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PubMed2004

High-throughput comparative proteome analysis using a quantitative cysteinyl-peptide enrichment technology.

A new quantitative cysteinyl-peptide enrichment technology (QCET) was developed to achieve higher efficiency, greater dynamic range, and higher throughput in quantitative proteomics that use stable-isotope labeling techniques combined with high-resolution liquid chromatography (LC)-mass spectrometry (MS). This approach involves (18)O labeling of tryptic peptides, high-efficiency enrichment of cysteine-containing peptides, and confident protein identification and quantification using the accurate mass and time tag strategy. Proteome profiling of naïve and in vitro-differentiated human mammary epithelial cells using QCET resulted in the identification and quantification of 603 proteins in a single LC-Fourier transform ion cyclotron resonance MS analysis. Advantages of this technology include the following: (1) a simple, highly efficient method for enriching cysteinyl-peptides; (2) a high-throughput strategy suitable for extensive proteome analysis; and (3) improved labeling efficiency for better quantitative measurements. This technology enhances both the functional analysis of biological systems and the detection of potential clinical biomarkers.

Cells, Cultured↗
PubMed2003

A correlation algorithm for the automated quantitative analysis of shotgun proteomics data.

Quantitative shotgun proteomic analyses are facilitated using chemical tags such as ICAT and metabolic labeling strategies with stable isotopes. The rapid high-throughput production of quantitative "shotgun" proteomic data necessitates the development of software to automatically convert mass spectrometry-derived data of peptides into relative protein abundances. We describe a computer program called RelEx, which uses a least-squares regression for the calculation of the peptide ion current ratios from the mass spectrometry-derived ion chromatograms. RelEx is tolerant of poor signal-to-noise data and can automatically discard nonusable chromatograms and outlier ratios. We apply a simple correction for systematic errors that improves the accuracy of the quantitative measurement by 32 +/- 4%. Our automated approach was validated using labeled mixtures composed of known molar ratios and demonstrated in a real sample by measuring the effect of osmotic stress on protein expression in Saccharomyces cerevisiae.

Algorithms↗
PubMed2006

Comparison of spectral counting and metabolic stable isotope labeling for use with quantitative microbial proteomics.

Spectral counting, a promising method for quantifying relative changes in protein abundance in mass spectrometry-based proteomic analysis, was compared to metabolic stable isotope labeling using (15)N/(14)N "heavy/light" peptide pairs. The data were drawn primarily from a Methanococcus maripaludis experiment comparing a wild-type strain with a mutant deficient in a key enzyme relevant to energy metabolism. The dataset contained both proteome and transcriptome measurements. The normalization technique used previously for the isotopic measurements was inappropriate for spectral counting, but a simple adjustment for sampling frequency was sufficient for normalization. This adjustment was satisfactory both for M. maripaludis, an organism that showed relatively little expression change between the wild-type and mutant strains, and Porphyromonas gingivalis, an intracellular pathogen that has demonstrated widespread changes between intracellular and extracellular conditions. Spectral counting showed lower overall sensitivity defined in terms of detecting a two-fold change in protein expression, and in order to achieve the same level of quantitative proteome coverage as the stable isotope method, it would have required approximately doubling the number of mass spectra collected.

Animals↗
PubMed2003

Quantitative chemical proteomics for identifying candidate drug targets.

We have developed a systematic strategy for drug target identification. This consists of the following sequential steps: (1) enrichment of total binding proteins using two differential affinity matrixes upon which are immobilized positive and negative chemical structures for drug activity, respectively; (2) covalent labeling of the proteins with a new cleavable isotope-coded affinity tag (ICAT) reagent, followed by proteolysis of the combined proteins; (3) isolation, identification, and relative quantification of the tagged peptides by liquid chromatography-mass spectrometry; (4) array-based transcription profiling to select candidate proteins; and (5) confirmation of direct interaction between the activity-associated structure and the selected proteins by using surface plasmon resonance. We present a typical application to identify the primary binding protein of a novel class of anticancer agents exemplified by E7070. Our results suggest that this approach provides a new aspect of quantitative proteomics to find specific binding proteins from protein mixture and should be applicable to a wide variety of biologically active small molecules with unidentified target proteins.

Antineoplastic Agents↗
PubMed2005

Non-stoichiometric relationship between clathrin heavy and light chains revealed by quantitative comparative proteomics of clathrin-coated vesicles from brain and liver.

