From the Institute for Systems Biology, Seattle, Washington 98103, ¶ Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, || Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, and ** Swiss Federal Institute of Technology (ETH) Zurich, and Faculty of Natural Sciences, University of Zurich, CH-8093 Zurich, Switzerland
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ABSTRACT |
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A number of new approaches that differ from the traditional two-dimensional gel electrophoresis method for the discovery of protein biomarkers in serum have recently been described (1). These include surface-enhanced laser desorption ionization mass spectrometry (SELDI-MS)2 (7), liquid chromatography tandem mass spectrometry (LC-MS/MS) of serum proteome digests (810), two- or three-dimensional (chromatography/gel electrophoresis) protein separation analyzed by differential fluorescent staining (11, 12), fractionation of the serum proteome on surface-modified magnetic beads followed by matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) (13), and combinations and variations of these approaches.
Any study of the serum proteome is confronted with the peculiar properties of serum samples. First, human blood serum is assumed to consist of minimally tens of thousands of different protein species that span a concentration range of an estimated 10 orders of magnitude (14). Second, the serum proteome is dominated by a few highly abundant proteins, i.e. the 22 most abundant human serum proteins combined constitute 99% of total protein mass (9). Indeed almost one-half of total serum protein mass is represented by just one protein, albumin. Third, many of the serum proteins show complex two-dimensional electrophoretic patterns, suggesting that they are extensively post-translationally modified with glycosylation apparently being the major source of protein heterogeneity (14). In fact when protein spots from two-dimensional electropherograms of serum were systematically identified by mass spectrometry, five to seven protein spots on average were identified as products of the same gene (15). Fourth, the serum proteome varies over time in an individual and among individuals in a population.
Useful platforms for serum proteome analysis should thus have minimally the following properties: first, sufficient analytical depth to reliably detect relatively low abundance proteins; second, quantitative accuracy to determine changes in the proteome pattern; third, reproducibility and robustness to detect disease-specific changes in a background of pattern changes unrelated to disease; fourth, the ability to identify distinct peptides for their cross-validation on different analytical platforms and comparison of results obtained from different research groups, studies, and diseases; and fifth, high sample throughput to support studies with sufficient statistical power.
Here we describe a new method for quantitative serum proteome analysis. It is based on the selective isolation of those peptides from serum proteins that are N-linked glycosylated in the native protein and the analysis of the complex peptide mixture representing the now deglycosylated forms of these peptides by LC-MS and MS/MS. By selectively isolating this subset of peptides, the procedure achieves a significant reduction in analyte complexity at two levels: first, a reduction of the total number of peptides due to the fact that every serum protein on average only contains a few N-linked glycosylation sites, and second, a reduction of pattern complexity by removing the oligosaccharides that contribute significantly to the peptide pattern heterogeneity. We go on to show that this method is reproducible and achieves increased analytical depth and higher throughput compared with the analysis of samples without selective analyte enrichment. Furthermore we demonstrate that, in a controlled experiment, peptide patterns distinguishing the serum proteome of cancer-bearing mice from genetically identical untreated normal mice could be detected, and discriminatory peptides could be subsequently identified. At present, this method affords one of the most comprehensive routine and high throughput analyses of the serum proteome. We therefore believe that it will find broad application in serum marker discovery research.
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EXPERIMENTAL PROCEDURES |
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Chemical Induction of Mouse Skin Tumors
Male mice of strain NIHO1a were subjected to the two-stage skin carcinogenesis protocol (16). Five littermates were used: two were untreated, and three were treated with carcinogen. The shaved backs of three 8-week-old mice were treated with a single dose of the carcinogen 7,12-dimethylbenz[a]anthracene (DMBA) (Sigma; 25 mg in 200 ml of acetone). Initiated cells were promoted with 12-O-tetradecanoylphorbol-13-acetate twice a week for 15 weeks, giving rise to papillomas that were hyperplastic, well differentiated, benign lesions consisting of keratinocytes together with stromal tissue. Papillomas appeared as early as 8 weeks after DMBA initiation and continued to grow for the next several months. A small percentage of these benign papillomas progressed to squamous cell carcinomas. At week 22 after DMBA initiation, all mice were sacrificed, and whole blood was collected by heart puncture with a 21-gauge needle and 1-ml syringe. Blood was allowed to clot for 1 h at room temperature. Sera were collected by centrifugation at 3000 rpm. The untreated mice contained no tumors, while the DMBA/12-O-tetradecanoylphorbol-13-acetate-treated mice each had at least one carcinoma as confirmed by histological analysis.
