Targeted comparative proteomics by liquid chromatography-tandem Fourier ion cyclotron resonance mass spectrometry

Identification of soybean proteins from a single cell type: the root hair

Root hairs (RH) are a terminally differentiated single cell type, mainly involved in water and nutrient uptake from the soil. The soybean RH cell represents an excellent model for the study of single cell systems biology. In this study, we identified 5702 proteins, with at least two peptides, from soybean RH using an accurate mass and time tag approach, establishing a comprehensive proteome reference map of this single cell type. We also showed that trypsin is the most appropriate enzyme for soybean proteomic studies by performing an in silico digestion of the soybean proteome using different proteases. Although the majority of proteins identified in this study are involved in basal metabolism, the function of others are more related to RH formation/function and include proteins involved in nutrient uptake (transporters) or vesicular trafficking (cytoskeleton and ras-associated binding proteins). Interestingly, some of these proteins appear to be specifically detected in RH and constitute promising candidates for further studies to elucidate unique features of this single-cell model.

Aging enhances the production of reactive oxygen species and bactericidal activity in peritoneal macrophages by upregulating classical activation pathways

Maintenance of macrophages in their basal state and their rapid activation in response to pathogen detection are central to the innate immune system, acting to limit nonspecific oxidative damage and promote pathogen killing following infection. To identify possible age-related alterations in macrophage function, we have assayed the function of peritoneal macrophages from young (3-4 months) and aged (14-15 months) Balb/c mice. In agreement with prior suggestions, we observe age-dependent increases in the extent of recruitment of macrophages into the peritoneum, as well as ex vivo functional changes involving enhanced nitric oxide production under resting conditions that contribute to a reduction in the time needed for full activation of senescent macrophages following exposure to lipopolysaccharides (LPS). Further, we observe enhanced bactericidal activity following Salmonella uptake by macrophages isolated from aged Balb/c mice in comparison with those isolated from young animals. Pathways responsible for observed phenotypic changes were interrogated using tandem mass spectrometry, which identified age-dependent increases in levels of proteins linked to immune cell pathways under basal conditions and following LPS activation. Immune pathways upregulated in macrophages isolated from aged mice include proteins critical to the formation of the immunoproteasome. Detection of these latter proteins is dramatically enhanced following LPS exposure for macrophages isolated from aged animals; in comparison, the identification of immunoproteasome subunits is insensitive to LPS exposure for macrophages isolated from young animals. Consistent with observed global changes in the proteome, quantitative proteomic measurements indicate that there are age-dependent abundance changes involving specific proteins linked to immune cell function under basal conditions. LPS exposure selectively increases the levels of many proteins involved in immune cell function in aged Balb/c mice. Collectively, these results indicate that macrophages isolated from old mice are in a preactivated state that enhances their sensitivities to LPS exposure. The hyper-responsive activation of macrophages in aged animals may act to minimize infection by general bacterial threats that arise due to age-dependent declines in adaptive immunity. However, this hypersensitivity and the associated increase in the level of formation of reactive oxygen species are likely to contribute to observed age-dependent increases in the level of oxidative damage that underlie many diseases of the elderly.

Effectiveness of CID, HCD, and ETD with FT MS/MS for degradomic-peptidomic analysis: comparison of peptide identification methods

We report on the effectiveness of CID, HCD, and ETD for LC-FT MS/MS analysis of peptides using a tandem linear ion trap-Orbitrap mass spectrometer. A range of software tools and analysis parameters were employed to explore the use of CID, HCD, and ETD to identify peptides (isolated from human blood plasma) without the use of specific "enzyme rules". In the evaluation of an FDR-controlled SEQUEST scoring method, the use of accurate masses for fragments increased the number of identified peptides (by ~50%) compared to the use of conventional low accuracy fragment mass information, and CID provided the largest contribution to the identified peptide data sets compared to HCD and ETD. The FDR-controlled Mascot scoring method provided significantly fewer peptide identifications than SEQUEST (by 1.3-2.3 fold) and CID, HCD, and ETD provided similar contributions to identified peptides. Evaluation of de novo sequencing and the UStags method for more intense fragment ions revealed that HCD afforded more contiguous residues (e.g., ≥ 7 amino acids) than either CID or ETD. Both the FDR-controlled SEQUEST and Mascot scoring methods provided peptide data sets that were affected by the decoy database used and mass tolerances applied (e.g., identical peptides between data sets could be limited to ~70%), while the UStags method provided the most consistent peptide data sets (>90% overlap). The m/z ranges in which CID, HCD, and ETD contributed the largest number of peptide identifications were substantially overlapping. This work suggests that the three peptide ion fragmentation methods are complementary and that maximizing the number of peptide identifications benefits significantly from a careful match with the informatics tools and methods applied. These results also suggest that the decoy strategy may inaccurately estimate identification FDRs.

Spectral archives: extending spectral libraries to analyze both identified and unidentified spectra

Tandem mass spectrometry (MS/MS) experiments yield multiple, nearly identical spectra of the same peptide in various laboratories, but proteomics researchers typically do not leverage the unidentified spectra produced in other labs to decode spectra they generate. We propose a spectral archives approach that clusters MS/MS datasets, representing similar spectra by a single consensus spectrum. Spectral archives extend spectral libraries by analyzing both identified and unidentified spectra in the same way and maintaining information about peptide spectra that are common across species and conditions. Thus archives offer both traditional library spectrum similarity-based search capabilities along with new ways to analyze the data. By developing a clustering tool, MS-Cluster, we generated a spectral archive from ∼1.18 billion spectra that greatly exceeds the size of existing spectral repositories. We advocate that publicly available data should be organized into spectral archives rather than be analyzed as disparate datasets, as is mostly the case today.

Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models

After hundreds of generations of adaptive evolution at exponential growth, Escherichia coli grows as predicted using flux balance analysis (FBA) on genome-scale metabolic models (GEMs). However, it is not known whether the predicted pathway usage in FBA solutions is consistent with gene and protein expression in the wild-type and evolved strains. Here, we report that >98% of active reactions from FBA optimal growth solutions are supported by transcriptomic and proteomic data. Moreover, when E. coli adapts to growth rate selective pressure, the evolved strains upregulate genes within the optimal growth predictions, and downregulate genes outside of the optimal growth solutions. In addition, bottlenecks from dosage limitations of computationally predicted essential genes are overcome in the evolved strains. We also identify regulatory processes that may contribute to the development of the optimal growth phenotype in the evolved strains, such as the downregulation of known regulons and stringent response suppression. Thus, differential gene and protein expression from wild-type and adaptively evolved strains supports observed growth phenotype changes, and is consistent with GEM-computed optimal growth states.

Analyzing protease specificity and detecting in vivo proteolytic events using tandem mass spectrometry

Although trypsin remains the most commonly used protease in MS, other proteases may be employed for increasing peptide coverage or generating overlapping peptides. Knowledge of the accurate specificity rules of these proteases is helpful for database search tools to detect peptides, and becomes crucial when label-free MS is used to discover in vivo proteolytic cleavages. Since in vivo cleavages are inferred by subtracting digestion-induced cleavages from all observed cleavages, it is important to ensure that the specificity rule used to identify digestion-induced cleavages are broad enough to capture even minor cleavages produced in digestion, to avoid erroneously identifying them as in vivo cleavages. In this study, we describe MS-Proteolysis, a software tool for identifying putative sites of in vivo proteolytic cleavage using label-free MS. The tool is used in conjunction with digestion by trypsin and three other proteases, whose specificity rules are revised and extended before inferring proteolytic cleavages. Finally, we show that comparative analysis of multiple proteases can be used to detect putative in vivo proteolytic sites on a proteome-wide scale.

An empirical strategy for characterizing bacterial proteomes across species in the absence of genomic sequences

Global protein identification through current proteomics methods typically depends on the availability of sequenced genomes. In spite of increasingly high throughput sequencing technologies, this information is not available for every microorganism and rarely available for entire microbial communities. Nevertheless, the protein-level homology that exists between related bacteria makes it possible to extract biological information from the proteome of an organism or microbial community by using the genomic sequences of a near neighbor organism. Here, we demonstrate a trans-organism search strategy for determining the extent to which near-neighbor genome sequences can be applied to identify proteins in unsequenced environmental isolates. In proof of concept testing, we found that within a CLUSTAL W distance of 0.089, near-neighbor genomes successfully identified a high percentage of proteins within an organism. Application of this strategy to characterize environmental bacterial isolates lacking sequenced genomes, but having 16S rDNA sequence similarity to Shewanella resulted in the identification of 300-500 proteins in each strain. The majority of identified pathways mapped to core processes, as well as to processes unique to the Shewanellae, in particular to the presence of c-type cytochromes. Examples of core functional categories include energy metabolism, protein and nucleotide synthesis and cofactor biosynthesis, allowing classification of bacteria by observation of conserved processes. Additionally, within these core functionalities, we observed proteins involved in the alternative lactate utilization pathway, recently described in Shewanella.

Blood peptidome-degradome profile of breast cancer

BACKGROUND

Cancer invasion and metastasis are closely associated with activities within the degradome; however, little is known about whether these activities can be detected in the blood of cancer patients.

METHODOLOGY AND PRINCIPAL FINDINGS

The peptidome-degradome profiles of pooled blood plasma sampled from 15 breast cancer patients (BCP) and age, race, and menopausal status matched control healthy persons (HP) were globally characterized using advanced comprehensive separations combined with tandem Fourier transform mass spectrometry and new data analysis approaches that facilitated top-down peptidomic analysis. The BCP pool displayed 71 degradome protein substrates that encompassed 839 distinct peptidome peptides. In contrast, the HP 50 degradome substrates found encompassed 425 peptides. We find that the ratios of the peptidome peptide relative abundances can vary as much as >4000 fold between BCP and HP. The experimental results also show differential degradation of substrates in the BCP sample in their functional domains, including the proteolytic and inhibitory sites of the plasmin-antiplasmin and thrombin-antithrombin systems, the main chains of the extracellular matrix protection proteins, the excessive degradation of innate immune system key convertases and membrane attack complex components, as well as several other cancer suppressor proteins.

CONCLUSIONS

Degradomics-peptidomics profiling of blood plasma is highly sensitive to changes not evidenced by conventional bottom-up proteomics and potentially provides unique signatures of possible diagnostic utility.

Proteomic and physiological responses of Kineococcus radiotolerans to copper

Copper is a highly reactive, toxic metal; consequently, transport of this metal within the cell is tightly regulated. Intriguingly, the actinobacterium Kineococcus radiotolerans has been shown to not only accumulate soluble copper to high levels within the cytoplasm, but the phenotype also correlated with enhanced cell growth during chronic exposure to ionizing radiation. This study offers a first glimpse into the physiological and proteomic responses of K. radiotolerans to copper at increasing concentration and distinct growth phases. Aerobic growth rates and biomass yields were similar over a range of Cu(II) concentrations (0–1.5 mM) in complex medium. Copper uptake coincided with active cell growth and intracellular accumulation was positively correlated with Cu(II) concentration in the growth medium (R² = 0.7). Approximately 40% of protein coding ORFs on the K. radiotolerans genome were differentially expressed in response to the copper treatments imposed. Copper accumulation coincided with increased abundance of proteins involved in oxidative stress and defense, DNA stabilization and repair, and protein turnover. Interestingly, the specific activity of superoxide dismutase was repressed by low to moderate concentrations of copper during exponential growth, and activity was unresponsive to perturbation with paraquot. The biochemical response pathways invoked by sub-lethal copper concentrations are exceptionally complex; though integral cellular functions are preserved, in part, through the coordination of defense enzymes, chaperones, antioxidants and protective osmolytes that likely help maintain cellular redox. This study extends our understanding of the ecology and physiology of this unique actinobacterium that could potentially inspire new biotechnologies in metal recovery and sequestration, and environmental restoration.

