistent estimate of random error came from applying the locally weighted scatter plot smoothing procedure (LOWESS) to the most extreme ratios generated from a control technical replicate, thus setting upper and lower bounds for the region of experimentally observed random error.

Quantitative proteomic measurements are of significant interest in studies aimed at discovering disease biomarkers and providing new insights into biological pathways. A quantitative cysteinyl-peptide enrichment technology (QCET) can be employed to achieve higher efficiency, greater dynamic range, and higher throughput in quantitative proteomic studies based upon the use of stable-isotope labeling techniques combined with high-resolution capillary or nano-scale liquid chromatography (LC)-mass spectrometry (MS) measurements. The QCET approach involves specific 16O/18O labeling of tryptic peptides, high-efficiency enrichment of cysteinyl-peptides, and confident protein identification and quantification using high mass accuracy LC-Fourier transform ion cyclotron resonance mass spectrometry (FTICR) measurements and a previously established database of accurate mass and LC elution time information for the labeled peptides. This methodology has been initially demonstrated by using proteome profiling of naïve and in vitro-differentiated human mammary epithelial cells (HMEC) as an example, which initially resulted in the identification and quantification of 603 proteins in a single LC-FTICR analysis. QCET provides not only highly efficient enrichment of cysteinyl-peptides for more extensive proteome coverage and improved labeling efficiency for better quantitative measurements, but more importantly, a high-throughput strategy suitable for quantitative proteome analysis where extensive or parallel proteomic measurements are required, such as in time course studies of specific pathways and clinical sample analyses for biomarker discovery.

The adipose tissue has important secretory and endocrine functions in humans. The regulation of adipocyte differentiation has been actively pursued using transcriptomic methods over the last several years. Quantitative proteomics has emerged as a promising approach to obtain temporal profiles of biological processes such as differentiation. Stable isotope labeling with amino acids in cell culture (SILAC) is a simple and robust method for labeling proteins in vivo. Here, we describe the development and application of a five-plex SILAC experiment using four different heavy stable isotopic forms of arginine to study the nuclear proteome and the secretome during the course of adipocyte differentiation. Tandem mass spectrometry analysis using a quadrupole time-of-flight instrument resulted in identification of a total 882 proteins from these two proteomes. Of these proteins, 427 were identified on the basis of one or more arginine containing peptides that allowed quantitation. In addition to previously reported molecules that are differentially expressed during the process of adipogenesis (e.g. adiponectin and lipoprotein lipase), we identified several proteins whose differential expression during adipocyte differentiation has not been documented previously. For example, THO complex 4, a context-dependent transcriptional activator in the T-cell receptor alpha enhancer complex, showed highest expression at middle stage of adipogenesis while SNF2 alpha, a chromatin remodeling protein, was downregulated upon initiation of adipogenesis and remained so during subsequent time points. This study using a 5-plex SILAC to investigate dynamics illustrates the power of this approach to identify differentially expressed proteins in a temporal fashion.

Summary

Mass spectrometry has been widely used to analyze biological samples and has evolved into an indispensable tool for proteomics research. Our desire to understand the proteome has led to new technologies that push the boundary of mass spectrometry capabilities, which in return has allowed mass spectrometry to address an ever-increasing array of biological questions. The recent development of a novel mass spectrometer (Orbitrap) and new dissociation methods such as electron transfer dissociation have made possible exciting new areas of proteomic application. Although bottom-up proteomics (analysis of proteolytic peptide mixtures) remains the workhorse for proteomic analysis, middle- and top-down strategies (analysis of longer peptides and intact proteins, respectively) should allow more complete characterization of protein isoforms and post-translational modifications. Finally, stable isotope labeling strategies have transformed mass spectrometry from merely descriptive to a tool for measuring dynamic changes in protein expression, interaction and modification.

