The use of a novel quantitation strategy based on Reductive Isotopic Di-Ethylation (RIDE) to evaluate the effect of glufosinate on the unicellular algae Ostreococcus tauri

We report a novel stable-isotope labeling strategy for quantitative proteomics analysis. The method consists of labeling N-termini and lysine ε-amino groups through reductive amination using acetaldehyde. This allows isotope labeling using pairs of either 2H/1H or 13C/12C without mass spectrum overlap. Our labeling procedure, which is significantly different than that developed for dimethylation, can be completed with little trace of partial ethylation; non-labeled peptides represent less than 0.05% of all peptides. Co-elution of both isotopic 13C/12C peptide pairs was observed in all cases, simplifying data analysis, which can be performed using standard commercial software such as Mascot Distiller. A 13C/12C labeled mix in a 1:1 ratio from a complex extract digest of the unicellular algae Ostreococcus tauri, showed a relative standard deviation of less than 14%. This quantitative method was used to characterize O. tauri in the presence of glufosinate, an herbicide which inhibits glutamine synthetase. Blocking glutamine synthetase significantly reduced the expression of several enzymes and transporters involved in nitrogen assimilation and the expression of a number of proteins involved in various stresses including oxidative damage response were up-regulated.     

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

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

Proteolytic 18O labeling for comparative proteomics: evaluation of endoprotease Glu-C as the catalytic agent

Recently, proteolytic 18O labeling has been demonstrated as a promising strategy for comparative proteomic studies (Yao, X.; Freas, A.; Ramirez, J.; Demirev, P. A.; Fenselau, C. Anal. Chem. 2001, 73, 2836-42). In this approach, protein mixtures are digested in parallel in H216O and H218O and the ratios of isotopically distinct peptide products are measured by mass spectrometry. In the initial report from this laboratory, trypsin was shown to catalyze incorporation of two 18O atoms into the carboxyl terminus of each new peptide formed by cleavage of the adenovirus proteome. In the present study, a second enzyme, endoprotease Glu-C, is evaluated as an agent for cleavage and labeling. Proteolytic 18O labeling by Glu-C is shown to occur readily with phosphorylated and glycosylated proteins and with cysteinealkylated and disulfide-linked proteins. A sequential double-labeling strategy is used to characterize N-linked glycopeptides. Labeled and unlabeled peptide pairs are found to coelute chromatographically, and measurements of isotope ratios by nanospray and capillary LC-MS are found to be accurate and precise.     

Quantitative proteomic analysis of mouse embryonic fibroblasts and induced pluripotent stem cells using 16O/18O labeling

Induced pluripotent stem cells (iPSC) hold great promise for regenerative medicine as well as for investigations into the pathogenesis and treatment of various diseases. Understanding of key intracellular signaling pathways and protein targets that control development of iPSC from somatic cells is essential for designing new approaches to improve reprogramming efficiency. Here, we report the development and application of an integrated quantitative proteomics platform for investigating differences in protein expressions between mouse embryonic fibroblasts (MEF) and MEF-derived iPSC. This platform consists of 16O/18O labeling, multidimensional peptide separation coupled with tandem mass spectrometry, and data analysis with UNiquant software. With this platform, a total of 2481 proteins were identified and quantified from the 16O/18O-labeled MEF-iPSC proteome mixtures with a false discovery rate of 0.01. Among them, 218 proteins were significantly upregulated, while 247 proteins were significantly downregulated in iPSC compared to MEF. Many nuclear proteins, including Hdac1, Dnmt1, Pcna, Ccnd1, Smarcc1, and subunits in DNA replication and RNA polymerase II complex, were found to be enhanced in iPSC. Protein network analysis revealed that Pcna functions as a hub orchestrating complicated mechanisms including DNA replication, epigenetic inheritance (Dnmt1), and chromatin remodeling (Smarcc1) to reprogram MEF and maintain stemness of iPSC.     

