Patent Publication Number: US-2006004525-A1

Title: System and method of determining proteomic differences

Description:
RELATED APPLICATIONS  
      This application claims priority from U.S. Provisional Application No. 60/305,169, filed on Jul. 13, 2001 and U.S. Provisional Application No. 60/359,524 filed on Feb. 21, 2002. 
    
    
     FIELD OF THE INVENTION  
      This invention relates to systems and methods for automatically calculating information received from a mass spectrometer. More specifically, this invention relates to systems and methods that determine proteomic differences between two samples by comparing mass spectrometer data from each sample.  
     BACKGROUND OF THE INVENTION  
      Recent advances in nucleotide sequencing and detection have made it possible to determine the complete DNA sequence for an entire genome of a living organism. With the sequencing of the human genome complete, as well as that of numerous other lower organisms, the attention of many researchers has turned towards how these sequences may be used to answer complex biological questions and provide useful information in the treatment of disease states.  
      More recently, comparative cDNA array analysis and related high-throughput nucleotide identification technologies have been used to globally assess gene expression at the messenger RNA (mRNA) level. These technologies are capable of quantitatively and simultaneously measuring mRNA levels for virtually every gene expressed in a cell or tissue to provide a complete expression profile for an organism. Furthermore, biological and computational techniques have been used to correlate specific biological functions or cellular activities with these expressed gene sequences.  
      While knowledge of expressed gene sequences or mRNAs is important to understanding biological mechanisms and states of a living organism, the interpretation of the data obtained by these techniques represents a formidable challenge and cannot be solely relied upon to answer many biological questions. In particular, it has become apparent that knowledge of nucleotide expression patterns must be correlated with peptide expression patterns in order to more thoroughly understand and explain the numerous mechanisms related to biological processes.  
      Proteins are essential for the control and execution of virtually every biological process. The rate of synthesis and the half-life which dictate a particular peptide&#39;s expression level are typically controlled post-transcriptionally. Furthermore, the activity of a peptide is frequently modulated by post-translational modifications and is thus dependent on the association of the peptide with other molecules. Examples of associated molecules include DNA, RNA, sugar residues and other peptides. Neither the level of expression nor the state of activity of peptides is therefore directly apparent from the gene sequence or even the expression level of the corresponding mRNA transcript. It is therefore essential that a complete description of a biological system include measurements that indicate the identity, quantity and the state of activity of the peptides which constitute the system. This requirement for large-scale (ultimately global) analysis of peptides expressed in a cell or tissue has been termed proteome analysis (Pennington et al.,  Trends Cell Bio  7:168-173 (1997)).  
      At present no peptide analytical technology approaches the throughput and level of automation of genomic technology. The most common implementation of proteome analysis is based on the separation of complex peptide samples by two-dimensional gel electrophoresis (2DE) and the subsequent sequential identification of the separated peptide species (Ducret et al.,  Prot Sci  7:706-719 (1998); Garrels et al.,  Electrophoresis  18:1347-1360 (1997); Link et al.,  Electrophoresis  18:1314-1334 (1997); Shevchenko et al.,  Proc Natl Acad Sci USA  93:14440-14445 (1996); Gygi et al.,  Electrophoresis  20:310-319 (1999); Boucherie et al.,  Electrophoresis  17:1683-1699 (1996)). This approach has been assisted by the development of mass spectrometric techniques and computational methods which correlate peptide and peptide mass spectral data with computer databases in order identify peptides (Eng et al.,  J Am Soc Mass Spectrom  5:976-980 (1994); Mann and Wilm,  Anal Chem  66:4390-4399 (1994); Yates et al.,  Anal Chem  67:1426-1436 (1995)).  
      Mass spectrometry based techniques for peptide identification identify peptide fragments based on a spectral signature uniquely generated for each peptide sequence. In this procedure, a peptide mixture is separated using a first mass spectrometer which separates the peptides according to their mass and charge characteristics to produce a spectrum indicative of the component peptides of the peptide mixture. Each separated peptide is then further subjected to a second tandem mass analysis where the peptide is fragmented and a second mass spectrum is produced. The second mass spectrum comprises a series of peaks (peptide signature) formed as a result of differences in the mass-to-charge ratios of fragments of the peptide. For peptides with differing sequences, the series of peaks uniquely identifies the particular sequence of the peptide undergoing analysis.  
      Computational methods for sequencing peptides subjected to mass analysis involve comparing the spectrum generated by the peptide of interest with known spectra. In these methods, the peptide spectrum is associated with a known sequence to indicate sequence homology. The results of the analysis typically contain many values and statistical correlations that identify associations between the peptide signature and the known spectra. The analysis may also include candidate sequences that are likely to match the experimental spectrum, as well as, correlation scores and probabilities indicating the degree of confidence of the match.  
      In conventional systems the results of the statistical analysis are reviewed and interpreted by an investigator to validate the peptide sequence. Sequence interpretation in this manner is a time consuming process and requires highly skilled individuals trained to understand the significance of the statistical analysis and correlation scores. Furthermore, validation of the peptide sequences can be inaccurate and is prone to investigator bias. As a result, analysis of increasingly complex peptide mixtures becomes impractical due to the inherent limitations in interpreting the data. Additionally, quantitating and comparing peptide concentrations in a mixed peptide population is also particularly time consuming due to the need to transform and interpret the results by hand.  
      U.S. Pat. No. 6,017,693 describes a system for correlating a peptide fragment mass spectrum with amino acid sequences derived from a database. This is one example of a conventional mass spectrometry-based method for peptide identification which compares an experimental peptide spectrum with a known database of spectra. In this system, mass spectra from an experiment are input into a computer containing a database of sequence-associated spectrum. The computer then performs a search of the database and outputs results of the search to the investigator in the form of an output file or summary. The resulting output file must then be reviewed and interpreted manually by the investigator to determine the peptide sequence. Such a system may have the analytical capability to process a relatively small sample peptide population, however, its utility is severely diminished when assessing the many thousands of proteins or peptides typically present in a cell or tissue extract. The resulting amount of time an investigator must devote to reviewing the output files therefore represents a significant bottleneck in the analytical process which must be alleviated if complex mixed-populations of peptides are to be assessed.  
      Thus, in the analysis of complex mixed peptide samples, there is a need for an automated method for processing mass spectral data in which peptide signatures generated during an experiment can be automatically queried against a database of spectral information to generate sequence information. Additionally, there is a need for a system which receives the results from the peptide sequence analysis and interprets the results automatically. Such a system is useful when identifying and comparing large numbers of proteins or peptides as are typically found in whole cell or tissue extracts. Furthermore, this system should be adapted to store the information in a central database permitting the comparison of results obtained from many experiments to facilitate global proteomic comparisons and data mining operations.  
      A further difficulty presented by the aforementioned peptide sequencing and identification methods relate to their limitations when applied to differential analysis. Differential analysis correlates protein expression between multiple populations of cells or tissues to identify differences between them. Such comparisons are essential to understand regulatory patterns and identify novel peptides or pathways. Existing mass spectroscopy based technologies typically asses each sample independently and are subject to experimental and instrumental variability between samples. This results in difficulties in correlating all of the components from each sample relative to one another and limits the utility of these techniques in assessing differential peptide expression on a global scale.  
      It is therefore apparent that current technologies are not suitable for rapidly quantitating nor determining the state of activity of each peptide within a complex mixture. Furthermore, existing technologies are not able to efficiently and accurately perform simultaneous analysis of more than one peptide population hindering the investigator&#39;s ability to conduct differential analysis. Accordingly, it would be useful to provide an efficient system for performing differential analysis which is capable of measuring peptide or protein expression changes between two or more biological samples. Such an analytical tool can provide important insight into how peptides interact and is useful in determining unknown peptide functions.  
     SUMMARY OF THE INVENTION  
      Embodiments of this invention include systems and methods for rapidly determining and quantifying proteomic differences between two or more biological samples. In one embodiment, proteomic analysis is performed by differentially labeling the two or more samples and subsequently quantifying the peptide levels or abundance in each sample. Differential labeling of the peptides derived from each sample provides a discernable means to identify each peptide population during the analysis and to provide a consistent, calculable molecular weight difference that can be observed during mass spectrometry of a mixed population peptide sample.  
      During the analysis, the mixed population peptide sample is passed through a peptide separation column and subjected to mass spectroscopy-based techniques. Knowledge of the difference in mass between the two populations, permits the system to identify pairs of the same (analogous) peptide from the mass spectrometry data, and determine their relative quantities or abundances. This results in the ability to rapidly and reliably calculate proteomic differences between the biological samples.  
      The approach described herein can be used for the quantitative analysis of peptide expression in complex samples (such as cells, tissues, and fractions thereof). Furthermore, the invention provides a suitable mechanism for differential expression analysis between multiple samples and the identification of novel peptides. Using a peptide labeling technique in conjunction with peptide separation and mass analysis methodologies, the peptide identification system resolves complex mixtures of peptides which are identified by database similarity lookups rather than traditional sequencing reactions. Additionally, this system evaluates peptide expression and regulation patterns in a rapid and quantifiable manner.  
      Embodiments of the invention include a mass spectrometry-based system and method for rapidly and quantitatively analyzing peptides in complex mixtures or isolates. The system also features automated processing capabilities used to analyze differentially expressed peptides in a single sample in order to reduce variability and increase accuracy. Differentially expressed peptides are identified by changes in expression patterns which, for example, may be affected by a stimulus (e.g., administration of a drug or contact with a potentially toxic material), by a change in environment (e.g., nutrient level, temperature, passage of time) or by a change in condition or cell state (e.g., disease state, malignancy, site-directed mutation, gene knockouts) of the cell, tissue or organism from which the sample originated. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
      These and other aspects, advantages, and novel features of the invention will become apparent upon reading the following detailed description and upon reference to the accompanying drawings. In the drawings, same elements have the same reference numerals in which:  
       FIG. 1  is a flow diagram illustrating a differential peptide identification methodology.  
       FIG. 2  is a block diagram illustrating a data analysis system used to identify differential peptide expression.  
       FIG. 3  is a flowchart illustrating a method of qualitative analysis of complex peptide mixtures.  
       FIG. 4  is a simplified mass spectrum intensity curve for a differentially labeled peptide in which markers create a mass differential between analogous peptides.  
       FIG. 5  is a flowchart illustrating a correlation process used for identifying differentially labeled peptides.  
      FIGS.  6 A-E are simplified mass spectrum scans illustrating states of differential expression that may be identified by the data analysis system.  
       FIG. 7  is a flow diagram illustrating a method for identifying and quantitating chromatographic peaks from a differentially labeled mass spectrum analysis.  
       FIG. 8  is a flow diagram illustrating a method for parallel processing of mass spectrum and sequence data.  
       FIG. 9  is a flow diagram illustrating computational activities performed by nodes within a parallel architecture that are used to resolve and quantitate differentially expressed peptides.  
       FIG. 10  is a chart showing the FPLC spectrum from the purification the synthesized PEPTag.  
       FIG. 11   a  is a printout showing the mass spectrum of the synthesized PEPTag.  
       FIG. 11   b  is a printout showing the mass spectrum from MS/MS experiment to sequence PEPTag.  
       FIGS. 12   a,b  show printouts of the MALDI MS analysis of PEPTag captured BSA peptides.  FIG. 12   a  is a printout wherein peaks are cysteinyl tryptic peptides from tagged BSA, which are captured by HA matrix and cleaved off by TEV.  FIG. 12   b  is a printout showing a control analysis of untagged BSA. The main peak in this spectrum is from TEV protease.  
       FIGS. 13   a,b  show the μLC MS/MS analysis of PEPTag captured BSA peptides.  FIG. 13   a  is a printout showing the base peak ion current profiles of all peptides released by TEV protease.  FIG. 13   b  is a printout showing the reconstructed ion chromatograms from A (m/z 956.0-957.0) of the eluted peptide, which is doubly charged ion (m/z=956.4).  
       FIGS. 14   a,b  show the MS and MS/MS spectra of the PEPTag modified peptide.  FIG. 14   a  is a printout showing the full-scan (600-1,500 m/z) mass spectrum at time 29.49 min of μLC-MS and μLC-MS/MS analysis.  FIG. 14   b  is a printout showing the tandem mass spectrum (250-1925 m/z) of the (M+2H) 2+  of the eluted peptide (m/z=957.25).  
       FIG. 15  is a printout showing the MALDI mass spectrum of a pair of PEPTag labeled peptides of identical sequences. The m/z difference depends on the charge state. It is either 14 or 7 for charge state one or two.  
       FIGS. 16   a - c  show the μLC-MS/MS analysis of captured peptides labeled by differential PEPTags.  FIG. 16   a  is a printout showing base peak ion current profiles of all the peptides released by TEV protease from combined two protein mixtures.  FIG. 16   b  is a printout showing the reconstructed ion chromatograms (m/z 1034.0-1035.0) of a cysteinyl peptide labeled by PEPTag  1   a .  FIG. 16   c  is a printout showing the reconstructed ion chromatograms (m/z 1027.0-1028.0) of the same cysteinyl peptide labeled by PEPTag lb.  
       FIG. 17  is a printout of the ESI mass spectrum of the pair of PEPTag labeled peptides of identical sequences. The m/z difference is 7 for doubly charged ions. 
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT  
      The system and methods presented herein are useful in identifying protein or peptide components when comparing mixed peptide populations for differential expression. In one embodiment, each population is labeled with an identifiable label or marker to resolve the mixed-population of peptides within the same sample or analysis. The resulting combined analysis provides improved resolution and identification capabilities and is not subject to the degree of instrumental or cross-sample experimental variations which confound conventional peptide identification techniques.  
      The peptide identification system further implements an automated sequencing routine in which tandem mass spectra identification resolves protein sequences by querying and correlation against a spectral database of known peptide spectra. This feature significantly improves data acquisition and sequencing throughput and provides a mechanism by which peptides within the mixed-population can be readily identified without additional sequencing steps or reactions.  
      As described below, in one embodiment an affinity labeling procedure is used to selectively isolate peptides that contain a desired label or tag. The isolated proteins, peptides, or reaction products are then characterized by mass spectrometry (MS) based techniques. In particular, the sequence of isolated peptides is determined using tandem MS (MS) n  techniques which are correlated with known peptide spectrum produced by the tandem MS (MS) n  techniques.  
      Prior to spectrometric analysis, the system for peptide identification and differential comparison incorporates a chromatographic/separation technique, such as microcapillary liquid chromatography or gas chromatography. These chromatographic techniques separate the mixed peptide sample or solution of interest thereby permitting selective analysis of each peptide sequence. Following the preliminary separation of the components, the sample is introduced into a mass spectrometer which serves as a detector of the individual components. Such a coupling between of these two technologies provides an efficient and high resolution method to identify the individual peptide components contained in the sample of interest.  
      The spectral database comprises a collection of tandem mass spectra which have been previously associated with known peptide sequences. One example of a mass spectral database is described in U.S. Pat. No. 5,538,897 to Yates, et al. Software comparison and identification routines correlate the output spectrum from mass spectrometry of the sample with those spectrum contained in the spectral database and returns the peptide identity of each peptide in the sample. Using these methods the spectrum of a complex peptide mixture is readily resolved and the corresponding sequences of the constituent peptides are identified as will be described in greater detail hereinbelow.  
      The following discussion provides examples of differential comparisons that are made based on treated and untreated cell or tissue populations. However, it will be appreciated that the peptide identification methods presented herein provide a flexible means for conducting comparisons between many different types of samples. Thus, these methods are applicable to a variety of instances where it is desirable to study differential peptide expression between two or more peptide populations. For example, in addition to comparing a treated versus untreated cell or tissue population, comparisons between different cell or tissue types may also be made. Furthermore, the analytical methods described herein can be used for multiplex analysis to simultaneously assess a complex mixture of peptides derived from more than two samples or peptide populations.  
      A. System Overview  
       FIG. 1  illustrates an overview of one embodiment of a peptide identification and differential analysis technique used to resolve, sequence, and identify complex peptide mixtures derived from two or more peptide populations. A typical comparison of differential expression is made using a starting cell population  105 . One portion of the cell population  105  is separated into a control cell population  109 A, while another portion of the population  105  is treated with a test compound to become test cell population  109 B.  
      The test cell population  109 B is treated with one or more conditions or treatments for which proteomic differences are to be identified. In one exemplary embodiment, the cell population  105  is analyzed by comparing the proteomes of the control population  109 A with the treated cell population  109 B.  
      Once the cells have been treated, the protein or peptide populations from each cell are isolated to yield a control peptide population  107  and a treated peptide population  108 . During this stage of analysis the peptide isolation procedure may additionally incorporate processing or purification steps designed to remove undesirable or contaminating biomolecules and chemicals. For example, during the harvest of peptides from a cell or tissue, biomolecules such as RNA, DNA, and proteases, as well as, extraction reagents and buffers may be removed from the peptide isolate to prevent interference with detection of the peptide molecules.  
      A subsequent labeling reaction is used to label each peptide population  107 ,  108  with an identifiable peptide labeling moiety or label  122 ,  124  which aids in resolving the peptide populations  107  during mass analysis. In one aspect, the labels  122 ,  124  comprise multi-functional synthetic peptide sequences with differing masses. During the analysis, the peptide populations  107 ,  108  are made differentially identifiable by incorporating the first label  122  into the first peptide population  107  and incorporating the second label  124  into the second peptide population  108 . Thus, the peptides  107 ,  108  derived from each condition or treatment  110  are made to contain an identifiable label  122 ,  124  of known mass. The difference in molecular weight between the first label  122  and the second label  124  serves as a basis for determining the peptide population  107 ,  108  of origin from which an identified peptide is derived by creating a mass differential between the two peptide populations. Examples of differential labels are described below.  
      The labels  122 ,  124  may additionally contain a peptide epitope tag or motif used for affinity purification of the labeled peptides  107 ,  108 . This feature of the labels  122 ,  124  is useful for isolating only those peptides which have been labeled and may further serve as a means for enriching the peptide populations  107 ,  108 . Enrichment of the peptide populations  107 ,  108  increases the sensitivity of the mass detection procedure and removes background “noise” that may be contributed by unlabeled or undesirable peptides.  
      Of course, it is not required to label both populations of peptides. Accordingly, only the treated peptide population  108  might be labeled in order for each peptide in the treated population to have a different mass from the control population. Additionally, it is contemplated that the peptides can be metabolically labeled prior to isolation from the cells or tissues. In this alternative method, discernable peptide populations  107 ,  108  are created through the use of isotopic labeling to create peptide populations  107 ,  108  with differing masses. In metabolic labeling, a heavy isotope label, such as a nitrogen isotope ( 15 N), may be incorporated into the first peptide population  107  and a lighter nitrogen isotope, such as  14 N, may be incorporated into the second peptide population  108 . The different isotopes are incorporated in-vivo to label all of the amino acids to create the discernable peptide populations without the requirement of a subsequent labeling step.  
      When using the peptide epitope tag for affinity purification, a specific protease site may further be incorporated into the label  122 ,  124  to facilitate the release of the affinity purified labeled peptides from an affinity matrix. Additional details of the chemical composition of the labels  122 ,  124  as well as details of the specialized peptide epitope motifs for purification of the peptide populations  107 ,  108  are described below.  
      Following peptide labeling, cleanup and purification procedures may be used to prepare the peptide populations  107 ,  108  for analysis. The control and treated peptide populations are then combined to form a single mixed-population peptide sample  130 . Combining the uniquely labeled peptide populations  107 ,  108  in this manner desirably simplifies subsequent mass analysis procedures while permitting peptides from each population  107 ,  108  to be resolved, identified, and compared using the incorporated labels  122 ,  124 .  
      Furthermore run-to-run inconsistencies, experimental variabilities, and user-induced inaccuracies are minimized by combining the peptide samples  107 ,  108  to result in improved data output and more definitive peptide identification. The improvement in analysis is due, in part, to the observation that by the combining peptide samples, the two peptide populations  107 ,  108  are subjected to identical conditions and manipulations thus reducing variability between the samples which would otherwise be treated and analyzed independently.  
      In preparation for mass analysis, the mixed peptide sample  130  is subjected to proteolysis to fragment the peptides  107 ,  108  into smaller molecules which are of suitable size for use in mass spectrometry-based techniques. Furthermore, protease cleavage can be used to release labeled peptides  107 ,  108  from the aforementioned affinity matrix.  
      Proteolysis is desirably conducted using a highly specific protease enzyme. Examples of protease enzymes which may be used for peptide digestion include: TEB protease, chymotrypsin, endopeptidease Arg-C, endopeptidease Asp-N, trypsin,  Staphylococcus aureus  protease, thermolysin, and pepsin. As described in greater detail below, protease selection may be directed by the type of label incorporated into the labeled peptides  107 ,  108 . These labels  122 ,  124  may contain amino acid sequences which define specific protease cleavage sites which are designed to release the labeled peptides from the affinity matrix to provide a purified or enriched peptide sample.  
      Quantitation of peptide expression levels is performed using mass analysis techniques which determine peptide quantities within the differentially labeled mixed-population peptide sample  130 . As discussed above, in one embodiment, the mixed-population sample  130  is first subjected to a preliminary separation step using liquid or gas chromatography methods or 2-dimensional gel electrophoresis. In another embodiment multidimensional protein identification technology (MudPIT) (Washburn et al.,  Nature Biotechnology,  19: 242-247 (2001)) is used as a preliminary means to separate the peptide components resulting from the aforementioned proteolysis reactions.  
      The MudPIT technique utilizes a fused-silica microcapillary column packed with a reverse-phase material (XDB-C18, Hewlett-Packard, CA) in addition to a strong cation exchange material (Partisphere SCX, Whatman, N.J.). The mixed-peptide sample is loaded onto the packed column and placed in-line with the mass spectrometer and a buffer solution is passed through the column to elute the peptides. The resulting peptide eluate provides a preliminary separation means for the peptides which are then passed through the mass spectrometer resulting in further separation of the peptides according to their mass-to-charge ratio.  
      As will be appreciated by one of skill in the art, numerous methodologies exist which may be used to provide a preliminary separation means for resolving the mixed-peptide sample prior to mass analysis. Thus, these preliminary separation means used in conjunction with the mass analysis techniques described herein represent alternate embodiments of the present invention.  
      The mass spectrometer, in addition to serving as a peptide-separation means, acts as a detector to provide information useful in the identification of each peptide species contained within the mixed-population sample  130 . Mass analysis, in this manner, provides a suitable method to compare expression levels between similar peptides  107 ,  108  derived from different sources, conditions, or treatments as will be described in greater detail hereinbelow.  