We used tandem mass spectrometry with peptide counts to identify and to determine the relative levels of expression of abundant protein components of highly enriched clathrin-coated vesicles (CCVs) from rat liver. The stoichiometry of stable protein complexes including clathrin heavy chain and clathrin light chain dimers and adaptor protein (AP) heterotetramers was assessed. We detected a deficit of clathrin light chain compared with clathrin heavy chain in non-brain tissues, suggesting a level of regulation of clathrin cage formation specific to brain. The high ratio of AP-1 to AP-2 in liver CCVs is reversed compared with brain where there is more AP-2 than AP-1. Despite this, general endocytic cargo proteins were readily detected in liver but not in brain CCVs, consistent with the previous demonstration that a major function for brain CCVs is recycling synaptic vesicles. Finally we identified 21 CCV-associated proteins in liver not yet characterized in mammals. Our results further validate the peptide accounting approach, reveal new information on the properties of CCVs, and allow for the use of quantitative proteomics to compare abundant components of organelles under different experimental and pathological conditions.

Animals↗
PubMed2005

Combination of two-dimensional electrophoresis and shotgun peptide sequencing in comparative proteomics.

Two-dimensional electrophoresis (2-DE) and shotgun peptide sequencing are the two major technologies to compare the expression profile of proteins, which is also referred to as comparative proteomics or quantitative proteomics. Although the methodologies, such as difference gel electrophoresis for 2-DE and isotope-coded affinity tags for shotgun peptide sequencing, have made rapid progress, these two approaches have their own strengths and weaknesses. Therefore, the combination of the two methodologies is beneficial for the purpose of better comparative proteomics, especially in comprehensive coverage of the proteome and protein information such as post-translational modifications.

Animals↗
PubMed2006

PEPPeR, a platform for experimental proteomic pattern recognition.

Quantitative proteomics holds considerable promise for elucidation of basic biology and for clinical biomarker discovery. However, it has been difficult to fulfill this promise due to over-reliance on identification-based quantitative methods and problems associated with chromatographic separation reproducibility. Here we describe new algorithms termed "Landmark Matching" and "Peak Matching" that greatly reduce these problems. Landmark Matching performs time base-independent propagation of peptide identities onto accurate mass LC-MS features in a way that leverages historical data derived from disparate data acquisition strategies. Peak Matching builds upon Landmark Matching by recognizing identical molecular species across multiple LC-MS experiments in an identity-independent fashion by clustering. We have bundled these algorithms together with other algorithms, data acquisition strategies, and experimental designs to create a Platform for Experimental Proteomic Pattern Recognition (PEPPeR). These developments enable use of established statistical tools previously limited to microarray analysis for treatment of proteomics data. We demonstrate that the proposed platform can be calibrated across 2.5 orders of magnitude and can perform robust quantification of ratios in both simple and complex mixtures with good precision and error characteristics across multiple sample preparations. We also demonstrate de novo marker discovery based on statistical significance of unidentified accurate mass components that changed between two mixtures. These markers were subsequently identified by accurate mass-driven MS/MS acquisition and demonstrated to be contaminant proteins associated with known proteins whose concentrations were designed to change between the two mixtures. These results have provided a real world validation of the platform for marker discovery.

Algorithms↗
PubMed2005

The dynamics of the proteome: strategies for measuring protein turnover on a proteome-wide scale.

Quantitative proteomics captures the steady-state amount of a protein in a cell but does not explain how a change in protein amount is manifest -- whether through a change in synthesis or a change in degradation. If we are to understand the changes in the proteome, we will need to define such processes. In this brief review, strategies for the determination of intracellular protein dynamics on a proteome-wide scale are discussed.

Animals↗
PubMed2005

Analysis of prostate cancer by proteomics using tissue specimens.