Differential dimethyl labeling of N-termini of peptides after guanidination for proteome analysis

We describe an enabling technique for proteome analysis based on isotope-differential dimethyl labeling of N-termini of tryptic peptides followed by microbore liquid chromatography (LC) matrix-assisted laser desorption and ionization (MALDI) mass spectrometry (MS). In this method, lysine side chains are blocked by guanidination to prevent the incorporation of multiple labels, followed by N-terminal labeling via reductive amination using d(0),(12)C-formaldehyde or d(2),(13)C-formaldehyde. Relative quantification of peptide mixtures is achieved by examining the MALDI mass spectra of the peptide pairs labeled with different isotope tags. A nominal mass difference of 6 Da between the peptide pair allows negligible interference between the two isotopic clusters for quantification of peptides of up to 3000 Da. Since only the N-termini of tryptic peptides are differentially labeled and the a(1) ions are also enhanced in the MALDI MS/MS spectra, interpretation of the fragment ion spectra to obtain sequence information is greatly simplified. It is demonstrated that this technique of N-terminal dimethylation (2ME) after lysine guanidination (GA) or 2MEGA offers several desirable features, including simple experimental procedure, stable products, using inexpensive and commercially available reagents, and negligible isotope effect on reversed-phase separation. LC-MALDI MS combined with this 2MEGA labeling technique was successfully used to identify proteins that included polymorphic variants and low abundance proteins in bovine milk. In addition, by analyzing a mixture of two equal amounts of milk whey fraction as a control, it is shown that the measured average ratio for 56 peptide pairs from 14 different proteins is 1.02, which is very close to the theoretical ratio of 1.00. The calculated percentage error is 2.0% and relative standard deviation is 4.6%.     

Combination of online enzyme digestion with stable isotope labeling for high‐throughput quantitative proteome analysis

Various enzyme reactors and online enzyme digestion strategies have been developed in recent years. These reactors greatly enhanced the detection sensitivity and proteome coverage in qualitative proteomics. However, these devices have higher rates of miscleavage in protein digestion. Therefore, we investigated the effect of online enzyme digestion on the quantification accuracy of quantitative proteomics using chemical or metabolic isotope labeling approaches. The incomplete digestion would introduce some unexpected variations in comparative quantification when the samples are digested and then chemically isotope labeled in different aliquots. Even when identical protein aliquots are processed on these devices using post‐digestion chemical isotope labeling and the CVs of the ratios controlled to less than 50% in replicate analyses, about 10% of the quantified proteins have a ratio greater than two‐fold, whereas in theory the ratio is 1:1. Interestingly, the incomplete digestion with enzyme reactor is not a problem when metabolic isotope labeling samples were processed because the proteins are isotopically labeled in vivo prior to their simultaneous digestion within the reactor. Our results also demonstrated that both high quantification accuracy and high proteome coverage can be achieved in comparative proteome quantification using online enzyme digestion even when a limited amount of metabolic isotope labeling samples is used (1683 proteins comparatively quantified from 10⁵ Hela cells).     

Stable isotope labeling by amino acids in cell culture (SILAC) and proteome quantitation of mouse embryonic stem cells to a depth of 5,111 proteins

Embryonic stem (ES) cells are pluripotent cells isolated from mammalian preimplantation embryos. They are capable of differentiating into all cell types and therefore hold great promise in regenerative medicine. Here we show that murine ES cells can be fully SILAC (stable isotope labeling by amino acids in cell culture)-labeled when grown feeder-free during the last phase of cell culture. We fractionated the SILAC-labeled ES cell proteome by one-dimensional gel electrophoresis and by isoelectric focusing of peptides. High resolution analysis on a linear ion trap-orbitrap instrument (LTQ-Orbitrap) at sub-ppm mass accuracy resulted in confident identification and quantitation of more than 5,000 distinct proteins. This is the largest quantified proteome reported to date and contains prominent stem cell markers such as OCT4, NANOG, SOX2, and UTF1 along with the embryonic form of RAS (ERAS). We also quantified the proportion of the ES cell proteome present in cytosolic, nucleoplasmic, and membrane/chromatin fractions. We compared two different preparation approaches, cell fractionation followed by one-dimensional gel separation and in-solution digestion of total cell lysate combined with isoelectric focusing, and found comparable proteome coverage with no apparent bias for any functional protein classes for either approach. Bioinformatics analysis of the ES cell proteome revealed a broad distribution of cellular functions with overrepresentation of proteins involved in proliferation. We compared the proteome with a recently published map of chromatin states of promoters in ES cells and found excellent correlation between protein expression and the presence of active and repressive chromatin marks.     

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.     