      As will be appreciated by one of skill in the art, a number of mass analysis techniques may be applied to the resolution and identification of the mixed-population peptide sample  130 . Examples of suitable mass analysis techniques include: electron ionization, fast atom/ion bombardment, matrix-assisted laser desorption/ionization (MALDI), and electrospray ionization. MALDI spectroscopy techniques in particular possess a number of desirable characteristics which improve the quality of the mass analysis. These characteristics include: large mass range of the input peptide species (greater the 300,000 daltons), high sensitivity (low picomole detectability), soft ionization (producing little or no observed fragmentation of the peptides), salt tolerance (in millimolar concentrations), and the ability to analyze complex mixtures of peptides in a resolvable manner.  
      Following the initial separation/quantitation step, a subsequent component analysis step is performed in which resolved peptides  146  undergo tandem mass analysis (MS (MS) n ) to produce a unique spectrum  147  characteristic of the particular sequence of the peptide  146 . In one embodiment, MS (MS) n  spectrum  147  are desirably acquired for each resolved peptide  146  using an automated procedure wherein the individual spectrum  147  are acquired and stored for later processing and sequence identification.  
      In a typical differential expression and characterization analysis, a large number of MS(MS) n  spectrum  147  are generated (at least one for each resolved peptide  146 ). While it is possible to visualize, review, and identify each spectrum manually, it is impractical and time consuming for an entire peptide population to be analyzed in this manner. Instead the MS(MS) n  spectrum  147  are well suited to be processed by an automated method using computer assisted identification in conjunction with a spectral or correlative database, as will be described in greater detail hereinbelow.  
      Based on the aforementioned overview, differential peptide analysis compares peptides present in two or more biological samples. The peptides are labeled with a discernable marker to allow the peptides from each biological sample to be identifiable from one another when they are combined. Combination of the samples is desirable as it permits simultaneous analysis of the peptides and provides a means of directly comparing related peptides. Direct peptide comparison is further useful in identifying expression differences between related peptides within the two or more biological samples and aids in the detection of novel peptides.  
      For example, in a peptide population A and a peptide population B derived from a similar cell or tissue type, it will be expected that the composition of the two peptide populations will be related (i.e. both cells will contain identical peptides which may be expressed at different levels). The differential peptide analysis identifies and quantitates the relative concentrations of the related peptides in these populations to provide information about the overall peptide expression state of each biological sample. This analysis further identifies differences in peptide expression between the two biological samples which are useful in determining the effect of a treatment or condition upon a cell or tissue.  
      Peptides are identified using mass analytical methods in which the peptides undergoing analysis are bombarded with an electron beam to produce identifiable fragments (cations and radical cations) that are accelerated in a vacuum through a magnetic field and are sorted on the basis of mass-to-charge ratios. Peptides are identified on the basis of the mass-to-charge ratio which is related to the molecular weight of the fragments produced. Subsequent tandem mass analysis produces a unique spectral signature for each identified fragment which is compared to a database of known spectral signatures and used to identify the sequences of the collection of peptide fragments. One device for performing this function is a tandem mass spectrometer LCQ Deca from Thermo Finnigan (San Jose, Calif.). See http://www.thermofinnigan.com on the Internet for more information.  
      This embodiment of the invention therefore is an automated method for identifying the many thousands of component peptides (i.e.: the proteome) of a biological sample. Furthermore, the expression levels of the component peptides can be rapidly quantitated and compared between samples to give a better understanding of global peptide expression within biological systems.  
      B. The Data Analysis System  
       FIG. 2  illustrates components of a data analysis system  200  which interact with instrumentation  205  used to perform the differential peptide analysis. The data analysis system  200  comprises a plurality of modules  210  that operate in conjunction with a microprocessor 215 to receive and process data output  208  produced by the mass analysis and MS (MS) n  techniques. Using these modules  210 , the data analysis system  200  identifies the peptide constituents whose mass spectrum and associated information make up the data output  208  and subsequently processes the data to obtain detailed sequence and expression information.  
      In the illustrated embodiment, an instrument control/data acquisition (ICDA) module  220  acts as an interface between the instrumentation  205  and the data analysis system  200 . The ICDA module  220  receives the data output  208  and performs necessary handshaking and error correcting functions to insure data integrity. The ICDA module  220  is further equipped to recognize and process various data types associated with the data output  208  which are native to the instrumentation being used  205 . The ICDA module  220  may additionally issue control signals  209  which coordinate run-time activities associated with the instrumentation  205 . For example, the control signals  209  may be used to modify configuration settings or parameters the instrumentation  205 , as well as, manage operational modes such as starting/stopping sample analysis. Furthermore, control signals  209  may be issued by the data analysis system  200  to direct a plurality of mass spectral analysis scans to be acquired by the instrumentation  205  over a specified time period or with a particular frequency. In this embodiment, the mixed-peptide population  130  is eluted from the preliminary separation means and passed through the mass analysis instrumentation over a time period of approximately 1-10 minutes. During this time, mass spectral scans are taken with a frequency of approximately 50 scans/sec generating a plurality of mass spectral scans which are representative of the peptide composition at various points throughout the peptide elution. As will be described in greater detail hereinbelow, this method of multiscan mass analysis is used to construct peptide elution profiles for each of the peptides in the mixed population and improves the ability of the data analysis system  200  to identify and quantify proteomic differences.  
      A data processing (DP) module  225  receives the data output  208  from the instruments  205 , formats the data output  208 , and stores it in a working database  226  in a suitable form for later retrieval and processing. Functions of the DP module  225  may include rearranging or organizing the data output  208 , performing operations to transform or change the format of the data output  208 , or other tasks to prepare the data output  208  for subsequent analysis. The DP module  225  additionally interacts with a working database  226  (used to store raw data and information) and a bioinformatic database or data warehouse  227  (used to archive the experimental results after the data has been processed and the mixed-peptide population analyzed, quantitated, and compared) to organize, categorize and store the data output  208  in a form that may be easily sorted, queried, and retrieved.  
      The working database  226  and the bioinformatic database  227  are desirably implemented using relational schemas to provide flexible analytical querying and data mining capabilities. Furthermore, use of the databases  226 ,  227  provide a means by which the data output  208  and expression results may be correlated with other information creating an integrated bioinformatic system. In one embodiment, the databases  226 ,  227  may be implemented using applications designed for relational database development and implementation, such as those sold by Oracle Corporation (Redwood Shores, Calif.), Sybase Corporation (Emeryville, Calif.), and MySQL AB (Postgirot, Stockholm, Sweden). In other embodiments, the databases  226 ,  227  comprise database designs implemented using numerous other programming languages such as JAVA, C/C++, Basic, Fortran, or the like, wherein the database structure, tables, and associations are defined by code of the programming languages.  
      It is also recognized that other types of databases may be used, such as object oriented databases, flat file databases, and so forth. Furthermore, the databases  226 ,  227  may be implemented as a single database with separate tables or as other data structures that are well known in the art such as linked lists, binary trees, and so forth. Additionally, the databases  226 ,  227  may be implemented as a plurality of databases which are collectively administered to store and analyze the data of the data analysis system  200 .  
      As will be subsequently described in greater detail, a communications module  235  of the data analysis system  200  interacts with a spectral database  250  to aid in the determination of the origin and sequence for each peptide component of the mixed peptide population under study. The spectral database  250  comprises stored spectra of known peptide sequences used to identify peptides from experimental tandem mass spectrum data  255 . The data analysis system  200  desirably utilizes a computer program or search routine to identify the peptides by comparison of tandem mass spectrum data  255  with the spectral database  255 . One such program for determining the identity of a peptide by matching tandem mass spectrum data with stored peptide spectra is the SEQUEST peptide identification program developed at the University of Washington (http://www.washington.edu). Information on the SEQUEST program and system can be found on the Internet at http://thompson.mbt.washington.edu.  
      Once the system  200  has searched the spectral database  250  in order to match tandem mass spec data with stored spectral data  208 , peptide-correlated output files  260  containing the putative identities of the peptides determined from the spectral data analysis are then returned to the data analysis system  200  for further processing.  
      In one embodiment, communication between the data analysis system  200  and the spectral database  250  occurs by way of a communications medium  252 , such as the Internet, with the communications module  235  providing functionality for sending and receiving data through a suitable means, such as a TCP/IP based protocol. The communications module may additionally provide accessibility to other remotely located bioinformatic information systems  254  such as GenBank, SwissProt, Entrez, PubMed, and the like to acquire other information which may be associated with the peptide-correlated output files  260  and information stored in the databases  226 ,  227 .  
      A quantitation module  230  is used by the data analysis system  200  to determine more precise relationships between the peptides identified in the mixed-population and their relative expression levels. This module confirms the identity of each peptide in the mixed population of peptides by evaluating the results of the peptide correlated output files  260  and the mass spectrum data  208 .  
      More specifically, the quantitation module  230  evaluates the peptide-correlated output files  260  and identifies peaks or intensity curves corresponding to resolved peptides in the mass spectrum data  208 . The quantitation module  230  also quantitates the amount of peptide associated with a particular resolved peak  146  or intensity curve within the mass spectrum data  208  by area calculations. Additionally, the quantitation module  230  identifies and evaluates the peaks corresponding to the same peptide from both control and treated samples. This process will be described in greater detail hereinbelow.  
      As previously indicated, peptides from the control population and the treated population may be determined by the differential masses of the labels  122 ,  124  which are integrated into each peptide undergoing analysis. The use of the label  122 ,  124  distinguishes analogous peptides from different samples which have similar spectrum  208  by creating a mass differential between the analogous peptides containing different labels  122 ,  124 . Identification of the peptides derived from each treatment or condition provides a means for the quantitation module  230  to perform cross-sample comparisons and identify changes in peptide expression.  
      The IR module  240  provides additional insight into the mixed population peptide samples under study by retrieving information from other bioinformatic databases  254  that may be correlated with peptide sequences identified by the data analysis system  200 . For example, the IR module  240  may read information stored in the working database  226  or the bioinformatic database  227  and perform automated information search queries directed towards collecting additional information about the identified peptides. The IR module  240 , therefore, provides an additional means for automatically associating bioinformatic information from other informational sources and repositories with the experimentally identified peptides to yield a detailed collection of information.  
      Based on the aforementioned system architecture, peptide expression data is acquired for the mixed population of differentially labeled peptides  130  and subsequently processed to identify the peptide constituents of the mixed population sample. The system  200  formats and stores the data in an organized manner and extracts relevant information to use to query the spectral database  250 . The spectral database  250  then returns correlated tandem mass spectra  260  which are associated with the spectra of individual peptides in the mixed population undergoing analysis.  
      Typically, many thousands of queries are generated by the system  200  and the amount of information returned from the spectral database  250  necessitates an automated method for identifying and quantitating the peptide constituents of the mixed population  130 . To this end, specialized modules  210  of the system  200  provide instructions which parse and process the correlated tandem mass spectra  260  in a rapid and efficient manner and store the results of the analysis in the bioinformatic database  227  for subsequent evaluation by the investigator.  
      As will be appreciated by one of skill in the art, the aforementioned automated analysis and correlation features of the data analysis system  200  free investigators from having to perform lengthy searches and associations on an individual basis. Furthermore, the data analysis system  200  provides a more complete collection of data and information to which subsequent data mining techniques can be applied to further investigate the components of the mixed-peptide population.  
      C. Analyzing Complex Mixtures  
       FIG. 3  further illustrates a method  300  for analyzing complex peptide mixtures using the aforementioned metabolic labeling or tagging methods to distinguish between different cell types or conditions. The process begins at a start state  302  and then moves to a state  304  wherein one cell population is treated differently from another cell population. Once the cell populations are treated, their peptides are isolated and labeled at a state  306 .  
      As previously indicated, the labeling method may include metabolic labeling methods incorporating isotopes directly into the peptides or subsequent post-growth labeling methods with incorporate peptides of known sequence and mass into the peptides. Several examples of labeling peptides are provided below.  
      Following labeling, the peptides are then processed and separated by mass spectroscopy-based techniques at a state  308 . In one embodiment, the mass spectroscopy-based techniques are preceded by the aforementioned MudPIT two-dimensional liquid chromatography methodology for separating the mixed-peptide population. Upon applying the mixed-peptide sample to the MudPIT column, the mixed-peptide sample is eluted off the column in a series of buffer washes (see Washburn et al.,  Nature Biotechnology,  19: 242-247 (2001) for additional information). Mass analysis of the eluted sample takes place as a plurality of independent “mass analysis snapshots” or scans which are performed sequentially over the time it takes for the mixed-peptide population to be eluted from the MudPIT column. In one aspect, mass analysis of the mixed-peptide eluate is performed at a rate of approximately 50 scans per second with approximately 9000 scans being acquired during the run of a typical mixed-peptide sample.  
      As the mixed-peptide population is eluted, the acquisition of sequential mass spectrum scans form a parent ion map or peptide elution profile for each of the peptides in the mixed population. Subsequently, peptide signatures or tandem mass spectrum are further generated by directing a portion of each eluted peptide through a second tandem mass analysis instrument to identify and characterize the peptides present in each parent mass spectrum scan. In one embodiment, the data analysis system  200  identifies the intensity of each of the peptide peaks within a particular mass spectrum scan or ion map and directs a tandem mass analysis to be performed for the most intense peaks using MS (MS) n . The resulting tandem mass spectrum or peptide signature is therefore generated for a limited number of intense peaks in the mass spectrum scan and the results of the scan are stored in the working database  226 .  
      In a subsequent mass spectrum scan a similar process of identification of peak intensity is performed. The mass analysis system  200  determines if the most intense peaks have already been identified in the previous mass spectrum scan and, if so, selects new peaks with lesser intensities to perform tandem mass analysis on. Thus, the data analysis system  200  avoids performing redundant tandem mass analysis on peptides which are eluted over the time for which a plurality of mass analysis scans have been acquired to reduce the size of the data set which must be subsequently processed. Furthermore, by performing tandem mass analysis on a limited number of intense peaks, the data analysis system  200  improves the likelihood that each resolved peptide will undergo tandem mass analysis during the point in the elution where the peak intensity corresponding to the peptide concentration or abundance is of sufficient intensity to generate a useful high resolution tandem mass spectrum or peptide signature. Alternatively, tandem mass spectrum may be acquired for each peak within a particular mass spectrum scan or tandem mass spectrum may be acquired in another user-defined manner as desired. In this manner, data acquisition is facilitated, yet comprehensive information may be readily obtained to aid in the subsequent sequence identification.  
      When this method is applied to each mass spectrum scan acquired during the elution process, a plurality of tandem mass spectra are obtained which correspond to the plurality of resolved peptides  146 . These spectra then undergo spectrum comparison at a state  312  by matching the spectrum from each peptide with the spectral database  250 .  
      In the analysis of whole cell lysates it is not uncommon to identify in excess of 40000 individual spectral peaks corresponding to different resolved peptides which are to be desirably processed. The spectrum comparison state  312  likewise produces a very large number of peptide-correlated output files  260  to be subsequently processed by the data analysis system  200 .  
      The data analysis system  200  facilitates the analysis of the peptide-correlated output files  260  by automating a number of the sorting and organizational tasks required to analyze the results returned from the spectrum comparison state  312  thereby reducing the burden to the investigator in identifying the components of the mixed-peptide population. In one aspect of this automation, the peptide data returned from the output files  260  is parsed and are stored to the working database  226 . This process is explained more completely below.  
      Following analysis and storage of the spectral data, a subsequent quantitation is performed in state  315  to determine the relative abundance of the peptides originating from the different samples which have been mixed together at the onset of the analysis. During the quantitation state  315  the identity of each peptide that was subjected to a spectrum analysis is retrieved from the working database  226  and correlated with the mass spectrum peak heights and areas to determine the relative abundance of the identified peptide. Differential comparisons are additionally performed to correlate the expression of analogous peptides arising from the different peptide samples within the mixed population.  
      During the analysis of the peptide-correlated output files and quantitation steps, the data analysis system  200  may further employ advanced processes to identify spectral peaks which were not positively correlated by spectral comparison. For example, in the analysis of a whole cell lysate containing many thousands of individual peptide components, the mass spectra data  208  produced vary greatly from one to the next in terms of quality and information. In some instances, the spectral peak  146  may not possess sufficient signal strength to be positively identified by the component identification  145  and spectrum comparison process.  
      The data analysis system  200  provides functionality to correlate these weak or diminished spectral peaks  146  with analogous spectral peaks arising from the same peptide from a different peptide population within the sample. Thus, low abundance peptides can be positively identified based on an analogous peptide with a different label  122 ,  124 . This feature of the data analysis system  200  improves the analysis of the peptide-correlated output files  260  and increases the sensitivity of the system in detecting and identifying low abundance peptides within the mixed-peptide population.  
      Upon completion of the analysis and quantitation of the mixed-peptide population, the resulting peptide identification and expression data is stored in the relational database  227  where it may be subsequently retrieved by the investigator and further utilized in a data mining operations state  320 . The process  300  then ends at an end state  325 .  
      The abovementioned peptide analysis method  300  desirably resolves the differentially labeled mixed-peptide population to produce a plurality of primary mass spectrum indicative of the individual components of the mixed population which are distributed based on their mass-to-charge ratio. Moreover, the mass analytical technique which produces the plurality of primary spectra possesses sufficient resolution capabilities to separate the mixed-peptide population into discrete and quantifiable units.  
      For each of the separated peptides, a subsequent tandem mass analysis is performed to generate a spectrum “signature” indicative of the peptide sequence of the separated peptide. The spectrum signatures are used as queries to interrogate the spectral database  250  which contains a plurality of previously associated peptide-correlated spectra. Typically, these queries produce a large number of results which must be correlated with the original spectrum signatures to verify the peptide sequence.  
      The peptide analysis method  300  comprises a series of instructions that determine the necessary associations between the spectrum signatures and the peptide-correlated spectra to identify each peptide in the mixed population. Furthermore, these instructions quantitate the individual peptides represented in the primary spectra and identify related peptides in the mixed-peptide population to assess differential expression in a manner that will be discussed in greater detail hereinbelow.  
       FIG. 4  illustrates a simplified mass spectrum scan diagram  400  for identical but differentially labeled peptides  402 A,  402 B. As previously described, the mass spectrum scan  400  comprises a plurality of individual mass analysis scans which are acquired over a designated time frame. Each individual mass analysis scan yields a snapshot of the peptides which are present in the portion of the eluate for which the mass analysis is conducted. By combining the results of the mass analysis scans an intensity curve  407  is generated for each peptide component of the mixed-peptide population. The intensity curve further represents the relative amount of the peptide component present at designated points in the mass analysis scan.  
      As shown in the illustrated embodiment, intensity measurements are assessed for a first peptide  402 A containing a first marker and a second peptide  402 B containing a second marker. At a designated scan number with a value of “178” (read from the z-axis of the mass spectrum scan diagram) the intensity for the first peptide  402 A has an approximate value of “73” (read from the y-axis of the mass spectrum scan diagram) and an approximate mass-to-charge value of “1028” (read from the x-axis of the mass spectrum scan diagram). In a similar manner, at the same scan number “178”, the second peptide  402 B has an approximate value of “98” and an approximate mass-to-charge value of “1035”. Using this method of data acquisition and comparison thus provides a means to compare the relative amounts of the two peptides  402 A, B at any point where a mass analysis scan is performed. Furthermore, expression levels for each peptide  402 A, B can be mapped over the time course of the elution and the maximal expression levels identified. In one embodiment, tracking of the maximal peptide expression levels as indicated by the intensity curves  407  is useful in improving the accuracy and sensitivity of peptides identification as will be discussed in greater detail hereinbelow.  
      A further feature of the data analysis system  200  resides in the mass differential created by analogous peptides whose sequence may be identical but whose mass-to-charge ratio differs as a result of the incorporated markers  122 ,  124 . This mass differential represents a known or expected value which may be used to identify analogous peptides on the basis of the mass-to-charge distribution with or without supplemental peptide-correlated sequence information  260 .  
      In an exemplary method demonstrating how the analogous peptide comparison feature may be applied, the data analysis system  200  identifies mass spectral scans comprising two or more peaks of interest where peptides  402 A, B are compared. Assessing the mass-to-charge value a first peptide peak  405  associated with the first peptide  402 A labeled with the first marker  122  yields a value of approximately 1027.6 mass-to-charge units while a second peptide peak  410  associated with the second peptide  402 A labeled with the second marker  124  yields a peak at approximately 1034.5 mass-to-charge units. The mass-to-charge difference between the first peptide peak  405  and the second peptide peak  410  is observed as a displacement, or offset, of approximately “7” mass units  425 . This displacement between the two peaks  405 ,  410  arises from the mass difference between the first and the second markers  122 ,  124  used to label each identical or analogous peptide  402 A, B prior to mass analysis.  
      Thus, when analogous peptides derived from different biological samples or peptide populations  109 A, B are labeled with discernable markers  122 ,  124  and these samples mixed, subsequent mass analysis scans resolve the peptides  402 A, B into discrete peaks  405 ,  410  and form distinguishable intensity curves  407  that are separated by a distance proportional to the mass difference between the labels  122 ,  124 . As will be shown in greater detail hereinbelow, this mass differential  420  may serve as a basis for separating and identifying analogous peaks in the mixed-population peptide sample. Additionally, the mass differential  420  may be used to identify peptides whose relative concentration within the mixed-peptide population is too low to be positively correlated with known peptide sequences within the spectral database  250 . Further details describing aspects of the differential labeling method used to discriminate analogous peptides based on the mass differential are described in the section entitled “Peptide Labeling Methods”.  
      Differential labeling of the mixed-population of peptides in the aforementioned manner provides a means for identifying peptides derived from each peptide population that are mixed prior to mass analysis. The separation distance of the exemplary analogous peptides illustrated in the mass analysis scan  400  is proportional to the mass of the markers  122 ,  124 . This mass differential  420  created between the labeled analogous peptide is used by the data analysis system  200  to validate that two peptide peaks found in the primary spectrum are analogous. Without a differential mass label, analogous peptides from each sample would have identical mass-to-charge ratios and thus be indistinguishable from one another. The resulting spectrum would therefore lack any discernable differences which could be used to identify analogous peptides and difficulties would arise in determining how much peptide was being contributed from each cell or tissue type under comparison.  