Preparation of Peptide Samples for Mass Spectrometry Analysis
Formerly N-linked glycosylated peptides were isolated and labeled using the N-linked glycopeptide capture procedure as described previously (17). Proteins from 100 µl of serum were used in isolation and isotope labeling of formerly N-linked glycopeptides, and peptides from 5 µl of original serum were used in each mass spectrometry analysis.
To prepare tryptic peptides from serum proteins, proteins from 1 µl (80 µg) of mouse serum were denatured in 20 µl of 8 M urea, 0.4 M NH4HCO3 for 30 min at room temperature. The proteins were diluted four times with water after which 1 µg of trypsin was added, and the proteins were digested at 37 °C overnight. The peptides were then reduced by adding 8 mM Tris(2-carboxyethyl)phosphine (Pierce) at room temperature for 30 min and alkylated by adding 10 mM iodoacetamide at room temperature for 30 min. The peptides were dried and resuspended in 0.4% acetic acid. Peptides from 0.05 µl of original serum (4 µg of original serum proteins) were used for each LC-MS analysis.
Analysis of Peptides by Mass Spectrometry
The peptides and proteins were identified using MS/MS analysis using an LCQ ion trap mass spectrometer (Thermo Finnigan, San Jose, CA) as described previously (18). For quantitative analysis of peptides using LC-MS, an ESI-QTOF mass spectrometer (Waters, Beverly, MA) was used. In both systems, peptides isolated from 5 µl of serum sample using the glycopeptide capture method were injected into a home-made peptide trap packed with Magic C18 resin (Michrome Bioresources, Auburn, CA) using a FAMOS autosampler (DIONEX, Sunnyvale, CA) and then passed through a 10-cm x 75-µm-inner diameter microcapillary HPLC column packed with Magic C18 resin (Michrome Bioresources). The effluent from the microcapillary HPLC column entered a home-built electrospray ionization source in which peptides were ionized and passed directly into the respective mass spectrometer. The C18 peptide trap cartridge, µ-ESI emitter/microcapillary HPLC pulled tip column combination, a high voltage line for ESI, and the waste line were each connected to separate ports of a four-port union (Upchurch Scientific, Oak Harbor, WA) constructed entirely out of polyetheretherketone (19). A linear gradient of acetonitrile from 532% over 100 min at a flow rate of 300 nl/min was applied. During the LC-MS mode, data were acquired with a profile mode in the mass range scan between m/z 400 and 2000 with 3.0-s scan duration and 0.1-s interscan. After completion of the LC/MS runs, inclusion peptide mass lists were created from data analysis software. The inclusion lists were then used for targeted LC-MS/MS analysis for peptide/protein identifications with the remaining of samples.
Data Analysis
For ESI-QTOF data analysis, a suite of software tools was developed or optimized in-house to analyze LC-MS data for this project and will be published separately.3 The software tools use LC-MS data generated by ESI-QTOF analysis of formerly N-linked glycopeptides from serum samples and sequentially perform the following tasks to determine peptides that are of different abundance in cancer and normal mice, respectively.
Peptide List
A list of peptide peaks was generated from each LC-ESI-MS run. The tool performing this operation was a straightforward extension of a previous tool for the analysis of LC-MALDI-MS data (20). That tool was modified to take into account the fact that in ESI-MS peptides are observed in different charge states. Peaks were selected if the signal to noise ratio exceeded 2.
Peptide Alignment
Peptides detected in individual LC-MS patterns were aligned mainly based on peptide mass. The retention time was then used to align peptides with the same m/z value. The software tool accounted for shifts in the retention time in different LC-MS analyses during peptide alignment. Peptide alignment was facilitated by the following factors: i) the glycopeptide capture procedure significantly simplifies the sample complexity, ii) the high mass accuracy achieved in the ESI-QTOF instrument, and iii) the optimized HPLC system that produced highly consistent and reproducible peptide patterns. In the mouse studies, peptides that appeared at least in two of three analyses in either group were selected for further quantitative analysis.