Predicting intensity ranks of peptide fragment ions

Accurate modeling of peptide fragmentation is necessary for the development of robust scoring functions for peptide-spectrum matches, which are the cornerstone of MS/MS-based identification algorithms. Unfortunately, peptide fragmentation is a complex process that can involve several competing chemical pathways, which makes it difficult to develop generative probabilistic models that describe it accurately. However, the vast amounts of MS/MS data being generated now make it possible to use data-driven machine learning methods to develop discriminative ranking-based models that predict the intensity ranks of a peptide's fragment ions. We use simple sequence-based features that get combined by a boosting algorithm into models that make peak rank predictions with high accuracy. In an accompanying manuscript, we demonstrate how these prediction models are used to significantly improve the performance of peptide identification algorithms. The models can also be useful in the design of optimal multiple reaction monitoring (MRM) transitions, in cases where there is insufficient experimental data to guide the peak selection process. The prediction algorithm can also be run independently through PepNovo+, which is available for download from http://bix.ucsd.edu/Software/PepNovo.html.

A ranking-based scoring function for peptide-spectrum matches

The analysis of the large volume of tandem mass spectrometry (MS/MS) proteomics data that is generated these days relies on automated algorithms that identify peptides from their mass spectra. An essential component of these algorithms is the scoring function used to evaluate the quality of peptide-spectrum matches (PSMs). In this paper, we present new approach to scoring of PSMs. We argue that since this problem is at its core a ranking task (especially in the case of de novo sequencing), it can be solved effectively using machine learning ranking algorithms. We developed a new discriminative boosting-based approach to scoring. Our scoring models draw upon a large set of diverse feature functions that measure different qualities of PSMs. Our method improves the performance of our de novo sequencing algorithm beyond the current state-of-the-art, and also greatly enhances the performance of database search programs. Furthermore, by increasing the efficiency of tag filtration and improving the sensitivity of PSM scoring, we make it practical to perform large-scale MS/MS analysis, such as proteogenomic search of a six-frame translation of the human genome (in which we achieve a reduction of the running time by a factor of 15 and a 60% increase in the number of identified peptides, compared to the InsPecT database search tool). Our scoring function is incorporated into PepNovo+ which is available for download or can be run online at http://bix.ucsd.edu.

Environmental proteomics: applications of proteome profiling in environmental microbiology and biotechnology

In this review, we present the use of proteomics to advance knowledge in the field of environmental biotechnology, including studies of bacterial physiology, metabolism and ecology. Bacteria are widely applied in environmental biotechnology for their ability to catalyze dehalogenation, methanogenesis, denitrification and sulfate reduction, among others. Their tolerance to radiation and toxic compounds is also of importance. Proteomics has an important role in helping uncover the pathways behind these cellular processes. Environmental samples are often highly complex, which makes proteome studies in this field especially challenging. Some of these challenges are the lack of genome sequences for the vast majority of environmental bacteria, difficulties in isolating bacteria and proteins from certain environments, and the presence of complex microbial communities. Despite these challenges, proteomics offers a unique dynamic view into cellular function. We present examples of environmental proteomics of model organisms, and then discuss metaproteomics (microbial community proteomics), which has the potential to provide insights into the function of a community without isolating organisms. Finally, the environmental proteomics literature is summarized as it pertains to the specific application areas of wastewater treatment, metabolic engineering, microbial ecology and environmental stress responses.

Proteome of Geobacter sulfurreducens grown with Fe(III) oxide or Fe(III) citrate as the electron acceptor

The mechanisms for Fe(III) oxide reduction in Geobacter species are of interest because Fe(III) oxides are the most abundant form of Fe(III) in many soils and sediments and Geobacter species are prevalent Fe(III)-reducing microorganisms in many of these environments. Protein abundance in G. sulfurreducens grown on poorly crystalline Fe(III) oxide or on soluble Fe(III) citrate was compared with a global accurate mass and time tag proteomic approach in order to identify proteins that might be specifically associated with Fe(III) oxide reduction. A total of 2991 proteins were detected in G. sulfurreducens grown with acetate as the electron donor and either Fe(III) oxide or soluble Fe(III) citrate as the electron acceptor, resulting in 86% recovery of the genes predicted to encode proteins. Of the total expressed proteins 76% were less abundant in Fe(III) oxide cultures than in Fe(III) citrate cultures, which is consistent with the overall slower rate of metabolism during growth with an insoluble electron acceptor. A total of 269 proteins were more abundant in Fe(III) oxide-grown cells than in cells grown on Fe(III) citrate. Most of these proteins were in the energy metabolism category: primarily electron transport proteins, including 13 c-type cytochromes and PilA, the structural protein for electrically conductive pili. Several of the cytochromes that were more abundant in Fe(III) oxide-grown cells were previously shown with genetic approaches to be essential for optimal Fe(III) oxide reduction. Other proteins that were more abundant during growth on Fe(III) oxide included transport and binding proteins, proteins involved in regulation and signal transduction, cell envelope proteins, and enzymes for amino acid and protein biosynthesis, among others. There were also a substantial number of proteins of unknown function that were more abundant during growth on Fe(III) oxide. These results indicate that electron transport to Fe(III) oxide requires additional and/or different proteins than electron transfer to soluble, chelated Fe(III) and suggest proteins whose functions should be further investigated in order to better understand the mechanisms of electron transfer to Fe(III) oxide in G. sulfurreducens.