A framework for intelligent data acquisition and real-time database searching for shotgun proteomics

In the analysis of complex peptide mixtures by MS-based proteomics, many more peptides elute at any given time than can be identified and quantified by the mass spectrometer. This makes it desirable to optimally allocate peptide sequencing and narrow mass range quantification events. In computer science, intelligent agents are frequently used to make autonomous decisions in complex environments. Here we develop and describe a framework for intelligent data acquisition and real-time database searching and showcase selected examples. The intelligent agent is implemented in the MaxQuant computational proteomics environment, termed MaxQuant Real-Time. It analyzes data as it is acquired on the mass spectrometer, constructs isotope patterns and SILAC pair information as well as controls MS and tandem MS events based on real-time and prior MS data or external knowledge. Re-implementing a top10 method in the intelligent agent yields similar performance to the data dependent methods running on the mass spectrometer itself. We demonstrate the capabilities of MaxQuant Real-Time by creating a real-time search engine capable of identifying peptides "on-the-fly" within 30 ms, well within the time constraints of a shotgun fragmentation "topN" method. The agent can focus sequencing events onto peptides of specific interest, such as those originating from a specific gene ontology (GO) term, or peptides that are likely modified versions of already identified peptides. Finally, we demonstrate enhanced quantification of SILAC pairs whose ratios were poorly defined in survey spectra. MaxQuant Real-Time is flexible and can be applied to a large number of scenarios that would benefit from intelligent, directed data acquisition. Our framework should be especially useful for new instrument types, such as the quadrupole-Orbitrap, that are currently becoming available.     

Validation of peptide MS/MS spectra using metabolic isotope labeling for spectral matching-based shotgun proteome analysis

We report an isotope labeling shotgun proteome analysis strategy to validate the spectrum-to-sequence assignments generated by using sequence-database searching for the construction of a more reliable MS/MS spectral library. This strategy is demonstrated in the analysis of the E. coli K12 proteome. In the workflow, E. coli cells were cultured in normal and (15)N-enriched media. The differentially labeled proteins from the cell extracts were subjected to trypsin digestion and two-dimensional liquid chromatography quadrupole time-of-flight tandem mass spectrometry (2D-LC QTOF MS/MS) analysis. The MS/MS spectra of the two samples were individually searched using Mascot against the E. coli proteome database to generate lists of peptide sequence matches. The two data sets were compared by overlaying the spectra of unlabeled and labeled matches of the same peptide sequence for validation. Two cutoff filters, one based on the number of common fragment ions and another one on the similarity of intensity patterns among the common ions, were developed and applied to the overlaid spectral pairs to reject the low quality or incorrectly assigned spectra. By examining 257,907 and 245,156 spectra acquired from the unlabeled and (15)N-labeled samples, respectively, an experimentally validated MS/MS spectral library of tryptic peptides was constructed for E. coli K12 that consisted of 9,302 unique spectra with unique sequence and charge state, representing 7,763 unique peptide sequences. This E. coli spectral library could be readily expanded, and the overall strategy should be applicable to other organisms. Even with this relatively small library, it was shown that more peptides could be identified with higher confidence using the spectral search method than by sequence-database searching.     

Regression analysis for comparing protein samples with 16O/18O stable-isotope labeled mass spectrometry

MOTIVATION: Using stable isotopes in global proteome scans, labeled molecules from one sample are pooled with unlabeled molecules from another sample and subsequently subjected to mass-spectral analysis. Stable-isotope methodologies make use of the fact that identical molecules of different stable-isotope compositions are differentiated in a mass spectrometer and are represented in a mass spectrum as distinct isotopic clusters with a known mass shift. We describe two multivariable linear regression models for (16)O/(18)O stable-isotope labeled data that jointly model pairs of resolved isotopic clusters from the same peptide and quantify the abundance present in each of the two biological samples while concurrently accounting for peptide-specific incorporation rates of the heavy isotope. The abundance measure for each peptide from the two biological samples is then used in down-stream statistical analyses, e.g. differential expression analysis. Because the multivariable regression models are able to correct for the abundance of the labeled peptide that appear as an unlabeled peptide due to the inability to exchange the natural C-terminal oxygen for the heavy isotope, they are particularly advantageous for a two-step digestion/labeling procedure. We discuss how estimates from the regression model are used to quantify the variability of the estimated abundance measures for the paired samples. Although discussed in the context of (16)O/(18)O stable-isotope labeled data, the multivariable regression models are generalizable to other stable-isotope labeled technologies.     