Peptide Abundance Ratio
An abundance ratio of matched peptides in different samples was determined for each peptide peak using the same method as described in the ASAPRatio software tool developed for LC-ESI-MS/MS data (21). Briefly the software uses spectra from multiple LC-MS analyses of a peptide peak (with same mass-to-charge ratio (m/z), same charge state, and close retention time) and calculates one ratio for each peptide peak. In the present study, ratios calculated for different charge states of the same peptide were not combined. The algorithm also estimates a noise background level in each spectrum and subtracts that value from the signal intensities when calculating the peak area.
Clustering Analysis
The lists of matched peptides with their relative signal intensities were subjected to unsupervised hierarchical clustering (22) to identify peptides distinguishing cancer samples from normal samples. Prior to clustering, the data were transformed to log value, and the mean intensity of each peptide across all samples was normalized. Peptides present at least in 50% of the total samples were used for clustering analysis.
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RESULTS |
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Pattern Generation
Isolated peptides were analyzed by LC-MS to generate three-dimensional (retention time, m/z, and intensity) patterns.
Pattern Analysis
Peptide patterns obtained from different samples were compared, and the discriminatory peptides were determined.
Peptide Identification
Discriminatory peptides and the proteins from which they originated were identified by tandem mass spectrometry and sequence data base searching.
Selectivity of the N-Linked Glycopeptide Capture Method
To determine the selectivity of the glycopeptide capture method for serum protein analysis, serum samples from four genetically identical mouse littermates were individually processed using the N-linked glycopeptide capture-and-release method, and the isolated peptides were analyzed by LC-MS/MS. The resulting collision-induced dissociation (CID) spectra were searched against the mouse International Protein Index sequence data base (Version 1.24), and the data base search results were further statistically analyzed using the PeptideProphet software tool (23). From four LC-MS/MS analyses of the mouse sera, 1722 CID spectra resulted in peptide identifications from the data base search with peptide probability scores of at least 0.99 (corresponding to a false positive error rate of 0.0007 (23)). The identified sequences were then examined for the presence of the known consensus N-linked glycosylation motif (NX(T/S) where X = any amino acid except proline). The number of proteins represented by the selected peptides were determined using INTERACT (24). The full list of peptides and proteins identified are given in Supplemental Table 1 on line, and the CID spectra of these peptides are given in Supplemental Fig. 1 on line. The number of identified proteins and peptides are summarized in Table I. A total of 319 unique peptides were identified, representing 93 unique proteins. 93.6% of the identifications, 81.8% of unique peptides, and 93.5% of identified proteins contained the consensus N-linked glycosylation motif (Table I).
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To determine whether the increased sensitivity achieved by reducing sample complexity was sufficient to detect serum protein biomarkers of clinically relevant concentration, we related data obtained in this study to the concentrations of human serum marker proteins (26, 27). We are not aware of a direct comparison of the protein compositions between the human and mouse serum proteomes. However, the serum two-dimensional maps of human and mouse are sufficiently similar to allow an approximate comparison of the concentrations of the proteins identified in this study between human and mouse (28). From the 93 proteins identified above (Supplemental Table 1), several proteins are known to be present in human serum at low µg/ml concentration (Table II). These include carboxypeptidase N and coagulation factors II, V, XII, and XIII. Except for epidermal growth factor receptor and serum amyloid P-component, none of the other proteins listed in Table II have been identified in the previous mouse two-dimensional map, suggesting that they are present at low abundance in mouse serum (28). To estimate the detection sensitivity, the peak intensities of the identified peptides from these proteins were calculated using the intensities of the chromatographic peaks at the charge states used for peptide identification. Examination of the peak intensities indicated an average peptide peak intensity of 2.7 x 107, which is 900 times greater than the observed background signal for these experiments (Table II). This indicates that even without multidimensional separation, serum proteins at concentrations on the order of ng/ml may be detected by LC-MS of formerly N-linked glycopeptides.
