An accurate mass tag strategy for quantitative and high-throughput proteome measurements

We describe and demonstrate a global strategy that extends the sensitivity, dynamic range, comprehensiveness, and throughput of proteomic measurements based upon the use of peptide "accurate mass tags" (AMTs) produced by global protein enzymatic digestion. The two-stage strategy exploits Fourier transform-ion cyclotron resonance (FT-ICR) mass spectrometry to validate peptide AMTs for a specific organism, tissue or cell type from "potential mass tags" identified using conventional tandem mass spectrometry (MS/MS) methods, providing greater confidence in identifications as well as the basis for subsequent measurements without the need for MS/MS, and thus with greater sensitivity and increased throughput. A single high resolution capillary liquid chromatography separation combined with high sensitivity, high resolution and accurate FT-ICR measurements has been shown capable of characterizing peptide mixtures of significantly more than 10(5) components with mass accuracies of < 1 ppm, sufficient for broad protein identification using AMTs. Other attractions of the approach include the broad and relatively unbiased proteome coverage, the capability for exploiting stable isotope labeling methods to realize high precision for relative protein abundance measurements, and the projected potential for study of mammalian proteomes when combined with additional sample fractionation. Using this strategy, in our first application we have been able to identify AMTs for >60% of the potentially expressed proteins in the organism Deinococcus radiodurans.     

Proteome analysis of Escherichia coli using high-performance liquid chromatography and Fourier transform ion cyclotron resonance mass spectrometry

The basic problem of complexity poses a significant challenge for proteomic studies. To date two-dimensional gel electrophoresis (2-DE) followed by enzymatic in-gel digestion of the peptides, and subsequent identification by mass spectrometry (MS) is the most commonly used method to analyze complex protein mixtures. However, 2-DE is a slow and labor-intensive technique, which is not able to resolve all proteins of a proteome. To overcome these limitations gel-free approaches are developed based on high performance liquid chromatography (HPLC) and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). The high resolution and excellent mass accuracy of FT-ICR MS provides a basis for simultaneous analysis of numerous compounds. In the present study, a small protein subfraction of an Escherichia coli cell lysate was prepared by size-exclusion chromatography and proteins were analyzed using C4 reversed phase (RP)-HPLC for pre-separation followed by C18 RP nanoHPLC/nanoESI FT-ICR MS for analysis of the peptide mixtures after tryptic digestion of the protein fractions. We identified 231 proteins and thus demonstrated that a combination of two RP separation steps - one on the protein and one on the peptide level - in combination with high-resolution FT-ICR MS has the potential to become a powerful method for global proteomics studies.     

A proteomic approach to characterize protein shedding

Shedding (i.e. proteolysis of ectodomains of membrane proteins) plays an important pathophysiological role. In order to study the feasibility of identifying shed proteins, we analyzed serum-free media of human mammary epithelial cells by mass spectrometry following induction of shedding by the phorbol ester, 4 beta-phorbol 12-myristate 13-acetate (PMA). Different means of sample preparation, including biotinylation of cell surface proteins, isolation of glycosylated proteins, and preparation of crude protein fractions, were carried out to develop the optimal method of sample processing. The collected proteins were digested with trypsin and analyzed by reversed-phase capillary liquid chromatography interfaced to an ion-trap mass spectrometer. The resulting peptide spectra were interpreted using the program SEQUEST. Analyzing the sample containing the crude protein mixture without chemical modification or separation resulted in the greatest number of identifications, including putatively shed proteins. Overall, 45 membrane-associated proteins were identified including 22 that contain at least one transmembrane domain and 23 that indirectly associate with the extracellular surface of the plasma membrane. Of the 22 transmembrane proteins, 18 were identified by extracellular peptides providing strong evidence they originate from regulated proteolysis or shedding processes. We combined results from the different experiments and used a peptide count method to estimate changes in protein abundance. Using this approach, we identified two proteins, syndecan-4 and hepatoma-derived growth factor, whose abundances increased in media of cells treated with PMA. We also detected proteins whose abundances decreased after PMA treatment such as 78 kDa glucose-regulated protein and lactate dehydrogenase A. Further analysis using immunoblotting validated the abundance changes for syndecan-4 and 78 kDa glucose-regulated protein as a result of PMA treatment. These results demonstrate that tandem mass spectrometry can be used to identify shed proteins and to estimate changes in protein abundance.     

