The ABRF Proteomics Research Group studies: educational exercises for qualitative and quantitative proteomic analyses

Resource (core) facilities have played an ever-increasing role in furnishing the scientific community with specialized instrumentation and expertise for proteomics experiments in a cost-effective manner. The Proteomics Research Group (PRG) of the Association of Biomolecular Resource Facilities (ABRF) has sponsored a number of research studies designed to enable participants to try new techniques and assess their capabilities relative to other laboratories analyzing the same samples. Presented here are results from three PRG studies representing different samples that are typically analyzed in a core facility, ranging from simple protein identification to targeted analyses, and include intentional challenges to reflect realistic studies. The PRG2008 study compares different strategies for the qualitative characterization of proteins, particularly the utility of complementary methods for characterizing truncated protein forms. The use of different approaches for determining quantitative differences for several target proteins in human plasma was the focus of the PRG2009 study. The PRG2010 study explored different methods for determining specific constituents while identifying unforeseen problems that could account for unanticipated results associated with the different samples, and included (15) N-labeled proteins as an additional challenge. These studies provide a valuable educational resource to research laboratories and core facilities, as well as a mechanism for establishing good laboratory practices.     

DeepQuanTR: MALDI‐MS‐based label‐free quantification of proteins in complex biological samples

The quantification of changes in protein abundance in complex biological specimens is essential for proteomic studies in basic and applied research. Here we report on the development and validation of the DeepQuanTR software for identification and quantification of differentially expressed proteins using LC‐MALDI‐MS. Following enzymatic digestion, HPLC peptide separation and normalization of MALDI‐MS signal intensities to the ones of internal standards, the software extracts peptide features, adjusts differences in HPLC retention times and performs a relative quantification of features. The annotation of multiple peptides to the corresponding parent protein allows the definition of a Protein Quant Value, which is related to protein abundance and which allows inter‐sample comparisons. The performance of DeepQuanTR was evaluated by analyzing 24 samples deriving from human serum spiked with different amounts of four proteins and eight complex samples of vascular proteins, derived from surgically resected human kidneys with cancer following ex vivo perfusion with a reactive ester biotin derivative. The identification and experimental validation of proteins, which were differentially regulated in cancerous lesions as compared with normal kidney, was used to demonstrate the power of DeepQuanTR. This software, which can easily be used with established proteomic methodologies, facilitates the relative quantification of proteins derived from a wide variety of different samples.     

A novel alignment method and multiple filters for exclusion of unqualified peptides to enhance label-free quantification using peptide intensity in LC-MS/MS

Though many software packages have been developed to perform label-free quantification of proteins in complex biological samples using peptide intensities generated by LC-MS/MS, two critical issues are generally ignored in this field: (i) peptides have multiple elution patterns across runs in an experiment, and (ii) many peptides cannot be used for protein quantification. To address these two key issues, we have developed a novel alignment method to enable accurate peptide peak retention time determination and multiple filters to eliminate unqualified peptides for protein quantification. Repeatability and linearity have been tested using six very different samples, i.e., standard peptides, kidney tissue lysates, HT29-MTX cell lysates, depleted human serum, human serum albumin-bound proteins, and standard proteins spiked in kidney tissue lysates. At least 90.8% of the proteins (up to 1,390) had CVs ≤ 30% across 10 technical replicates, and at least 93.6% (up to 2,013) had R(2) ≥ 0.9500 across 7 concentrations. Identical amounts of standard protein spiked in complex biological samples achieved a CV of 8.6% across eight injections of two groups. Further assessment was made by comparing mass spectrometric results to immunodetection, and consistent results were obtained. The new approach has novel and specific features enabling accurate label-free quantification.     

Evaluation of reproducibility of protein identification results after multidimensional human serum protein separation