Comparative LC-MS: a landscape of peaks and valleys

Quantitative proteomics approaches using stable isotopes are well-known and used in many labs nowadays. More recently, high resolution quantitative approaches are reported that rely on LC-MS quantitation of peptide concentrations by comparing peak intensities between multiple runs obtained by continuous detection in MS mode. Characteristic of these comparative LC-MS procedures is that they do not rely on the use of stable isotopes; therefore the procedure is often referred to as label-free LC-MS. In order to compare at comprehensive scale peak intensity data in multiple LC-MS datasets, dedicated software is required for detection, matching and alignment of peaks. The high accuracy in quantitative determination of peptide abundance provides an impressive level of detail. This approach also requires an experimental set-up where quantitative aspects of protein extraction and reproducible separation conditions need to be well controlled. In this paper we will provide insight in the critical parameters that affect the quality of the results and list an overview of the most recent software packages that are available for this procedure.

Ecology and biotechnology of the genus Shewanella

The shewanellae are aquatic microorganisms with worldwide distribution. Their hallmark features include unparalleled respiratory diversity and the capacity to thrive at low temperatures. As a genus the shewanellae are physiologically diverse, and this review provides an overview of the varied roles they serve in the environment and describes what is known about how they might survive in such extreme and harsh environments. In light of their fascinating physiology, these organisms have several biotechnological uses, from bioremediation of chlorinated compounds, radionuclides, and other environmental pollutants to energy-generating biocatalysis. The ecology and biotechnology of these organisms are intertwined, with genomics playing a key role in our understanding of their physiology.

Clustering millions of tandem mass spectra

Tandem mass spectrometry (MS/MS) experiments often generate redundant data sets containing multiple spectra of the same peptides. Clustering of MS/MS spectra takes advantage of this redundancy by identifying multiple spectra of the same peptide and replacing them with a single representative spectrum. Analyzing only representative spectra results in significant speed-up of MS/MS database searches. We present an efficient clustering approach for analyzing large MS/MS data sets (over 10 million spectra) with a capability to reduce the number of spectra submitted to further analysis by an order of magnitude. The MS/MS database search of clustered spectra results in fewer spurious hits to the database and increases number of peptide identifications as compared to regular nonclustered searches. Our open source software MS-Clustering is available for download at http://peptide.ucsd.edu or can be run online at http://proteomics.bioprojects.org/MassSpec.

Phosphopeptide elution times in reversed-phase liquid chromatography

Elution time shifts between 33 different peptides and their corresponding phosphopeptides ranging from 4 amino acid residues to 35 amino acids in length were systematically investigated using high-resolution reversed-phase liquid chromatography (RPLC)-tandem mass spectrometry (MS/MS) analysis with trifluoroacetic acid as the ion pairing agent. Observed peptide elution time shifts for a single phosphorylation ranged from -5.28 min (for pYVPML) to +0.59 min (for HRDpSGLLDSLGR). Peptides containing a phosphotyrosine residue displayed a significant decrease in elution time following phosphorylation compared to their similar-sized peptides with phosphoserine or phosphothreonine residues. While peptide phosphorylation generally led to a decrease in the observed elution time, five peptides displayed increased elution times as a result of phosphorylation. For large peptides (> or =18 amino acids), the elution time shifts due to single phosphorylation were limited (ranging between -0.48 and +0.03 min), while the elution time shifts for small peptides (<18 amino acids) were characterized by a larger deviation (ranging between -5.28 and +0.59 min). The predictive capability for the observed RPLC elution time change due to phosphorylation has been suggested, which will aid in assigning confident phosphopeptide identifications and their subsequent confirmation.

Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer

Despite their potential to impact diagnosis and treatment of cancer, few protein biomarkers are in clinical use. Biomarker discovery is plagued with difficulties ranging from technological (inability to globally interrogate proteomes) to biological (genetic and environmental differences among patients and their tumors). We urgently need paradigms for biomarker discovery. To minimize biological variation and facilitate testing of proteomic approaches, we employed a mouse model of breast cancer. Specifically, we performed LC-MS/MS of tumor and normal mammary tissue from a conditional HER2/Neu-driven mouse model of breast cancer, identifying 6758 peptides representing >700 proteins. We developed a novel statistical approach (SASPECT) for prioritizing proteins differentially represented in LC-MS/MS datasets and identified proteins over- or under-represented in tumors. Using a combination of antibody-based approaches and multiple reaction monitoring-mass spectrometry (MRM-MS), we confirmed the overproduction of multiple proteins at the tissue level, identified fibulin-2 as a plasma biomarker, and extensively characterized osteopontin as a plasma biomarker capable of early disease detection in the mouse. Our results show that a staged pipeline employing shotgun-based comparative proteomics for biomarker discovery and multiple reaction monitoring for confirmation of biomarker candidates is capable of finding novel tissue and plasma biomarkers in a mouse model of breast cancer. Furthermore, the approach can be extended to find biomarkers relevant to human disease.

Targeted tandem mass spectrometry for high-throughput comparative proteomics employing NanoLC-FTICR MS with external ion dissociation

Targeted tandem mass spectrometry (MS/MS) is an attractive proteomic approach that allows selective identification of peptides exhibiting abundance differences, e.g., between culture conditions and/or diseased states. Herein, we report on a targeted LC-MS/MS capability realized with a hybrid quadrupole-7 tesla Fourier transform ion cyclotron resonance (FTICR) mass spectrometer that provides data-dependent ion selection, accumulation, and dissociation external to the ICR trap, and a control software that directs intelligent MS/MS target selection based on LC elution time and m/z ratio. We show that the continuous on-the-fly alignment of the LC elution time during the targeted LC-MS/MS experiment, combined with the high mass resolution of FTICR MS, is crucial for accurate selection of targets, whereas high mass measurement accuracy MS/MS data facilitate unambiguous peptide identifications. Identification of a subset of differentially abundant proteins from Shewanella oneidensis grown under suboxic versus aerobic conditions demonstrates the feasibility of such approach.

