Abundance ratio-dependent proteomic analysis by mass spectrometry

Mass spectrometry-based proteome profile may be useful to differentiate adenoid cystic carcinoma from polymorphous adenocarcinoma of salivary glands

OBJECTIVE

The aim of this study was to determine the proteome of adenoid cystic carcinoma (AdCC) and polymorphous adenocarcinoma (PAc) and to identify a protein signature useful in distinguishing these two neoplasms.

STUDY DESIGN

Ten cases of AdCC and 10 cases of PAc were microdissected for enrichment of neoplastic tissue. The samples were submitted to liquid chromatography-tandem mass spectrometry (LC-MS/MS), and the proteomics data were analyzed by using the MaxQuant software. LC-MS/MS spectra were searched against the Human UniProt database, and statistical analyses were performed with Perseus software. Bioinformatic analyses were performed by using discovery-based proteomic data on both tumors.

RESULTS

LC-MS/MS analysis identified 1957 proteins. The tumors shared 1590 proteins, and 261 were exclusively identified in AdCC and 106 in PAc. Clustering analysis of the statistically significant proteins clearly separated AdCC from PAc. Protein expression 10 times higher in one group than in the other led to a signature of 16 proteins-6 upregulated in AdCC and 10 in PAc. A new clustering analysis showed reverse regulation and also differentiated both tumors.

CONCLUSIONS

Global proteomics may be useful in discriminating these two malignant salivary neoplasms that frequently show clinical and microscopic overlaps, but additional validation studies are still necessary to determine the diagnostic potential of the protein signature obtained.

Isobaric labeling and data normalization without requiring protein quantitation

Isobaric multiplexed quantitative proteomics can complement high-resolution sample isolation techniques. Here, we report a simple workflow exponentially modified protein abundance index (emPAI)-MW deconvolution (EMMOL) for normalizing isobaric reporter ratios within and between experiments, where small or unknown amounts of protein are used. EMMOL deconvolutes the isobaric tags for relative and absolute quantification (iTRAQ) data to yield the quantity of each protein of each sample in the pool, a new approach that enables the comparison of many samples without including a channel of reference standard. Moreover, EMMOL allows using a sufficient quantity of control sample to facilitate the peptide fractionation (isoelectric-focusing was used in this report), and mass spectrometry MS/MS sequencing yet relies on the broad dynamic range of iTRAQ quantitation to compare relative protein abundance. We demonstrated EMMOL by comparing four pooled samples with 20-fold range differences in protein abundance and performed data normalization without using prior knowledge of the amounts of proteins in each sample, simulating an iTRAQ experiment without protein quantitation prior to labeling. We used emPAI, the target protein MW, and the iTRAQ reporter ratios to calculate the amount of each protein in each of the four channels. Importantly, the EMMOL-delineated proteomes from separate iTRAQ experiments can be assorted for comparison without using a reference sample. We observed no compression of expression in iTRAQ ratios over a 20-fold range for all protein abundances. To complement this ability to analyze minute samples, we report an optimized iTRAQ labeling protocol for using 5 μg protein as the starting material.

Mass spectrometry for protein quantification in biomarker discovery

Major technological advances have made proteomics an extremely active field for biomarker discovery in recent years due primarily to the development of newer mass spectrometric technologies and the explosion in genomic and protein bioinformatics. This leads to an increased emphasis on larger scale, faster, and more efficient methods for detecting protein biomarkers in human tissues, cells, and biofluids. Most current proteomic methodologies for biomarker discovery, however, are not highly automated and are generally labor-intensive and expensive. More automation and improved software programs capable of handling a large amount of data are essential to reduce the cost of discovery and to increase throughput. In this chapter, we discuss and describe mass spectrometry-based proteomic methods for quantitative protein analysis.

MSQ: a tool for quantification of proteomics data generated by a liquid chromatography/matrix-assisted laser desorption/ionization time-of-flight tandem mass spectrometry based targeted quantitative proteomics platform

Mass spectrometry (MS)-based quantitative proteomics has become a critical component of biological and clinical research for identification of biomarkers that can be used for early detection of diseases. In particular, MS-based targeted quantitative proteomics has been recently developed for the detection and validation of biomarker candidates in complex biological samples. In such approaches, synthetic reference peptides that are the stable isotope labeled version of proteotypic peptides of proteins to be quantitated are used as internal standards enabling specific identification and absolute quantification of targeted peptides. The quantification of targeted peptides is achieved using the intensity ratio of a native peptide to the corresponding reference peptide whose spike-in amount is known. However, a manual calculation of the ratios can be time-consuming and labor-intensive, especially when the number of peptides to be tested is large. To establish a liquid chromatography/matrix-assisted laser desorption/ionization time-of-flight tandem mass spectrometry (LC/MALDI TOF/TOF)-based targeted quantitative proteomics pipeline, we have developed a software named Mass Spectrometry based Quantification (MSQ). This software can be used to automate the quantification and identification of targeted peptides/proteins by the MALDI TOF/TOF platform. MSQ was applied to the detection of a selected group of targeted peptides in pooled human cerebrospinal spinal fluid (CSF) from patients with Alzheimer's disease (AD) in comparison with age-matched control (OC). The results for the automated quantification and identification of targeted peptides/proteins in CSF were in good agreement with results calculated manually.