      Additionally, the mass differential created by the markers  122 ,  124  may be used by the data analysis system  200  to determine the region of the primary spectrum which should be scanned for analogous peptides rather than comparing each spectrum signature with all others produced by peptides of the primary spectrum scans. As will be subsequently shown, this feature is useful in dividing the comparison and quantitation calculations into smaller subsets that may be operated on in parallel to improve acquisition of experimental results.  
      1. Correlation of Mass Spectral Information  
      Matched Peptide Correlation  
       FIG. 5  illustrates one embodiment of a correlation process  500  used by the data analysis system  200  to identify and correlate peptide peaks corresponding to resolved peptides  146  obtained by mass analysis. The process begins at a start state  502  and proceeds to a state  503  where scanning of the primary mass spectra  208  takes place. The primary mass spectra  208  comprises a plurality of mass analysis scans corresponding to sequential time points in the elution of the mixed-peptide population. Each mass analysis scan further corresponds to an ion map, snapshot, or image of the proteins which are present in the eluate during the time at which the mass analysis scan was performed.  
      As will be described in greater detail in subsequent figures, eluted peptides that are detected in the primary mass spectra  208  are further analyzed be tandem mass analysis to generate peptide signatures characteristic of each of the peptide sequences. The collection of signatures are then used to query the spectral database  250  to aid in the identification of the peptides by correlation with tandem mass analysis spectrum of known sequences.  
      In one embodiment, peptide matching against the spectral database  250  takes place in a batch process where peptides associated with the first discernable population are processed and the results stored in the working database  226 . Subsequently, peptides associated with the second discernable population are then processed and results similarly stored in the database  226 . The data analysis system  200  may recognize peptides arising from each peptide population by identifying the characteristic mass difference between the peaks in the mass spectrum scans.  
      The results  260  obtained from the queries of the spectral database  250  include information which aids in the identification of each peptide sequence. One component of the query result  260  comprises a correlation result which identifies a known peptide sequence that is likely to be similar to the experimental peptide sequence from which the query was formed. Additionally, a correlation score may be used to indicate the degree of certainty of the correlation result. A high correlation score is indicative of a high degree of certainty for the identification of the experimental peptide sequence. In a similar manner a lower correlation score is indicative of a lesser degree of certainty for the identification of the experimental peptide sequence. The value of the correlation score is desirably used in conjunction with the mass-differential created by the peptide markers  122 ,  124  to identify the peptide components of the mixed-population and determine the proteonomic differences as will be described in greater detail hereinbelow.  
      The process of peptide correlation  500  continues in a state  505  where the elution profile for each of the peptides is assessed. During this state  505 , the peptide peak intensity across the plurality of mass analysis scans obtained during the time course of the elution is evaluated to produce an intensity curve indicative of the relative abundance of the protein during the elution. Using the information obtained from the intensity curve, quantitation of the peptide can be made by evaluating the summation of the peak intensities for all mass analysis scans along the intensity curve where the peptide is found. Additionally, in evaluating the intensity profile  505  for each peptide, the data analysis system  200  further identifies the time frame of the elution corresponding to a particular mass analysis scan where the intensity of the peptide is maximal and stores this value in the working database  226  for use in identifying analogous peptides labeled with different markers  122 ,  124 .  
      In a decision state  510 , the correlation process  500  scans each mass spectrum scan incrementally and upon identifying a peptide, determines if a corresponding analogous peptide or partner exists in the spectral vicinity. In one aspect, corresponding analogous peptides can be identified by scanning for peaks displaced by an appropriate mass distance, dependent on the marker or label  122 ,  124  used to tag the mixed-peptide population. For example, as shown in the previous illustration, the correlation process  500  identifies the first peak  405  and scans the primary mass spectrum in the regions that are displaced approximately 7 mass units away from the first peak of interest to determine if the second peptide peak  410  is present.  
      While in the decision state  510 , if the data analysis system  200  determines that the identified peptide possesses a potentially analogous partner, as indicated by the presence of the second peak  410  with the appropriate mass difference, the process  500  proceeds to a state  515  where the sequence identity of both peaks  405 ,  410  is confirmed. Alternatively, if the data analysis system  200  determines that the identified peptide does not possess and analogous partner, the process  500  proceeds to a state  535  where the correlation score for the identified peptide is reviewed (see section below entitled Un-matched Peptide Correlation).  
      In the case of identified peptide partners where the process  500  has reached the sequence confirmation state  515 , the peptide sequences for each identified peptide are confirmed using information obtained from the MS (MS) n  analysis and subsequent peptide-correlated output files  260 . During the sequence confirmation state  515 , the data analysis system processes correlate analogous peptides by both sequence-related information, as well as, expected mass differences to establish the relationship between the two discernibly labeled peptides with a high degree of certainty.  
      The sequence confirmation state  515  additionally incorporates an intensity scanning feature that is useful in identifying peptides of low abundance or whose tandem mass analysis scans produce inconclusive results. Using this feature, the data analysis system  200  may proceed identify a different region of the intensity curve  407  for the particular peptide of interest which is associated with a different mass analysis scan. Typically, the region of the intensity curve  407  selected corresponds to a region where the peptide is present in greater abundance (as indicated by a higher intensity). The data analysis system  200  may then review the results of the tandem mass analysis taken in this higher intensity region and any spectral database queries performed for the peptide to improve the positive identification of peptide sequences and facilitate analogous peptide identification. Additionally, when using this method, the data analysis system  200  is able to acquire useful peptide sequence information from other regions or mass analysis scans which may be correlated with the region where the tandem mass analysis of the peptide produced inconclusive results. Thus, if one peptide is below the threshold of resolvability of the MS (MS) n  analysis at a particular time point or if the peptide-correlated output files  260  do not imply a clear sequence identity, the data acquisition system  200  may utilize the plurality of mass analysis scans and tandem mass analysis taken over different times to better resolve the each peptide sequence and confirm the sequence identities between two analogous peptides.  
      Following the confirmation state  515 , the process  500  proceeds to a state  520  where peak or intensity curve areas for analogous peptides are determined. As previously indicated, these calculations are representative of the amount of peptide present in the mixed-population sample and may be used to determine changes in peptide expression by computing the difference between analogous peptides. As will be described in greater detail in subsequent illustrations and discussion, the analysis of the peak area and intensity curves desirably employs a specialized method for identifying and resolving each peptide associated data set to improve the quantitation and integration of the area defined by the bounds of the data set. The quantitation methods used in this state  520  desirably provide improved accuracy in assessing the relative abundance of each peptide in the mixed population and aid in identifying proteomic differences in the cells or tissues under comparison. Additionally, the quantitation methods may be used to identify peptide abundance at specific times during the elution of the peptide (corresponding to individual mass analysis scans), as well as, across the overall time frame for which the elution of the peptide takes place (corresponding to the plurality of mass analysis scans).  
      After quantitating the analogous peptides the process  500  proceeds to a state  525  where the peptide abundances or concentrations are compared. In this state  525 , differences in abundance between the analogous peptides are identified by calculating the difference between the quantities of peptides determined in state  520 . This information provides valuable insight into proteomic differences between analogous peptides in the mixed-population and serves as an indicator of differences in expression or regulation of the peptides as will be shown in greater detail in subsequent figures.  
      The process  500  then proceeds to a state  530  where the results of the aforementioned calculations are stored within the relational database  227 . As will be appreciated by one of skill in the art, the relational database  227  may comprise a plurality of tables or fields which may be interrelated via associations. These associations are used to generate meaningful queries, such as those used to produce reports, which display the associations between analogous peptides in the cell or tissue samples. The use of the relational database  227  also provides a means of interrelating data obtained from a plurality of different mass analysis experiments and aids in data mining operations used to evaluate and associate differential peptide expression in various conditions and biological samples of interest. In one aspect, the peptide calculations may include a confidence score which is used to order the results based on the degree of confidence with which the peptide identification and/or comparison is made. Furthermore, other identifiers or relationships can be stored in the relational database  227 , including information that correlates the identified peptides to other resolved peptides within the mass analysis spectrum. As previously discussed, at least a portion of this information may be obtained from other bioinformatic databases  254  which are queried by the data analysis system  200  and the results stored with the associated peptide sequence and quantitation results.  
      Un-Matched Peptide Correlation  
      In those instances where the correlation process  500  reaches the decision state  510  and determines that the resolved peptide does not possess an identifiable partner (analogous peptide), the process  500  proceeds to a state  535  wherein the correlation score of the peptide comparison is reviewed. In this state  535 , results (in the form of peptide-correlated output files) are obtained from queries of the spectral database  250  (corresponding to the tandem mass analysis spectrum of the resolved peptide). The process  500  proceeds to a decision state  540  wherein an assessment of the results of the spectral database queries is made. In this state  540 , the data analysis system  200  identifies if significant correlation exists between the resolved peptide and any mass analysis spectrum in the spectral database  250 . If a significant correlation is determined to exist between the resolved peptide and an entry in the spectral database  250 , the process  500  moves to the state  530  wherein the putative sequence of the resolved peptide is stored along with an indicator of the relative confidence level of the correlation.  
      If a significant correlation is not found at the decision state  540 , the process  500  moves to a state  545  wherein novel or un-matched peptides (which are identified by a lack of significant correlation with existing entries in the spectral database  250 ) are stored in the relational database  227  with an appropriate identifier denoting that the peptide is unidentifiable or possesses a low correlation score indicating that the resolved peptide&#39;s sequence was not known with certainty.  
      Upon storing the results for analogous or identifiable peptides in state  520  or storing the results for peptides with little or no sequence homology in state  545  the process proceeds to a decision state  550  and determines if all resolved peptides have been assessed. If additional peptides remain to be correlated, the process returns to the scan spectrum state  503  and performs the indicated functions. When all peptides have been processed in the aforementioned manner, the process  500  proceeds to a state  560  where the results of the analysis may be output to the investigator. In this state  560  data summaries and automated calculations may be made which are subsequently output in a user-defined manner to provide the investigator with one or more flexible reports of the experimental results including peptide sequence identifications and correlation, differential expression analysis of analogous peptides, novel peptide identification, and confidence level assessments for the peptide correlations. Finally, the process proceeds to an end state  562  completing the peak analysis process  500 .  
      The aforementioned correlation process  500  therefore implements a method to identify each peptide in the primary mass analysis spectrum and, if possible, associate analogous peptides labeled with the different markers  122 ,  124 . Furthermore, the correlation process  500  quantitates the relative abundance of each peptide and may use this information to aid in the determination of proteomic differences. Proteomic differences between analogous peptides are subsequently used to identify changes in peptide expression or abundance corresponding to the treatment or condition which the cells or tissues were exposed to and provides an important tool for investigators to use in assessing complex peptide populations and biological processes.  
      As will be subsequently described in greater detail, the amount of data which must be analyzed during the correlation process is quite large. As a result, the time required to perform the analysis can take many hours to complete. Although it is possible to perform the necessary calculations on a single computing device, the correlation process  500  is desirably implemented in a clustered environment to improve computing performance and yield results more quickly. In the clustered computing environment the correlation process  500  is performed in a parallel computational manner where the work of identifying and comparing peptides is subdivided and distributed across a plurality of computing devices configured to process the spectra in a distributed manner.  
      2. Exemplary Mass Spectra Data  
       FIGS. 6A-6F  illustrate a collection of exemplary mass spectrum scans depicting states of differential expression which may be identified by the data analysis system  200 . In each figure, a collection of peaks  605  is shown with each peak indicative of a peptide component of the mixed-population that has been separated by mass analysis. The correlation process  500  subsequently identifies a first peak  405  and a corresponding partner or analogous second peak  410 . Confirmation of both the appropriate mass difference (seven mass units in the illustrated embodiment) and the tandem mass spectrum (not shown in the illustration) results in the comparison process  500  identifying these peaks  405 ,  410  as analogous and having the same peptide composition with different labels or tags. Confirmation further prevents other peaks  610  in the mass spectrum from being inappropriately associated with the two analogous peaks  405 ,  410 . As previously indicated, upon confirming the relationship between the peaks  405 ,  410  the data analysis system  200  performs a quantitation of peak areas and intensity values to determine the relative amount of peptide within the sample and compares these values to one another to determine proteomic differences.  
      In  FIG. 6A , a first peak area  615  is associated with the first peak  405  and has a value of “1000” with a second peak area  620  associated with the second peak  410  also having a value of “1000”. A calculation of the difference between the peak areas  615 ,  620  of the analogous peaks  405 ,  410 , results in a difference value of “30” (1010-980=30). This difference in peak areas is representative of resolved peptides that do not possess substantially altered differences in expression.  
       FIG. 6B  illustrates an exemplary mass spectrum scan for a labeled peptide having an up-regulated expression pattern. Similar to the manner of identification and confirmation as described above, the data analysis system  200  identifies the first peak  405  and the second peak  410  as analogous based on their mass difference and tandem mass spectrum. In the case of up-regulated expression the first peak  405  possesses a substantially reduced peak area  615  compared to the area  620  of the second peak  410 . The data analysis system therefore recognizes this pattern of expression as being up-regulated when comparing the quantity of peptide  402  labeled with the first label  122  relative to the quantity of peptide  402  labeled with the second label (see  FIG. 4 ). Conversely, peptide down-regulation as illustrated in  FIG. 6C , may be determined by the data analysis system  200  when the first peak  405  possesses a substantially increased peak area  615  relative the area  620  of the second peak  410 .  
       FIG. 6D  illustrates an exemplary mass spectrum scan for a labeled peptide exhibiting de-novo expression. As shown in the illustrated embodiment, the lack of the first peak at the expected position  630  in the mass spectrum in addition to the presence of the unpaired second peak  410  is indicative of only the peptide population labeled with the second label  124  containing the indicated peptide. In one aspect, an expression pattern where an unmatched peak is present in the mass spectrum scan may indicate de-novo expression of a peptide which is potentially of significant interest to investigators.  
      Alternatively,  FIG. 6E  illustrates and exemplary mass spectrum scan for a labeled peptide exhibiting repression. As shown in the illustrated embodiment, the presence of the first peak  405  in addition to the lack of a corresponding or paired second peak at the indicated position  635  may identify a peptide that is found only in the first peptide population labeled with the first label  122 .  
      In the case of unpaired peptides encountered in the mass analysis, further characterization by the correlation process  500  may be performed to determine if there is significant correlation between the tandem mass spectrum of the peptide with those in the spectral database  250 . This information is useful in identifying peptides with novel sequences, as well as, flagging those peptides whose level of expression changes dramatically when comparing the two peptide populations.  
       FIG. 6F  illustrates an exemplary mass spectrum where low signal strength in the second peptide peak  410  may be correlated with a positive identification of the first peptide peak  405  to yield a putative identification of an otherwise unidentifiable peptide. As shown in the illustrated embodiment the second peak possesses a peak area  620  indicative of a peptide whose low abundance prevents identification by tandem mass spectroscopy. The peak analysis process  500  however is able to associate the second peak  420  with the first peak  405  on the basis of the mass differential. In the absence of confirming tandem mass spectroscopy data, this type of identification can be important in identifying peptides which fall below the threshold of detectability of the instrumentation in one mixed peptide population but are readily detectable in a second peptide population.  
      The aforementioned exemplary mass spectra demonstrate an overview of how peptide expression between two or more samples may be correlated to identify differences in peptide expression. Based upon the identification of analogous peaks  405 ,  410  that are appropriately displaced by incorporation of the markers  122 ,  124 , the data analysis system quantitates relative amounts of peptide expression and readily compares these values in the cells or tissues under study. Comparison of peptide expression in this manner provides important insight into changes or alterations in differential peptide expression and may identify peptide expression states of interest.  
      Another useful feature of this system relates to the aspects of analysis whereby the majority of peptides contained within a cell or tissue of interest may be analyzed simultaneously. This feature provides a global assessment of peptide expression which is in many cases necessary to better understand important biological relationships between related peptides and pathways.  
      A further feature of this system relates to the simultaneous analysis of two or more peptide populations within the sample mixed population sample. Analysis within the same sample desirably reduces problems associated with background, noise, and spurious or stray data which might otherwise confound differential expression analysis. These problems are commonly found in experimental mass analysis where each peptide population is evaluated independently of one another and increases the difficulty in positively and accurately identifying and associating peptides across multiple sample sets.  
      In one embodiment the aforementioned mass spectra depict mass spectrum scans taken at particular time intervals during the elution of the mixed peptide population. As will be appreciated by those of skill in the art, the principles and methods for mass spectral analysis to identify proteomic differences can additionally be carried out using the intensity curves  407  formed from the aggregate of the plurality of mass spectral scans taken over a designated time interval. In this embodiment, peptides are quantitated and compared based on the total peptide concentrations within the mixed population sample. This method of proteomic analysis desirably normalizes the difference analysis over the plurality of mass analysis scans and reduces quantitation errors which might arise from slight differences in elution at particular times during the mass spectrum acquisition process. In a manner similar to that used in comparing analogous peptides in the mass analysis scans, the intensity curves  407  may be used for analogous peptide comparison. Thus, proteomic differences, peptide identification, and peptide quantitation can be performed both on individual mass analysis scans and on the intensity curves as a whole.  
      3. Quantitating Sample Differences in Parallel  
       FIG. 7  illustrates a flow diagram used by the data analysis system  200  to identify and quantitate the chromatographic scans of the mass spectra associated with the differentially labeled peptides. The process of identification and quantitation is a computationally demanding task as there are typically thousands of individual scans which must be analyzed to associate and identify analogous peptides. Furthermore, the relative abundance of the peptides represented in each scan must be evaluated and correlated between analogous, but differentially labeled, peptides. In the illustrated embodiment parallelization of tasks is used to improve computational performance by distributing the computational work to be performed among a network of computers. Although, the data analysis system  200  can be readily adapted to process the mass spectra in a non-parallel manner, such a system may lack the improvement in performance gained by distributing the computational workload over a number of computers within a cluster.  
      Parallel computational methods utilize a plurality of independent microprocessors and/or computers to solve complex problems in a more rapid manner than can be accomplished using a single computer or processing device. In a parallel architecture, computers are typically interconnected by networking connections forming a plurality of nodes within a clustered environment which exchange information and operate in a coordinated manner using a parallel computational language. The parallel computational language is designed to implement specialized programming and communication requirements necessary for solving problems in a distributed manner. Examples of commonly utilized parallel computational paradigms include Parallel Virtual Machine (PVM), Message Passing Interface (MPI), load sharing facility (LSF), or other similar methods to create programming instructions and processes that can be simultaneously executed on a plurality of computational devices to solve problems rapidly and efficiently. For additional details relating to these parallel implementations the reader is directed to the following references:  Pvm: Parallel Virtual Machine: A Users&#39; Guide and Tutorial for Networked Parallel Computing , Al Geist, MIT Press (1994);  Using Mpi: Portable Parallel Programming With the Message - Passing Interface , William Gropp, Ewing Lusk, Anthony Skjellum, MIT Press (1999);  Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers , Barry Wilkinson, C. Michael Allen, Prentice Hall (1998).  
      The data analysis system  200  typically stores the necessary information about each chromatographic peak and intensity curve  407  in one or more tables of the working database  226 . This information includes the results  260  of the sequence queries directed towards the spectral database  250 . As previously discussed, these queries are created by the data analysis system  200  using the tandem mass spectra  147  generated from each resolved peptide  146 . The resulting peptide-correlated output files  260  obtained by comparison of the tandem mass spectrum  147  against the spectral database  250  provides a preliminary basis of knowledge and information used to evaluate the sequence and composition of the resolved peptides  146 . As the data analysis system  200  receives the peptide-correlated output files  260  the associated information is stored in the aforementioned database  226  where it is subsequently processed in a manner that will be described in greater detail hereinbelow.  
      Additional information which may be stored in the database  226  includes information identifying chromatographic peak or intensity curve areas, mass-to-charge ratios, peptide-correlated data output, or other information useful in associating or pairing the differentially labeled peptides from the mixed-population. In one aspect, this information is stored in tables or arrays within the database  226  to facilitate cataloging, sorting, querying, and storage/retrieval of the information used to determine the peptide sequences and proteomic differences in the biological samples. These tables may additionally be arranged according to the results of the tandem mass spectroscopy obtained for each condition, cell treatment, peptide-population, and/or label and are used to distinguish between the peptides in the mixed-population that underwent mass analysis.  
      In an exemplary differential analysis comparing a wild-type peptide population with a mutant or treated peptide population, two tables are generated and compared which correspond to a first table containing information relating to the wild-type condition and a second table containing information relating to the mutant condition.  
      Thus, the process  700  for identification and quantitation of the chromatographic peaks and intensity curves proceeds from a start state  702  to a state  710  where the data analysis system  200  reads data from the tables and acquires information contained in the fields of interest. The process  700  then moves to a state  715  wherein a first summary file is created containing information necessary to perform the peptide identification and quantitation analysis, while removing unnecessary information which might otherwise reduce the performance of the parallel processing routines. The process then proceeds to a state  720  where the quantitation summary is broken into a plurality of data sub-sections  720  to divide the data into smaller pieces which may be operated upon individually. The creation of data subsections at the state  720  additionally facilitates the distribution of the experimental data across the plurality of nodes improving the ability to perform the identification and quantitation in parallel.  