We developed a gel- and label-free proteomics platform for comparative studies of human serum. The method involves the depletion of the six most abundant proteins, protein fractionation by Off-Gel IEF and RP-HPLC, followed by tryptic digestion, LC-MS/MS, protein identification, and relative quantification using probabilistic peptide match score summation (PMSS). We evaluated performance and reproducibility of the complete platform and the individual dimensions, by using chromatograms of the RP-HPLC runs, PMSS based abundance scores and abundance distributions as objective endpoints. We were interested if a relationship exists between the quantity ratio and the PMSS score ratio. The complete analysis was performed four times with two sets of serum samples containing different concentrations of spiked bovine beta-lactoglobulin (0.1 and 0.3%, w/w). The two concentrations resulted in significantly differing PMSS scores when compared to the variability in PMSS scores of all other protein identifications. We identified 196 proteins, of which 116 were identified four times in corresponding fractions whereof 73 qualified for relative quantification. Finally, we characterized the PMSS based protein abundance distributions with respect to the two dimensions of fractionation and discussed some interesting patterns representing discrete isoforms. We conclude that combination of Off-Gel electrophoresis (OGE) and HPLC is a reproducible protein fractionation technique, that PMSS is applicable for relative quantification, that the number of quantifiable proteins is always smaller than the number of identified proteins and that reproducibility of protein identifications should supplement probabilistic acceptance criteria.     

Abundance of tegument surface proteins in the human blood fluke Schistosoma mansoni determined by QconCAT proteomics

The schistosome tegument provides a major interface with the host blood stream in which it resides. Our recent proteomic studies have identified a range of proteins present in the complex tegument structure, and two models of protective immunity have implicated surface proteins as mediating antigens. We have used the QconCAT technique to evaluate the relative and absolute amounts of tegument proteins identified previously. A concatamer comprising R- or K-terminated peptides was generated with [(13)C(6)] lysine/arginine amino acids. Two tegument surface preparations were each spiked with the purified SmQconCAT as a standard, trypsin digested, and subjected to MALDI ToF-MS. The absolute amounts of protein in the biological samples were determined by comparing the areas under the pairs of peaks, separated by 6m/z units, representing the light and heavy peptides derived from the biological sample and SmQconCAT, respectively. We report that aquaporin is the most abundant transmembrane protein, followed by two phosphohydrolases. Tetraspanin Tsp-2 and Annexin-2 are also abundant but transporters are scarce. Sm200 surface protein comprised the bulk of the GPI-anchored fraction and likely resides in the secreted membranocalyx. Two host IgGs were identified but in amounts much lower than their targets. The findings are interpreted in relation to the models of protective immunity.     

Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures

A novel database search algorithm is presented for the qualitative identification of proteins over a wide dynamic range, both in simple and complex biological samples. The algorithm has been designed for the analysis of data originating from data independent acquisitions, whereby multiple precursor ions are fragmented simultaneously. Measurements used by the algorithm include retention time, ion intensities, charge state, and accurate masses on both precursor and product ions from LC‐MS data. The search algorithm uses an iterative process whereby each iteration incrementally increases the selectivity, specificity, and sensitivity of the overall strategy. Increased specificity is obtained by utilizing a subset database search approach, whereby for each subsequent stage of the search, only those peptides from securely identified proteins are queried. Tentative peptide and protein identifications are ranked and scored by their relative correlation to a number of models of known and empirically derived physicochemical attributes of proteins and peptides. In addition, the algorithm utilizes decoy database techniques for automatically determining the false positive identification rates. The search algorithm has been tested by comparing the search results from a four‐protein mixture, the same four‐protein mixture spiked into a complex biological background, and a variety of other “system” type protein digest mixtures. The method was validated independently by data dependent methods, while concurrently relying on replication and selectivity. Comparisons were also performed with other commercially and publicly available peptide fragmentation search algorithms. The presented results demonstrate the ability to correctly identify peptides and proteins from data independent acquisition strategies with high sensitivity and specificity. They also illustrate a more comprehensive analysis of the samples studied; providing approximately 20% more protein identifications, compared to a more conventional data directed approach using the same identification criteria, with a concurrent increase in both sequence coverage and the number of modified peptides.     

Mass spectrometric quantitation of C-reactive protein using labeled tryptic peptides

Given the extensive efforts applied toward proteomics and research in biomarkers, methods for the simultaneous measurement of proteins, peptides, metabolic intermediates, hormones, etc. in a complex sample may be required in the foreseeable future. Assays based on mass spectrometric detection may be suitable for meeting the demands of such complex samples with sensitivity and specificity. An analytical method for the quantitation of C-reactive protein (CRP), a well-known marker of inflammation, is described. Exact quantities of two synthetic (13)C-labeled CRP tryptic peptides were added as internal standards directly to the sample prior to chemical treatment, trypsinization, and liquid chromatography/mass spectrometry quantitation. C-reactive protein levels based on isotopic response ratios were measured. Intact C-reactive protein was spiked into blank rat urine for chemical and enzymatic treatment, producing linear response ratios of labeled to unlabeled peptides. For rigorous quantitation, standard curves, and quality control samples were prepared in rat urine with highly purified labeled and unlabeled peptides over the 50 pg-5 ng/muL concentration range. Using the same chemical and enzymatic treatment used for digestion of intact CRP, data from these samples demonstrated excellent analytical performance. The method was successfully applied toward the quantitation of urinary C-reactive protein from a study of drug-induced nephrotoxicity.     