Sample preparation and fractionation for proteome analysis and cancer biomarker discovery by mass spectrometry

Sample preparation and fractionation technologies are one of the most crucial processes in proteomic analysis and biomarker discovery in solubilized samples. Chromatographic or electrophoretic proteomic technologies are also available for separation of cellular protein components. There are, however, considerable limitations in currently available proteomic technologies as none of them allows for the analysis of the entire proteome in a simple step because of the large number of peptides, and because of the wide concentration dynamic range of the proteome in clinical blood samples. The results of any undertaken experiment depend on the condition of the starting material. Therefore, proper experimental design and pertinent sample preparation is essential to obtain meaningful results, particularly in comparative clinical proteomics in which one is looking for minor differences between experimental (diseased) and control (nondiseased) samples. This review discusses problems associated with general and specialized strategies of sample preparation and fractionation, dealing with samples that are solution or suspension, in a frozen tissue state, or formalin-preserved tissue archival samples, and illustrates how sample processing might influence detection with mass spectrometric techniques. Strategies that dramatically improve the potential for cancer biomarker discovery in minimally invasive, blood-collected human samples are also presented.

An integrated, directed mass spectrometric approach for in-depth characterization of complex peptide mixtures

LC-MS/MS has emerged as the method of choice for the identification and quantification of protein sample mixtures. For very complex samples such as complete proteomes, the most commonly used LC-MS/MS method, data-dependent acquisition (DDA) precursor selection, is of limited utility. The limited scan speed of current mass spectrometers along with the highly redundant selection of the most intense precursor ions generates a bias in the pool of identified proteins toward those of higher abundance. A directed LC-MS/MS approach that alleviates the limitations of DDA precursor ion selection by decoupling peak detection and sequencing of selected precursor ions is presented. In the first stage of the strategy, all detectable peptide ion signals are extracted from high resolution LC-MS feature maps or aligned sets of feature maps. The selected features or a subset thereof are subsequently sequenced in sequential, non-redundant directed LC-MS/MS experiments, and the MS/MS data are mapped back to the original LC-MS feature map in a fully automated manner. The strategy, implemented on an LTQ-FT MS platform, allowed the specific sequencing of 2,000 features per analysis and enabled the identification of more than 1,600 phosphorylation sites using a single reversed phase separation dimension without the need for time-consuming prefractionation steps. Compared with conventional DDA LC-MS/MS experiments, a substantially higher number of peptides could be identified from a sample, and this increase was more pronounced for low intensity precursor ions.

Label-free mass spectrometry-based protein quantification technologies in proteomic analysis

Major technological advances have made proteomics an extremely active field for biomarker discovery and validation in recent years. These improvements have lead to an increased emphasis on larger scale, faster and more efficient methods for protein biomarker discoveries in human tissues, cells and biofluids. However, most current proteomic methodologies for biomarker discovery and validation are not highly automated and generally labour intensive and expensive. Improved automation as well as software programs capable of handling a large amount of data are essential in order to reduce the cost of discovery and increase the throughput. In this review, we will discuss and describe the label-free mass spectrometry-based protein quantification technologies and a case study utilizing one of these methods for biomarker discovery.

Identification of carbonylated proteins from enriched rat skeletal muscle mitochondria using affinity chromatography-stable isotope labeling and tandem mass spectrometry

We describe a strategy for the identification of carbonylated proteins from complex protein mixtures that combines biotin hydrazide labeling of protein carbonyl groups, avidin affinity chromatography, multiplexed iTRAQ reagent stable isotope labeling, and analysis using pulsed Q dissociation (PQD) operation on an LTQ linear ion trap mass spectrometer. This strategy provided the ability to distinguish biotin hydrazide labeled, avidin purified, carbonylated proteins from non-carbonylated background proteins with affinity for the avidin column, derived from a control sample. Applying this strategy to the identification of crudely enriched rat skeletal muscle mitochondrial protein isolates, we generated a catalogue of over 200 carbonylated proteins by virtue of their quantitative enrichment compared to the control sample. The catalogue contains many mitochondrial localized proteins shown to be susceptible to carbonyl modification for the first time, including numerous transmembrane proteins involved in oxidative phosphorylation. Other oxidative modifications (e.g. nitrosylation, hydroxylation) were also identified on many of the carbonylated proteins, providing further evidence of the susceptibility of these proteins to oxidative damage. The results also demonstrate the utility of PQD operation on the LTQ instrument for quantitative analysis of iTRAQ reagent-labeled peptide mixtures, as well as the quantitative reproducibility of the avidin-affinity enrichment method.