Proteome analysis of Desulfovibrio desulfuricans G20 mutants using the accurate mass and time (AMT) tag approach

Abundance values obtained from direct LC-MS analyses were used to compare the proteomes of six transposon-insertion mutants of Desulfovibrio desulfuricans G20, the lab strain (G20lab) and a sediment-adapted strain (G20sediment). Three mutations were in signal transduction histidine kinases, and three mutations were in other regulatory proteins. The high-throughput accurate mass and time (AMT) tag proteomic approach was utilized to analyze the proteomes. A total of 1318 proteins was identified with high confidence, approximately 35% of all predicted proteins in the D. desulfuricans G20 genome. Proteins from all functional categories were identified. Significant differences in the abundance of 30 proteins were detected between the G20lab strain and the G20sediment strain. Abundances of proteins for energy metabolism, ribosomal synthesis, membrane biosynthesis, transport, and flagellar synthesis were affected in the mutants. Specific examples of proteins down-regulated in mutants include a putative tungstate transport system substrate-binding protein and several proteins related to energy production, for example, 2-oxoacid:acceptor oxidoreductase, cytochrome c-553, and formate acetyltransferase. In addition, several signal transduction mechanism proteins were regulated in one mutant, and the abundances of ferritin and hybrid cluster protein were reduced in another mutant. However, the similar abundance of universal stress proteins, heat shock proteins, and chemotaxis proteins in the mutants revealed that regulation of chemotactic behavior and stress regulation might not be observed under our growth conditions. This study provides the first proteomic overview of several sediment fitness mutants of G20, and evidence for the difference between lab strains and sediment-adapted strains at the protein level.

Proteomic Profiling of Intact Proteins Using WAX-RPLC 2-D Separations and FTICR Mass Spectrometry

We investigated the combination of weak anion exchange (WAX) fractionation and on-line reversed-phase liquid chromatography (RPLC) separation using a 12 T FTICR mass spectrometer for the detection of intact proteins from a Shewanella oneidensis MR-1 cell lysate. This work aimed at optimizing intact protein detection for profiling proteins at a level that incorporates their modification state. A total of 715 intact proteins were detected, and the combined results from the WAX fractions and the unfractionated cell lysate were aligned using LC-MS features to facilitate protein abundance measurements. Protein identifications and post-translational modifications were assigned for approximately 10% of the detected proteins by comparing intact protein mass measurements to proteins identified in peptide MS/MS analysis of an aliquot of the same fraction. Intact proteins were also detected for S. oneidensis lysates obtained from cells grown on 13C-, 15N-depleted media under aerobic and sub-oxic conditions. The strategy can be readily applied for measuring differential protein abundances and provides a platform for high-throughput selection of biologically relevant targets for further characterization.

Spatial mapping of protein abundances in the mouse brain by voxelation integrated with high-throughput liquid chromatography-mass spectrometry

Temporally and spatially resolved mapping of protein abundance patterns within the mammalian brain is of significant interest for understanding brain function and molecular etiologies of neurodegenerative diseases; however, such imaging efforts have been greatly challenged by complexity of the proteome, throughput and sensitivity of applied analytical methodologies, and accurate quantitation of protein abundances across the brain. Here, we describe a methodology for comprehensive spatial proteome mapping that addresses these challenges by employing voxelation integrated with automated microscale sample processing, high-throughput liquid chromatography (LC) system coupled with high-resolution Fourier transform ion cyclotron resonance (FTICR) mass spectrometer, and a "universal" stable isotope labeled reference sample approach for robust quantitation. We applied this methodology as a proof-of-concept trial for the analysis of protein distribution within a single coronal slice of a C57BL/6J mouse brain. For relative quantitation of the protein abundances across the slice, an 18O-isotopically labeled reference sample, derived from a whole control coronal slice from another mouse, was spiked into each voxel sample, and stable isotopic intensity ratios were used to obtain measures of relative protein abundances. In total, we generated maps of protein abundance patterns for 1028 proteins. The significant agreement of the protein distributions with previously reported data supports the validity of this methodology, which opens new opportunities for studying the spatial brain proteome and its dynamics during the course of disease progression and other important biological and associated health aspects in a discovery-driven fashion.

Biomarker candidate identification in Yersinia pestis using organism-wide semiquantitative proteomics

The accurate mass and time tag mass spectrometry method and clustering analysis were used to compare the abundance change of 992 Yersinia pestis proteins under four contrasting growth conditions (26 and 37 degrees C, with or without Ca2+) that mimicked growth states in either a flea vector or mammalian host. Eighty-nine proteins were observed to have similar abundance change profiles to 29 known virulence associated proteins, providing identification of additional biomarker candidates. Eighty-seven hypothetical proteins, which clustered into 5 distinct clusters of like-protein abundance change, were identified as unique biomarkers related specifically to growth condition.