Life‐style changes of a halophilic archaeon analyzed by quantitative proteomics

Quantitative proteomics based on isotopic labeling has become the method of choice to accurately determine changes in protein abundance in highly complex mixtures. Isotope‐coded protein labeling (ICPL), which is based on the nicotinoylation of proteins at lysine residues and free N‐termini was used as a simple, reliable and fast method for the comparative analysis of three different cellular states of the halophilic archaeon Halobacterium salinarum through pairwise comparison. The labeled proteins were subjected to SDS‐PAGE, in‐gel digested and the proteolytic peptides were separated by LC and analyzed by MALDI‐TOF/TOF MS. Automated quantitation was performed by comparing the MS peptide signals of ¹²C and ¹³C nicotinoylated isotopic peptide pairs. The transitions between (i) aerobic growth in complex versus synthetic medium and (ii) aerobic versus anaerobic/phototrophic growth, both in complex medium, provide a wide span in nutrient and energy supply for the cell and thus allowed optimal studies of proteome changes. In these two studies, 559 and 643 proteins, respectively, could be quantified allowing a detailed analysis of the adaptation of H. salinarum to changes of its living conditions. The subtle cellular response to a wide variation of nutrient and energy supply demonstrates a fine tuning of the cellular protein inventory.     

Quantitative proteome analysis of CD95 (Fas/Apo-1)-induced apoptosis by stable isotope labeling with amino acids in cell culture, 2-DE and MALDI-MS

Proteome analysis of Jurkat T cells induced to undergo apoptosis by CD95 (Fas/Apo-1) treatment was performed to identify modified proteins. We used stable isotope labeling with amino acids in cell culture (SILAC) using leucine to identify proteins of apoptotic and control Jurkat T cells by 2-DE and MALDI-MS. Out of 224 spots analyzed, we quantified 213 spots with 3.5 leucine-containing peptide pairs on average; 28 proteins with a relative abundance of higher than 1.5 were found. Five new modified proteins including calcyclin binding protein, cytosolic acyl coenzyme A thioester hydrolase, heterogeneous ribonucleoprotein M, replication factor C 37-kDa subunit, and tropomyosin 4 chain were identified as being modified in response to apoptosis. In comparison to differential proteome analysis via silver-stained 2-D gels and PMF of total Jurkat T cell lysates, 15 additional apoptosis-modified proteins were identified though 8 proteins were not found. The described approach using SILAC instead of silver staining for relative quantification was simpler to perform regarding the number of required 2-D gels, that cumbersome gel comparisons were avoided, and more apoptosis-modified proteins were identified, but with a higher demand on data interpretation of the mass spectra obtained.     

A label-free quantification method by MS/MS TIC compared to SILAC and spectral counting in a proteomics screen

In order to assess the biological function of proteins and their modifications for understanding signaling mechanisms within cells as well as specific biomarkers to disease, it is important that quantitative information be obtained under different experimental conditions. Stable isotope labeling is a powerful method for accurately determining changes in the levels of proteins and PTMs; however, isotope labeling experiments suffer from limited dynamic range resulting in signal change ratios of less than approximately 20:1 using most commercial mass spectrometers. Label-free approaches to relative quantification in proteomics such as spectral counting have gained popularity since no additional chemistries are needed. Here, we show a label-free method for relative quantification based on the TIC from peptide MS/MS spectra collected from data-dependent runs can be used effectively as a quantitative measure and expands the dynamic range over isotope labeling experiments allowing for abundance differences up to approximately 60:1 in a screen for proteins that bind to phosphotyrosine residues.     

Stable isotope labeling with amino acids in Drosophila for quantifying proteins and modifications

Drosophila melanogaster is a common animal model for genetics studies, and quantitative proteomics studies of the fly are emerging. Here, we present in detail the development of a procedure to incorporate stable isotope-labeled amino acids into the fly proteome. In the method of stable isotope labeling with amino acids in Drosophila melanogaster (SILAC fly), flies were fed with SILAC-labeled yeast grown with modified media, enabling near complete labeling in a single generation. Biological variation in the proteome among individual flies was evaluated in a series of null experiments. We further applied the SILAC fly method to profile proteins from a model of fragile X syndrome, the most common cause of inherited mental retardation in human. The analysis identified a number of altered proteins in the disease model, including actin-binding protein profilin and microtubulin-associated protein futsch. The change of both proteins was validated by immunoblotting analysis. Moreover, we extended the SILAC fly strategy to study the dynamics of protein ubiquitination during the fly life span (from day 1 to day 30), by measuring the level of ubiquitin along with two major polyubiquitin chains (K48 and K63 linkages). The results show that the abundance of protein ubiquitination and the two major linkages do not change significantly within the measured age range. Together, the data demonstrate the application of the SILAC principle in D. melanogaster, facilitating the integration of powerful fly genomics with emerging proteomics.     