Proteomics in clinical prostate research

The incidence of early prostate cancer (PCa) has increased rapidly in recent years. The majority of newly diagnosed PCa are in early tumor phase. Presently, we do not have adequate biomarkers to assess tumor aggressiveness in individual cases. Consequently, too many patients are given curatively intended treatment. An exploration of the human proteome may provide clinically useful markers. 2-DE has been successfully used for analysis of the protein phenotype using clinical samples. Proteins are separated according to size and charge, gels are compared by image analysis, protein spots of interest are excised, and proteins identified by MS. This method is exploratory and allows protein identification. However, low-abundance proteins are difficult to detect and 2-DE is currently too labor-intensive for routine use. In recent years, nongel based techniques, such as LC-MS, SELDI-MS, and protein arrays have emerged. They require smaller sample sizes and can be more automated than 2-DE. In this review, we describe studies of the protein expression of benign prostatic tissue and PCa, which is likely to serve as the first step in prognostic biomarker discovery. The prostate proteome is still far from a complete mapping which would enhance our understanding of PCa biology.

A study of reproducibility of guanidination-dimethylation labeling and liquid chromatography matrix-assisted laser desorption ionization mass spectrometry for relative proteome quantification

The combination of dimethylation after guanidination (2MEGA) isotope labeling with microbore liquid chromatography (LC)-matrix-assisted laser desorption ionization (MALDI) MS and MS/MS [C. Ji, N. Guo, L. Li, J. Proteome Res. 4 (2005) 2099] has been reported as a promising strategy for abundance ratio-dependent quantitative proteome analysis. A critical step in using this integrated strategy is to set up the abundance ratio threshold of peptide pairs, above which the peptide pairs are used for quantifying and identifying the protein that is considered to be differentially expressed between two different samples. The threshold is determined by technical variation (i.e., the overall abundance ratio variation caused by the experimental process including sample workup, MS analysis and data processing) as well as biological variation (i.e., the abundance ratio variation caused by the biological process including cell growth), which can be defined and assessed by a coefficient of variation (CV). We have designed experiments and measured three different levels of variations, starting with the same membrane protein preparation, the same batch of cells and three batches of cells from the same cell line grown under the same conditions, respectively. It is shown that technical variation from the experimental processes involved in 2MEGA labeling LC-MALDI MS has a CV of <15%. In addition, the measured biological variation from cell growth was much smaller than the measured technical variation. From the studies of the occurrence rate of outliers in the distribution of the abundance ratio data within a comparative dataset of peptide pairs, it is concluded that, to compare the proteome changes between two sets of cultured cells without the use of replicate experiments, a relative abundance ratio of greater than 2X or less than 0.5X (X is the average abundance ratio of the dataset) on peptide pairs can be used as a stringent threshold to quantify and identify differentially expressed proteins with high confidence.

Gel-free mass spectrometry-based high throughput proteomics: Tools for studying biological response of proteins and proteomes

Revolutionary advances in biological mass spectrometry (MS) have provided a basic tool to make possible comprehensive proteomic analysis. Traditionally, two-dimensional gel electrophoresis has been used as a separation method coupled with MS to facilitate analysis of complex protein mixtures. Despite the utility of this method, the many challenges of comprehensive proteomic analysis has motivated the development of gel-free MS-based strategies to obtain information not accessible using two-dimensional gel separations. These advanced strategies have enabled researchers to dig deeper into complex proteomes, gaining insights into the composition, quantitative response, covalent modifications and macromolecular interactions of proteins that collectively drive cellular function. This review describes the current state of gel-free, high throughput proteomic strategies using MS, including (i) the separation approaches commonly used for complex mixture analysis; (ii) strategies for large-scale quantitative analysis; (iii) analysis of post-translational modifications; and (iv) recent advances and future directions. The use of these strategies to make new discoveries at the proteome level into the effects of disease or other cellular perturbations is discussed in a variety of contexts, providing information on the potential of these tools in electromagnetic field research.