      The identification of the peptides commences when the data sub-sections are processed in a state  725  and distributed across the plurality of nodes within a computing cluster. After receiving the data sub-sections, the process  700  proceeds to a state  730  where each node quantifies the chromatographic peaks and intensity curves. The quantitated data is then sent back to the database  226  in state  735  where results are captured and collated.  
      After the initial quantification is complete, the process  700  moves to a state  740  wherein a comparison function is performed to identify any chromatographic peaks whose tandem mass analysis spectrum can not be correlated with an associated entry in the spectral database  250 , thus indicating that the peptide may not be identified accurately.  
      Subsequently, the process  700  proceeds to a new state  745  where the chromatographic peaks and their associated information fields are used to build a second summary table which is redistributed for parallel processing in the aforementioned manner. The process  700  then moves to a state  750  wherein the peaks and intensity curves  407  are requantified by extrapolation to improve the level of confidence of the identification of the peptide.  
      The extrapolation state  750  is performed by identifying the paired or analogous peptide which reside an appropriate number of mass units away from the unidentified peptide (mass shift), depending on the differential mass labeling technique chosen. During state  750 , differentially labeled peptides which are analogous (having similar sequences but different labels and derived from different biological samples) are identified based upon knowledge of the expected mass differential between the markers  122 ,  124  used to label the two or more peptide population being compared. Following identification, the process advances to an end state  757  where quantitation is completed and the results stored in the relational database  227 .  
      During the identification and correlation of analogous peptides, the data analysis system may proceed through a first collection of resolved peptides whose sequence identity are confirmed by spectral database  250  comparison. Furthermore, these peptides may be associated with partner (analogous) peptides whose mass-to-charge ratio is displaced or offset from that of the resolved peptide. The data analysis system  200  confirms the relationship between the resolved peptide and the analogous peptide by verifying that the mass difference between the two peptides occurs with an expected value dependent upon the markers  122 ,  124  incorporated into the peptide populations. Furthermore, the data analysis system  200  may confirm the peptide-correlated output files  260  for the two peptides are consistent with the peptides having the same sequence. In this manner, the data analysis system  200  is able to identify and associate peptides with similar sequences that have been derived from different cells, tissues, treatments, and/or conditions. The results of this identification procedure are then stored in the aforementioned database  226  where they may be formatted, queried, and presented in user-defined manners.  
      For those peptides whose sequence cannot be identified with certainty based upon the peptide-correlated output file  260 , a subsequent identification process may be attempted in order to maximize the chances for identifying the peptide sequence. In this process the data analysis system  200  reviews the primary mass analysis scans and identifies the unknown peak or intensity curve. Subsequently, the data analysis system  200  scans the mass-to-charge region of the spectra coinciding with a region where an analogous peptide (containing the different marker) might be expected. If an analogous peptide peak or intensity curve is identified, the data analysis system  200  may correlate the tandem mass spectrum of the peptides and determine if the spectra are similar enough to associate the sequence information of the analogous peptide with that of the unidentified peptide.  
      In certain instances, the tandem mass spectrum produced for the peptide is of low resolution or quality. This is typically due to a low abundance or concentration of the peptide in the eluate which was used to generate the tandem mass spectrum. The resulting low resolution tandem mass spectrum may contribute to a low confidence sequence match with the spectral database  250 . To improve in the identification of peptides which posses such low resolution spectra, the data analysis system  200  may scan through the intensity curve of the peptide and locate an area or region where the peptide intensity is maximal. The data analysis system  200  may then assess the tandem mass spectrum for the peptide taken in this region to improve the quality or resolution of the spectrum which may be subsequently compared against the spectrum database  250 . This process desirably improves sequence identification and increases the confidence of matches. Upon identifying the sequence of the peptide in the region of maximal intensity, the data analysis system  200  may correlate this information with the mass spectrum scan having low peptide abundance or concentration to identify each peptide with greater accuracy and sensitivity.  
      Furthermore, the intensity curve scanning technique described above can be applied to instances where analogous peptides are difficult to determine in a particular mass spectrum scan. Using this method, the data analysis system  200  may scan peptide intensity curves for both the peptide of interest and the putative analogous peptide to identify areas of maximal intensity. In these regions of maximal intensity, the tandem mass spectra can be assessed to improve the accuracy and sensitivity of the identification of each peptide. The results of the identification can then be correlated with one another to aid in identification of the analogous peptides and proteomic differences.  
      Peptides which are identified using the intensity curve scanning methods are requantified and the results summarized and returned as before. Those peptides which cannot be conclusively identified are flagged during the quantification procedure and the results returned to the working database  226  where they may be summarized independently. Unidentified peptides are significant in that they may represent novel peptides whose expression cannot be correlated with information in existing spectral databases and are typically of interest to investigators.  
      The aforementioned method  700  for identifying and quantitating data uses parallelizable tasks to improve the ability of the data analysis system  200  to process the large numbers of peptides that might be found within an entire organism or tissue sample. To improve the efficiency of processing, each parallelizable task is desirably divided in such a way so as to associate the specific data files and information required for analysis of the resolved peptides  146 . This association of information improves the computational efficiency of identifying and quantitating the resolved peptides and reduces the amount of data that must be transferred between nodes.  
       FIG. 8  illustrates a flow diagram of a process  800  in which the data output comprising the mass spectrum information  208  is analyzed by the data analysis system  200 . Beginning in a start state  802  the process proceeds to a state  805  where analysis of the labeled mixed-peptide population  130  takes place. In this state  805 , the primary mass analysis is performed to separate the components of the mixed-peptide population  130 . Furthermore, the subsequent tandem mass analysis is performed on each resolved peptide to generate the unique mass spectrum which is dependent on the sequence or composition of the peptide.  
      The resulting spectral information including the primary mass spectrum and the plurality of tandem mass spectra, as well as, associated data and information produced by the instrumentation  205  are received by the data acquisition module  220  of the data analysis system  200  in a state  810 . In this state  810 , the spectral data and information may be re-arranged, cataloged, formatted, or otherwise processed into a form suitable for storage in the working database  226 . Additionally, the data processing module  225  of the data analysis system  200  may associate the spectral data and information with informational identifiers such as investigator-input descriptions of the experimental conditions, cell types, sample quantities, markers used, and other information which is useful in identifying and assessing the spectral data. Processed spectral data and information is stored in the database  226  according to an organizational schema that separates the data into component parts and stores it within the database  227  in a plurality of data tables and fields as will be subsequently illustrated in greater detail.  
      Upon completion of the aforementioned database population, the process  800  proceeds to a state  812  where the spectral database query is prepared. In this state  812  the data processing module  225  retrieves information from the database  226  including experimental tandem spectra and associated information from one or more of the resolved peptides. This information is further formatted and organized to form a query command or file which is submitted by the communications module  235  to the spectral database  250 . In one embodiment, the data analysis system  200  forms and submits a combined or composite query in which a plurality of spectrum and information to be analyzed is submitted as a batch file to be processed by the spectral database  250 . Additionally, the spectrum and information can be reviewed by the investigator and customized queries developed which are submitted in a manner similar to the automated queries generated by the data analysis system  200 .  
      Queries which are received by the spectral database  250  are then compared against the plurality of mass spectra with known peptide sequences. As previously discussed, the results of the query comprise one or more peptide-correlated output files  260  which contain information indicating the correlation between the experimentally resolved peptide and those contained in the spectral database  250 . The output files  260  are sent back to the data analysis system  200  in a subsequent step  815  where they are processed and stored in the database  226 .  
      In an experiment where many thousands of peptides are simultaneously assessed, the amount of information contained in the uploaded output files  260  is quite large. Furthermore, each output file  260  typically comprises numerous fields and types of information which are associated with the analysis and identification of each peptide. In order to more efficiently complete the analysis of the mixed-peptide population, the data analysis system  200  desirably performs a number of steps of the analysis in parallel  818 . As previously indicated, parallel processing comprises subdividing or partitioning the analysis into sub-processes that may be independently operated upon by a plurality of nodes within a clustered computer environment.  
      Parallelization of the data analysis commences in a state  820  where both the experimental mass analysis data and the results returned from the spectral database query  260  are split into jobs that are operated on by nodes within the cluster. In this state  820 , information is extracted and stored in fields of tables which are integrated into the database schema. As shown in subsequent figures, these tables are populated with information which characterize each peptide component and provide links or associations to allow the information stored in the tables to be analyzed and correlated.  
      In a subsequent state  825  the information retrieval module  210  of the data analysis system  210  may additionally acquire supplemental information from other external or bioinformatic databases  254  which is desirably associated with the experimental results and peptide-correlated output file information. This supplemental information may, for example, include descriptions and information further detailing the matched peptides from FASTA databases, as well as, other sources of information such as GenBank search results and nucleic acid expression data.  
      Additional information may be computed by the data analysis system  200  in a state  830  where parameter calculations based on the associated data are made. In this state  830 , the information contained in the fields of the tables may be used to calculate information such as the molecular weight of the peptides undergoing analysis, charge distributions, or other information which may be of interest to the investigators. Furthermore, links or associations may be created within the tables which serve as pointers or hyperlinks to the stored mass spectra or peptide-correlated output files  260  to facilitate subsequent investigator retrieval of the information stored in the database  226 .  
      As each node completes the aforementioned operations to prepare and analyze the subset of information which has been distributed to it, the process enters a state  835  where the information is uploaded to the database  226 . This state  835  utilizes the database  226  as a centralized storage area to organize the data output  208 , peptide-correlated output files  260 , and any newly created information/associations in a manner that is readily accessible to the investigator. Additionally, the informational upload  835  to the database  226  prepares the data analysis system  200  for subsequent operations in which differential analysis and proteomic expression evaluation are performed. The process  800  subsequently reaches an end state  842  where the informational processing and upload is complete and the data analysis system  200  made ready to perform other functions.  
      The foregoing method of parallel data processing efficiently acquires the necessary data and information to associate the experimentally obtained mass spectra with spectra obtained from known peptide sequences. This method may further be scaled up or down as necessary to accommodate various amounts of data and provides an improved method for populating the bioinformatic database  227  so as reduce the amount of time necessary to complete the analysis of the experimental results.  
      A distinctive feature of the data analysis system  200  resides in its ability to dynamically create links or identifiers during the processing of the data output  208  and sequence-correlated data output files  260 . These links are automatically created and stored in the bioinformatic database  227  in response to a number of definable events which the data analysis system  200  is programmed to recognize. In one aspect, when a particular database match or sequence homology is encountered with a peptide undergoing analysis. The data analysis system  200  may create the identifier which flags the data of interest for subsequent review by the investigator.  
      The identifier may additionally comprise a hyperlink to an actual image of the spectrum stored in the database  227  whereby the investigator can quickly review the visual representation (picture) of the mass analysis. These identifiers are desirably stored in the database  227  and may be subsequently used by the investigator to selectively retrieve data of interest. Additionally, the investigator may create similar links or identifiers in a user-defined manner to flag desired data or information selectively.  
      The hyperlinked association of data and information can also be represented by a link which contains the address of a computer that runs script to generate an image of the spectrum on the fly, based upon the numerical values of the mass spectrum analysis. Thus, actual images of the spectrum need not necessarily be stored in the database  227  and may instead be generated upon request of the investigator.  
      In one embodiment, images of the experimental spectrum are desirably stored within the database to provide an additional source of information which may be used for data analysis. For example, neural network analysis of the images of the experimental spectrum may be performed to aid in the identification of proteomic differences and data mining operations. In a neural network processing paradigm, information is analyzed by methods such as pattern recognition or data classification. Furthermore, the neural network is an adaptive process that “learns” or creates associations based on previously encountered data input. The storage of images within the database  227  therefore may be desirably used in conjunction with the neural network processing paradigm to provide improved information analysis as compared to using more traditional processing methodologies alone. Furthermore, storage of images within the database  227  improves access times for investigators wishing to view the mass spectrum compared to that of rendering the images from the numerical representations of the data and information.  
       FIG. 9  provides a detailed flow diagram of a quantification method  900  used by each node during parallel peptide assessment. Beginning in a start state  902  the process advances to a state  905  where quantification is performed by extracting peptide information from the relevant correlated database files  260  and comparing this information with the peptide associated peak or intensity curve  407  undergoing analysis. One component of the correlated database file  260  comprises a summary of expected peaks and intensities at various charge states for the associated known peptide sequence. These peaks and intensities are extracted in a subsequent state  910  to within one atomic mass unit (amu) of the calculated masses of the peptide at the different charge states which the peptide exists as during the mass analysis. During this state  910 , appropriate peaks are isolated from the spectrum to isolate and identify relevant portions of the spectrum from which quantitation will subsequently be made.  
      As will be appreciated by those of skill in the art, during mass analysis, peptides resolved in the primary mass spectrum are present in a number of different charge states. These charge states are indicative of states of ionization of the peptide when subjected to the energy of the mass analysis. Each ionization state results in a different mass-to-charge ratio for the peptide and results in a plurality of independently resolved peaks or charge intensities appearing in the primary spectrum. The exact number of peaks or charge intensities is therefore dependent on the number of different charges states possible for each peptide.  
      A significant feature of the quantification method  900  resides in its ability to identify the aforementioned charge states for each peptide and determine which charge states are appropriate for assessing quantitation. To accomplish this task, the quantification method  900  enters a state  915  to determine the most abundant charge state of the peptide undergoing analysis based on the expected charge states for the associated known peptide. In one embodiment, the most abundant charge state is identified by extracting stored peptide intensities from the correlated database file  260  to identify peaks in the mass spectrum which correlate with the plurality of charge states of the peptide under analysis. During this state  915 , the node identifies the highest intensity charge state and takes the peak  146  associated with this charge state to be the most relevant for the purposes of quantitation.  
      Upon identifying the peak  146  of the mass spectrum to be quantified, the quantification method  900  proceeds to a state  920  where a numerical filter is used to smooth the data contained in the identified peak  146  of the mass spectrum. In one aspect the numerical filter comprises a Butterworth or Chebyshev filter applied to the peaks  146  of the mass spectrum to isolate each peak of interest from any intervening peaks or background noise. Subsequently, the method proceeds to a new state  925  wherein an endpoint determination is made to define the bounds of the peak area to be quantified. The peak smoothing and endpoint identification states  920 ,  925  are useful in isolating the peptide-associated peak of interest, for which quantitation of peak area will be made, from any background noise or other closely positioned peaks within the mass spectrum.  
      The method  900  then proceeds to a state  930  where an area determination is made to determine the relative amount of peptide present. Information related to the calculated peak area and quantitation of the peptide is subsequently summarized to a file or table in a new state  935  and is written back to the working database  226  for storage in the bioinformatic database  227 .  
      In another embodiment, the method  900  contains an additional module for optimizing the peptide data stored in the correlated database file  226 . The additional peptide module is configured to detect identical peptides (with the same marker or label) that have been identified in immediately adjacent peaks. This result may be due, for example, to a long elution time for a particular peptide, so that the measured peak for the peptide extends beyond the dynamic exclusion window specified for the analysis. Thus, the area beyond the exclusion window is detected as a separate, second peak, even though it relates to the same peptide as the prior peak. By comparing the back border value of the first peak with the front border value of the second peak, the module detects that the second peak is in fact the tail end of the first. In that case, the module will combine their areas and record the combined value as the actual area of the first peak while eliminating the second peak from the data set.  
      Another optional module can also be implemented with the method  900  to double check the accuracy of the Sequest peptide identifications. This check module is designed to eliminate duplicate Sequest peptide identity files from the collected data, and also to ensure that the most accurate peptide identity is used for each peak. Two data loops run within this module. A first outer loop gathers and stores to a “consensus” table all of the Sequest peptide data that comes from a first run of a sample through the system. Each entry in the table includes a peak identifier, and a step and charge state for each peak, along with the Sequest Xcorr score and peptide that was identified for the peak. Once this data is stored, a Sequest data from a second run of sample through the system is stored to a second data table.  
      Each entry for each peak is then matched against all entries in the consensus table in order to find matches. If a peak from the first run is matched with a peak from the second run, the module determines whether the step and charge states for the compared peaks are the same. If they are the same, the module determines whether the correlation (Xcorr) score is greater for the data in the consensus table, or the second table. The data with the highest Xcorr score is retained in the consensus table so that at the completion of the process, the consensus table has a list of the Sequest data having the highest correlation to particular peptides for each peak. This ensures that each peak is assigned to a correct peptide, and artifacts are not entered into the database. If the step and charge states for the two peaks are not the same, the module determines whether the charge state is plus 2 for each set of data. If the charge state of the data from the second run is not plus 2, then the data stored in the consensus table from the first run is maintained. However, if the charge state of the data from the second run is plus 2, then the data from the second run is copied into the consensus table for that peak.  
      The aforementioned quantitation method  900  defines a principle functionality of the distributed node processing for each resolved peptide  146  in the primary mass spectrum. This method  900  features an efficient peak isolation and quantitation approach that identifies the most relevant peak associated with a peptide having a plurality of charge states. Furthermore, the identified mass spectrum associated with each peptide of interest is isolated from the surrounding information contained in the spectrum so that an accurate assessment of the peak area may be obtained. This feature of the invention contributes to increased sensitivity in identifying relative peptide abundances and improves the determination of proteomic differences when comparing analogous peptides within the mass spectrum.  
      4. Exemplary Pseudocode for Parallel Processing  
      The following pseudocode illustrates one example for implementing a parallel processing routine for analysis of the primary mass spectrum and subsequent determination of peptide quantitation and proteomic differences. A master/slave paradigm is used to perform the calculations associated with the data analysis and, as previously indicated, the functions are implemented in a parallel programming language such as PVM, MPI or LSF. The comments provided within the pseudocode describe the functionality of the procedure calls used to perform the data analysis which can be coded in numerous different ways as will be appreciated by one of skill in the art.  
      The software of the data analysis system  200  therefore desirably provides easy and open access to data contained within the relational database  227  and is designed to be independent of system architecture. These features permit the software to be readily extended to larger scale installations to accommodate the vast quantities of data which are typically associated with identifying and comparing the many thousands of peptides found in most biological samples.  
                                                  a. PSEUDOCODE FOR PARALLEL PROCESSING (MASTER)           /* start by building the parallel virtual machine - see how many nodes(slaves)           are available and what is their computational load; launch slave tasks on the           remote nodes */                         initiate(parallel_virtual_machine);                         /* the master node first compiles a list of all the output files from the           spectral database search; these files (*.out) contain information regarding           the matched peptides from a given database such as the correlation score, the           preliminary score, the sequence, the number of matched ions and so on */                         read(*.out files);                         /* once this list has been compiled, workload packets need to be constructed;           these are sublists of output files, computed such that the total number of           matches per packet is constant. This guarantees a fair workload for all the           slaves in the cluster */                         compute(workload);                         /* next the summary parameters are broadcasted to the slaves, e.g. - FASTA           database used for search and/or description, database to be uploaded with the           results from the search */                         broadcast(parameters);                         /* here the main work of the master begins: keep sending workload packets to           the nodes */                         while (there is work to be done) {                         wait(request from slave);           send(workload_packet, slave);           receive(acknowledgement);                         }                         /* when there is no more work to be done, signals are sent to the slaves in           the cluster so they can exit gracefully */                         shutdown(slaves);                         /* and the process is finished */                         exit;                         b. PSEUDOCODE FOR PARALLEL PROCESSING (SLAVE)           /* once the slave process has been started, it needs to know the general           parametes of the parallel job */                         receive(broadcasted parameters);                         /* signal to the master node that we are ready to begin */                             1   communicate(availability to master);                         /* meet all the communication requirements imposed by the master:                         get ready to receive workload packet... */           receive(workload_packet);                         /* acknowledge the transmission */                         send(acknowledgement);                         /* examine the workload packet; open the corresponding output files */                         forall(files in workload_packet) {                         open(file);                         /* make connection with the database that stores the search summary */                         initiate(database_connection);                         /* and now start the real work: get all the details for each hit... */                         forall(entries in file) {                         get_search_results(entry);           compute_peptide_molecular_weight(entry);           get_description_from_fasta-db(entry); /* this                             is   the database that Sequest used */                         /* and upload the details */                         upload_db(tablename, entry.details);                         }                         }                         /* done with this packet of data - communicate the master that we are ready           for more */                         goto(1);                      
 