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From the sera of three cancer-bearing male mice (C1, C2, and C3) and two untreated normal male mice (N1 and N2) from the same litter, glycopeptides were isolated and analyzed by LC-MS as described above. The sample from N1 was analyzed by LC-MS twice (N1a and N1b); thus a total of six LC-MS patterns were generated. After aligning peptide peaks from all six patterns, over 3000 peptide peaks were found to occur in at least two of the three analyses from either normal or cancer-bearing mice. The six LC-MS patterns consisting of the peptide peaks matched between the samples and their associated intensities were next subjected to unsupervised hierarchical clustering (22). Neither predefined reference vectors nor prior knowledge about the nature of each pattern (untreated normal versus cancer-bearing) was used. The results of this unsupervised hierarchical clustering analysis are represented by the tree structure (Fig. 5). A section of the clustered peptides illustrating a cluster of discriminatory peptides is also shown in Fig. 5 in which each row represents a peptide and each column represents a different serum sample. The lengths of the branches among different samples are proportional to the similarity of the obtained peptide patterns. From this clustering, it is apparent that the cancer-bearing mice (C1, C2, and C3) were clustered together and clearly differentiated from the patterns obtained from their sex- and litter-matched normal mice (N1a, N1b, and N2), which were also clustered together.
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The glycopeptide-enriched samples were then further analyzed by MS/MS to identify peptides that increase in abundance in cancer-bearing mice compared with untreated normal animals. The m/z and retention time coordinates of these peptides were added to the inclusion list on a tandem mass spectrometer and identified by LC-MS/MS and sequence data base searching. Fig. 6A shows a peptide at m/z of 709.7 (eluted at 65 min) that, while showing variation between individuals, also clearly showed consistently increased abundance in cancer-bearing mice (C1, C2, and C3) compared with normal animals (N1a, N1b, and N2). The signal at m/z of 709.7 was subsequently identified as a peptide with the amino acid sequence LIPHLEKPLQN#FTLCFR (in which N# indicates the formerly N-linked glycosylation site) derived from serum amyloid P-component in mouse. This is an acute phase protein whose expression is known to be elevated during inflammation (32).
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DISCUSSION |
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Ideally, for the detection and validation of protein biomarkers in serum, the complete serum proteomes of multiple individuals representing different clinical states would be completely and quantitatively analyzed. Due to the enormous complexity of the serum proteome and technical limitations, all the current proteomic technologies for such analyses can only sample a small part of the proteome, predominantly the most abundant proteins (14, 33). For example, two-dimensional gel electrophoresis-based studies have identified about 300 serum proteins collectively (14, 15, 34). It has also been estimated that SELDI-TOF approaches have limited detection of low abundance proteins due to the high dynamic range of serum proteins and the limited binding capacity of the SELDI chip (6). In the method presented here, the selective isolation of the N-linked glycosylated peptides resulted in a substantial improvement in the concentration limit of protein detected due to the reduction in sample complexity.
A number of factors contribute to this effect. First, the number of peptides per protein isolated after applying the glycopeptide capture-and-release method is significantly reduced. The 93 proteins identified in this study are predicted to generate an average of 28.8 tryptic peptides per protein. Of these, only 3.6 on average contain the N-linked glycosylation consensus motif and can be potentially glycosylated, and an average of 3.6 peptides representing 1.8 unique N-linked glycosylation sites per protein were actually identified. By comparison, a similar number of N-linked glycosylation sites identified per protein was reported by Kaji and colleagues (35) (1.8 sites per protein) in a study in which N-linked glycopeptides were isolated from Caenorhabditis elegans proteins using lectin enrichment. Second, the most abundant serum protein, albumin, does not contain N-linked glycosylation motifs and therefore is effectively transparent to the analysis. Since albumin itself comprises almost 50% of total serum protein content, exclusion of albumin eliminates numerous peptides that otherwise dominate serum peptide samples. Indeed quantitative removal of albumin, a goal that is normally attempted by use of costly affinity depletion methods (36), is an automatic by-product of the glycopeptide capture method. Third, the method only selects peptides from the constant region of immunoglobulins and thus dramatically reduces the number of immunoglobulin-derived peptides. This is important since immunoglobulins constitute 20% of total protein mass in serum (26) and comprise a population of an estimated 10 million different molecules (14). The difficulty of penetrating the population of immunoglobulins in unbiased serum proteome analyses was recently illustrated in a study in which a tryptic digest of serum was analyzed by ultrahigh efficiency strong cation exchange LC/reversed-phase LC/MS/MS. Of the 1061 plasma protein identifications reported, 38% were immunoglobulins (10). It is also likely that an even more significant fraction of peptides observed in LC-MS patterns of unbiased serum protein digests are derived from immunoglobulins since nucleic acid and protein sequence data bases dramatically underreport the contribution of somatic combinatorial gene rearrangement to immunoglobulin diversity. Fourth, many serum proteins are post-translationally altered by phosphorylation, glycosylation, acetylation, methionine oxidation, protease processing, and other mechanisms, resulting in multiple forms for each protein. It has been estimated that one protein may generate on the order of 100 species (14). In the case of glycosylation, the oligosaccharide structures attached at each site are typically diverse, compounding the complexity of the peptide mixture. The peptides isolated by the glycopeptide capture method remove the heterogeneous oligosaccharides and thus, by isolating a few peptides per protein only, also eliminate other significant sources of pattern heterogeneity.