Selective isolation of multiple positively charged peptides for 2-DE-free quantitative proteomics

A method for quantitative proteomic analysis based on the selective isolation of multiply charged peptides (RH peptides) containing arginine and histidine residues is described. Two pools of proteins are digested in tandem with lysyl-endopeptidase and trypsin and the primary amino groups of proteolytic peptides are separately labeled with d3- and d0-acetic anhydride. This reaction has a dual purpose: (i) to allow the relative protein quantification in two different conditions and (ii) to restrict the positive charges of peptides to the presence of arginine and histidine. The N-acylated peptides are separated by cation-exchange chromatography into two groups, neutral and singly charged peptides (R+H<or=1) that are neither retained nor analyzed, whereas the multiply charged peptides (R+H>1) are retained into the column and can be eluted in batch or further fractionated using a saline gradient before LC-MS/MS analysis. In silico analysis revealed that the selective isolation of RH peptides considerably simplifies the complex mixture of peptides (three RH peptides/protein) and at the same time they represent 84% of the whole proteomes. The selectivity, and recovery of the method were evaluated with model proteins and with a complex mixture of proteins extracted from Vibrio cholerae.

A quantitative results-driven approach to analyzing multisite protein phosphorylation: the phosphate-dependent phosphorylation profile of the transcription factor Pho4

Multisite protein phosphorylation appears to be quite common. Nevertheless our understanding of how multiple phosphorylation events regulate the function of a protein is limited in many cases. The ability to measure temporal changes in the site-specific phosphorylation profile of a protein in response to a given stimulus or cellular activity would provide an immediate indication of the functional significance of any phosphorylation site to a given process. Here we describe a mass spectrometry-based method to identify functionally relevant phosphorylation sites on a protein. It combines stable isotope labeling with a highly selective mass spectrometry analysis to detect and quantitate phosphorylation sites in response to a cellular signal. This approach requires no a priori knowledge of the phosphorylation state of the protein, does not require purification of phosphopeptides, and reliably detects substoichiometric levels of phosphorylation. Following a review of the quantitative results, only those phosphorylation sites that show a change in relative abundance are selected for identification and further study. We used this results-driven approach to study phosphorylation of the budding yeast transcription factor Pho4 in response to phosphate starvation. Phosphorylation of Pho4 on five cyclin-dependent kinase (Cdk) consensus sites has been shown to regulate the transcriptional activity of Pho4 in response to changes in environmental phosphate levels. Here we show that in phosphate-rich medium Pho4 is phosphorylated on at least 15 distinct sites including the five Cdk sites described previously. In excellent agreement with the known mechanism for regulation of Pho4 we found that phosphorylation at all five of the Cdk sites was repressed in phosphate-depleted medium. In addition to these five sites, we identified four novel phosphorylation sites that were also responsive to changes in phosphate availability. Selecting a limited number of Pho4 phosphorylation sites, we performed a more detailed kinetic analysis using an isotope-free strategy. We used LC-MS with selected reaction monitoring to greatly improve the accuracy, sensitivity, and dynamic range of the subsequent experiments. A detailed analysis of the cell-based phosphorylation at the selected Pho4 sites confirmed an apparent site preference for the Pho80-Pho85 cyclin-cyclin-dependent kinase complex.

Direct cancer tissue proteomics: a method to identify candidate cancer biomarkers from formalin-fixed paraffin-embedded archival tissues

Successful treatment of multiple cancer types requires early detection and identification of reliable biomarkers present in specific cancer tissues. To test the feasibility of identifying proteins from archival cancer tissues, we have developed a methodology, termed direct tissue proteomics (DTP), which can be used to identify proteins directly from formalin-fixed paraffin-embedded prostate cancer tissue samples. Using minute prostate biopsy sections, we demonstrate the identification of 428 prostate-expressed proteins using the shotgun method. Because the DTP method is not quantitative, we employed the absolute quantification method and demonstrate picogram level quantification of prostate-specific antigen. In depth bioinformatics analysis of these expressed proteins affords the categorization of metabolic pathways that may be important for distinct stages of prostate carcinogenesis. Furthermore, we validate Wnt-3 as an upregulated protein in cancerous prostate cells by immunohistochemistry. We propose that this general strategy provides a roadmap for successful identification of critical molecular targets of multiple cancer types.

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

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

ProbIDtree: an automated software program capable of identifying multiple peptides from a single collision-induced dissociation spectrum collected by a tandem mass spectrometer

In MS/MS experiments with automated precursor ion, selection only a fraction of sequencing attempts lead to the successful identification of a peptide. A number of reasons may contribute to this situation. They include poor fragmentation of the selected precursor ion, the presence of modified residues in the peptide, mismatches with sequence databases, and frequently, the concurrent fragmentation of multiple precursors in the same CID attempt. Current database search engines are incapable of correctly assigning the sequences of multiple precursors to such spectra. We have developed a search engine, ProbIDtree, which can identify multiple peptides from a CID spectrum generated by the concurrent fragmentation of multiple precursor ions. This is achieved by iterative database searching in which the submitted spectra are generated by subtracting the fragment ions assigned to a tentatively matched peptide from the acquired spectrum and in which each match is assigned a tentative probability score. Tentatively matched peptides are organized in a tree structure from which their adjusted probability scores are calculated and used to determine the correct identifications. The results using MALDI-TOF-TOF MS/MS data demonstrate that multiple peptides can be effectively identified simultaneously with high confidence using ProbIDtree.