 D. Exemplary Data Tables for Storing Spectral Data 
 
      The following Tables illustrate a schema that may be used in the relational database  227  for storing and processing the aforementioned mass spectra. Experimental information, data output and subsequent results from spectral database queries are stored in fields of these Tables and are used in the identification of proteomic differences between the two or more biological samples. As previously described, these Tables are desirably implemented using a specialized database programming language such as SQL or MySQL in order to permit the fields and information stored in these Tables to be flexibly associated. This implementation also provides search, query, and processing routines used to identify the primary mass spectrum peaks. The information retrieved from the spectral database  250  and stored in the Tables is further used to associate peptide-specific sequences with the primary mass spectrum peaks, and assess differential peptide expression between analogous peptides in the mixed-population. It will be appreciated that the following combination of Tables illustrate one of many possible schemas that may be used to process and analyze the mass spectral data and evaluate peptide expression. As such, other implementations and Table schemas should be considered to be but other embodiments of the present invention.  
      Tables 1 and 2 illustrate peptide and peptide tables or entities that store information about the peptides and peptides identified by mass spectral analysis. In these tables, the peptide and peptide entities are defined by a plurality of fields which identify features and information related to the peptide. The peptide and peptide entities, as well as other related entities, serve as a basis for storing and associating information useful in identifying the peptides, relating the peptides with the mass spectra information, and describing information that may be of interest to the investigator.  
      Each field may additionally be associated with a number of database properties or attributes used to define the type of data in the table and describe functionality used by the relational database to manipulate the information within the table. For example, each field of the table may be associated with attributes including: Type, Null, Key, Default, and Extra. The Type attribute defines the type of information or value which is to be stored within the table such as an integer, character, text, or other variable identifier. The Null attribute indicates whether the field must contain an associated data value or may be stored within the relational database as an empty field. The Key attribute defines a unique instance of the entity and is used by the relational database  227  to maintain links or associations in the table and interrelate the table with other tables in the database  226 . The Default attribute defines the contents of the field when an instance of the Table is created in the database  226 ,  227 . The Extra attribute defines properties or functionality which the database programming language uses to perform operations on fields of the table such as auto incrementing values to facilitate user interaction.  
      Table 1 further comprises a peptide_id field (defines a unique peptide identifier for the matched peptide), a name field (defines the name of the peptide), and a sequence field (defines the peptide sequence). These fields define attributes of the Peptide entity which may be associated with other fields of other tables or entities to aid in the organization of the database schema. In a similar manner, Table 2 comprises a peptide_id field (defines the unique peptide identifier for the matched peptide), a name field (defines the name of the peptide sequence, with the corresponding peptide belonging to the named peptide), and a peptide_id field (defines a unique peptide identifier for the corresponding peptide).  
      Table 3 illustrates a global table that is used in conjunction with peptide and peptide tables to store and relate information used in the processing of the tandem mass spectra obtained from the spectral database  250 . The fields of this table comprise: a peptide_id field (defines a peptide identifier similar to that of the peptide and peptide tables), a species field (defines species, conditions, or treatments of the biological samples), a charge_state field (defines the charge state of the peptide of interest), a quantitation_value field (defines the computed quantitation value), a ratio field (defines the relative abundance of one biological sample to another), a mass field (defines the mass of the peptide), a identified_charge_state field (defines the charge state of the peptide as identified by the spectral database or the data analysis program  200 ), and a duplicate field (defines whether or not the peptide has been found elsewhere in the mass spectrum or database).  
      Table 4 illustrates a quantitation table used by the data analysis program  200  to maintain state information and run indicators used in the identification and quantitation of the peaks of the primary mass spectrum. The fields of this table comprise: a run_id field (defines the identifiers used by the data analysis program  200  to determine what operations are being performed), a Qvalue field (defines the quantitation value obtained by the data analysis program), a start_scan field (defines a number corresponding to the scan number where the peak under analysis starts), end_scan (defines a number corresponding to the scan number where the peak under analysis ends), a duplicate field (defines whether or not the peptide is a duplicate), a xcorr field (defines a correlation score as computed by the spectral database analysis), a DCn field (defines a delta Cn value as computed by the spectral database analysis), a valley field (defines whether or not the start_scan analysis commences in a valley of the spectrum), and an extrapolation field (defines whether or not extrapolation has been performed during the analysis).  
      Table 5 illustrates a node table used by the data analysis system  200  as a data structure to pass information between nodes of the parallel computing distributed system for data analysis. The fields of this table comprise: a dirname field (defines a name of a directory which contains the data files  260  produced by the spectral database  250 ), a filename field (defines the filenames of the data files  260  files produced by the spectral database  250  and may include a hyperlink to the actual raw spectrum data), a charge state field (defines the charge state [1, 2 or 3] for the top rated peptide in a given data file  260 ), a mass field (defines the mass of the peptide), a tol field (defines the mass tolerance of the analysis), a tot_icurrent field (defines the total ion current per mass spectrum), a Xcorr field (defines the correlation score for the peptide), a dCn field (defines the delta Cn between the peptide and one defined in the data file  260 ), a Sp field (defines a preliminary scoring of the peptide under analysis), a RSp field (defines a ranking for the preliminary scoring of the peptide under analysis), a IonsMatch field (defines the number of matched ions found in the mass spectrum), a IonsTot field (defines the total number of ions expected), a SpecLink field (defines a hyperlink to a plot of the actual spectrum), a PeptideWeight field (defines the weight of the peptide under study), a resultPI field (defines the pH of the peptide at the specified temperature), a Ref field (defines a database reference for the matched peptide), a DuplicateCount field (defines a number of places where the peptide occurs and may further contain a hyperlink to other information such as BLAST sequence information), a tryptic field (defines the tryptic nature of the peptide), a Sequence field (defines the actual sequence of the peptide under study), and a PeptideHeader field (defines references and annotations for the matched peptide).  
      The aforementioned tables and descriptors summarize some of the primary fields and attributes associated with performing the data analysis used to identify the sequence of each peak within the primary mass spectrum. Furthermore, these tables are used by the data analysis system  200  to store the information useful in comparing the analogous peptides in the mixed-population and to identify proteomic differences using the data analysis system peak identification algorithms.  
               TABLE 1                          PEPTIDE                                     Field   Type   Null   Key   Default   Extra               peptide_id   int(11)       PRI   NULL   auto_increment       name   varchar(255)   YES       NULL       sequence   mediumtext   YES       NULL                  
 
     
       
         
           
               
             
               
                 TABLE 2 
               
             
            
               
                   
               
               
                   
               
               
                 PEPTIDE 
               
            
           
           
               
               
               
               
               
               
            
               
                 Field 
                 Type 
                 Null 
                 Key 
                 Default 
                 Extra 
               
               
                   
               
               
                 peptide_id 
                 int(11) 
                   
                   
                 0 
                   
               
               
                 sequence 
                 varchar(255) 
                 YES 
                   
                 NULL 
               
               
                 peptide_id 
                 int(11) 
                   
                 PRI 
                 NULL 
                 auto_increment 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 3 
               
             
            
               
                   
               
               
                   
               
               
                 GLOBAL 
               
            
           
           
               
               
               
               
               
               
            
               
                 Field 
                 Type 
                 Null 
                 Key 
                 Default 
                 Extra 
               
               
                   
               
               
                 peptide_id 
                 int(11) 
                   
                   
                 0 
                   
               
               
                 species 
                 tinyint(4) 
                 YES 
                   
                 NULL 
               
               
                 charge_state 
                 tinyint(4) 
                 YES 
                   
                 NULL 
               
               
                 quantitation_value 
                 float 
                 YES 
                   
                 NULL 
               
               
                 ratio 
                 float 
                 YES 
                   
                 NULL 
               
               
                 mass 
                 float 
                 YES 
                   
                 NULL 
               
               
                 identified_charge_state 
                 tinyint(4) 
                 YES 
                   
                 NULL 
               
               
                 duplicate 
                 tinyint(4) 
                 YES 
                   
                 NULL 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 4 
               
             
            
               
                   
               
               
                   
               
               
                 QUANTITATION 
               
            
           
           
               
               
               
               
               
               
            
               
                 Field 
                 Type 
                 Null 
                 Key 
                 Default 
                 Extra 
               
               
                   
               
               
                 run_id 
                 tinyint(4) 
                 YES 
                   
                 NULL 
                   
               
               
                 qvalue 
                 float 
                 YES 
                   
                 NULL 
               
               
                 start_scan 
                 smallint(6) 
                 YES 
                   
                 NULL 
               
               
                 end_scan 
                 smallint(6) 
                 YES 
                   
                 NULL 
               
               
                 duplicate 
                 tinyint(4) 
                 YES 
                   
                 NULL 
               
               
                 XCorr 
                 float 
                 YES 
                   
                 NULL 
               
               
                 DCn 
                 float 
                 YES 
                   
                 NULL 
               
               
                 valley 
                 tinyint(4) 
                 YES 
                   
                 NULL 
               
               
                 extrapolation 
                 tinyint(4) 
                 YES 
                   
                 NULL 
               
               
                   
               
            
           
         
       
     
                     TABLE 5                          NODE                                     Field   Type   Null   Key   Default   Extra               dirname   varchar(255)   YES       NULL           filename   varchar(255)   YES       NULL       charge_state   tinyint(4)   YES       NULL       Mass   float   YES       NULL       tol   float   YES       NULL       tot_icurrent   float   YES       NULL       XCorr   float   YES       NULL       dCn   float   YES       NULL       Sp   float   YES       NULL       RSp   smallint(6)   YES       NULL       IonsMatch   smallint(6)   YES       NULL       IonsTot   smallint(6)   YES       NULL       SpecLink   varchar(255)   YES       NULL       PeptideWeight   mediumint(9)   YES       NULL       resultPI   float   YES       NULL       Ref   text   YES       NULL       DuplicateCount   varchar(255)   YES       NULL       tryptic   tinyint(4)   YES       NULL       Sequence   text   YES       NULL       PeptideHeader   text   YES       NULL                    
 E. Peptide Labeling Methods 
 