Site-specific degree of phosphorylation in proteins measured by liquid chromatography-electrospray mass spectrometry

This review focuses on quantitative protein phosphorylation analysis based on coverage of both the phosphorylated and nonphosphorylated forms. In this way, site-specific data on the degree of phosphorylation can be measured, generating the most detailed level of phosphorylation status analysis of proteins. To highlight the experimental challenges in this type of quantitative protein phosphorylation analysis, we discuss the typical workflows for mass spectrometry-based proteomics with a focus on the quantitative analysis of peptide/phosphopeptide ratios. We review workflows for measuring site-specific degrees of phosphorylation including the label-free approach, differential stable isotope labeling of analytes, and methods based on the addition of stable isotope labeled peptide/phosphopeptide pairs as internal standards. The discussion also includes the determination of phosphopeptide isoform abundance data for multiply phosphorylated motifs that contain information about the connectivity of phosphorylation events. The review closes with a prospective on the use of intact stable isotope labeled proteins as internal standards and a summarizing discussion of the typical accuracies of the individual methods.     

Protein abundance ratios for global studies of prokaryotes

The use of multidimensional capillary HPLC combined with MS/MS 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 nonlinearity, 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.     

hmSEEKER: Identification of hmSILAC Doublets in MaxQuant Output Data

Heavy methyl Stable Isotope Labeling with Amino acids in Cell culture (hmSILAC) is a metabolic labeling strategy employed in proteomics to increase the confidence of global identification of methylated peptides by MS. However, to this day, the automatic and robust identification of heavy and light peak doublets from MS‐raw data of hmSILAC experiments is a challenging task, for which the choice of computational methods is very limited. Here, hmSEEKER, a software designed to work downstream of a MaxQuant analysis for in‐depth search of MS peak pairs that correspond to light and heavy methyl‐peptide within MaxQuant‐generated tables is described with good sensitivity and specificity. The software is written in Perl, and its code and user manual are freely available at Bitbucket ( https://bit.ly/2scCT9u ).     

Quantitative proteome analysis of human plasma following in vivo lipopolysaccharide administration using 16O/18O labeling and the accurate mass and time tag approach

Identification of novel diagnostic or therapeutic biomarkers from human blood plasma would benefit significantly from quantitative measurements of the proteome constituents over a range of physiological conditions. Herein we describe an initial demonstration of proteome-wide quantitative analysis of human plasma. The approach utilizes postdigestion trypsin-catalyzed 16O/18O peptide labeling, two-dimensional LC-FTICR mass spectrometry, and the accurate mass and time (AMT) tag strategy to identify and quantify peptides/proteins from complex samples. A peptide accurate mass and LC elution time AMT tag data base was initially generated using MS/MS following extensive multidimensional LC separations to provide the basis for subsequent peptide identifications. The AMT tag data base contains >8,000 putative identified peptides, providing 938 confident plasma protein identifications. The quantitative approach was applied without depletion of high abundance proteins for comparative analyses of plasma samples from an individual prior to and 9 h after lipopolysaccharide (LPS) administration. Accurate quantification of changes in protein abundance was demonstrated by both 1:1 labeling of control plasma and the comparison between the plasma samples following LPS administration. A total of 429 distinct plasma proteins were quantified from the comparative analyses, and the protein abundances for 25 proteins, including several known inflammatory response mediators, were observed to change significantly following LPS administration.     

Find Pairs: The Module for Protein Quantification of the PeakQuant Software Suite

Abstract Accurate quantification of proteins is one of the major tasks in current proteomics research. To address this issue, a wide range of stable isotope labeling techniques have been developed, allowing one to quantitatively study thousands of proteins by means of mass spectrometry. In this article, the FindPairs module of the PeakQuant software suite is detailed. It facilitates the automatic determination of protein abundance ratios based on the automated analysis of stable isotope-coded mass spectrometric data. Furthermore, it implements statistical methods to determine outliers due to biological as well as technical variance of proteome data obtained in replicate experiments. This provides an important means to evaluate the significance in obtained protein expression data. For demonstrating the high applicability of FindPairs, we focused on the quantitative analysis of proteome data acquired in (14)N/(15)N labeling experiments. We further provide a comprehensive overview of the features of the FindPairs software, and compare these with existing quantification packages. The software presented here supports a wide range of proteomics applications, allowing one to quantitatively assess data derived from different stable isotope labeling approaches, such as (14)N/(15)N labeling, SILAC, and iTRAQ. The software is publicly available at http://www.medizinisches-proteom-center.de/software and free for academic use.