Application of mass spectrometry to the discovery of biomarkers for detection of prostate cancer

There has been an impressive emergence of mass spectrometry based technologies applied toward the study of proteins. Equally notable is the rapid adaptation of these technologies to biomedical approaches in the realm of clinical proteomics. Concerted efforts toward the elucidation of the proteomes of organ sites or specific disease state are proliferating and from these efforts come the promise of better diagnostics/prognostics and therapeutic intervention. Prostate cancer has been a focus of many such studies with the promise of improved care to patients via biomarkers derived from these proteomic approaches. The newer technologies provide higher analytical capabilities, employ automated liquid handling systems, fractionation techniques and bioinformatics tools for greater sensitivity and resolving power, more robust and higher throughput sample processing, and greater confidence in analytical results. In this prospects, we summarize the proteomic technologies applied to date in prostate cancer, along with their respective advantages and disadvantages. The development of newer proteomic strategies for use in future applications is also discussed.

Matrix-assisted laser desorption/ionization Fourier transform mass spectrometry quantitation via in cell combination

Herein we describe a novel method for quantitation using a Fourier transform mass spectrometer (FTMS) equipped with a MALDI ion source. The unique instrumental configuration of FTMS and its ion trapping and storing capabilities enable ion packets originating from two physically distinct samples to be combined in the ion cyclotron resonance (ICR) cell prior to detection. These features are exploited to combine analyte ions from two differentially labeled samples spotted separately and then combined in the ICR cell to generate a single mass spectrum containing isotopically paired peaks for quantitative comparison of relative ion abundances. The utility of this new quantitation via in cell combination (QUICC) approach is explored using peptide standards, a bovine serum albumin tryptic digest, and a crude neuronal tissue extract. We show that spectra acquired using the QUICC scheme are comparable to those obtained from premixing the isotopically labeled samples in solution. In addition, we show direct tissue in situ isotopic formaldehyde labeling of a crustacean neuroendocrine organ, thus demonstrating the potential application of the QUICC methodology for direct tissue quantitative analysis.

Quantitative proteome analysis using differential stable isotopic labeling and microbore LC-MALDI MS and MS/MS

We demonstrate an approach for global quantitative analysis of protein mixtures using differential stable isotopic labeling of the enzyme-digested peptides combined with microbore liquid chromatography (LC) matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS). Microbore LC provides higher sample loading, compared to capillary LC, which facilitates the quantification of low abundance proteins in protein mixtures. In this work, microbore LC is combined with MALDI MS via a heated droplet interface. The compatibilities of two global peptide labeling methods (i.e., esterification to carboxylic groups and dimethylation to amine groups of peptides) with this LC-MALDI technique are evaluated. Using a quadrupole-time-of-flight mass spectrometer, MALDI spectra of the peptides in individual sample spots are obtained to determine the abundance ratio among pairs of differential isotopically labeled peptides. MS/MS spectra are subsequently obtained from the peptide pairs showing significant abundance differences to determine the sequences of selected peptides for protein identification. The peptide sequences determined from MS/MS database search are confirmed by using the overlaid fragment ion spectra generated from a pair of differentially labeled peptides. The effectiveness of this microbore LC-MALDI approach is demonstrated in the quantification and identification of peptides from a mixture of standard proteins as well as E. coli whole cell extract of known relative concentrations. It is shown that this approach provides a facile and economical means of comparing relative protein abundances from two proteome samples.

Comparison of label-free methods for quantifying human proteins by shotgun proteomics

Measurements of mass spectral peak intensities and spectral counts are promising methods for quantifying protein abundance changes in shotgun proteomic analyses. We describe Serac, software developed to evaluate the ability of each method to quantify relative changes in protein abundance. Dynamic range and linearity using a three-dimensional ion trap were tested using standard proteins spiked into a complex sample. Linearity and good agreement between observed versus expected protein ratios were obtained after normalization and background subtraction of peak area intensity measurements and correction of spectral counts to eliminate discontinuity in ratio estimates. Peak intensity values useful for protein quantitation ranged from 10(7) to 10(11) counts with no obvious saturation effect, and proteins in replicate samples showed variations of less than 2-fold within the 95% range (+/-2sigma) when >or=3 peptides/protein were shared between samples. Protein ratios were determined with high confidence from spectral counts when maximum spectral counts were >or=4 spectra/protein, and replicates showed equivalent measurements well within 95% confidence limits. In further tests, complex samples were separated by gel exclusion chromatography, quantifying changes in protein abundance between different fractions. Linear behavior of peak area intensity measurements was obtained for peptides from proteins in different fractions. Protein ratios determined by spectral counting agreed well with those determined from peak area intensity measurements, and both agreed with independent measurements based on gel staining intensities. Overall spectral counting proved to be a more sensitive method for detecting proteins that undergo changes in abundance, whereas peak area intensity measurements yielded more accurate estimates of protein ratios. Finally these methods were used to analyze differential changes in protein expression in human erythroleukemia K562 cells stimulated under conditions that promote cell differentiation by mitogen-activated protein kinase pathway activation. Protein changes identified with p<0.1 showed good correlations with parallel measurements of changes in mRNA expression.