A new algorithm using cross-assignment for label-free quantitation with LC-LTQ-FT MS

A new algorithm is described for label-free quantitation of relative protein abundances across multiple complex proteomic samples. Q-MEND is based on the denoising and peak picking algorithm, MEND, previously developed in our laboratory. Q-MEND takes advantage of the high resolution and mass accuracy of the hybrid LTQ-FT MS mass spectrometer (or other high-resolution mass spectrometers, such as a Q-TOF MS). The strategy, termed "cross-assignment", is introduced to increase substantially the number of quantitated proteins. In this approach, all MS/MS identifications for the set of analyzed samples are combined into a master ID list, and then each LC-MS run is searched for the features that can be assigned to a specific identification from that master list. The reliability of quantitation is enhanced by quantitating separately all peptide charge states, along with a scoring procedure to filter out less reliable peptide abundance measurements. The effectiveness of Q-MEND is illustrated in the relative quantitative analysis of Escherichia coli samples spiked with known amounts of non-E. coli protein digests. A mean quantitation accuracy of 7% and mean precision of 15% is demonstrated. Q-MEND can perform relative quantitation of a set of LC-MS data sets without manual intervention and can generate files compatible with the Guidelines for Proteomic Data Publication.     

ABRF-PRG04: differentiation of protein isoforms.

Accurate protein identification sometimes requires careful discrimination between closely related protein isoforms that may differ by as little as a single amino acid substitution or post-translational modification. The ABRF Proteomics Research Group sent a mixture of three picomoles each of three closely related proteins to laboratories who requested it in the form of intact proteins, and participating laboratories were asked to identify the proteins and report their results. The primary goal of the ABRF-PRG04 Study was to give participating laboratories a chance to evaluate their capabilities and practices with regards to sample fractionation (1D- or 2D-PAGE, HPLC, or none), protein digestion methods (in-solution, in-gel, enzyme choice), and approaches to protein identification (instrumentation, use of software, and/or manual techniques to facilitate interpretation), as well as determination of amino acid or post-translational modifications. Of the 42 laboratories that responded, 8 (19%) correctly identified all three isoforms and N-terminal acetylation of each, 16 (38%) labs correctly identified two isoforms, 9 (21%) correctly identified two isoforms but also made at least one incorrect identification, and 9 (21%) made no correct protein identifications. All but one lab used mass spectrometry, and data submitted enabled a comparison of strategies and methods used.     

Finnish quality assurance programme for biological monitoring of organic solvents

The FIOH quality assurance programme for organic solvents and their metabolites consists of analyses for 2,5-hexadione, phenol, mandelic acid, methylenedianiline, methylhippuric acid, trans,trans-muconic acid and trichloroacetic acid in urine, and creatinine and relative density for standardisation. Four times a year two levels of spiked urine or urine specimens collected from occupationally exposed workers are distributed to the participants in 22 countries. RSD and recovery were studied during 1997-2000. Average RSDs of all participants varied between 23 and 56% and were clearly dependent on the analytical method used and the concentration level of the samples. Since 1997 the target values have been determined in reference laboratories for five of the analytes. Lower RSDs (9-21%) and good recoveries were obtained for all analytes in these laboratories, indicating that good performance can be achieved even in the complex analyses performed in biological monitoring of exposure to industrial chemicals.     