PURPOSE: Prostate cancer cells differ from their normal counterpart in gene expression. Therefore, cancer and normal tissue/cells would show differences in their proteomes. From a diagnostic standpoint differences in the composition of secreted protein species are especially relevant. We used ProteinChip Array (Ciphergen Biosystems, Fremont, California) surface enhanced laser desorption/ionization (SELDI) time of flight mass spectrometry to profile prostate tissue samples to generate phenomic fingerprints. We used quantitative proteomics based on glycopeptide capture followed by tandem mass spectrometry to identify differentially expressed proteins. MATERIALS AND METHODS: Patient matched cancer and noncancer specimens were digested by collagenase to single cells. After digestion the cells were pelleted and the cell-free supernatant was used for analysis. A reversed phase hydrophobic ProteinChip Array was used to generate SELDI patterns from 43 primary prostate tumors, including 26 with matched noncancer specimens. Quantitative proteomics was applied to 1 specimen and the expression pattern was verified by Western blotting and immunohistochemistry. RESULTS: SELDI profiles showed that cancers of similar TNM stages were more likely to have similar profiles. On quantitative proteomics tissue metalloproteinase inhibitor-1 was identified to be down-regulated in cancer. Tissue metalloproteinase inhibitor-1 expression was localized to secretory cells. CONCLUSIONS: Protein profiling by SELDI is relatively easy to perform and it has great potential in prostate cancer diagnosis through pattern recognition. Quantitative proteomics can potentially determine the identity of many biomarkers specific for prostate cancer.

Biomarkers, Tumor↗
PubMed2006

A proteomic analysis of the acute effects of high-intensity exercise on skeletal muscle proteins in fasted rats.

Quantitative proteomics is a technique that allows for large-scale comparison of the levels of individual proteins present in a biological sample. This technique has not previously been applied to examine the response of skeletal muscle proteins to an acute bout of exercise. In the present study, quantitative proteomics was applied to investigate whether the levels of individual skeletal muscle proteins are acutely affected by a short bout of high-intensity exercise. Gastrocnemius muscle was sampled from fasted rats either at rest, immediately following 3 min of high-intensity exercise or after 30 min of recovery. Muscle samples were submitted to two-dimensional gel electrophoresis and 61 of the resulting protein spots were selected for quantitative analysis. It was found that skeletal muscle protein levels were generally not acutely affected by a short bout of high-intensity exercise, with only four of the 61 proteins selected for analysis being significantly altered. These altered proteins were identified using liquid chromatography electrospray ionization-tandem mass spectrometry as creatine kinase, troponin T and a combination of heat shock 20 kDa protein and adenylate kinase 1. In conclusion, quantitative proteomics is sensitive enough to detect acute changes in skeletal muscle protein levels in response to exercise. We have found that the levels of most individual skeletal muscle proteins are not immediately altered in response to a short bout of high-intensity exercise and recovery in fasted rats.

Animals↗
PubMed2003

Abundance ratio-dependent proteomic analysis by mass spectrometry.

The goal of quantitative proteomics is to determine the identity and relative quantity of each protein present in two or more complex protein samples. Here we describe a novel approach to quantitative proteomics. It is based on a highly accurate algorithm for the automated quantification of chromatographically fractionated, isotope-coded affinity-tagged peptides and MALDI quadrupole time-of-flight tandem mass spectrometry for their identification. The method is capable of detecting and selectively identifying those proteins within a complex mixture that show a difference in relative abundance. We demonstrate the effectiveness and the versatility of this approach in the analysis of a standard protein mixture, protein expression profiling in a human prostate cancer cell line model, and identification of the specific components of the multiprotein transcriptional machinery in S. cerevisiae.

Humans↗
PubMed2006

Robust estimation of peptide abundance ratios and rigorous scoring of their variability and bias in quantitative shotgun proteomics.

The abundance ratio between the light and heavy isotopologues of an isotopically labeled peptide can be estimated from their selected ion chromatograms. However, quantitative shotgun proteomics measurements yield selected ion chromatograms at highly variable signal-to-noise ratios for tens of thousands of peptides. This challenge calls for algorithms that not only robustly estimate the abundance ratios of different peptides but also rigorously score each abundance ratio for the expected estimation bias and variability. Scoring of the abundance ratios, much like scoring of sequence assignment for tandem mass spectra by peptide identification algorithms, enables filtering of unreliable peptide quantification and use of formal statistical inference in the subsequent protein abundance ratio estimation. In this study, a parallel paired covariance algorithm is used for robust peak detection in selected ion chromatograms. A peak profile is generated for each peptide, which is a scatterplot of ion intensities measured for the two isotopologues within their chromatographic peaks. Principal component analysis of the peak profile is proposed to estimate the peptide abundance ratio and to score the estimation with the signal-to-noise ratio of the peak profile (profile signal-to-noise ratio). We demonstrate that the profile signal-to-noise ratio is inversely correlated with the variability and bias of peptide abundance ratio estimation.

Algorithms↗
PubMed2005

High throughput proteome screening for biomarker detection.