Proteome analysis of prostate cancer

In this paper, we briefly review cancer proteomics in general, with particular attention to our proteome analyses of prostate cancer. Our efforts include development of new tools and novel approaches to discovering proteins potentially useful as cancer diagnostic and/or prognostic biomarkers or as therapeutic targets. To this end, we analyzed prostate cancer proteomes using two-dimensional gel electrophoresis employing agarose gels for the initial isoelectric focusing step (agarose 2-DE), with mass spectrometry used for protein identification. Agarose 2-DE offers advantages over the more widely used immobilized pH gradient 2-DE for separating high molecular mass proteins (15-500 kDa), thereby increasing its power to detect changes in the cancer's high-molecular mass proteomes.

N-acetylation and ubiquitin-independent proteasomal degradation of p21(Cip1)

Expression of the CDK inhibitor p21(Cip1) is tightly regulated by signals that control cell division. p21 is an unstable protein that is degraded by the proteasome; however, the pathway that leads to proteasomal degradation of p21 has proven to be enigmatic. An important issue is whether proteasomal degradation of p21 occurs independently of ubiquitylation or, alternatively, whether ubiquitylation on its N terminus is crucial. We resolve this uncertainty by showing that endogenous cellular p21 is completely acetylated at its amino terminus and is therefore not a substrate for N-ubiquitylation. We further show that inactivation of essential components of the ubiquitylation machinery does not directly impact endogenous p21 degradation. Our results underscore the importance of N-acetylation in restricting N-ubiquitylation and show, in particular, that ubiquitylation of endogenous p21 either at internal lysines or on the N terminus is unlikely to control its degradation by the proteasome.

Protein interaction networks from yeast to human

Protein interaction networks summarize large amounts of protein-protein interaction data, both from individual, small-scale experiments and from automated high-throughput screens. The past year has seen a flood of new experimental data, especially on metazoans, as well as an increasing number of analyses designed to reveal aspects of network topology, modularity and evolution. As only minimal progress has been made in mapping the human proteome using high-throughput screens, the transfer of interaction information within and across species has become increasingly important. With more and more heterogeneous raw data becoming available, proper data integration and quality control have become essential for reliable protein network reconstruction, and will be especially important for reconstructing the human protein interaction network.

High throughput quantitative analysis of serum proteins using glycopeptide capture and liquid chromatography mass spectrometry

It is expected that the composition of the serum proteome can provide valuable information about the state of the human body in health and disease and that this information can be extracted via quantitative proteomic measurements. Suitable proteomic techniques need to be sensitive, reproducible, and robust to detect potential biomarkers below the level of highly expressed proteins, generate data sets that are comparable between experiments and laboratories, and have high throughput to support statistical studies. Here we report a method for high throughput quantitative analysis of serum proteins. It consists of the selective isolation of peptides that are N-linked glycosylated in the intact protein, the analysis of these now deglycosylated peptides by liquid chromatography electrospray ionization mass spectrometry, and the comparative analysis of the resulting patterns. By focusing selectively on a few formerly N-linked glycopeptides per serum protein, the complexity of the analyte sample is significantly reduced and the sensitivity and throughput of serum proteome analysis are increased compared with the analysis of total tryptic peptides from unfractionated samples. We provide data that document the performance of the method and show that sera from untreated normal mice and genetically identical mice with carcinogen-induced skin cancer can be unambiguously discriminated using unsupervised clustering of the resulting peptide patterns. We further identify, by tandem mass spectrometry, some of the peptides that were consistently elevated in cancer mice compared with their control littermates.

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.