The cumulative effect of these factors is the generation of a peptide sample from the serum proteome with a moderate redundancy of an average of 3.6 unique peptides per protein. Theoretically an average of 3.6 potential N-linked glycopeptides (containing an NX(T/S) motif) is predicted for the 93 identified serum proteins (Supplemental Table 1). However, not all of these potential N-linked glycosylation sites were observed. Some of these potential N-linked glycosylation sites may not actually be occupied (25), the peptides from certain sites may not be detectable by mass spectrometry, or protein digestion may be hindered by the protein post-translational modifications such as oligosaccharide attachment and/or disulfide bond formation. On the other hand, the number of peptides from each glycosylation site was increased due to other types of protein modifications (e.g. methionine oxidation and protease processing) in the glycosylation region. It is expected that the same factors would also lead to an inflation of the number of peptides observed if digests of non-selected serum samples were analyzed. In our analyses of peptides generated from 5 µl of mouse serum using the glycopeptide capture-and-release method, we were able to detect and quantify over 3000 peptide peaks that were present at least at two of three samples in either group with intensity at least at 2-fold above background noise level. In MS/MS analysis, only a small fraction of peptides (319 unique peptides, Supplemental Table 1) were identified. This was due to the complexity of the sample and the fact that the mass spectrometer only had time to sequence a small portion of the peptides, predominantly the highly abundant peptides in each sample (undersampling). The same undersampling factor was also the major cause of the inconsistency of protein identifications using LC-MS/MS. In this study, we used reproducible LC-MS for quantitative analyses, and this allowed us to analyze all the peptide ions in each sample, including those from proteins of low abundance.
While the reduction of peptide redundancy is beneficial for achieving higher coverage of the proteome per analysis, it is also apparent that it leads to the loss of some, potentially important information. First, non-glycosylated proteins are transparent in this system. While it is believed that the majority of serum-specific proteins are in fact glycosylated (37), intracellular proteins (typically non-glycosylated) that may represent a rich source of biomarkers if leaked into serum might go undetected. Second, the availability of fewer peptides per protein increases the challenge of identifying the corresponding protein. Third, this approach will reveal differences in protein level or glycosylation level (glycosylation site occupancy). Disease markers that alter other protein post-translational modifications including proteolytic processing will not be detected on a glycopeptide level. Finally collapsing peptides modified by different oligosaccharide structures into a single signal will obscure potential disease markers that are due to oligosaccharide structure alteration (37).
In this study, we successfully used the glycopeptide capture and LC-MS analysis platforms to differentiate serum from mice with chemically induced skin cancer from that of non-treated littermates. In this experiment, the mice with skin cancer and their untreated littermates had the same genetic background and lived in the same environment. The study therefore represents a controlled experiment with chemically induced skin cancer being the sole variable. The sera were clearly distinguished by numerous distinct peptides, the abundance of which was consistently increased or decreased between the cancer and control sera. While in this controlled experiment, the low number of samples was sufficient to detect disease-associated signatures, the application of the method to identification of potential biomarkers in much more variable human samples will require the analysis of much larger sample numbers to facilitate statistical validation of the data. The current method, at present, has sufficient throughput to perform studies involving a few hundred samples, a number that appears sufficient to generate statistically significant results within a reasonable time frame (38, 39). By developing a robotic procedure to allow automated sample preparation and by further optimizing LC-MS analysis procedures and the development of a robust, automated data analysis platform, we are further increasing the performance of the system.