Stabilization of a binary protein complex by intein-mediated cyclization

The study of protein-protein interactions can be hampered by the instability of one or more of the protein complex components. In this study, we showed that intein-mediated cyclization can be used to engineer an artificial intramolecular cyclic protein complex between two interacting proteins: the largely unstable LIM-only protein 4 (LMO4) and an unstructured domain of LIM domain binding protein 1 (ldb1). The X-ray structure of the cyclic complex is identical to noncyclized versions of the complex. Chemical and thermal denaturation assays using intrinsic tryptophan fluorescence and dynamic light scattering were used to compare the relative stabilities of the cyclized complex, the intermolecular (or free) complex, and two linear versions of the intramolecular complex (in which the interacting domains of LMO4 and ldb1 were fused, via a flexible linker, in either orientation). In terms of resistance to denaturation, the cyclic complex is the most stable variant and the intermolecular complex is the least stable; however, the two linear intramolecular variants show significant differences in stability. These differences appear to be related to the relative contact order (the average distance in sequence between residues that make contacts within a structure) of key binding residues at the interface of the two proteins. Thus, the restriction of the more stable component of a complex may enhance stability to a greater extent than restraining less stable components.     

Custom 4-Plex DiLeu Isobaric Labels Enable Relative Quantification of Urinary Proteins in Men with Lower Urinary Tract Symptoms (LUTS)

The relative quantification of proteins using liquid chromatography mass spectrometry (LC-MS) has allowed researchers to compile lists of potential disease markers. These complex quantitative workflows often include isobaric labeling of enzymatically-produced peptides to analyze their relative abundances across multiple samples in a single LC-MS run. Recent efforts by our lab have provided scientists with cost-effective alternatives to expensive commercial labels. Although the quantitative performance of these dimethyl leucine (DiLeu) labels has been reported using known ratios of complex protein and peptide standards, their potential in large-scale proteomics studies using a clinically relevant system has never been investigated. Our work rectifies this oversight by implementing 4-plex DiLeu to quantify proteins in the urine of aging human males who suffer from lower urinary tract symptoms (LUTS). Protein abundances in 25 LUTS and 15 control patients were compared, revealing that of the 836 proteins quantified, 50 were found to be differentially expressed (>20% change) and statistically significant (p-value <0.05). Gene ontology (GO) analysis of the differentiated proteins showed that many were involved in inflammatory responses and implicated in fibrosis. While confirmation of individual protein abundance changes would be required to verify protein expression, this study represents the first report using the custom isobaric label, 4-plex DiLeu, to quantify protein abundances in a clinically relevant system.     

Strategies for structural proteomics of prokaryotes: Quantifying the advantages of studying orthologous proteins and of using both NMR and X-ray crystallography approaches

Only about half of non-membrane-bound proteins encoded by either bacterial or archaeal genomes are soluble when expressed in Escherichia coli (Yee et al., Proc Natl Acad Sci USA 2002;99:1825-1830; Christendat et al., Prog Biophys Mol Biol 200;73:339-345). This property limits genome-scale functional and structural proteomics studies, which depend on having a recombinant, soluble version of each protein. An emerging strategy to increase the probability of deriving a soluble derivative of a protein is to study different sequence homologues of the same protein, including representatives from thermophilic organisms, based on the assumption that the stability of these proteins will facilitate structural analysis. To estimate the relative merits of this strategy, we compared the recombinant expression, solubility, and suitability for structural analysis by NMR and/or X-ray crystallography for 68 pairs of homologous proteins from E. coli and Thermotoga maritima. A sample suitable for structural studies was obtained for 62 of the 68 pairs of homologs under standardized growth and purification procedures. Fourteen (eight E. coli and six T. maritima proteins) samples generated NMR spectra of a quality suitable for structure determination and 30 (14 E. coli and 16 T. maritima proteins) samples formed crystals. Only three (one E. coli and two T. maritima proteins) samples both crystallized and had excellent NMR properties. The conclusions from this work are: (1) The inclusion of even a single ortholog of a target protein increases the number of samples for structural studies almost twofold; (2) there was no clear advantage to the use of thermophilic proteins to generate samples for structural studies; and (3) for the small proteins analyzed here, the use of both NMR and crystallography approaches almost doubled the number of samples for structural studies.     

A comparison of nLC-ESI-MS/MS and nLC-MALDI-MS/MS for GeLC-based protein identification and iTRAQ-based shotgun quantitative proteomics.