The potentials of MS-based subproteomic approaches in medical science: the case of lysosomes and breast cancer

Because of the great number of women who are diagnosed with breast cancer each year, and though this disease presents the lowest mortality rate among cancers, breast cancer remains a major public health problem. As for any cancer, the tumorigenic and metastatic processes are still hardly understood, and the biochemical markers that allow either a precise monitoring of the disease or the classification of the numerous forms of breast cancer remain too scarce. Therefore, great hopes are put on the development of high-throughput genomic and proteomic technologies. Such comprehensive techniques should help in understanding the processes and in defining steps of the disease by depicting specific genes or protein profiles. Because techniques dedicated to the current proteomic challenges are continuously improving, the probability of the discovery of new potential protein biomarkers is rapidly increasing. In addition, the identification of such markers should be eased by lowering the sample complexity; e.g., by sample fractionation, either according to specific physico-chemical properties of the proteins, or by focusing on definite subcellular compartments. In particular, proteins of the lysosomal compartment have been shown to be prone to alterations in their localization, expression, or post-translational modifications (PTMs) during the cancer process. Some of them, such as the aspartic protease cathepsin D (CatD), have even been proven as participating actively in the disease progression. The present review aims at giving an overview of the implication of the lysosome in breast cancer, and at showing how subproteomics and the constantly refining MS-based proteomic techniques may help in making breast cancer research progress, and thus, hopefully, in improving disease treatment.

N-Terminal Isotope Tagging Strategy for Quantitative Proteomics: Results-Driven Analysis of Protein Abundance Changes

Comparing the relative abundance of each protein present in two or more complex samples can be accomplished using isotope-coded tags incorporated at the peptide level. Here we describe a chemical labeling strategy for the incorporation of a single isotope label per peptide, which is completely sequence-independent so that it potentially labels every peptide from a protein including those containing posttranslational modifications. It is based on a gentle chemical labeling strategy that specifically labels the N-terminus of all peptides in a digested sample with either a d5- or d0-propionyl group. Lysine side chains are blocked by guanidination prior to N-terminal labeling to prevent the incorporation of multiple labels. In this paper, we describe the optimization of this N-terminal isotopic tagging strategy and validate its use for peptide-based protein abundance measurements with a 10-protein standard mixture. Using a results-driven strategy, which targets proteins for identification based on MALDI TOF-MS analysis of isotopically labeled peptide pairs, we also show that this labeling strategy can detect a small number of differentially expressed proteins in a mixture as complex as a yeast cell lysate. Only peptides that show a difference in relative abundance are targeted for identification by tandem MS. Despite the fact that many peptides are quantitated, only those few showing a difference in abundance are targeted for protein identification. Proteins are identified by either targeted LC-ES MS/MS or MALDI TOF/TOF. Identifications can be accomplished equally well by either technique on the basis of multiple peptides. This increases the confidence level for both identification and quantitation. The merits of ES MS/MS or MALDI MS/MS for protein identification in a results-driven strategy are discussed.

Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra

To take advantage of the potential quantitative benefits offered by tandem mass spectrometry, we have modified the method in which tandem mass spectrum data are acquired in 'shotgun' proteomic analyses. The proposed method is not data dependent and is based on the sequential isolation and fragmentation of precursor windows (of 10 m/z) within the ion trap until a desired mass range has been covered. We compared the quantitative figures of merit for this method to those for existing strategies by performing an analysis of the soluble fraction of whole-cell lysates from yeast metabolically labeled in vivo with (15)N. To automate this analysis, we modified software (RelEx) previously written in the Yates lab to generate chromatograms directly from tandem mass spectra. These chromatograms showed improvements in signal-to-noise ratio of approximately three- to fivefold over corresponding chromatograms generated from mass spectrometry scans. In addition, to demonstrate the utility of the data-independent acquisition strategy coupled with chromatogram reconstruction from tandem mass spectra, we measured protein expression levels in two developmental stages of Caenorhabditis elegans.

Informatics platform for global proteomic profiling and biomarker discovery using liquid chromatography-tandem mass spectrometry

We have developed an integrated suite of algorithms, statistical methods, and computer applications to support large-scale LC-MS-based gel-free shotgun profiling of complex protein mixtures using basic experimental procedures. The programs automatically detect and quantify large numbers of peptide peaks in feature-rich ion mass chromatograms, compensate for spurious fluctuations in peptide signal intensities and retention times, and reliably match related peaks across many different datasets. Application of this toolkit markedly facilitates pattern recognition and biomarker discovery in global comparative proteomic studies, simplifying mechanistic investigation of physiological responses and the detection of proteomic signatures of disease.

High-throughput proteomic-based identification of oxidatively induced protein carbonylation in mouse brain

PURPOSE

The major initiative of this study was to implement a novel proteomic approach in order to detect protein carbonylation in aged mouse brain. Several lines of evidence indicate that reactive oxygen species (ROS)-induced protein oxidation plays an essential role in the initiation of age-related neuropathologies. Therefore, the identification of free radical or peroxide substrates would provide further insight into key biochemical mechanisms that contribute to the progression of certain neurological disorders.