Parallel processing of large datasets from NanoLC-FTICR-MS measurements

A new approach for automatic parallel processing of large mass spectral datasets in a distributed computing environment is demonstrated to significantly decrease the total processing time. The implementation of this novel approach is described and evaluated for large nanoLC-FTICR-MS datasets. The speed benefits are determined by the network speed and file transfer protocols only and allow almost real-time analysis of complex data (e.g., a 3-gigabyte raw dataset is fully processed within 5 min). Key advantages of this approach are not limited to the improved analysis speed, but also include the improved flexibility, reproducibility, and the possibility to share and reuse the pre- and postprocessing strategies. The storage of all raw data combined with the massively parallel processing approach described here allows the scientist to reprocess data with a different set of parameters (e.g., apodization, calibration, noise reduction), as is recommended by the proteomics community. This approach of parallel processing was developed in the Virtual Laboratory for e-Science (VL-e), a science portal that aims at allowing access to users outside the computer research community. As such, this strategy can be applied to all types of serially acquired large mass spectral datasets such as LC-MS, LC-MS/MS, and high-resolution imaging MS results.

Protein composition of the vaccinia virus mature virion

The protein content of vaccinia virus mature virions, purified by rate zonal and isopycnic centrifugations and solubilized by SDS or a solution of urea and thiourea, was determined by the accurate mass and time tag technology which uses both tandem mass spectrometry and Fourier transform-ion cyclotron resonance mass spectrometry to detect tryptic peptides separated by high-resolution liquid chromatography. Eighty vaccinia virus-encoded proteins representing 37% of the 218 genes annotated in the complete genome sequence were detected in at least three analyses. Ten proteins accounted for approximately 80% of the virion mass. Thirteen identified proteins were not previously reported as components of virions. On the other hand, 8 previously described virion proteins were not detected here, presumably due to technical reasons including small size and hydrophobicity. In addition to vaccinia virus-encoded proteins, 24 host proteins omitting isoforms were detected. The most abundant of these were cytoskeletal proteins, heat shock proteins and proteins involved in translation.

Proteome Analysis of Myxococcus xanthus by Off-Line Two-Dimensional Chromatographic Separation Using Monolithic Poly-(styrene-divinylbenzene) Columns Combined with Ion-Trap Tandem Mass Spectrometry

Myxobacteria are potent producers of secondary metabolites exhibiting diverse biological activities and pharmacological potential. The proteome of Myxococcus xanthus DK1622 was characterized by two-dimensional chromatographic separation of tryptic peptides from a lysate followed by tandem mass spectrometric identification. The high degree of orthogonality of the separation system employing polymer-based strong cation-exchange and monolithic reversed-phase stationary phases was clearly demonstrated. Upon automated database searching, 1312 unique peptides were identified, which were associated with 631 unique proteins. High-molecular polyketide synthetases and nonribosomal peptide synthetases, known to be involved in the biosynthesis of various secondary metabolites, were readily detected. Besides the identification of gene products associated with the production of known secondary metabolites, proteins could also be identified for six gene clusters, for which no biosynthetic product has been known so far.

Using size exclusion chromatography-RPLC and RPLC-CIEF as two-dimensional separation strategies for protein profiling

Bottom-up proteomics (analyzing peptides that result from protein digestion) has demonstrated capability for broad proteome coverage and good throughput. However, due to incomplete sequence coverage, this approach is not ideally suited to the study of modified proteins. The modification complement of a protein can best be elucidated by analyzing the intact protein. 2-DE, typically coupled with the analysis of peptides that result from in-gel digestion, is the most frequently applied protein separation technique in MS-based proteomics. As an alternative, numerous column-based liquid phase techniques, which are generally more amenable to automation, are being investigated. In this work, the combination of size-exclusion chromatography (SEC) fractionation with RPLC-Fourier-transform ion cyclotron resonance (FTICR)-MS is compared with the combination of RPLC fractionation with CIEF-FTICR-MS for the analysis of the Shewanella oneidensis proteome. SEC-RPLC-FTICR-MS allowed the detection of 297 proteins, as opposed to 166 using RPLC-CIEF-FTICR-MS, indicating that approaches based on LC-MS provide better coverage. However, there were significant differences in the sets of proteins detected and both approaches provide a basis for accurately quantifying changes in protein and modified protein abundances.

Quality control metrics for LC-MS feature detection tools demonstrated on Saccharomyces cerevisiae proteomic profiles

Quantitative proteomic profiling using liquid chromatography-mass spectrometry is emerging as an important tool for biomarker discovery, prompting development of algorithms for high-throughput peptide feature detection in complex samples. However, neither annotated standard data sets nor quality control metrics currently exist for assessing the validity of feature detection algorithms. We propose a quality control metric, Mass Deviance, for assessing the accuracy of feature detection tools. Because the Mass Deviance metric is derived from the natural distribution of peptide masses, it is machine- and proteome-independent and enables assessment of feature detection tools in the absence of completely annotated data sets. We validate the use of Mass Deviance with a second, independent metric that is based on isotopic distributions, demonstrating that we can use Mass Deviance to identify aberrant features with high accuracy. We then demonstrate the use of independent metrics in tandem as a robust way to evaluate the performance of peptide feature detection algorithms. This work is done on complex LC-MS profiles of Saccharomyces cerevisiae which present a significant challenge to peptide feature detection algorithms.

Advances and challenges in liquid chromatography-mass spectrometry-based proteomics profiling for clinical applications

Recent advances in proteomics technologies provide tremendous opportunities for biomarker-related clinical applications; however, the distinctive characteristics of human biofluids such as the high dynamic range in protein abundances and extreme complexity of the proteomes present tremendous challenges. In this review we summarize recent advances in LC-MS-based proteomics profiling and its applications in clinical proteomics as well as discuss the major challenges associated with implementing these technologies for more effective candidate biomarker discovery. Developments in immunoaffinity depletion and various fractionation approaches in combination with substantial improvements in LC-MS platforms have enabled the plasma proteome to be profiled with considerably greater dynamic range of coverage, allowing many proteins at low ng/ml levels to be confidently identified. Despite these significant advances and efforts, major challenges associated with the dynamic range of measurements and extent of proteome coverage, confidence of peptide/protein identifications, quantitation accuracy, analysis throughput, and the robustness of present instrumentation must be addressed before a proteomics profiling platform suitable for efficient clinical applications can be routinely implemented.