Mass spectrometry-based quantitative proteomics has become an important component of biological and clinical research. Current methods, while highly developed and powerful, are falling short of their goal of routinely analyzing whole proteomes mainly because the wealth of proteomic information accumulated from prior studies is not used for the planning or interpretation of present experiments. The consequence of this situation is that in every proteomic experiment the proteome is rediscovered. In this report we describe an approach for quantitative proteomics that builds on the extensive prior knowledge of proteomes and a platform for the implementation of the method. The method is based on the selection and chemical synthesis of isotopically labeled reference peptides that uniquely identify a particular protein and the addition of a panel of such peptides to the sample mixture consisting of tryptic peptides from the proteome in question. The platform consists of a peptide separation module for the generation of ordered peptide arrays from the combined peptide sample on the sample plate of a MALDI mass spectrometer, a high throughput MALDI-TOF/TOF mass spectrometer, and a suite of software tools for the selective analysis of the targeted peptides and the interpretation of the results. Applying the method to the analysis of the human blood serum proteome we demonstrate the feasibility of using mass spectrometry-based proteomics as a high throughput screening technology for the detection and quantification of targeted proteins in a complex system.

Automation↗
PubMed2006

Exploring glycopeptide-resistance in Staphylococcus aureus: a combined proteomics and transcriptomics approach for the identification of resistance-related markers.

BACKGROUND: To unravel molecular targets involved in glycopeptide resistance, three isogenic strains of Staphylococcus aureus with different susceptibility levels to vancomycin or teicoplanin were subjected to whole-genome microarray-based transcription and quantitative proteomic profiling. Quantitative proteomics performed on membrane extracts showed exquisite inter-experimental reproducibility permitting the identification and relative quantification of >30% of the predicted S. aureus proteome. RESULTS: In the absence of antibiotic selection pressure, comparison of stable resistant and susceptible strains revealed 94 differentially expressed genes and 178 proteins. As expected, only partial correlation was obtained between transcriptomic and proteomic results during stationary-phase. Application of massively parallel methods identified one third of the complete proteome, a majority of which was only predicted based on genome sequencing, but never identified to date. Several over-expressed genes represent previously reported targets, while series of genes and proteins possibly involved in the glycopeptide resistance mechanism were discovered here, including regulators, global regulator attenuator, hyper-mutability factor or hypothetical proteins. Gene expression of these markers was confirmed in a collection of genetically unrelated strains showing altered susceptibility to glycopeptides. CONCLUSION: Our proteome and transcriptome analyses have been performed during stationary-phase of growth on isogenic strains showing susceptibility or intermediate level of resistance against glycopeptides. Altered susceptibility had emerged spontaneously after infection with a sensitive parental strain, thus not selected in vitro. This combined analysis allows the identification of hundreds of proteins considered, so far as hypothetical protein. In addition, this study provides not only a global picture of transcription and expression adaptations during a complex antibiotic resistance mechanism but also unravels potential drug targets or markers that are constitutively expressed by resistant strains regardless of their genetic background, amenable to be used as diagnostic targets.

Anti-Bacterial Agents↗
PubMed2005

A software suite for the generation and comparison of peptide arrays from sets of data collected by liquid chromatography-mass spectrometry.

There is an increasing interest in the quantitative proteomic measurement of the protein contents of substantially similar biological samples, e.g. for the analysis of cellular response to perturbations over time or for the discovery of protein biomarkers from clinical samples. Technical limitations of current proteomic platforms such as limited reproducibility and low throughput make this a challenging task. A new LC-MS-based platform is able to generate complex peptide patterns from the analysis of proteolyzed protein samples at high throughput and represents a promising approach for quantitative proteomics. A crucial component of the LC-MS approach is the accurate evaluation of the abundance of detected peptides over many samples and the identification of peptide features that can stratify samples with respect to their genetic, physiological, or environmental origins. We present here a new software suite, SpecArray, that generates a peptide versus sample array from a set of LC-MS data. A peptide array stores the relative abundance of thousands of peptide features in many samples and is in a format identical to that of a gene expression microarray. A peptide array can be subjected to an unsupervised clustering analysis to stratify samples or to a discriminant analysis to identify discriminatory peptide features. We applied the SpecArray to analyze two sets of LC-MS data: one was from four repeat LC-MS analyses of the same glycopeptide sample, and another was from LC-MS analysis of serum samples of five male and five female mice. We demonstrate through these two study cases that the SpecArray software suite can serve as an effective software platform in the LC-MS approach for quantitative proteomics.