METHODS

Historically, ROS targets have been identified by conventional immunological two-dimensional (2-D) gel electrophoresis and mass spectrometric analyses. However, specific classes of proteins, such as transmembrane-spanning proteins, high-molecular-weight proteins, and very acidic or basic proteins, are frequently excluded or underrepresented by these analyses. In order to fill this technologic gap, we have used a functional proteomics approach using a liquid chromatography tandem mass spectrometric (LC-MS/MS) analysis coupled with a hydrazide biotin-streptavidin methodology in order to identify protein carbonylation in aged mice.

RESULTS

Our initial studies suggest an ability to identify at least 100 carbonylated proteins in a single LC-MS/MS experiment. In addition to high-abundance cytosolic proteins that have been previously identified by 2-D gel electrophoresis and mass spectrometric analyses, we are able to identify several low-abundance receptor proteins, mitochondrial proteins involved in glucose and energy metabolism, as well as a series of receptors and tyrosine phosphatases known to be associated with insulin and insulin-like growth factor metabolism and cell-signaling pathways.

CONCLUSIONS

Here we describe a rapid and sensitive proteomic analysis for the identification of carbonylated proteins in mouse brain homogenates through the conjunction of liquid chromatography and tandem mass spectrometry methods. We believe the ability to detect these post-translationally modified proteins specifically associated with brain impairments during the course of aging should allow one to more closely and objectively monitor the efficacy of various clinical treatments. In addition, the discovery of these unique brain biomarkers could also provide a conceptual framework for the future design of alternative drugs in the treatment of a variety of age-related neurodegenerative disorders.

MONITORING THE SEROLOGICAL PROTEOME: THE LATEST MODALITY IN PROSTATE CANCER DETECTION

PURPOSE

Various strategies have recently emerged to improve the diagnostic prediction of prostate cancer (CaP). One such strategy includes the mass profiling of serum protein fractions selectively adsorbed onto chemically modified probes. In the current study we further validated this approach, while offering a more versatile, less expensive and yet equally predictive alternative to existing technologies.

MATERIALS AND METHODS

A solid core lipophilic C-18 resin was used to extract and enrich the low molecular weight protein fraction from patient serum for further analysis by mass spectrometry. Mass spectra generated from a 48 patient training set were data mined using multivariate analysis to identify diagnostically significant protein peaks. These peaks were then used to test a blinded study set comprising 168 patients with common statistical algorithms and commercially available software packages.

RESULTS

A total of 36 peaks generated from the training set were used to test the combined set of 168 serum samples obtained from 98 healthy individuals and 70 patients with CaP. We report a sensitivity of 94.1% and a specificity of 99.0% with 1 false-positive, 4 false-negative and 5 nondiagnosed cases.

CONCLUSIONS

Our results further indicate that mass profiling of serological proteins provides a means for the accurate detection of CaP. In addition, our approach was found to be superior to chip based protocols, generating rich, sharp, highly reproducible spectra attainable in a high throughput manner and at minimal cost. This technique is also scaleable for subsequent protein characterization using multidimensional protein identification technologies. Finally, analyses of mass spectra with commercially available statistical applications was found to be highly effective in generating highly discriminatory m/z values for CaP diagnosis.

A Tool To Visualize and Evaluate Data Obtained by Liquid Chromatography-Electrospray Ionization-Mass Spectrometry

We present a software tool for visualizing data obtained from analyzing complex peptide mixtures by liquid chromatography (LC) electrospray ionization (ESI) mass spectrometry (MS). The data are represented as a two-dimensional density plot. For experiments employing collision-induced dissociation (CID), links are embedded in the image to the CID spectra and the corresponding peptide sequences that are represented by the respective feature. The image provides an intuitive method to evaluate sample quality and the performance of an LC-ESI-MS system and can be used to optimize experimental conditions. Local patterns of the image can also be used to identify chemical contaminants and specific peptide features. Therefore, this software tool may have broad application in MS-based proteomics.

Identification and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry

Quantitative proteome profiling using stable isotope protein tagging and automated tandem mass spectrometry (MS/MS) is an emerging technology with great potential for the functional analysis of biological systems and for the detection of clinical diagnostic or prognostic marker proteins. Owing to the enormous complexity of proteomes, their comprehensive analysis is an as-yet-unresolved technical challenge. However, biologically or clinically important information can be obtained if specific, information-rich protein classes, or sub-proteomes, are isolated and analyzed. Glycosylation is the most common post-translational modification. Here we describe a method for the selective isolation, identification and quantification of peptides that contain N-linked carbohydrates. It is based on the conjugation of glycoproteins to a solid support using hydrazide chemistry, stable isotope labeling of glycopeptides and the specific release of formerly N-linked glycosylated peptides via peptide- N-glycosidase F (PNGase F). The recovered peptides are then identified and quantified by MS/MS. We applied the approach to the analysis of plasma membrane proteins and proteins contained in human blood serum.