      Embodiments of this invention provide analytical reagents and mass spectrometry-based methods using these reagents for the rapid and quantitative analysis of proteins or protein function in mixtures of proteins. The analytical method can be used for qualitative and particularly for quantitative analysis of global protein expression profiles in cells and tissues, i.e., the quantitative analysis of proteomes. The method can also be employed to screen for and identify proteins whose expression level in cells, tissue or biological fluids is affected by a stimulus (e.g., administration of a drug or contact with a potentially toxic material), by a change in environment (e.g., nutrient level, temperature, passage of time) or by a change in condition or cell state (e.g., disease state, malignancy, site-directed mutation, gene knockouts) of the cell, tissue or organism from which the sample originated. The proteins identified in such a screen can function as markers for the changed state. For example, comparisons of protein expression profiles of normal and malignant cells can result in the identification of proteins whose presence or absence is characteristic and diagnostic of the malignancy.  
      In an exemplary embodiment, the methods herein can be employed to screen for changes in the expression or state of enzymatic activity of specific proteins. These changes may be induced by a variety of chemicals, including pharmaceutical agonists or antagonists, or potentially harmful or toxic materials. The knowledge of such changes may be useful for diagnosing enzyme-based diseases and for investigating complex regulatory networks in cells.  
      The methods herein can also be used to implement a variety of clinical and diagnostic analyses to detect the presence, absence, deficiency or excess of a given protein or protein function in a biological fluid (e.g., blood), or in cells or tissue. The method is particularly useful in the analysis of complex mixtures of proteins, i.e., those containing 5 or more distinct proteins or protein functions.  
      One method employs affinity-labeled protein reactive reagents that allow for the selective isolation of peptide fragments or the products of reaction with a given protein (e.g., products of enzymatic reaction) from complex mixtures. The isolated peptide fragments or reaction products are characteristic of the presence of a protein or the presence of a protein function, e.g., an enzymatic activity, respectively, in those mixtures. Isolated peptides or reaction products are characterized by mass spectrometric (MS) techniques. In particular, the sequence of isolated peptides can be determined using tandem MS (MS) n  techniques, and by application of sequence database searching techniques, the protein from which the sequenced peptide originated can be identified.  
      I. Peptide Labeling Reagents  
      Embodiments of the present invention provide trifunctional synthetic reagents that can be used for reducing the complexity of peptide mixtures by labeling peptides at a specific amino acid residue and then selectively enriching only those peptides containing the labeled amino acid. By preparing this reagent in two forms with detectably different masses, this technique can be used to provide accurate relative quantification of peptide amounts using mass spectrometry.  
      In one embodiment of the invention, peptide labeling reagents are used that consist of heavier isotopes of atoms normally found in those reagents. In a preferred embodiment, cells or tissues that will be used to prepare proteins for the control or the experimental protein samples are grown with reagents containing  15 N, whereas cells or tissues that will be used to prepare proteins for the other sample are grown with reagents containing  14 N. These reagents can be amino acids or amino acid precursors containing the required nitrogen isotope. Peptides from biological samples grown with  15 N containing reagents will be heavier and distinguishable from peptides from other samples grown with  14 N reagents when the peptide samples are mixed and analyzed with ms/ms techniques.  
      In some embodiments of the invention, the peptide labeling moiety consists of a lysine residue modified with an iodoacetamide functional group on the ε-amino group of the side chain. The synthetic peptides contain two additional motifs: a peptide epitope tag for high affinity purification; and a highly specific protease site for releasing the affinity purified labeled peptides from the affinity matrix. In addition, these synthetic peptides can readily be prepared as isoforms of two different masses by the simple expedient of using an ornithine in place of lysine to introduce a 14 mass unit difference in the carboxyl terminal acid.  
      In other embodiments of the invention, the peptide labeling moiety consists of a molecule modified with an iodo-containing organic substituent, which may be an iodide on a primary carbon, an acid iodide, or an iodoacetamide functional group. In addition, the peptide labeling moiety comprises a substituted benzyl moiety, which undergoes heterolytic cleavage upon exposure to light of a certain wavelength. In addition, these molecules can readily be prepared as isoforms of two different masses by the simple expedient of using an alkylene chain that has additional methylene groups or is missing methylene groups to introduce an integer multiple of 14 mass unit difference in the carboxyl terminal acid.  
      Thus, in a first aspect, the invention provides a compound of Formula I 
 
Immobilization Site-Cleavage Site-Link  (I) 
 
 where: 
      Immobilization Site is selected from the group consisting of an epitope tag, a linker to a solid surface, a metal chelating site, a magnetic site, and a specific oligonucleotide sequence, or a combination thereof;     Cleavage Site is selected from the group consisting of a protease cleavage site, a photocleavable linker, a restriction enzyme cleavage site, a chemical cleavage site, and a thermal cleavage site, or a combination thereof;     Link is selected from the group consisting of an amino acid reactive site and a mass variance site, or a combination thereof.    

      At some point during their use, the compounds of the present invention are immobilized on, for example, a surface, such that they do not move when washed with a fluid. The surface on which the compounds are immobilized may be a solid surface. Examples, without limitation of solid surfaces include beads (glass, plastic or other material), plastic, glass, silicon chip, multi-well plates, and membranes (such as PVDF or nylon).  
      There are a number of ways by which the compounds of the invention may be immobilized. For instance, the solid surface may comprise an amino acid sequence. The Immobilization Site of the compounds of the present invention will then comprise another amino acid sequence which is the epitope tag of the amino acid sequence on the surface. An epitope tag binds exclusively to its target amino acid sequence.  
      In other embodiments, the solid surface may comprise a metal chelating column, comprising for example nickel atoms. The Immobilization Site of the compounds of the invention may then comprise, for example, amino acid residues, such as histidines, or other residues, such as ethylenediaminetetraacetate, that will chelate to the metal atom on the column. The solid surface can be an oligonucleotide and the Immobilization Site can be the complimentary oligonucleotide. Those skilled in the art and familiar with metal affinity chromatography will know which chelating groups are best used with which metals on the column to be used.  
      In other embodiments of the present invention, the solid surface may comprise magnetic residues. In this case, the Immobilization Site of the compounds of the present invention will also comprise magnetic residues that are designed to bind magnetically to the magnetic residues of the solid surface.  
      In certain other embodiments, the Immobilization Site is a direct link between the solid surface and the compounds of the present invention. The direct link may be an acyl group or other chemical moieties that are capable of reacting with the solid surface, in some cases reversibly, so that the compounds of the present invention are immobilized on the surface.  
      The Cleavage Site is a part of the compound of the present invention that is capable of breaking the molecule in two different parts: One part of the molecule remains immobilized on the solid surface, while the other part of the molecule can move away from the solid surface by a wash fluid.  
      In certain embodiments, the Cleavage Site may be an amino acid sequence, comprising at least one amino acid residue, which is a cleavage site for a protease.  
      In other embodiments, the Cleavage Site may be a photocleavable linker. A photocleavable linker is a residue that breaks in two parts, either heterolytically or homolytically, when exposed to light of a certain wavelength, whether visible, infrared, or ultraviolet.  
      Other embodiments of the invention include a Cleavage Site which comprises a polynucleotide residue, of at least two nucleotides in length, that can be cleaved with a restriction enzyme.  
      In certain other embodiments, the Cleavage Site is a site that can be chemically cleaved, for example, by addition of an acid or a base.  
      In other embodiments, the Cleavage Site may be cleaved thermally. This embodiment may include a Cleavage Site that comprises a polynucleotide reside that can hybridize to another polynucleotide residue connected to the Immobilization Site. Heating the compounds can then result in the hybridized polynucleotides to “melt” and separate, as a DNA double helix would.  
      The Link comprises a residue that can react with an amino acid. The Link may react with a side-chain of an amino acid, or with the N- or C-terminus of a polypeptide. Thus, the Link residue comprises a reactive group. The reactive group may be a moiety that can undergo nucleophilic substitution with a portion of the amino acid, or can form an amide or an ester bond with the amino acid. However, in general, the invention contemplates any reactive group that can form a bond with any part of an amino acid.  
      Optionally, the Link comprises a portion that allows mass variance to be introduced into a series of molecules. Thus, for example, the Link residue comprises a alkylene group, which may be a methylene in one embodiment, an ethylene in another embodiment, and a propylene in yet another embodiment, thereby introducing a mass difference of a multiple of 14 mass units between the different embodiments. The mass variance portion of the Link residue may be a series of methylene residues, or a series of —NH— residues, or a series of amide bonds, —NH—C(O)—. Any other repeating unit may work for introducing mass variance. The mass variance may be a variance that is measurable under the conditions of the experiment. Thus, mass variances in the range of 1 to 1000 mass units, or in the range of about 1 to about 500 mass units, or in the range of about 1 to about 250 mass units, or in the range of about 1 to about 100, or in the range of about 1 to about 50, or in the range of about 1 to about 30, or in the range of about 1 to about 20, or in the range of about 3 to about 20, or in the range of about 4 to about 20 are contemplated. In general, the mass variance portion of the Link affects chromatographic properties of the compound of the invention consistently. In another aspect, the invention provides a compound of Formula II or III: 
 
Acyl-NH—X-[Epitope Tag Site] A -Y-[Protease Cleavage Site]-Z-Link  (II) 
 
Acyl-NH—X-alk-O-Ph-CH 2 -Z-Link  (III) 
 
 where: 
          A is an integer from 0 to 12;     X is selected from the group consisting of an amide bond of formula —C(O)—NR—, a carbonyl of formula —C(O)—, and an amino acid sequence comprising between 10 to 30 amino acids, where R is hydrogen or lower alkyl;     Y is an amide bond of formula —C(O)—NR—, where R is hydrogen or lower alkyl, or Y is an amino acid sequence comprising between 0 to 20 amino acids;     Z is selected from the group consisting of an amide bond of formula —(CH 2 ) B —C(O)—NR—, an amide bond of formula —(CH 2 ) B —NR—C(O)—, and an amino acid sequence comprising between 0 to 3 amino acids, 
            where R is hydrogen or lower alkyl, and     where B is an integer from 0 to 20;    
            alk is straight or branched chain of alkylene comprising between 0 and 20 carbon atoms;     Ph is a phenyl group optionally substituted with one or more electron withdrawing groups ortho or para to the —CH 2 — group;     Link is selected from the group consisting of —(CH 2 ) C —I, —(CH 2 ) D —CH(—(CH 2 ) E CH 3 )—(CH 2 ) F —X—I, Lys-ε-iodoacetamide, Arg-δ-iodoacetamide, and Orn-δ-iodoacetamide 
            where C, D, E, and F are each independently an integer from 0 to 20;    
                Epitope Tag Site is a sequence of amino acids, 
        where when A is two or more, the amino acid sequence of each Epitope Tag Site can be the same or different; and    
        Protease Cleavage Site is a sequence of amino acids that is a cleavage site for a highly specific protease enzyme.    

      By “Acyl” it is meant a chemical substituent of the formula R—C(O)—, where R is an organic group selected from the group consisting of straight chain, branched, or cyclic alkyl, aryl, and five-membered or six-membered heteroaryl, each being optionally substituted with one or more protected substituents, which are selected from the group consisting of hydroxyl (—OH), sulfhydryl (—SH), amino (—NH 2 ), nitro (—NO 2 ), carboxyl (—COOH), ester (—COOR), and carboxamido (—CONH 2 ). These substituents may be protected by any common organic protecting group as set forth in, for example, Greene &amp; Wutts, Protective Groups in Organic Chemistry, 3 rd  Ed., John Wiley &amp; Sons, New York, N.Y., 1999.  
      Electron withdrawing groups are well-known to those of skill in the art. These groups include, without limitation, —OH, —OR, —NO 2 , —N(CH 3 ) 3   + , —CN, —COOH, —COOR, —SO 3 H, —CHO, and —CRO. In general, these groups are the ones that increase the rate of nucleophilic aromatic substitution when they are located at the ortho or para position with respect to the site of attack.  
      One of the functional groups of the compounds is the Epitope Tag Site. Suitable Epitope Tag Sites bind selectively either covalently or non-covalently and with high affinity to a capture reagent. The “capture reagent” is an amino acid sequence bound to solid support. The solid supports, with the capture reagent attached thereto, are packed into a column, preferably a column for chromatography. The amino acid sequence of the capture reagent and the amino acid sequence of the Epitope Tag Site are designed to bind to each other with high selectivity and high affinity. The binding may be either covalently or non-covalently. Examples of non-covalent binding include ionic interactions, van der Waals interactions, and hydrophobic or hydrophilic interactions. The binding between the Epitope Tag Site and the capture reagent may be similar to the binding of an antibody to an epitope of a protein for which the antibody is specific.  
      The interaction or bond between the Epitope Tag Site and the capture agent preferably remains intact after extensive and multiple washings with a variety of solutions to remove non-specifically bound components. The Epitope Tag Site binds minimally or preferably not at all to components in the assay system, except the capture agent, and does not significantly bind to surfaces of reaction vessels. Any non-specific interaction of the Epitope Tag Site with other components or surfaces should be disrupted by multiple washes that leave Epitope Tag Site-capture agent interaction intact. Further, the interaction of Epitope Tag Site and the capture agent can be disrupted to release peptide, substrates or reaction products, for example, by addition of a displacing ligand or by changing the temperature or solvent conditions. Preferably, neither capture agent nor Epitope Tag Site react chemically with other components in the assay system and both groups should be chemically stable over the time period of an assay or experiment.  
      The Epitope Tag Site is preferably soluble in the sample liquid to be analyzed and the capture reagent should remain soluble in the sample liquid even though attached to an insoluble resin such as Agarose. In the case of the capture reagent, the term “soluble” means that the capture reagent is sufficiently hydrated or otherwise solvated such that it functions properly for binding to the Epitope Tag Site. The capture reagent or capture reagent-containing conjugates should not be present in the sample to be analyzed, except when added to capture the Epitope Tag Site.  
      A displacement ligand is optionally used to displace the Epitope Tag Site from the capture reagent. Suitable displacement ligands are not typically present in samples unless added. The displacement ligand should be chemically and enzymatically stable in the sample to be analyzed and should not react with or bind to components (other than the capture reagent) in samples or bind non-specifically to reaction vessel walls. The displacement ligand preferably does not undergo peptide-like fragmentation during mass spectral analysis, and its presence in sample should not significantly suppress the ionization of tagged peptide, substrate or reaction product conjugates.  
      Another functional group of the compounds disclosed herein is the Protease Cleavage Site. This site is an amino acid sequence, which in some embodiments comprises between 1 and 15 amino acids, and in other embodiments comprises between 4 and 8 amino acids, while in certain other embodiments comprises at least four amino acids. In one embodiment, the Protease Cleavage Site is an amino acid sequence of formula ENLYFQG (SEQ ID NO: 1).  
      The Protease Cleavage Site is designed to be cleaved once it is exposed to a highly specific protease enzyme. In certain embodiments, the protease enzyme is selected from the group consisting of TEV protease, chymotrypsin, endoproteinase Arg-C, endoproteinase Asp-N, trypsin,  Staphylococcus aureus  protease, thermolysin, and pepsin. In other embodiments, the protease enzyme is TEV protease. Preferably, the Protease Cleavage Site is not cleaved by the enzyme for the initial proteolysis of the lysed cell sample, nor would the cleavage site be lysed by any contaminating proteases from the cell sample.  
      The third functional group of the compounds disclosed herein is the protein reactive group, designated as “Link” in the above formula. This group may selectively react with certain protein functional groups or may be a substrate of an enzyme of interest. Any selectively reactive protein reactive group should react with a functional group of interest that is present in at least a portion of the proteins in a sample. Reaction of Link with functional groups on the protein should occur under conditions that do not lead to substantial degradation of the compounds in the sample to be analyzed. Examples of selectively reactive Links suitable for use in the affinity tagged reagents include those which react with sulfhydryl groups to tag proteins containing cysteine, those that react with amino groups, carboxylate groups, ester groups, phosphate reactive groups, and aldehyde and/or ketone reactive groups or, after fragmentation with CNBr, with homoserine lactone.  
      Thiol reactive groups include epoxides, α-haloacyl groups, nitriles, sulfonated alkyls or aryl thiols and maleimides. Amino reactive groups tag amino groups in proteins and include sulfonyl halides, isocyanates, isothiocyantes, active esters, including tetrafluorophenyl esters, and N-hydroxysuccinimidyl esters, acid halides, and acid anyhydrides. In addition, amino reactive groups include aldehydes or ketones in the presence or absence of NaBH 4  or NaCNBH 3 .  
      Carboxylic acid reactive groups include amines or alcohols in the presence of a coupling agent such as dicyclohexylcarbodiimide, or 2,3,5,6-tetrafluorophenyl trifluoroacetate and in the presence or absence of a coupling catalyst such as 4-dimethylaminopyridine; and transition metal-diamine complexes including Cu(II)phenanthroline.  
      Ester reactive groups include amines which, for example, react with homoserine lactone.  
      Phosphate reactive groups include chelated metal where the metal is, for example Fe(III) or Ga(III), chelated to, for example, nitrilotriacetiac acid or iminodiacetic acid.  
      Aldehyde or ketone reactive groups include amine plus NaBH 4  or NaCNBH 3 , or these reagents after first treating a carbohydrate with periodate to generate an aldehyde or ketone.  
      The Link group should be soluble in the sample liquid to be analyzed and it should be stable with respect to chemical reaction, e.g., substantially chemically inert, with components of the sample as well as the Epitope Tag Site, Protease Cleavage Site, and the capture reagent groups. The Link group when bound to the molecule should not interfere with the specific interaction of the Epitope Tag Site with the capture reagent or interfere with the displacement of the Epitope Tag Site from the capture reagent by a displacing ligand or by a change in temperature or solvent. The Link group should bind minimally or preferably not at all to other components in the system, to reaction vessel surfaces or to the capture reagent. Any non-specific interactions of the Link group should be broken after multiple washes which leave the Epitope Tag Site-capture reagent complex intact.  
      The Link group may be selected from a group of substituents that differ from one another by the presence or absence of one or more repeating units, such as methylene (—CH 2 —) groups. Thus, groups that contain straight chain alkylene moieties within them are particularly well-suited for this purpose.  
      In certain embodiments, the invention contemplates using lysine, ornithine, or arginine, coupled with iodoacetamide, as the Link group. “Orn” is the three letter designation for “L-ornithine,” which is (S)-(+)-2,5-diaminopentanoic acid, H 2 N(CH 2 ) 3 CH(NH 2 )COOH. “Iodoacetamide” is an organic substituent group with the structure I—CH 2 —C(O)—NH—. When an amino acid group of a compound is derivatized by the iodoacetamide group, the iodoacetamide group is chemically bound to the side-chain amino group of the amino acid moiety. Thus, the designation “ε” or “δ” following the amino acids in the above formula designate the position at which the amino acid is derivatized by the iodoacetamide group. For example, Lys-ε-iodoacetamide has the formula 
 
ICH 2 C(O)NH(CH 2 ) 4 CH(NH 2 )COOH 
 
      It is also understood within the context of the invention that the incorporation of the designation “ε” or “δ” is optional. Therefore, Lys-ε-iodoacetamide and Lys-iodoacetamide (K-iodoacetamide), Arg-δ-iodoacetamide and Arg-iodoacetamide (R-iodoacetamide), and Orn-δ-iodoacetamide and Orn-iodoacetamide refer to the same compound or moiety, respectively.  
      Specific embodiments provided herein include, but are in no way limited to, the following compounds:  
                          (SEQ ID NO: 2)                         Acyl-NH-AYPYDVPDYASENLYFQGK-iodoacetamide,                                 (SEQ ID NO: 3)                         Acyl-NH-AYPYDVPDYASENLYFQGGK-iodoacetamide,                                 (SEQ ID NO: 4)                         Acyl-NH-AYPYDVPDYASENLYFQGAK-iodoacetamide,                                 (SEQ ID NO: 5)                         Acyl-NH-AYPYDVPDYASENLYFQG(GABA)K-iodoacetamide,                                 (SEQ ID NO: 6)                         Acyl-NH-AYPYDVPDYASENLYFQGVK-iodoacetamide,                                 (SEQ ID NO: 7)                         Acyl-NH-AYPYDVPDYASENLYFQGOrn-iodoacetamide,                                 (SEQ ID NO: 8)                         Acyl-NH-AYPYDVPDYASENLYFQGGOrn-iodoacetamide,                                 (SEQ ID NO: 9)                         Acyl-NH-AYPYDVPDYASENLYFQGAOrn-iodoacetamide,                                 (SEQ ID NO: 10)                         Acyl-NH-AYPYDVPDYASENLYFQG(GABA)Orn-iodoacetamide,                                 (SEQ ID NO: 11)                         Acyl-NH-AYPYDVPDYASENLYFQGVOrn-iodoacetamide,                                 (SEQ ID NO: 12)                         Acyl-NH-AYPYDVPDYASENLYFQGR-iodoacetamide,                                 (SEQ ID NO: 13)                         Acyl-NH-AYPYDVPDYASENLYFQGGR-iodoacetamide,                                 (SEQ ID NO: 14)                         Acyl-NH-AYPYDVPDYASENLYFQGAR-iodoacetamide,                                 (SEQ ID NO: 15)                         Acyl-NH-AYPYDVPDYASENLYFQG(GABA)R-iodoacetamide,           and                             (SEQ ID NO: 16)                         Acyl-NH-AYPYDVPDYASENLYFQGVR-iodoacetamide.              
 