In contrast to the widely used SELDI-TOF and similar polypeptide profiling methods, the signals detected in the present method are defined molecular species, mostly peptides ranging in length between 7 and 30 amino acids. These peptides, if selected for CID in a tandem mass spectrometer, are readily sequenced. By adding the coordinates of selected discriminatory peptides to an inclusion list, we have identified several serum proteins for which the abundance is increased in correlation with the chemical induction of skin cancer in mice (Table III). While these proteins are indicators of interesting biology and have been reported to change the abundance in different types of cancer (40), they are likely not markers for the specific diagnosis of skin cancer. Proteins useful for cancer detection, diagnosis, or stratification might be proteins released in small amounts from the primary lesion, indicators of a specific response of the system to the lesion, or other subtle changes in the serum proteome. For the reliable detection of such proteins or patterns of proteins, it is imperative that all candidate molecules are identified so that potential markers or signatures observed in different diseases, studies, and laboratories can be validated, correlated, and compared. This will allow the proteomic biomarker discovery community to establish defined molecular signatures as the currency of communication and to distinguish between true biomarkers and coincidental changes. The identification of discriminatory peptides in this study furthermore indicates that at least some of the proteins changing in abundance in the skin cancer model are moderately to highly expressed. In contrast, serum cancer markers currently in clinical use have concentrations in the ng/ml range. Diamandis (6) has argued that the SELDI-TOF method and by implication similar methods are about 3 orders of magnitude too insensitive from the sensitivity required to detect such proteins. The method presented here has the potential to reach ng/ml sensitivity levels and even lower concentration limits if high performance Fourier transform ion cyclotron resonance-MS instruments are used (data not shown). For example, at a concentration of 4 ng/ml, 5 µl of serum sample contains 20 pg (
700 amol) of prostate-specific antigen, an amount that is readily detected in a modern mass spectrometer. In comparison, if non-biased serum digests are analyzed on the same capillary LC-MS system, the total amount of serum that can be applied to the system would be 50 nl, and therefore the concentration limit of detection would be 100-fold reduced compared with the glycopeptide-selected sample. Thus prostate-specific antigen would be well outside the detection limit of such an analysis. If further increases in the concentration limit of detection were required, the glycopeptide capture-and-release method could easily be combined with other peptide fractionation methods, including electrophoresis (gel-based or free flow electrophoresis) chromatography or affinity depletion.
In summary, selectively isolating peptides from N-linked glycosylated serum proteins has been found to be a powerful method for the analysis of the serum proteome. Together with the high reproducibility of this method, the high level of serum proteome coverage achieved at a moderate throughput suggests that this method will be most useful for the detection of proteins or protein patterns that distinguish individuals in different physiological states.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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Published, MCP Papers in Press, December 17, 2004, DOI 10.1074/mcp.M400090-MCP200
1 In this paper, the term serum is used to indicate serum or plasma.
2 The abbreviations used are: SELDI, surface-enhanced laser desorption ionization; MS, mass spectrometry; LC, liquid chromatography; ESI, electrospray ionization; MS/MS, tandem mass spectrometry; MALDI, matrix-assisted laser desorption ionization; CID, collision-induced dissociation; TOF, time-of-flight; QTOF, quadrupole time-of-flight; CV, coefficient of variance; DMBA, 7,12-dimethylbenz[a]anthracene; HPLC, high performance liquid chromatography.
3 X. J. Li, E. C. Yi, H. Zhang, and R. Aebersold, manuscript in preparation.
* This work was supported in part by NCI, National Institutes of Health Grants R33 and CA93302, by Grant N01-HV-28179 from the NHLBI, National Institutes of Health, Proteomics Initiative, and by a sponsored research agreement from Industrial Technology Research Institute in Taiwan. The Institute for Systems Biology was supported by a generous gift from Merck & Co. The LC-FTICR analyses at PNNL were supported by NIH National Center for Research Resources (RR-18522). Pacific Northwest National Laboratory is operated by the Battelle Memorial Institute for the U. S. Department of Energy through Contract DE-AC06-76RLO1830. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
S The on-line version of this article (available at http://www.mcponline.org) contains supplemental material.
To whom correspondence should be addressed. E-mail: hzhang{at}systemsbiology.org
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REFERENCES |
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