The main goal of comparative proteomics is the quantitation of the differences in abundance of many proteins between two different biological samples in a single experiment. By differentially labeling the peptides from the two samples and combining them in a single analysis, relative ratios of protein abundance can be accurately determined. Protease catalyzed 18O exchange is a simple method to differentially label peptides, but the lack of robust software tools to analyze the data from mass spectra of 18O labeled peptides generated by common ion trap mass spectrometers has been a limitation. ZoomQuant is a stand-alone computational tool that analyzes the mass spectra of 18O labeled peptides from ion trap instruments and determines relative abundance ratios between two samples. Starting with a filtered list of candidate peptides that have been successfully identified by Sequest, ZoomQuant analyzes the isotopic forms of the peptides using high-resolution zoom scan spectrum data. The theoretical isotope distribution is determined from the peptide sequence and is used to deconvolute the peak areas associated with the unlabeled, partially labeled, and fully labeled species. The ratio between the labeled and unlabeled peptides is then calculated using several different methods. ZoomQuant’s graphical user interface allows the user to view and adjust the parameters for peak calling and quantitation and select which peptides should contribute to the overall abundance ratio calculation. Finally, ZoomQuant generates a summary report of the relative abundance of the peptides identified in the two samples.

Mass spectrometry has served as a major tool for the discipline of proteomics to catalogue proteins in an unprecedented scale. With chemical and metabolic techniques for stable isotope labeling developed over the past decade, it is now routinely used as a method for relative quantification to provide valuable information on alteration of protein abundance in a proteome-wide scale. More recently, absolute or stoichiometric quantification of proteome is becoming feasible, in particular, with the development of strategies with isotope-labeled standards composed of concatenated peptides. On the other hand, remarkable progress has been also made in label-free quantification methods based on the number of identified peptides. Here we review these mass spectrometry-based approaches for absolute quantification of proteome and discuss their implications.

Quantitative proteomic mass spectrometry involves comparison of the amplitudes of peaks resulting from different isotope labeling patterns, including fractional atomic labeling and fractional residue labeling. We have developed a general and flexible analytical treatment of the complex isotope distributions that arise in these experiments, using Fourier transform convolution to calculate labeled isotope distributions and least-squares for quantitative comparison with experimental peaks. The degree of fractional atomic and fractional residue labeling can be determined from experimental peaks at the same time as the integrated intensity of all of the isotopomers in the isotope distribution. The approach is illustrated using data with fractional 15N-labeling and fractional 13C-isoleucine labeling. The least-squares Fourier transform convolution approach can be applied to many types of quantitive proteomic data, including data from stable isotope labeling by amino acids in cell culture and pulse labeling experiments.

Background

Isotope-coded affinity tags (ICAT) is a method for quantitative proteomics based on differential isotopic labeling, sample digestion and mass spectrometry (MS). The method allows the identification and relative quantification of proteins present in two samples and consists of the following phases. First, cysteine residues are either labeled using the ICAT Light or ICAT Heavy reagent (having identical chemical properties but different masses). Then, after whole sample digestion, the labeled peptides are captured selectively using the biotin tag contained in both ICAT reagents. Finally, the simplified peptide mixture is analyzed by nanoscale liquid chromatography-tandem mass spectrometry (LC-MS/MS). Nevertheless, the ICAT LC-MS/MS method still suffers from insufficient sample-to-sample reproducibility on peptide identification. In particular, the number and the type of peptides identified in different experiments can vary considerably and, thus, the statistical (comparative) analysis of sample sets is very challenging. Low information overlap at the peptide and, consequently, at the protein level, is very detrimental in situations where the number of samples to be analyzed is high.

Results

We designed a method for improving the data processing and peptide identification in sample sets subjected to ICAT labeling and LC-MS/MS analysis, based on cross validating MS/MS results. Such a method has been implemented in a tool, called EIPeptiDi, which boosts the ICAT data analysis software improving peptide identification throughout the input data set. Heavy/Light (H/L) pairs quantified but not identified by the MS/MS routine, are assigned to peptide sequences identified in other samples, by using similarity criteria based on chromatographic retention time and Heavy/Light mass attributes. EIPeptiDi significantly improves the number of identified peptides per sample, proving that the proposed method has a considerable impact on the protein identification process and, consequently, on the amount of potentially critical information in clinical studies. The EIPeptiDi tool is available at with a demo data set.