Randomized sequence databases for tandem mass spectrometry peptide and protein identification

Tandem mass spectrometry (MS/MS) combined with database searching is currently the most widely used method for high-throughput peptide and protein identification. Many different algorithms, scoring criteria, and statistical models have been used to identify peptides and proteins in complex biological samples, and many studies, including our own, describe the accuracy of these identifications, using at best generic terms such as "high confidence." False positive identification rates for these criteria can vary substantially with changing organisms under study, growth conditions, sequence databases, experimental protocols, and instrumentation; therefore, study-specific methods are needed to estimate the accuracy (false positive rates) of these peptide and protein identifications. We present and evaluate methods for estimating false positive identification rates based on searches of randomized databases (reversed and reshuffled). We examine the use of separate searches of a forward then a randomized database and combined searches of a randomized database appended to a forward sequence database. Estimated error rates from randomized database searches are first compared against actual error rates from MS/MS runs of known protein standards. These methods are then applied to biological samples of the model microorganism Shewanella oneidensis strain MR-1. Based on the results obtained in this study, we recommend the use of use of combined searches of a reshuffled database appended to a forward sequence database as a means providing quantitative estimates of false positive identification rates of peptides and proteins. This will allow researchers to set criteria and thresholds to achieve a desired error rate and provide the scientific community with direct and quantifiable measures of peptide and protein identification accuracy as opposed to vague assessments such as "high confidence."     

Quantitative proteome analysis of breast cancer cell lines using 18O-labeling and an accurate mass and time tag strategy

Proteome comparison of cell lines derived from cancer and normal breast epithelium provide opportunities to identify differentially expressed proteins and pathways associated with specific phenotypes. We employed 16O/18O peptide labeling, FT-ICR MS, and an accurate mass and time (AMT) tag strategy to simultaneously compare the relative abundance of hundreds of proteins in non-cancer and cancer cell lines derived from breast tissue. A cell line reference panel allowed relative protein abundance comparisons among multiple cell lines and across multiple experiments. A peptide database generated from multidimensional LC separations and MS/MS analysis was used for subsequent AMT tag-based peptide identifications. This peptide database represented a total of 2299 proteins, including 514 that were quantified in five cell lines using the AMT tag and 16O/18O strategies. Eighty-six proteins showed at least a threefold protein abundance change between cancer and non-cancer cell lines. Hierarchical clustering of protein abundance ratios revealed that several groups of proteins were differentially expressed between the cancer cell lines.     

Depth of proteome issues: a yeast isotope-coded affinity tag reagent study

As a test case for optimizing how to perform proteomics experiments, we chose a yeast model system in which the UPF1 gene, a protein involved in nonsense-mediated mRNA decay, was knocked out by homologous recombination. The results from five complete isotope-coded affinity tag (ICAT) experiments were combined, two using matrix-assisted laser desorption/ionization (MALDI) tandem mass spectrometry (MS/MS) and three using electrospray MS/MS. We sought to assess the reproducibility of peptide identification and to develop an informatics structure that characterizes the identification process as well as possible, especially with regard to tenuous identifications. The cleavable form of the ICAT reagent system was used for quantification. Most proteins did not change significantly in expression as a consequence of the upf1 knockout. As expected, the Upf1 protein itself was down-regulated, and there were reproducible increases in expression of proteins involved in arginine biosynthesis. Initially, it seemed that about 10% of the proteins had changed in expression level, but after more thorough examination of the data it turned out that most of these apparent changes could be explained by artifacts of quantification caused by overlapping heavy/light pairs. About 700 proteins altogether were identified with high confidence and quantified. Many peptides with chemical modifications were identified, as well as peptides with noncanonical tryptic termini. Nearly all of these modified peptides corresponded to the most abundant yeast proteins, and some would otherwise have been attributed to "single hit" proteins at low confidence. To improve our confidence in the identifications, in MALDI experiments, the parent masses for the peptides were calibrated against nearby components. In addition, five novel parameters reflecting different aspects of identification were collected for each spectrum in addition to the Mascot score that was originally used. The interrelationship between these scoring parameters and confidence in protein identification is discussed.     

Probability-based evaluation of peptide and protein identifications from tandem mass spectrometry and SEQUEST analysis: the human proteome