      Other specific embodiments include: 
      Acyl-NH-CASENLYFQGK-CH 2 CH 2 CH 2 CH 2 —NH—C(O)—CH 2 I,     Acyl-NH-CASENLYFQGOrn-CH 2 CH 2 CH 2 —NH—C(O)—CH 2 I,     Acyl-NH-CASENLYFQGPK-CH 2 CH 2 CH 2 CH 2 —NH—C(O)—CH 2 I, and     Acyl-NH-CASENLYFQGPOrn-CH 2 CH 2 CH 2 CH 2 —NH—C(O)—CH 2 I.    

      Other embodiments of the invention include compounds in which the Link moiety is a non-amino acid organic group. In these embodiments, the Link moiety is —(CH 2 )C—I or —(CH 2 ) D —CH(—(CH 2 ) E CH 3 )—(CH 2 ) F —X—I, where C, D, E, and F are each independently an integer from 0 to 20, and X is as defined herein. In some embodiments, the Link group is iodoacetamide. In other embodiments, the Link group is selected from the group consisting of —CH(CH 2 C(O)I)CH 2 CH 3 , —C(C(O)I)CH 2 CH 2 CH 3 , —CH(CH 2 I)CH 2 CH 3 , —CH 2 CH(CH 2 I)CH 2 CH 2 CH 3 .  
      In other embodiments, the invention relates to a compound of Formula III. In some embodiments, alk is a straight or branched chain of alkylene comprising between 0 and 20, between 0 and 15, between 0 and 10, between 0 and 5, or between 0 and 3 carbon atoms carbon atoms. In some embodiments alk is a straight chain of alkylene. alk may be selected from the group consisting of methylene, ethylene, propylene, n-butylene, and n-pentylene. In certain embodimets, alk is propylene.  
      In some embodiments Ph is a substituted phenyl group. It may be substituted with electron withdrawing groups. The substitutions may take place at positions ortho or para to the methylene group to which Ph is connected. In certain embodiments, the substituents on Ph are methoxy or nitro. In some embodiments, Ph is the following:  
                 
 
      The Ph groups is such that when the molecule is exposed to a light of certain wavelength, for example ultraviolet light, the bond between the CH 2  group and Z undergoes heterolytic cleavage. Therefore, the substituents on Ph are situated to stabilize the resulting benzylic free radical.  
      In embodiments, Z is an amino acid sequence comprising between 1 and 3 amino acids. In certain embodiments, Z is a single amino acid. It may be any of the natural or synthetic amino acids known in the art. In some embodiments, Z is selected from the group consisting of glycine, alanine, and valine. In certain other embodiments, Z may be a synthetic amino acid, where the amino group in a position other than a to the carboxyl group. For instance, the amino group may be β, δ, ε, φ, or ε, or any other position, to the carboxyl group. In some embodiments Z is γ-aminobutyric acid.  
      Certain other specific embodiments of the invention include, without limitation, 
      Acyl-CH 2 CH 2 CH 2 —O-Ph-CH 2 -G-NH—C(O)—CH 2 I,     Acyl-CH 2 CH 2 CH 2 —O-Ph-CH 2 -A-NH—C(O)—CH 2 I,     Acyl-CH 2 CH 2 CH 2 —O-Ph-CH 2 -γ-aminobutyric acid-NH—C(O)—CH 2 I, and     Acyl-CH 2 CH 2 CH 2 —O-Ph-CH 2 —V—NH—C(O)—CH 2 I, 
 
 where Ph is  
                 
 
 II. Peptide Labeling Process 
   

      In another aspect, the invention provides for a method for simultaneously identifying and determining the levels of expression of cysteine-containing proteins in normal and perturbed cells, comprising: 
      a) preparing a first protein sample or a first peptide sample from the normal cells;     b) reacting the first protein sample or the first peptide sample with a reagent of Formula II or III: 
 
Acyl-NH—X-[Epitope Tag Site] A -Y-[Protease Cleavage Site]-Z-Link  (II) 
 
Acyl-NH—X-alk-O-Ph-CH 2 -Z-Link  (III) 
        where:     A is an integer from 0 to 12;     X is selected from the group consisting of an amide bond of formula —C(O)—NR—, a carbonyl of formula —C(O)—, and an amino acid sequence comprising between 10 to 30 amino acids, where R is hydrogen or lower alkyl;     Y is an amide bond of formula —C(O)—NR—, where R is hydrogen or lower alkyl, or Y is an amino acid sequence comprising between 0 to 20 amino acids;     Z is selected from the group consisting of an amide bond of formula —(CH 2 ) B —C(O)—NR—, an amide bond of formula —(CH 2 ) B —NR—C(O)—, and an amino acid sequence comprising between 0 to 3 amino acids, 
            where R is hydrogen or lower alkyl, and     where B is an integer from 0 to 20;    
            alk is straight or branched chain of alkylene comprising between 0 and 20 carbon atoms;     Ph is a phenyl group optionally substituted with one or more electron withdrawing groups ortho or para to the —CH 2 — group;     Link is selected from the group consisting of —(CH 2 )C—I, —(CH 2 ) D —CH(—(CH 2 ) E CH 3 )—(CH 2 ) F —X—I, Lys-ε-iodoacetamide, Arg-δ-iodoacetamide, and Orn-δ-iodoacetamide 
            where C, D, E, and F are each independently an integer from 0 to 20;    
            Epitope Tag Site is a sequence of amino acids, 
            where when A is two or more, the amino acid sequence of each Epitope Tag Site can be the same or different; and    
            Protease Cleavage Site is a sequence of amino acids that is a cleavage site for a highly specific protease enzyme;    
        c) preparing a second protein sample or a second peptide sample from the perturbed cells;     d) reacting the second protein sample or the second peptide sample of step c) with a second reagent of Formula II or III: 
 
Acyl-NH—X-[Epitope Tag Site] A -Y-[Protease Cleavage Site]-Z-Link  (II) 
 
Acyl-NH—X-alk-O-Ph-CH 2 -Z-Link  (III) 
 
 where: 
        A is an integer from 0 to 12;     X is selected from the group consisting of an amide bond of formula —C(O)—NR—, a carbonyl of formula —C(O)—, and an amino acid sequence comprising between 10 to 30 amino acids, where R is hydrogen or lower alkyl;     Y is an amide bond of formula —C(O)—NR—, where R is hydrogen or lower alkyl, or Y is an amino acid sequence comprising between 0 to 20 amino acids;     Z is selected from the group consisting of an amide bond of formula —(CH 2 ) B —C(O)—NR—, an amide bond of formula —(CH 2 ) B —NR—C(O)— and an amino acid sequence comprising between 0 to 3 amino acids, 
            where R is hydrogen or lower alkyl, and     where B is an integer from 0 to 20;    
            alk is straight or branched chain of alkylene comprising between 0 and 20 carbon atoms;     Ph is a phenyl group optionally substituted with one or more electron withdrawing groups ortho or para to the —CH 2 — group;     Link is selected from the group consisting of —(CH 2 ) C —I, —(CH 2 ) D —CH(—(CH 2 ) E CH 3 )—(CH 2 ) F —X—I, Lys-ε-iodoacetamide, Arg-δ-iodoacetamide, and Orn-δ-iodoacetamide 
            where C, D, E, and F are each independently an integer from 0 to 20;    
            Epitope Tag Site is a sequence of amino acids, 
            where when A is two or more, the amino acid sequence of each Epitope Tag Site can be the same or different; and    
            Protease Cleavage Site is a sequence of amino acids that is a cleavage site for a highly specific protease enzyme, such that the molecular weight of the first reagent and the molecular weight of the second reagent are different by an integer multiple of 14 atomic mass units;    
        e) combining the reacted the first and the second protein samples or the reacted the first and the second peptide sample from steps b) and d);     f) subjecting the combined protein samples or the combined peptide samples from step e) to proteolysis at a site on the protein samples or at a site on the peptide samples, the site being other than the Protease Cleavage Site;     g) subjecting the proteolyzed combined protein samples or the proteolyzed peptide samples from step f) to an affinity chromatography system comprising a second amino acid sequence attached to a solid, thereby forming bound proteins and non-bound proteins, where the Epitope Tag Site of the reagent and the second amino acid sequence bind with high specificity to each other;     h) eluting the non-bound proteins from the affinity chromatography system;     i) subjecting the affinity chromatography system from step h) to a protease specific for the Protease Cleavage Site, thereby forming a cleaved protein mixture;     j) eluting the cleaved protein mixture from the affinity chromatography system of step i);     k) isolating the eluted protein mixture obtained from step j);     l) subjecting the eluted protein mixture from step k) to chromatographic separation, followed by mass analysis;     m) comparing the results of step 1) to: 
        1) determining the ratio of amounts of compounds in the two samples, where the molecular weights thereof are separated by an integer multiple of 14 atomic mass units; and     2) comparing the results obtained for each compound to protein databases containing chromatographic and molecular weight correlations.    
       

      In another aspect, the invention provides for a method for simultaneously identifying and determining the levels of expression of cysteine-containing proteins in normal and perturbed cells, comprising: 
      a) preparing a first protein sample or a first peptide sample from the normal cells;     b) subjecting the first protein sample or the first peptide sample from step a) to proteolysis;     c) reacting the proteolyzed first protein sample or the proteolyzed first peptide sample with a reagent of Formula II or III: 
 
Acyl-NH—X-[Epitope Tag Site] A -Y-[Protease Cleavage Site]-Z-Link  (II) 
 
Acyl-NH—X-alk-O-Ph-CH 2 -Z-Link  (III) 
 
 where: 
        A is an integer from 0 to 12;     X is selected from the group consisting of an amide bond of formula —C(O)—NR—, a carbonyl of formula —C(O)—, and an amino acid sequence comprising between 10 to 30 amino acids, where R is hydrogen or lower alkyl;     Y is an amide bond of formula —C(O)—NR—, where R is hydrogen or lower alkyl, or Y is an amino acid sequence comprising between 0 to 20 amino acids;     Z is selected from the group consisting of an amide bond of formula —(CH 2 ) B —C(O)—NR—, an amide bond of formula —(CH 2 ) B —NR—C(O)—, and an amino acid sequence comprising between 0 to 3 amino acids, where R is hydrogen or lower alkyl, and where B is an integer from 0 to 20;     alk is straight or branched chain of alkylene comprising between 0 and 20 carbon atoms;     Ph is a phenyl group optionally substituted with one or more electron withdrawing groups ortho or para to the —CH 2 — group;     Link is selected from the group consisting of —(CH 2 )C—I, —(CH 2 ) D —CH(—(CH 2 ) E CH 3 )—(CH 2 ) F —X—I, Lys-ε-iodoacetamide, Arg-δ-iodoacetamide, and Orn-δ-iodoacetamide where C, D, E, and F are each independently an integer from 0 to 20;     Epitope Tag Site is a sequence of amino acids, where when A is two or more, the amino acid sequence of each Epitope Tag Site can be the same or different; and     Protease Cleavage Site is a sequence of amino acids that is a cleavage site for a highly specific protease enzyme;    
        d) preparing a second protein sample or a second peptide sample from the perturbed cells;     e) subjecting the second protein sample or the second peptide sample from step d) to proteolysis;     f) reacting the proteolyzed second protein sample or the proteolyzed second peptide sample of step e) with a second reagent of Formula II or III: 
 
Acyl-NH—X-[Epitope Tag Site] A -Y-[Protease Cleavage Site]-Z-Link  (II) 
 
Acyl-NH—X-alk-O-Ph-CH 2 -Z-Link  (III) 
 
 where: 
        A is an integer from 0 to 12;     X is selected from the group consisting of an amide bond of formula —C(O)—NR—, a carbonyl of formula —C(O)—, and an amino acid sequence comprising between 10 to 30 amino acids, where R is hydrogen or lower alkyl;     Y is an amide bond of formula —C(O)—NR—, where R is hydrogen or lower alkyl, or Y is an amino acid sequence comprising between 0 to 20 amino acids;     Z is selected from the group consisting of an amide bond of formula —(CH 2 ) B —C(O)—NR—, an amide bond of formula —(CH 2 ) B —NR—C(O)—, and an amino acid sequence comprising between 0 to 3 amino acids, 
            where R is hydrogen or lower alkyl, and     where B is an integer from 0 to 20;    
            alk is straight or branched chain of alkylene comprising between 0 and 20 carbon atoms;     Ph is a phenyl group optionally substituted with one or more electron withdrawing groups ortho or para to the —CH 2 — group;     Link is selected from the group consisting of —(CH 2 ) C —I, —(CH 2 ) D —CH(—(CH 2 ) E CH 3 )—(CH 2 ) F —X—I, Lys-ε-iodoacetamide, Arg-δ-iodoacetamide, and Orn-δ-iodoacetamide 
            where C, D, E, and F are each independently an integer from 0 to 20;    
            Epitope Tag Site is a sequence of amino acids, where when A is two or more, the amino acid sequence of each Epitope Tag Site can be the same or different; and     Protease Cleavage Site is a sequence of amino acids that is a cleavage site for a highly specific protease enzyme, such that the molecular weight of the first reagent and the molecular weight of the second reagent are different by an integer multiple of 14 atomic mass units;    
        g) combining the reacted the first and the second protein samples or the reacted the first and the second peptide sample from steps c) and f);     h) subjecting the combined protein samples or the combined peptide samples from step e) to proteolysis at a site on the protein samples or at a site on the peptide samples, the site being other than the Protease Cleavage Site;     i) subjecting the proteolyzed combined protein samples or the proteolyzed peptide samples from step f) to an affinity chromatography system comprising a second amino acid sequence attached to a solid, thereby forming bound proteins and non-bound proteins, 
        where the Epitope Tag Site of the reagent and the second amino acid sequence bind with high specificity to each other;    
        j) eluting the non-bound proteins from the affinity chromatography system;     k) subjecting the affinity chromatography system from step j) to a protease specific for the Protease Cleavage Site, thereby forming a cleaved protein mixture;     l) eluting the cleaved protein mixture from the affinity chromatography system of step k);     m) isolating the eluted protein mixture obtained from step 1);     n) subjecting the eluted protein mixture from step m) to chromatographic separation, followed by mass analysis;     o) comparing the results of step n) to: 
        1) determining the ratio of amounts of compounds in the two samples, where the molecular weights thereof are separated by an integer multiple of 14 atomic mass units; and     2) comparing the results obtained for each compound to protein databases containing chromatographic and molecular weight correlations.    
       

      In certain embodiments, if in step b) in the above method Link is Lys-ε-iodoacetamide, then in step d) Link is Orn-δ-iodoacetamide. Alternatively, if in step b) Link is Orn-δ-iodoacetamide, then in step d) Link is Lys-δ-iodoacetamide. In another embodiment, the Z substituent in the first reagent, i.e., in step b) has a molecular weight that is an integer multiple of 14 atomic mass units different than the Z substituent in the second reagent, i.e., in step d). For example, and without limitation, the Z in the first reagent contains valine whereas the Z in the second reagent contains leucine instead of valine, all the other amino acids in Z, if any, remaining the same between the two reagents.  
      In an embodiment, the reagent of step b) is selected from the group consisting of  
                          (SEQ ID NO: 17)                         Acyl-NH-AYPYDVPDYASENLYFQGK-iodoacetamide,                                 (SEQ ID NO: 18)                         Acyl-NH-AYPYDVPDYASENLYFQGGK-iodoacetamide,                                 (SEQ ID NO: 19)                         Acyl-NH-AYPYDVPDYASENLYFQGAK-iodoacetamide,                                 (SEQ ID NO: 20)                         Acyl-NH-AYPYDVPDYASENLYFQG(GABA)K-iodoacetamide,                                 (SEQ ID NO: 21)                         Acyl-NH-AYPYDVPDYASENLYFQGVK-iodoacetamide,                                 (SEQ ID NO: 22)                         Acyl-NH-AYPYDVPDYASENLYFQGR-iodoacetamide,                                 (SEQ ID NO: 23)                         Acyl-NH-AYPYDVPDYASENLYFQGGR-iodoacetamide,                                 (SEQ ID NO: 24)                         Acyl-NH-AYPYDVPDYASENLYFQGAR-iodoacetamide,                                 (SEQ ID NO: 25)                         Acyl-NH-AYPYDVPDYASENLYFQG(GABA)R-iodoacetamide,                                 (SEQ ID NO: 26)                         Acyl-NH-AYPYDVPDYASENLYFQGVR-iodoacetamide,                                 (SEQ ID NO: 27)                         Acyl-NH-AYPYDVPDYASENLYFQGOrn-iodoacetamide,                                 (SEQ ID NO: 28)                         Acyl-NH-AYPYDVPDYASENLYFQGGOrn-iodoacetamide,                                 (SEQ ID NO: 29)                         Acyl-NH-AYPYDVPDYASENLYFQGAOrn-iodoacetamide,                                 (SEQ ID NO: 30)                         Acyl-NH-AYPYDVPDYASENLYFQG(GABA)Orn-iodoacetamide,           and                             (SEQ ID NO: 31)                         Acyl-NH-AYPYDVPDYASENLYFQGVOrn-iodoacetamide.              
 
      Therefore, by way of example only, if the reagent of step b) is  
                          (SEQ ID NO: 32)                                 Acyl-NH-AYPYDVPDYASENLYPQGK-iodoacetamide               the reagent of step d) would be                                 (SEQ ID NO: 33)                                 Acyl-NH-AYPYDVPDYASENLYPQGOrn-iodoacetamide;              
 
      and if the reagent of step b) is  
                          (SEQ ID NO: 34)                                 Acyl-NH-AYPYDVPDYASENLYPQGOrn-iodoacetamide,              
 
      the reagent of step d) would be  
                          (SEQ ID NO: 35)                                 Acyl-NH-AYPYDVPDYASENLYPQGK-iodoacetamide.              
 