Amino Acid Sequence↗
PubMed2004

Applications of affinity chromatography in proteomics.

Affinity chromatography is a powerful protein separation method that is based on the specific interaction between immobilized ligands and target proteins. Peptides can also be separated effectively by affinity chromatography through the use of peptide-specific ligands. Both two-dimensional electrophoresis (2-DE)- and non-2-DE-based proteomic approaches benefit from the application of affinity chromatography. Before protein separation by 2-DE, affinity separation is used primarily for preconcentration and pretreatment of samples. Those applications entail the removal of one protein or a class of proteins that might interfere with 2-DE resolution, the concentration of low-abundance proteins to enable them to be visualized in the gel, and the classification of total protein into two or more groups for further separation by gel electrophoresis. Non-2-DE-based approaches have extensively employed affinity chromatography to reduce the complexity of protein and peptide mixtures. Prior to mass spectrometry (MS), preconcentration and capture of specific proteins or peptides to enhance sensitivity can be accomplished by using affinity adsorption. Affinity purification of protein complexes followed by identification of proteins by MS serves as a powerful tool for generating a map of protein-protein interactions and cellular locations of complexes. Affinity chromatography of peptide mixtures, coupled with mass spectrometry, provides a tool for the study of protein posttranslational modification (PTM) sites and quantitative proteomics. Quantitation of proteomes is possible via the use of isotope-coded affinity tags and isolation of proteolytic peptides by affinity chromatography. An emerging area of proteomics technology development is miniaturization. Affinity chromatography is becoming more widely used for exploring PTM and protein-protein interactions, especially with a view toward developing new general tag systems and strategies of chemical derivatization on peptides for affinity selection. More applications of affinity-based purification can be expected, including increasing the resolution in 2-DE, improving the sensitivity of MS quantification, and incorporating purification as part of multidimensional liquid chromatography experiments.

Chromatography, Affinity↗
PubMed2006

Relative and absolute quantitative shotgun proteomics: targeting low-abundance proteins in Arabidopsis thaliana.

The plant system is a highly dynamic structure on all molecular levels, transcripts, proteins, and metabolites. Thus, protein analysis has to cope with a highly dynamic range of concentrations. A severe problem is the detection of low-abundance proteins in the presence of housekeeping proteins. Basically three approaches are facilitated to measure protein abundance in a comprehensive manner: 2DE and one- or multi-dimensional shotgun proteomics, with or without stable-isotope labelling. These comparative techniques allow for the identification of altered protein levels compared with a reference state. However, they are limited to the analysis of medium/high-abundance proteins. Using stable-isotope dilution techniques it is possible to target the quantitative analysis to low-abundance proteins and to measure absolute concentrations of proteins. Based on multi-dimensional non-gel shotgun proteomics in Arabidopis thaliana, a list of tryptic peptides comprising >1000 proteins was generated. A strategy for quantitative plant proteomics is proposed using this master-list for selecting signature peptides of proteins. To prove the concept, a liquid chromatography-high-resolution triple quadrupole multiple reaction monitoring-mass spectrometry technique is described to determine the absolute amount of a low-abundance sucrose synthase isoform out of an ultra-complex A. thaliana protein extract.

Arabidopsis↗
PubMed2005

Normalization and analysis of residual variation in two-dimensional gel electrophoresis for quantitative differential proteomics.

Although two-dimensional gel electrophoresis (2-DE) has long been a favorite experimental method to screen proteomes, its reproducibility is seldom analyzed with the assistance of quantitative error models. The lack of models of residual distributions that can be used to assign likelihood to differential expression reflects the difficulty in tackling the combined effect of variability in spot intensity and uncertain recognition of the same spot in different gels. In this report we have analyzed a series of four triplicate two-dimensional gels of chicken embryo heart samples at two distinct development stages to produce such a model of residual distribution. In order to achieve this reference error model, a nonparametric procedure for consistent spot intensity normalization had to be established, and is also reported here. In addition to variability in normalized intensity due to various sources, the residual variation between replicates was observed to be compounded by failure to identify the spot itself (gel alignment). The mixed effect is reflected by variably skewed bimodal density distributions of residuals. The extraction of a global error model that accommodated such distribution was achieved empirically by machine learning, specifically by bootstrapped artificial neural networks. The model described is being used to assign confidence values to observed variations in arbitrary 2-DE gels in order to quantify the degree of over-expression and under-expression of protein spots.

Animals↗