Large-scale protein identifications from highly complex protein mixtures have recently been achieved using multidimensional liquid chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) and subsequent database searching with algorithms such as SEQUEST. Here, we describe a probability-based evaluation of false positive rates associated with peptide identifications from three different human proteome samples. Peptides from human plasma, human mammary epithelial cell (HMEC) lysate, and human hepatocyte (Huh)-7.5 cell lysate were separated by strong cation exchange (SCX) chromatography coupled offline with reversed-phase capillary LC-MS/MS analyses. The MS/MS spectra were first analyzed by SEQUEST, searching independently against both normal and sequence-reversed human protein databases, and the false positive rates of peptide identifications for the three proteome samples were then analyzed and compared. The observed false positive rates of peptide identifications for human plasma were significantly higher than those for the human cell lines when identical filtering criteria were used, suggesting that the false positive rates are significantly dependent on sample characteristics, particularly the number of proteins found within the detectable dynamic range. Two new sets of filtering criteria are proposed for human plasma and human cell lines, respectively, to provide an overall confidence of >95% for peptide identifications. The new criteria were compared, using a normalized elution time (NET) criterion (Petritis et al. Anal. Chem. 2003, 75, 1039-1048), with previously published criteria (Washburn et al. Nat. Biotechnol. 2001, 19, 242-247). The results demonstrate that the present criteria provide significantly higher levels of confidence for peptide identifications from mammalian proteomes without greatly decreasing the number of identifications.     

Optimization of human dendritic cell sample preparation for mass spectrometry-based proteomic studies

Dendritic cells (DCs) are specialized leukocytes that orchestrate the adaptive immune response. Mass spectrometry (MS)-based proteomic study of these cells presents technical challenges, especially when the DCs are human in origin due to the paucity of available biological material. Here, to maximize MS coverage of the global human DC proteome, different cell disruption methods, lysis conditions, protein precipitation, and protein pellet solubilization and denaturation methods were compared. Mechanical disruption of DC cell pellets under cryogenic conditions, coupled with the use of RIPA (radioimmunoprecipitation assay) buffer, was shown to be the method of choice based on total protein extraction and on the solubilization and identification of nuclear proteins. Precipitation by acetone was found to be more efficient than that by 10% trichloroacetic acid (TCA)/acetone, allowing in excess of 28% more protein identifications. Although being an effective strategy to eliminate the detergent residue, the acetone wash step caused a loss of protein identifications. However, this potential drawback was overcome by adding 1% sodium deoxycholate into the dissolution buffer, which enhanced both solubility of the precipitated proteins and digestion efficiency. This in turn resulted in 6 to 11% more distinct peptides and 14 to 19% more total proteins identified than using 0.5 M triethylammonium bicarbonate alone, with the greatest increase (34%) for hydrophobic proteins.     

Evaluation of the compact high-field orbitrap for top-down proteomics of human cells

Mass spectrometry based proteomics generally seeks to identify and fully characterize protein species with high accuracy and throughput. Recent improvements in protein separation have greatly expanded the capacity of top-down proteomics (TDP) to identify a large number of intact proteins. To date, TDP has been most tightly associated with Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry. Here, we couple the improved separations to a Fourier-transform instrument based not on ICR but using the Orbitrap Elite mass analyzer. Application of this platform to H1299 human lung cancer cells resulted in the unambiguous identification of 690 unique proteins and over 2000 proteoforms identified from proteins with intact masses <50 kDa. This is an early demonstration of high throughput TDP (>500 identifications) in an Orbitrap mass spectrometer and exemplifies an accessible platform for whole protein mass spectrometry.     

A database of unique protein sequence identifiers for proteome studies

In proteome studies, identification of proteins requires searching protein sequence databases. The public protein sequence databases (e.g., NCBInr, UniProt) each contain millions of entries, and private databases add thousands more. Although much of the sequence information in these databases is redundant, each database uses distinct identifiers for the identical protein sequence and often contains unique annotation information. Users of one database obtain a database-specific sequence identifier that is often difficult to reconcile with the identifiers from a different database. When multiple databases are used for searches or the databases being searched are updated frequently, interpreting the protein identifications and associated annotations can be problematic. We have developed a database of unique protein sequence identifiers called Sequence Globally Unique Identifiers (SEGUID) derived from primary protein sequences. These identifiers serve as a common link between multiple sequence databases and are resilient to annotation changes in either public or private databases throughout the lifetime of a given protein sequence. The SEGUID Database can be downloaded (http://bioinformatics.anl.gov/SEGUID/) or easily generated at any site with access to primary protein sequence databases. Since SEGUIDs are stable, predictions based on the primary sequence information (e.g., pI, Mr) can be calculated just once; we have generated approximately 500 different calculations for more than 2.5 million sequences. SEGUIDs are used to integrate MS and 2-DE data with bioinformatics information and provide the opportunity to search multiple protein sequence databases, thereby providing a higher probability of finding the most valid protein identifications.     