The use of nLC-ESI-MS/MS in shotgun proteomics experiments and GeLC-MS/MS analysis is well accepted and routinely available in most proteomics laboratories. However, the same cannot be said for nLC-MALDI MS/MS, which has yet to experience such widespread acceptance, despite the fact that the MALDI technology offers several critical advantages over ESI. As an illustration, in an analysis of moderately complex sample of E. coli proteins, the use MALDI in addition to ESI in GeLC-MS/MS resulted in a 16% average increase in protein identifications, while with more complex samples the number of additional protein identifications increased by an average of 45%. The size of the unique peptides identified by MALDI was, on average, 25% larger than the unique peptides identified by ESI, and they were found to be slightly more hydrophilic. The insensitivity of MALDI to the presence of ionization suppression agents was shown to be a significant advantage, suggesting it be used as a complement to ESI when ion suppression is a possibility. Furthermore, the higher resolution of the TOF/TOF instrument improved the sensitivity, accuracy, and precision of the data over that obtained using only ESI-based iTRAQ experiments using a linear ion trap. Nevertheless, accurate data can be generated with either instrument. These results demonstrate that coupling nanoLC with both ESI and MALDI ionization interfaces improves proteome coverage, reduces the deleterious effects of ionization suppression agents, and improves quantitation, particularly in complex samples.     

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.     

Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition

Relative quantification methods have dominated the quantitative proteomics field. There is a need, however, to conduct absolute quantification studies to accurately model and understand the complex molecular biology that results in proteome variability among biological samples. A new method of absolute quantification of proteins is described. This method is based on the discovery of an unexpected relationship between MS signal response and protein concentration: the average MS signal response for the three most intense tryptic peptides per mole of protein is constant within a coefficient of variation of less than +/-10%. Given an internal standard, this relationship is used to calculate a universal signal response factor. The universal signal response factor (counts/mol) was shown to be the same for all proteins tested in this study. A controlled set of six exogenous proteins of varying concentrations was studied in the absence and presence of human serum. The absolute quantity of the standard proteins was determined with a relative error of less than +/-15%. The average MS signal responses of the three most intense peptides from each protein were plotted against their calculated protein concentrations, and this plot resulted in a linear relationship with an R(2) value of 0.9939. The analyses were applied to determine the absolute concentration of 11 common serum proteins, and these concentrations were then compared with known values available in the literature. Additionally within an unfractionated Escherichia coli lysate, a subset of identified proteins known to exist as functional complexes was studied. The calculated absolute quantities were used to accurately determine their stoichiometry.     

Targeted protein-omic methods are bridging the gap between proteomic and hypothesis-driven protein analysis approaches

While proteomic methods have illuminated many areas of biological protein space, many fundamental questions remain with regard to systems-level relationships between mRNAs, proteins and cell behaviors. While mass spectrometric methods offer a panoramic picture of the relative expression and modification of large numbers of proteins, they are neither optimal for the analysis of predefined targets across large numbers of samples nor for assessing differences in proteins between individual cells or cell compartments. Conversely, traditional antibody-based methods are effective at sensitively analyzing small numbers of proteins across small numbers of conditions, and can be used to analyze relative differences in protein abundance and modification between cells and cell compartments. However, traditional antibody-based approaches are not optimal for analyzing large numbers of protein abundances and modifications across many samples. In this article, we will review recent advances in methodologies and philosophies behind several microarray-based, intermediate-level, ‘protein-omic’ methods, including a focus on reverse-phase lysate arrays and micro-western arrays, which have been helpful for bridging gaps between large- and small-scale protein analysis approaches and have provided insight into the roles that protein systems play in several biological processes.     

Prediction of binding hot spot residues by using structural and evolutionary parameters

In this work, we present a method for predicting hot spot residues by using a set of structural and evolutionary parameters. Unlike previous studies, we use a set of parameters which do not depend on the structure of the protein in complex, so that the predictor can also be used when the interface region is unknown. Despite the fact that no information concerning proteins in complex is used for prediction, the application of the method to a compiled dataset described in the literature achieved a performance of 60.4%, as measured by F-Measure, corresponding to a recall of 78.1% and a precision of 49.5%. This result is higher than those reported by previous studies using the same data set.     