Conclusion

EIPeptiDi significantly increases the number of peptides identified and quantified in analyzed samples, thus reducing the number of unassigned H/L pairs and allowing a better comparative analysis of sample data sets.

Background

Effective and economical methods for quantitative analysis of high throughput mass spectrometry data are essential to meet the goals of directly identifying, characterizing, and quantifying proteins from a particular cell state. Multidimensional Protein Identification Technology (MudPIT) is a common approach used in protein identification. Two types of methods are used to detect differential protein expression in MudPIT experiments: those involving stable isotope labelling and the so-called label-free methods. Label-free methods are based on the relationship between protein abundance and sampling statistics such as peptide count, spectral count, probabilistic peptide identification scores, and sum of peptide Sequest XCorr scores (ΣXCorr). Although a number of label-free methods for protein quantification have been described in the literature, there are few publicly available tools that implement these methods. We describe ProtQuant, a Java-based tool for label-free protein quantification that uses the previously published ΣXCorr method for quantification and includes an improved method for handling missing data.

Results

ProtQuant was designed for ease of use and portability for the bench scientist. It implements the ΣXCorr method for label free protein quantification from MudPIT datasets. ProtQuant has a graphical user interface, accepts multiple file formats, is not limited by the size of the input files, and can process any number of replicates and any number of treatments. In addition,ProtQuant implements a new method for dealing with missing values for peptide scores used for quantification. The new algorithm, called ΣXCorr*, uses "below threshold" peptide scores to provide meaningful non-zero values for missing data points. We demonstrate that ΣXCorr* produces an average reduction in false positive identifications of differential expression of 25% compared to ΣXCorr.

Conclusion

ProtQuant is a tool for protein quantification built for multi-platform use with an intuitive user interface. ProtQuant efficiently and uniquely performs label-free quantification of protein datasets produced with Sequest and provides the user with facilities for data management and analysis. Importantly, ProtQuant is available as a self-installing executable for the Windows environment used by many bench scientists.

The use of multidimensional capillary HPLC combined with tandem mass spectrometry has allowed high qualitative and quantitative proteome coverage of prokaryotic organisms. The determination of protein abundance change between two or more conditions has matured to the point that false discovery rates can be very low and for smaller proteomes coverage is sufficiently high to explicitly consider false negative error. Selected aspects of using these methods for global protein abundance assessments are reviewed. These include instrumental issues that influence the reliability of abundance ratios; a comparison of sources of non-linearity, errors, and data compression in proteomics and spotted cDNA arrays; strengths and weaknesses of spectral counting versus stable isotope metabolic labeling; and a survey of microbiological applications of global abundance analysis at the protein level. Proteomic results for two organisms that have been studied extensively using these methods are reviewed in greater detail. Spectral counting and metabolic labeling data are compared and the utility of proteomics for global gene regulation studies are discussed for the methanogenic Archaeon Methanococcus maripaludis. The oral pathogen Porphyromonas gingivalis is discussed as an example of an organism where a large percentage of the proteome differs in relative abundance between the intracellular and extracellular phenotype.

Isotope labeling combined with liquid chromatography–mass spectrometry (LC–MS) provides a robust platform for analyzing differential protein expression in proteomics research. We present a web service, called MaXIC-Q Web (http://ms.iis.sinica.edu.tw/MaXIC-Q_Web/), for quantitation analysis of large-scale datasets generated from proteomics experiments using various stable isotope-labeling techniques, e.g. SILAC, ICAT and user-developed labeling methods. It accepts spectral files in the standard mzXML format and search results from SEQUEST, Mascot and ProteinProphet as input. Furthermore, MaXIC-Q Web uses statistical and computational methods to construct two kinds of elution profiles for each ion, namely, PIMS (projected ion mass spectrum) and XIC (extracted ion chromatogram) from MS data. Toward accurate quantitation, a stringent validation procedure is performed on PIMSs to filter out peptide ions interfered with co-eluting peptides or noise. The areas of XICs determine ion abundances, which are used to calculate peptide and protein ratios. Since MaXIC-Q Web adopts stringent validation on spectral data, it achieves high accuracy so that manual validation effort can be substantially reduced. Furthermore, it provides various visualization diagrams and comprehensive quantitation reports so that users can conveniently inspect quantitation results. In summary, MaXIC-Q Web is a user-friendly, interactive, robust, generic web service for quantitation based on ICAT and SILAC labeling techniques.