A comparative evaluation of software for the analysis of liquid chromatography-tandem mass spectrometry data from isotope coded affinity tag experiments

The options available for processing quantitative data from isotope coded affinity tag (ICAT) experiments have mostly been confined to software specific to the instrument of acquisition. However, recent developments with data format conversion have subsequently increased such processing opportunities. In the present study, data sets from ICAT experiments, analysed with liquid chromatography/tandem mass spectrometry (MS/MS), using an Applied Biosystems QSTAR Pulsar quadrupole-TOF mass spectrometer, were processed in triplicate using separate mass spectrometry software packages. The programs Pro ICAT, Spectrum Mill and SEQUEST with XPRESS were employed. Attention was paid towards the extent of common identification and agreement of quantitative results, with additional interest in the flexibility and productivity of these programs. The comparisons were made with data from the analysis of a specifically prepared test mixture, nine proteins at a range of relative concentration ratios from 0.1 to 10 (light to heavy labelled forms), as a known control, and data selected from an ICAT study involving the measurement of cytokine induced protein expression in human lymphoblasts, as an applied example. Dissimilarities were detected in peptide identification that reflected how the associated scoring parameters favoured information from the MS/MS data sets. Accordingly, there were differences in the numbers of peptides and protein identifications, although from these it was apparent that both confirmatory and complementary information was present. In the quantitative results from the three programs, no statistically significant differences were observed.     

Pepitome: evaluating improved spectral library search for identification complementarity and quality assessment

Spectral libraries have emerged as a viable alternative to protein sequence databases for peptide identification. These libraries contain previously detected peptide sequences and their corresponding tandem mass spectra (MS/MS). Search engines can then identify peptides by comparing experimental MS/MS scans to those in the library. Many of these algorithms employ the dot product score for measuring the quality of a spectrum-spectrum match (SSM). This scoring system does not offer a clear statistical interpretation and ignores fragment ion m/z discrepancies in the scoring. We developed a new spectral library search engine, Pepitome, which employs statistical systems for scoring SSMs. Pepitome outperformed the leading library search tool, SpectraST, when analyzing data sets acquired on three different mass spectrometry platforms. We characterized the reliability of spectral library searches by confirming shotgun proteomics identifications through RNA-Seq data. Applying spectral library and database searches on the same sample revealed their complementary nature. Pepitome identifications enabled the automation of quality analysis and quality control (QA/QC) for shotgun proteomics data acquisition pipelines.     

IPM: An integrated protein model for false discovery rate estimation and identification in high-throughput proteomics

In high-throughput mass spectrometry proteomics, peptides and proteins are not simply identified as present or not present in a sample, rather the identifications are associated with differing levels of confidence. The false discovery rate (FDR) has emerged as an accepted means for measuring the confidence associated with identifications. We have developed the Systematic Protein Investigative Research Environment (SPIRE) for the purpose of integrating the best available proteomics methods. Two successful approaches to estimating the FDR for MS protein identifications are the MAYU and our current SPIRE methods. We present here a method to combine these two approaches to estimating the FDR for MS protein identifications into an integrated protein model (IPM). We illustrate the high quality performance of this IPM approach through testing on two large publicly available proteomics datasets. MAYU and SPIRE show remarkable consistency in identifying proteins in these datasets. Still, IPM results in a more robust FDR estimation approach and additional identifications, particularly among low abundance proteins. IPM is now implemented as a part of the SPIRE system.     

A hierarchical statistical model to assess the confidence of peptides and proteins inferred from tandem mass spectrometry

MOTIVATION: Statistical evaluation of the confidence of peptide and protein identifications made by tandem mass spectrometry is a critical component for appropriately interpreting the experimental data and conducting downstream analysis. Although many approaches have been developed to assign confidence measure from different perspectives, a unified statistical framework that integrates the uncertainty of peptides and proteins is still missing. RESULTS: We developed a hierarchical statistical model (HSM) that jointly models the uncertainty of the identified peptides and proteins and can be applied to any scoring system. With data sets of a standard mixture and the yeast proteome, we demonstrate that the HSM offers a reliable or at least conservative false discovery rate (FDR) estimate for peptide and protein identifications. The probability measure of HSM also offers a powerful discriminating score for peptide identification. AVAILABILITY: The algorithm is available upon request from the authors.     