Proteomic technology for biomarker profiling in cancer: an update

The progress in the understanding of cancer progression and early detection has been slow and frustrating due to the complex multifactorial nature and heterogeneity of the cancer syndrome. To date, no effective treatment is available for advanced cancers, which remain a major cause of morbidity and mortality. Clearly, there is urgent need to unravel novel biomarkers for early detection. Most of the functional information of the cancer-associated genes resides in the proteome. The later is an exceptionally complex biological system involving several proteins that function through posttranslational modifications and dynamic intermolecular collisions with partners. These protein complexes can be regulated by signals emanating from cancer cells, their surrounding tissue microenvironment, and/or from the host. Some proteins are secreted and/or cleaved into the extracellular milieu and may represent valuable serum biomarkers for diagnosis purpose. It is estimated that the cancer proteome may include over 1.5 million proteins as a result of posttranslational processing and modifications. Such complexity clearly highlights the need for ultra-high resolution proteomic technology for robust quantitative protein measurements and data acquisition. This review is to update the current research efforts in high-resolution proteomic technology for discovery and monitoring cancer biomarkers.

The proteome of dissimilatory metal-reducing microorganism Geobacter sulfurreducens under various growth conditions

The proteome of Geobacter sulfurreducens, a model for the Geobacter species that predominate in many Fe(III)-reducing subsurface environments, was characterized with ultra high-pressure liquid chromatography and mass spectrometry using accurate mass and time (AMT) tags as well as with more traditional two-dimensional polyacrylamide gel electrophoresis (2-D PAGE). Cells were grown under six different growth conditions in order to enhance the potential that a wide range of genes would be expressed. The AMT tag approach was able to identify a much greater number of proteins than could be detected with the 2-D PAGE approach. With the AMT approach over 3,000 gene products were identified, representing about 90% of the total predicted gene products in the genome. A high proportion of predicted proteins in most protein role categories were detected; the highest number of proteins was identified in the hypothetical protein role category. Furthermore, 91 c-type cytochromes of 111 predicted genes in the G. sulfurreducens genome were identified. Differences in the abundance of cytochromes and other proteins under different growth conditions provided information for future functional analysis of these proteins. These results demonstrate that a high percentage of the predicted proteins in the G. sulfurreducens genome are produced and that the AMT tag approach provides a rapid method for comparing differential expression of proteins under different growth conditions in this organism.

Advances in proteomics data analysis and display using an accurate mass and time tag approach

Proteomics has recently demonstrated utility for increasing the understanding of cellular processes on the molecular level as a component of systems biology approaches and for identifying potential biomarkers of various disease states. The large amount of data generated by utilizing high efficiency (e.g., chromatographic) separations coupled with high mass accuracy mass spectrometry for high-throughput proteomics analyses presents challenges related to data processing, analysis, and display. This review focuses on recent advances in nanoLC-FTICR-MS-based proteomics approaches and the accompanying data processing tools that have been developed to display and interpret the large volumes of data being produced.

Ultra-sensitive and quantitative characterization of proteomes

Electrospray ionization mass spectrometry combined with high efficiency capillary liquid chromatography provides high sensitivity and broad dynamic range measurements for the characterization of biological macromolecules in complex matrices, and is an increasingly powerful analytical tool for systems biology research.

Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics

Central tendency, linear regression, locally weighted regression, and quantile techniques were investigated for normalization of peptide abundance measurements obtained from high-throughput liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR MS). Arbitrary abundances of peptides were obtained from three sample sets, including a standard protein sample, two Deinococcus radiodurans samples taken from different growth phases, and two mouse striatum samples from control and methamphetamine-stressed mice (strain C57BL/6). The selected normalization techniques were evaluated in both the absence and presence of biological variability by estimating extraneous variability prior to and following normalization. Prior to normalization, replicate runs from each sample set were observed to be statistically different, while following normalization replicate runs were no longer statistically different. Although all techniques reduced systematic bias to some degree, assigned ranks among the techniques revealed that for most LC-FTICR-MS analyses linear regression normalization ranked either first or second. However, the lack of a definitive trend among the techniques suggested the need for additional investigation into adapting normalization approaches for label-free proteomics. Nevertheless, this study serves as an important step for evaluating approaches that address systematic biases related to relative quantification and label-free proteomics.

"Zero-length" cross-linking in solid state as an approach for analysis of protein-protein interactions

We have developed a new approach for the analysis of interacting interfaces in protein complexes and protein quaternary structure based on cross-linking in the solid state. Protein complexes are freeze-dried under vacuum, and cross-links are introduced in the solid phase by dehydrating the protein in a nonaqueous solvent creating peptide bonds between amino and carboxyl groups of the interacting peptides. Cross-linked proteins are digested into peptides with trypsin in both H2(16)O and H(2)18O and then readily distinguished in mass spectra by characteristic 8 atomic mass unit (amu) shifts reflecting incorporation of two 18O atoms into each C terminus of proteolytic peptides. Computer analysis of mass spectrometry (MS) and MS/MS data is used to identify the cross-linked peptides. We demonstrated specificity and reproducibility of our method by cross-linking homo-oligomeric protein complexes of glutathione-S-transferase (GST) from Schistosoma japonicum alone or in a mixture of many other proteins. Identified cross-links were predominantly of amide origin, but six esters and thioesters were also found. The cross-linked peptides were validated against the GST monomer and dimer X-ray structures and by experimental (MS/MS) analyses. Some of the identified cross-links matched interacting peptides in the native 3D structure of GST, indicating that the structure of GST and its oligomeric complex remained primarily intact after freeze-drying. The pattern of oligomeric GST obtained in solid state was the same as that obtained in solution by Ru (II) Bpy(3)2+ catalyzed, oxidative "zero-length" cross-linking, confirming that it is feasible to use our strategy for analyzing the molecular interfaces of interacting proteins or peptides.