Interlaboratory Study Characterizing a Yeast Performance Standard for Benchmarking LC-MS Platform Performance

Optimal performance of LC-MS/MS platforms is critical to generating high quality proteomics data. Although individual laboratories have developed quality control samples, there is no widely available performance standard of biological complexity (and associated reference data sets) for benchmarking of platform performance for analysis of complex biological proteomes across different laboratories in the community. Individual preparations of the yeast Saccharomyces cerevisiae proteome have been used extensively by laboratories in the proteomics community to characterize LC-MS platform performance. The yeast proteome is uniquely attractive as a performance standard because it is the most extensively characterized complex biological proteome and the only one associated with several large scale studies estimating the abundance of all detectable proteins. In this study, we describe a standard operating protocol for large scale production of the yeast performance standard and offer aliquots to the community through the National Institute of Standards and Technology where the yeast proteome is under development as a certified reference material to meet the long term needs of the community. Using a series of metrics that characterize LC-MS performance, we provide a reference data set demonstrating typical performance of commonly used ion trap instrument platforms in expert laboratories; the results provide a basis for laboratories to benchmark their own performance, to improve upon current methods, and to evaluate new technologies. Additionally, we demonstrate how the yeast reference, spiked with human proteins, can be used to benchmark the power of proteomics platforms for detection of differentially expressed proteins at different levels of concentration in a complex matrix, thereby providing a metric to evaluate and minimize preanalytical and analytical variation in comparative proteomics experiments.Access to proteomics performance standards is essential for several reasons. First, to generate the highest quality data possible, proteomics laboratories routinely benchmark and perform quality control (QC)1 monitoring of the performance of their instrumentation using standards. Second, appropriate standards greatly facilitate the development of improvements in technologies by providing a timeless standard with which to evaluate new protocols or instruments that claim to improve performance. For example, it is common practice for an individual laboratory considering purchase of a new instrument to require the vendor to run “demo” samples so that data from the new instrument can be compared head to head with existing instruments in the laboratory. Third, large scale proteomics studies designed to aggregate data across laboratories can be facilitated by the use of a performance standard to measure reproducibility across sites or to compare the performance of different LC-MS configurations or sample processing protocols used between laboratories to facilitate development of optimized standard operating procedures (SOPs).Most individual laboratories have adopted their own QC standards, which range from mixtures of known synthetic peptides to digests of bovine serum albumin or more complex mixtures of several recombinant proteins (1). However, because each laboratory performs QC monitoring in isolation, it is difficult to compare the performance of LC-MS platforms throughout the community.Several standards for proteomics are available for request or purchase (2, 3). RM8327 is a mixture of three peptides developed as a reference material in collaboration between the National Institute of Standards and Technology (NIST) and the Association of Biomolecular Resource Facilities. Mixtures of 15–48 purified human proteins are also available, such as the HUPO (Human Proteome Organisation) Gold MS Protein Standard (Invitrogen), the Universal Proteomics Standard (UPS1; Sigma), and CRM470 from the European Union Institute for Reference Materials and Measurements. Although defined mixtures of peptides or proteins can address some benchmarking and QC needs, there is an additional need for more complex reference materials to fully represent the challenges of LC-MS data acquisition in complex matrices encountered in biological samples (2, 3).Although it has not been widely distributed as a reference material, the yeast Saccharomyces cerevisiae proteome has been extensively used by the proteomics community to characterize the capabilities of a variety of LC-MS-based approaches (4–15). Yeast provides a uniquely attractive complex performance standard for several reasons. Yeast encodes a complex proteome consisting of ∼4,500 proteins expressed during normal growth conditions (7, 16–18). The concentration range of yeast proteins is sufficient to challenge the dynamic range of conventional mass spectrometers; the abundance of proteins ranges from fewer than 50 to more than 10⁶ molecules per cell (4, 15, 16). Additionally, it is the most extensively characterized complex biological proteome and the only one associated with several large scale studies estimating the abundance of all detectable proteins (5, 9, 16, 17, 19, 20) as well as LC-MS/MS data sets showing good correlation between LC-MS/MS detection efficiency and the protein abundance estimates (4, 11, 12, 15). Finally, it is inexpensive and easy to produce large quantities of yeast protein extract for distribution.In this study, we describe large scale production of a yeast S. cerevisiae performance standard, which we offer to the community through NIST. Through a series of interlaboratory studies, we created a reference data set characterizing the yeast performance standard and defining reasonable performance of ion trap-based LC-MS platforms in expert laboratories using a series of performance metrics. This publicly available data set provides a basis for additional laboratories using the yeast standard to benchmark their own performance as well as to improve upon the current status by evolving protocols, improving instrumentation, or developing new technologies. Finally, we demonstrate how the yeast performance standard, spiked with human proteins, can be used to benchmark the power of proteomics platforms for detection of differentially expressed proteins at different levels of concentration in a complex matrix.