Comparative proteome analyses of human plasma following in vivo lipopolysaccharide administration using multidimensional separations coupled with tandem mass spectrometry

There is significant interest in characterization of the human plasma proteome due to its potential for providing biomarkers applicable to clinical diagnosis and treatment and for gaining a better understanding of human diseases. We describe here a strategy for comparative proteome analyses of human plasma, which is applicable to biomarker identifications for various disease states. Multidimensional liquid chromatography-mass spectrometry (LC-MS/MS) has been applied to make comparative proteome analyses of plasma samples from an individual prior to and 9 h after lipopolysaccharide (LPS) administration. Peptide peak areas and the number of peptide identifications for each protein were used to evaluate the reproducibility of LC-MS/MS and to compare relative changes in protein concentration between the samples following LPS treatment. A total of 804 distinct plasma proteins (not including immunoglobulins) were confidently identified with 32 proteins observed to be significantly increased in concentration following LPS administration, including several known inflammatory response or acute-phase mediators such as C-reactive protein, serum amyloid A and A2, LPS-binding protein, LPS-responsive and beige-like anchor protein, hepatocyte growth factor activator, and von Willebrand factor, and thus, constituting potential biomarkers for inflammatory response.     

Quantitative proteomic profiling of pancreatic cancer juice

Pancreatic juice is an exceptionally rich source of cancer-specific proteins shed from cancerous ductal cells into the pancreatic juice. Quantitative proteomic analysis of the proteins specific to pancreatic cancer juice has not previously been reported. We used isotope-code affinity tag (ICAT) technology and MS/MS to perform quantitative protein profiling of pancreatic juice from pancreatic cancer patients and normal controls. ICAT technology coupled with MS/MS allows the systematic study of the proteome and measures the protein abundance in pancreatic juice with the potential for development of biomarkers. A total of 105 proteins were identified and quantified in the pancreatic juice from a pancreatic cancer patient, of which 30 proteins showed abundance changes of at least twofold in pancreatic cancer juice compared to normal controls. Many of these proteins have been externally validated. This is the first comprehensive study of the pancreatic juice proteome by quantitative global protein profiling, and the study reveals numerous proteins that are shown for the first time to be associated with pancreatic cancer, providing candidates for diagnostic biomarkers. One of the identified proteins, insulin-like growth factor binding protein-2 was further validated by Western blotting to be elevated in pancreatic cancer juice and overexpressed in pancreatic cancer tissue.     

Global proteome discovery using an online three-dimensional LC-MS/MS

We have developed a proteomics technology featuring on-line three-dimensional liquid chromatography coupled to tandem mass spectrometry (3D LC-MS/MS). Using 3D LC-MS/MS, the yeast-soluble, urea-solubilized peripheral membrane and SDS-solubilized membrane protein samples collectively yielded 3019 unique yeast protein identifications with an average of 5.5 peptides per protein from the 6300-gene Saccharomyces Genome Database searched with SEQUEST. A single run of the urea-solubilized sample yielded 2255 unique protein identifications, suggesting high peak capacity and resolving power of 3D LC-MS/MS. After precipitation of SDS from the digested membrane protein sample, 3D LC-MS/MS allowed the analysis of membrane proteins. Among 1221 proteins containing two or more predicted transmembrane domains, 495 such proteins were identified. The improved yeast proteome data allowed the mapping of many metabolic pathways and functional categories. The 3D LC-MS/MS technology provides a suitable tool for global proteome discovery.     

Optimization and use of peptide mass measurement accuracy in shotgun proteomics

Mass spectrometers that provide high mass accuracy such as FT-ICR instruments are increasingly used in proteomic studies. Although the importance of accurately determined molecular masses for the identification of biomolecules is generally accepted, its role in the analysis of shotgun proteomic data has not been thoroughly studied. To gain insight into this role, we used a hybrid linear quadrupole ion trap/FT-ICR (LTQ FT) mass spectrometer for LC-MS/MS analysis of a highly complex peptide mixture derived from a fraction of the yeast proteome. We applied three data-dependent MS/MS acquisition methods. The FT-ICR part of the hybrid mass spectrometer was either not exploited, used only for survey MS scans, or also used for acquiring selected ion monitoring scans to optimize mass accuracy. MS/MS data were assigned with the SEQUEST algorithm, and peptide identifications were validated by estimating the number of incorrect assignments using the composite target/decoy database search strategy. We developed a simple mass calibration strategy exploiting polydimethylcyclosiloxane background ions as calibrant ions. This strategy allowed us to substantially improve mass accuracy without reducing the number of MS/MS spectra acquired in an LC-MS/MS run. The benefits of high mass accuracy were greatest for assigning MS/MS spectra with low signal-to-noise ratios and for assigning phosphopeptides. Confident peptide identification rates from these data sets could be doubled by the use of mass accuracy information. It was also shown that improving mass accuracy at a cost to the MS/MS acquisition rate substantially lowered the sensitivity of LC-MS/MS analyses. The use of FT-ICR selected ion monitoring scans to maximize mass accuracy reduced the number of protein identifications by 40%.