Protonated molecular peptide ions and their product ions generated by tandem mass spectrometry appear as isotopologue clusters due to the natural isotopic variations of carbon, hydrogen, nitrogen, oxygen and sulfur. Quantitation of the isotopic composition of peptides can be employed in experiments involving isotope effects, isotope exchange, isotopic labeling by chemical reactions, and studies of metabolism by stable isotope incorporation. Both ion trap and quadrupole-time of flight mass spectrometry are shown to be capable of determining the isotopic composition of peptide product ions obtained by tandem mass spectrometry with both precision and accuracy. Tandem mass spectra obtained in profile-mode of clusters of isotopologue ions are fit by non-linear least squares to a series of Gaussian peaks (described in the accompanying manuscript) which quantify the Mn/M0 values which define the isotopologue distribution (ID). To determine the isotopic composition of product ions from their ID, a new algorithm that predicts the Mn/M0 ratios is developed which obviates the need to determine the intensity of all of the ions of an ID. Consequently a precise and accurate determination of the isotopic composition a product ion may be obtained from only the initial values of the ID, however the entire isotopologue cluster must be isolated prior to fragmentation. Following optimization of the molecular ion isolation width, fragmentation energy and detector sensitivity, the presence of isotopic excess (2H, 13C, 15N, 18O) is readily determined within 1%. The ability to determine the isotopic composition of sequential product ions permits the isotopic composition of individual amino acid residues in the precursor ion to be determined.

The identification of differentially regulated proteins in animal models of psychiatric diseases is essential for a comprehensive analysis of associated psychopathological processes. Mass spectrometry is the most relevant method for analyzing differences in protein expression of tissue and body fluid proteomes. However, standardization of sample handling and sample-to-sample variability are problematic. Stable isotope metabolic labeling of a proteome represents the gold standard for quantitative mass spectrometry analysis. The simultaneous processing of a mixture of labeled and unlabeled samples allows a sensitive and accurate comparative analysis between the respective proteomes. Here, we describe a cost-effective feeding protocol based on a newly developed 15N bacteria diet based on Ralstonia eutropha protein, which was applied to a mouse model for trait anxiety. Tissue from 15N-labeled vs. 14N-unlabeled mice was examined by mass spectrometry and differences in the expression of glyoxalase-1 (GLO1) and histidine triad nucleotide binding protein 2 (Hint2) proteins were correlated with the animals' psychopathological behaviors for methodological validation and proof of concept, respectively. Additionally, phenotyping unraveled an antidepressant-like effect of the incorporation of the stable isotope 15N into the proteome of highly anxious mice. This novel phenomenon is of considerable relevance to the metabolic labeling method and could provide an opportunity for the discovery of candidate proteins involved in depression-like behavior. The newly developed 15N bacteria diet provides researchers a novel tool to discover disease-relevant protein expression differences in mouse models using quantitative mass spectrometry.

Background

Quantitative proteomics technologies have been developed to comprehensively identify and quantify proteins in two or more complex samples. Quantitative proteomics based on differential stable isotope labeling is one of the proteomics quantification technologies. Mass spectrometric data generated for peptide quantification are often noisy, and peak detection and definition require various smoothing filters to remove noise in order to achieve accurate peptide quantification. Many traditional smoothing filters, such as the moving average filter, Savitzky-Golay filter and Gaussian filter, have been used to reduce noise in MS peaks. However, limitations of these filtering approaches often result in inaccurate peptide quantification. Here we present the WaveletQuant program, based on wavelet theory, for better or alternative MS-based proteomic quantification.