Alignment and statistical difference analysis of complex peptide data sets generated by multidimensional LC-MS

A method for high-resolution proteomics analyses of complex protein mixtures is presented using multidimensional HPLC coupled to MS (MDLC-MS). The method was applied to identify proteins that are differentially expressed during fruit ripening of tomato. Protein extracts from red and green tomato fruits were digested by trypsin. The resulting highly complex peptide mixtures were separated by strong cation exchange chromatography (SCX), and subsequently analyzed by RP nano-LC coupled to quadrupole-TOF MS. For detailed quantitative comparison, triplicate RP-LC-MS runs were performed for each SCX fraction. The resulting data sets were analyzed using MetAlign software for noise and data reduction, multiple alignment and statistical variance analysis. For each RP-LC-MS chromatogram, up to 7000 mass components were detected. Peak intensity data were compared by multivariate and statistical analysis. This revealed a clear separation between the green and red tomato samples, and a clear separation of the different SCX fractions. MS/MS spectra were collected using the data-dependent acquisition mode from a selected set of differentially detected peptide masses, enabling the identification of proteins that were differentially expressed during ripening of tomato fruits. Our approach is a highly sensitive method to analyze proteins in complex mixtures without the need of isotope labeling.

Utilizing human blood plasma for proteomic biomarker discovery

Candidate proteomic biomarker discovery from human plasma holds both incredible clinical potential as well as significant challenges. The dynamic range of proteins within plasma is known to exceed 10(10), and many potential biomarkers are likely present at lower protein abundances. At present, proteomic based MS analyses provide a dynamic range typically not exceeding approximately 10(3) in a single spectrum, and approximately 10(4)-10(6) when combined with on-line separations (e.g., reversed-phase gradient liquid chromatography), and thus are generally insufficient for low level biomarker detection directly from human plasma. This limitation is providing an impetus for the development of experimental methodologies and strategies to increase the possible number of detections within this biofluid. Discussed is the diversity of available approaches currently used by our laboratory and others to utilize human plasma as a viable medium for biomarker discovery. Various separation, depletion, enrichment, and quantitative efforts as well as recent improvements in MS capabilities have resulted in measurable improvements in the detection and identification of lower abundance proteins (by approximately 10-10(2)). Despite these improvements, further advances are needed to provide a basis for discovery of candidate biomarkers at very low levels. Continued development of depletion and enrichment techniques, coupled with improved pre-MS separations (both at the protein and peptide level) holds promise in extending the dynamic range of proteomic analysis.

A proteomic study of the HUPO Plasma Proteome Project's pilot samples using an accurate mass and time tag strategy

Characterization of the human blood plasma proteome is critical to the discovery of routinely useful clinical biomarkers. We used an accurate mass and time (AMT) tag strategy with high-resolution mass accuracy cLC-FT-ICR MS to perform a global proteomic analysis of pilot study samples as part of the HUPO Plasma Proteome Project. HUPO reference serum and citrated plasma samples from African Americans, Asian Americans, and Caucasian Americans were analyzed, in addition to a Pacific Northwest National Laboratory reference serum and plasma. The AMT tag strategy allowed us to leverage two previously published "shotgun" proteomics experiments to perform global analyses on these samples in triplicate in less than 4 days total analysis time. A total of 722 (22% with multiple peptide identifications) International Protein Index redundant proteins, or 377 protein families by ProteinProphet, were identified over the six individual HUPO serum and plasma samples. The samples yielded a similar number of identified redundant proteins in the plasma samples (average 446 +/- 23) as found in the serum samples (average 440 +/- 20). These proteins were identified by an average of 956 +/- 35 unique peptides in plasma and 930 +/- 11 unique peptides in serum. In addition to this high-throughput analysis, the AMT tag approach was used with a Z-score normalization to compare relative protein abundances. This analysis highlighted both known differences in serum and citrated plasma such as fibrinogens, and reproducible differences in peptide abundances from proteins such as soluble activin receptor-like kinase 7b and glycoprotein m6b. The AMT tag strategy not only improved our sample throughput but also provided a basis for estimated quantitation.

Ultrahigh-Throughput Proteomics Using Fast RPLC Separations with ESI-MS/MS

We describe approaches for proteomics analysis using electrospray ionization-tandem mass spectrometry coupled with fast reversed-phase liquid chromatography (RPLC) separations. The RPLC separations used 50-microm-i.d. fused-silica capillaries packed with submicrometer-sized C18-bonded porous silica particles and achieved peak capacities of 130-420 for analytes from proteome tryptic digests. When these separations were combined with linear ion trap tandem mass spectrometry measurements, approximately 1000 proteins could be identified in 50 min from approximately 4000 identified tryptic peptides; approximately 550 proteins in 20 min from approximately 1800 peptides; and approximately 250 proteins in 8 min from approximately 700 peptides for a S. oneidensis tryptic digest. The dynamic range for protein identification with the fast separations was determined to be approximately 3-4 orders of magnitude of relative protein abundance on the basis of known proteins in human blood plasma analyses. We found that 55% of the MS/MS spectra acquired during the entire analysis (and up to 100% of the MS/MS spectra acquired from the most data-rich zone) provided sufficient quality for identifying peptides. The results confirm that such analyses using very fast (minutes) RPLC separations based on columns packed with microsized porous particles are primarily limited by the MS/MS analysis speed.