Results

We developed a novel discrete wavelet transform (DWT) and a 'Spatial Adaptive Algorithm' to remove noise and to identify true peaks. We programmed and compiled WaveletQuant using Visual C++ 2005 Express Edition. We then incorporated the WaveletQuant program in the Trans-Proteomic Pipeline (TPP), a commonly used open source proteomics analysis pipeline.

Conclusions

We showed that WaveletQuant was able to quantify more proteins and to quantify them more accurately than the ASAPRatio, a program that performs quantification in the TPP pipeline, first using known mixed ratios of yeast extracts and then using a data set from ovarian cancer cell lysates. The program and its documentation can be downloaded from our website at http://systemsbiozju.org/data/WaveletQuant.

Determining global proteome changes is important for advancing a systems biology view of cellular processes and for discovering biomarkers. Liquid chromatography, coupled to mass spectrometry, has been widely used as a proteomics technique for discovering differentially expressed proteins in biological samples. However, although a large number of high-throughput studies have identified differentially regulated proteins, only a small fraction of these results have been reproduced and independently verified. The use of different approaches to data processing and analyses is among the factors which contribute to inconsistent conclusions. This perspective provides a comprehensive and critical overview of bioinformatics methods for commonly used mass spectrometry-based quantitative proteomics, employing both stable isotope labeling and label-free approaches. We evaluate the challenges associated with current quantitative proteomics techniques, placing particular emphasis on data analyses. The complexity of processing and interpreting proteomics datasets has become a central issue as sensitivity, mass resolution, mass accuracy and throughput of mass spectrometers have improved. A number of computer programs are available to address these challenges, and are reviewed here. We focus on approaches for signal processing, noise reduction, and methods for protein abundance estimation.

Recently, several research groups have published methods for the determination of proteomic expression profiling by mass spectrometry without the use of exogenously added stable isotopes or stable isotope dilution theory. These so-called label-free, methods have the advantage of allowing data on each sample to be acquired independently from all other samples to which they can later be compared in silico for the purpose of measuring changes in protein expression between various biological states. We developed label free software based on direct measurement of peptide ion current area (PICA) and compared it to two other methods, a simpler label free method known as spectral counting and the isotope coded affinity tag (ICAT) method. Data analysis by these methods of a standard mixture containing proteins of known, but varying, concentrations showed that they performed similarly with a mean squared error of 0.09. Additionally, complex bacterial protein mixtures spiked with known concentrations of standard proteins were analyzed using the PICA label-free method. These results indicated that the PICA method detected all levels of standard spiked proteins at the 90% confidence level in this complex biological sample. This finding confirms that label-free methods, based on direct measurement of the area under a single ion current trace, performed as well as the standard ICAT method. Given the fact that the label-free methods provide ease in experimental design well beyond pair-wise comparison, label-free methods such as our PICA method are well suited for proteomic expression profiling of large numbers of samples as is needed in clinical analysis.

Quantitative comparative analyses of protein abundances using peptide ion intensities and their modifications have become a widely used technique in studying various biological questions. In the past years, several methods for quantitative proteomics were established using stable-isotope labeling and label-free approaches. We systematically evaluated the application of reference protein normalization (RPN) for proteomic experiments using a high mass accuracy LC-MS/MS platform. In RPN all sample peptide intensities were normalized to an average protein intensity of a spiked reference protein. The main advantage of this method is that it avoids fraction of total based relative analysis of proteomic data, which is often very much dependent on sample complexity. We could show that reference protein ion intensity sums are sufficiently reproducible to ensure a reliable normalization. We validated the RPN strategy by analyzing changes in protein abundances induced by nutrient starvation in Arabidopsis thaliana. Beyond that, we provide a principle guideline for determining optimal combination of sample protein and reference protein load on individual LC-MS/MS systems.