      Preferably, the reagent of step b) or of step d) reacts with the reactive side chain of one or more of the amino acid residues of the proteins in the first or second protein sample. By “reactive side chain” it is meant the amino acid side chain that is functionalized, or an amino acid side chain that is other than straight chain or branched alkyl. Therefore, the reagent reacts with the first or second protein at an amino acid residue selected from the group consisting of tyrosine, tryptophan, cysteine, methionine, proline, serine, threonine, lysine, histidine, arginine, aspartic acid, glutamic acid, asparagine, and glutamine. In certain embodiments, the reagent reacts at an amino acid residue selected from the group consisting of tyrosine, cysteine, proline, and histidine. In another embodiment, the site of reaction is a cysteine.  
      In some embodiments of the present invention, the chromatographic separation of step 1) is a multi-dimensional liquid chromatographic separation, which may be a two-dimensional liquid chromatographic separation or a three-dimensional liquid chromatographic separation. The dimensions of the multi-dimensional liquid chromatographic separation are selected from the group consisting of size differentiation, charge differentiation, hydrophobicity, hydrophilicity, and polarity. In some embodiments, at least one dimension of the multi-dimensional liquid chromatographic separation is separation using size differentiation. Embodiments of the invention include those in which one dimension of the multi-dimensional liquid chromatographic separation is separation using charge differentiation. In other embodiments, one dimension of the multi-dimensional liquid chromatographic separation is separation using hydrophobicity or hydrophilicity.  
      In another embodiment the mass analysis of step m) is a multi-dimensional mass analysis, which may be a two-dimensional mass analysis (i.e., tandem mass spectrometry).  
      It is well-known in the art to separate fragments of a solution using chromatography and, in tandem thereto, analyze the mass spectra of each fragment. The technique is formally known in the art as LC-MS or LC-MS/MS analysis. Multi-dimensional chromatography is also well-known in the art, where multiple columns are used in tandem, or the same column is packed with segments of different material that can separate the sample using different criteria. See, for example, Link et al., (1999) or Opitek et al. (1997), above. Multi-dimensional mass analysis is a technique known to those skilled in the art as well. In this technique, following an initial ionization, an ion of interest is selected. The selected ion is fragmented and each fragment (known as “daughter ion” or “progeny ion”) is now capable of being either analyzed or be subjected to further fragmentation. The technique is fully described in Siuzdak, Mass Spectrometry for Biotechnology, Academic Press, San Diego, Calif., 1996, which is incorporated by reference herein in its entirety.  
      In certain embodiments, the preparation of proteins from step a) is subjected to orthogonal chromatography before proceeding with the labeling in step b). Orthogonal chromatography is a technique well-known in the art.  
      Quantitative relative amounts of proteins in one or more different samples containing protein mixtures (e.g., biological fluids, cell or tissue lysates, etc.) can be determined using chemically similar, affinity tagged and differentially labeled reagents to affinity tag and differentially label proteins in the different samples. The label may be differentiated by having additional methylene groups, which would result in the mass of the two labels be different by an integer multiple of 14.  
      In this method, each sample to be compared is treated with a different labeled reagent to tag certain proteins therein with the affinity label. The treated samples are then combined, preferably in equal amounts, and the proteins in the combined sample are enzymatically digested, if necessary, to generate peptides. Some of the peptides are affinity tagged and in addition tagged peptides originating from different samples are differentially labeled. As described above, affinity labeled peptides are isolated, released from the capture reagent and analyzed by (LC/MS). Peptides characteristic of their protein origin are sequenced using (MS) n  techniques allowing identification of proteins in the samples. The relative amounts of a given protein in each sample is determined by comparing relative abundance of the ions generated from any differentially labeled peptides originating from that protein. The method can be used to assess relative amounts of known proteins in different samples. The method is described in U.S. Pat. No. 5,538,897, issued Jul. 23, 1996, to Yates et al., which is incorporated herein by reference in its entirety, including any drawings.  
      Further, since the method does not require any prior knowledge of the type of proteins that may be present in the samples, it can be used to identify proteins which are present at different levels in the samples examined. More specifically, the method can be applied to screen for and identify proteins which exhibit differential expression in cells, tissue or biological fluids. It is also possible to determine the absolute amount of specific proteins in a complex mixture. In this case, a known amount of internal standard, one for each specific protein in the mixture to be quantified, is added to the sample to be analyzed. The internal standard is an affinity tagged peptide that is identical in chemical structure to the affinity tagged peptide to be quantified except that the internal standard is differentially labeled, either in the peptide or in the affinity tagged portion, to distinguish it from the affinity tagged peptide to be quantified. The internal standard can be provided in the sample to be analyzed in other ways. For example, a specific protein or set of proteins can be chemically tagged with a labeled affinity tagging reagent. A known amount of this material can be added to the sample to be analyzed. Alternatively, a specific protein or set of proteins may be labeled with additional methylene groups and then derivatized with an affinity tagging reagent.  
      Also, it is possible to quantify the levels of specific proteins in multiple samples in a single analysis (multiplexing). For example, a set of five different samples can be reacted with one of SEQ ID NO:27-SEQ ID NO:31, then follow with subsequent steps as described herein. In this case, affinity tagging reagents used to derivatize proteins present in different affinity tagged peptides from different samples can be selectively quantified by mass spectrometry. This may be achieved by using reagents whose molecular mass varies from one sample to another by an integer multiple of 14. So, for example, the Link group in one reagent may feature ornithine whereas the Link group in another reagent may feature arginine or lysine. Similarly, the Z groups in the different reagent may vary such that the molecular mass of the reagent varies by an integer multiple of 14. It is also understood that other amino acids may also be featured. For example, the lighter reagent may have valine whereas the heavier reagent may feature leucine or isoluecine in its stead. The same would be true for having asparagine in the lighter reagent and glutamine in the heavier reagent, or aspartic acid in the lighter reagent and glutamic acid in the heavier reagent.  
      In this aspect of the invention, the method provides for quantitative measurement of specific proteins in biological fluids, cells or tissues and can be applied to determine global protein expression profiles in different cells and tissues. The same general strategy can be broadened to achieve the proteome-wide, qualitative and quantitative analysis of the state of modification of proteins, by employing affinity reagents with differing specificity for reaction with proteins. The method and reagents can be used to identify low abundance proteins in complex mixtures and can be used to selectively analyze specific groups or classes of proteins such as membrane or cell surface proteins, or proteins contained within organelles, sub-cellular fractions, or biochemical fractions such as immunoprecipitates. Further, these methods can be applied to analyze differences in expressed proteins in different cell states. For example, the methods and reagents herein can be employed in diagnostic assays for the detection of the presence or the absence of one or more proteins indicative of a disease state, such as cancer.  
      The methods described herein can also be applied to determine the relative quantities of one or more proteins in two or more protein samples. The proteins in each sample are reacted with affinity tagging reagents which are substantially chemically identical but differentially labeled. The samples are combined and processed as one.  
      The relative quantity of each tagged peptide which reflects the relative quantity of the protein from which the peptide originates is determined by the integration of the respective mass peaks by mass spectrometry.  
      The methods described herein can be applied to the analysis or comparison of multiple different samples. Samples that can be analyzed by methods of this invention include cell homogenates; cell fractions; biological fluids including urine, blood, and cerebrospinal fluid; tissue homogenates; tears; feces; saliva; lavage fluids such as lung or peritoneal ravages; mixtures of biological molecules including proteins, lipids, carbohydrates and nucleic acids generated by partial or complete fractionation of cell or tissue homogenates.  
      The methods described herein employ MS and (MS) n  methods. While a variety of MS and (MS) n  are available and may be used in these methods, Matrix Assisted Laser Desorption Ionization MS (MALDI/MS) and Electrospray ionization MS (ESI/MS) methods are preferred.  
      III. Analytical Methodology  
      Another aspect of the present invention relates to a method for proteomic analysis, comprising: 
      a) preparing a protein sample or a peptide sample from cells;     b) reacting the protein sample or the peptide sample with a reagent of the formula: 
 
Acyl-NH—X-[Epitope Tag Site] A -Y-[Protease Cleavage Site]-Z-Link 
 
 where: 
        A is an integer from 1 to 12;     X is an amide bond of formula —C(O)—NR—, where R is hydrogen or lower alkyl, or X is an amino acid sequence comprising between 10 to 30 amino acids;    
       

      Y is an amide bond of formula —C(O)—NR—, where R is hydrogen or lower alkyl, or Y is an amino acid sequence comprising between 0 to 20 amino acids; 
          Z is an amide bond of formula —C(O)—NR—, where R is hydrogen or lower alkyl, or Z is an amino acid sequence comprising between 0 to 3 amino acids;     Link is selected from the group consisting of Lys-ε-iodoacetamide, Arg-δ-iodoacetamide, and Orn-δ-iodoacetamide;     Epitope Tag Site is a sequence of amino acids, and     Protease Cleavage Site is a sequence of amino acids that is a cleavage site for a highly specific protease enzyme;         c) subjecting the reacted proteins or peptides from step b) to proteolysis at a site on the protein samples or at a site on the peptide samples, the site being other than the Protease Cleavage Site;     d) subjecting the proteolyzed reacted proteins or the proteolyzed reacted peptides from step c) to an affinity chromatography system comprising a second amino acid sequence attached to a solid support, thereby forming bound proteins and non-bound proteins, 
        where the Epitope Tag Site of the reagent and the second amino acid sequence bind with high specificity to each other;    
        e) eluting the non-bound proteins from the affinity chromatography system;     f) subjecting the affinity chromatography system from step e) to a protease specific for the Protease Cleavage Site, thereby forming a cleaved protein mixture;     g) eluting the cleaved protein mixture from the affinity chromatography system of step f);     h) isolating the cleaved protein mixture obtained from step g);     i) subjecting the cleaved protein mixture from step h) to chromatographic separation, followed by mass analysis;     j) comparing the results of step i) to: 
        1) determine the ratio of amounts of compounds in the sample separated by a molecular weight of 14 atomic mass units; and     2) identify the various modified proteins by comparing the results obtained for each modified protein to protein databases containing chromatographic and molecular weight correlations.    
       

      “Proteomic analysis” refers to identifying the proteome of a cell. The “proteome” of a cell is the collection of all the proteins expressed by the cell at the time the proteomic analysis is undertaken. It is understood that, unlike the genome of a cell, which is invariable, the proteome of a cell varies depending on many factors, including the age of the cell, the environmental conditions surrounding the cell, and the position of the cell in its life cycle.  
      In the above methods, the reagent reacts with the reactive side chain of one or more of the amino acid residues of the first or second protein. Therefore, the reagent reacts with the protein at an amino acid residue selected from the group consisting of tyrosine, tryptophan, cysteine, methionine, proline, serine, threonine, lysine, histidine, arginine, aspartic acid, glutamic acid, asparagine, and glutamine. In certain embodiments, the reagent reacts at an amino acid residue selected from the group consisting of tyrosine, cysteine, proline, and histidine. In another preferred embodiment, the site of reaction is a cysteine.  
      In some embodiments of the present invention, the chromatographic separation of step i) is a multi-dimensional liquid chromatographic separation, which may be a two-dimensional liquid chromatographic separation or a three-dimensional liquid chromatographic separation. The dimensions of the multi-dimensional liquid chromatographic separation are selected from the group consisting of size differentiation, charge differentiation, hydrophobicity, hydrophilicity, and polarity. In some embodiments, at least one dimension of the multi-dimensional liquid chromatographic separation is separation using size differentiation. Embodiments of the invention include those in which one dimension of the multi-dimensional liquid chromatographic separation is separation using charge differentiation. In other embodiments, one dimension of the multi-dimensional liquid chromatographic separation is separation using hydrophobicity or hydrophilicity.  
      In another embodiment the mass analysis of step i) is a multi-dimensional mass analysis, which more preferably, may be a two-dimensional mass analysis. In certain embodiments, the preparation of proteins from step a) is subjected to orthogonal chromatography before proceeding with the labeling in step b).  
      In one aspect, the invention provides a mass spectrometric method for identification and quantification of one or more proteins in a complex mixture which employs affinity labeled reagents in which the Link group is a group that selectively reacts with certain groups that are typically found in peptides (e.g., sulfhydryl, amino, carboxy, homoserine, or lactone groups). One or more affinity labeled reagents with different Link groups are introduced into a mixture containing proteins and the reagents react with certain proteins to tag them with the affinity label. It may be necessary to pretreat the protein mixture to reduce disulfide bonds or otherwise facilitate affinity labeling. After reaction with the affinity labeled reagents, proteins in the complex mixture are cleaved, e.g., enzymatically, into a number of peptides. This digestion step may not be necessary, if the proteins are relatively small. Peptides that remain tagged with the affinity label are isolated by an affinity isolation method, e.g., affinity chromatography, via their selective binding to the capture reagent. Isolated peptides are released from the capture reagent by displacement of the Epitope Tag Site or cleavage of the linker, and released materials are analyzed by liquid chromatography/mass spectrometry (LC/MS). The sequence of one or more tagged peptides is then determined by (MS) n  techniques. At least one peptide sequence derived from a protein will be characteristic of that protein and be indicative of its presence in the mixture. Thus, the sequences of the peptides typically provide sufficient information to identify one or more proteins present in a mixture.  
      IV. Proteome Analysis Methodology  
      The method comprises the following steps:  
      Reduction. Disulfide bonds of proteins in the sample and reference mixtures are chemically reduced to free SH groups. The preferred reducing agent is tri-n-butylphosphine which is used under standard conditions. Alternative reducing agents include mercaptoethanol, 2-methylthioethanol, 2-methylthio-1-hexanol, and dithiothreitol. If required, this reaction can be performed in the presence of solubilizing agents including high concentrations of urea and detergents to maintain protein solubility. The reference and sample protein mixtures to be compared are processed separately, applying identical reaction conditions.  
      Derivatization of SH groups with an affinity tag. Free SH groups of the sample protein are derivatized with a reagent of the invention. The reagent reacts with the free SH group through the Link group.  
      Each sample is derivatized with a different reagent having a different mass. Derivatization of SH groups is preferably performed under slightly basic conditions (pH 8.5) for 90 min at about room temperature. For the quantitative, comparative analysis of two samples, one sample each (termed “reference sample” and “sample”) are derivatized with two different reagents, whose molecular mass differs by an integer multiple of 14. For the comparative analysis of several samples one sample is designated a reference to which the other samples are related.  
      Combination of labeled samples. After completion of the affinity tagging reaction defined aliquots of the samples labeled with different reagents are combined and all the subsequent steps are performed on the pooled samples. Combination of the differentially labeled samples at this early stage of the procedure eliminates variability due to subsequent reactions and manipulations. Preferably equal amounts of each sample are combined.  
      Removal of excess affinity tagged reagent. Excess reagent is adsorbed, for example, by adding an excess of SH-containing beads to the reaction mixture after protein SH groups are completely derivatized. Beads are added to the solution to achieve about a 5-fold molar excess of SH groups over the reagent added and incubated for 30 min at about room temperature. After the reaction the beads are removed by centrifugation.  
      Protein digestion. The proteins in the sample mixture are digested, typically with trypsin. Alternative proteases are also compatible with the procedure as in fact are chemical fragmentation procedures. In cases in which the preceding steps were performed in the presence of high concentrations of denaturing solubilizing agents, the sample mixture is diluted until the denaturant concentration is compatible with the activity of the proteases used. This step may be omitted in the analysis of small proteins.  
      Affinity isolation of the affinity tagged peptides by interaction with a capture reagent.  
      The tagged peptides are isolated on anti-HA antibodies-agarose. After digestion the pH of the peptide samples is lowered to 6.5 and the tagged peptides are immobilized on beads coated with anti-HA. The beads are extensively washed. The last washing solvent includes 10% methanol to remove residual SDS.  
      Release of the captured peptides with specific protease. A solution of TEV in TRIS at pH 7.5 is added to the column and digestion is allowed to proceed. The bound peptides are cleaved from the column by incubation at 30° C. for 6 hours.  
      Analysis of the isolated, derivatized peptides by μLC-(MS) n  or CE-(MS) n  with data dependent fragmentation. Methods and instrument control protocols well-known in the art and described, for example, in Ducret et al. (1998); Figeys and Aebersold (1998); Figeys et al. (1996); or Haynes et al. ( Electrophoresis  19:939-945 (1998)) are used. In this last step, both the quantity and sequence identity of the proteins from which the tagged peptides originated can be determined by automated multistage MS. This is achieved by the operation of the mass spectrometer in a dual mode in which it alternates in successive scans between measuring the relative quantities of peptides eluting from the capillary column and recording the sequence information of selected peptides. Peptides are quantified by measuring in the MS mode the relative signal intensities for pairs of peptide ions of identical sequence that are tagged with the lighter or heavier forms of the reagent, respectively, and which therefore differ in mass by the mass differential encoded within the affinity tagged reagent. Peptide sequence information is automatically generated by selecting peptide ions of a particular mass-to-charge (m/z) ratio for collision-induced dissociation (CID) in the mass spectrometer operating in the (MS) n  mode. (Link et al.  Electrophoresis  18:1314-1334 (1997); Gygi et al.  Nature Biotechnol  17:994-999 (1999); Gygi et al.,  Cell Biol  19:1720-1730 (1999)). The resulting CID spectra are then automatically correlated with sequence databases to identify the protein from which the sequenced peptide originated. Combination of the results generated by MS and (MS) n  analyses of affinity tagged and differentially labeled peptide samples therefore determines the relative quantities as well as the sequence identities of the components of protein mixtures in a single, automated operation. This method can also be practiced using other affinity tags and other protein reactive groups, including amino reactive groups, carboxyl reactive groups, or groups that react with homoserine lactones.  
      The approach employed herein for quantitative proteome analysis is based on two principles. First, a short sequence of contiguous amino acids from a protein contains sufficient information to uniquely identify that protein. Protein identification by (MS) n  is accomplished by correlating the sequence information contained in the CID mass spectrum with sequence databases, using sophisticated computer searching algorithms (Yates, III et al. U.S. Pat. No. 5,538,897). Second, pairs of peptides tagged with lighter and heavier Link groups or Z groups, respectively, are chemically similar and therefore serve as mutual internal standards for accurate quantification. The MS measurement readily differentiates between peptides originating from different samples, representing for example different cell states, because of the difference between the distinct reagents attached to the peptides. The ratios between the intensities of the differing weight components of these pairs or sets of peaks provide an accurate measure of the relative abundance of the peptides (and hence the proteins) in the original cell pools.  
      Specifically, the peptide labeling moiety consists of a lysine residue modified with an iodoacetamido functional group on the ε-amino side chain. The synthetic chemistry necessary for this modification reaction is readily available in the literature. The synthetic peptides contain two additional motifs: a peptide epitope tag for high affinity purification; and a highly specific protease site for releasing the affinity purified labeled peptides from the affinity matrix. In addition, these synthetic peptides can readily be prepared as isoforms of two different masses by the simple expedient of using an ornithine in place of lysine to introduce a 14 mass unit difference in the carboxyl terminal acid.  
      Examples of the reagents (SEQ ID NO: 36 and SEQ ID NO: 37) are thus:  
                      Ala-[Tyr-Pro-Tyr-Asp-Val-Pro-Asp-Tyr-Ala]-Ser-(Glu-Asn-Leu-Tyr-Phe-Gln-Gly)- Lys--- Iodoacetamide                       |                                        |                 (Epitope Tag Site)                      (Protease Cleavage Site)                       |                                        |       Ala-[Tyr-Pro-Tyr-Asp-Val-Pro-Asp-Tyr-Ala]-Ser-(Glu-Asn-Leu-Tyr-Phe-Gln-Gly)- Orn--- Iodoacetamide          
 
      The peptide sequence in the square brackets is an Epitope Tag Site and the sequence in parentheses is a Protease Cleavage Site. In the case shown here, the peptide sequence YPYDVPDYA (SEQ ID NO: 38) is an influenza hemagglutinin (HA) epitope tag. This part of the reagent could be replaced by any other epitope tag, or multiple copies of a single tag for higher efficiency purification, or parallel copies of different tags for higher specificity purification. Examples of other Epitope Tag Sites include Flag, His-6, and c-myc.  
      The protease cleavage site shown here is that of TEV protease, which is commercially available. This enzyme has been shown to cleave at only one protein site in the entire yeast genome, thus indicating that the enzyme is highly specific for an extremely rare sequence. This part of the reagent could be replaced by any other highly specific protease cleavage site, either commercially available, such as Factor Xa, or Pharmacia Prescission Enzyme, or one that is newly discovered. The amino acid indicated in bold is used to provide a site of attachment for the iodoacetamide group, hence we have used lysine which contains an E-amino side chain that is suitable for the purpose. This amino acid is also used to introduce a differential mass between the two reagents, and this can be readily accomplished by using ornithine in place of lysine. Ornithine is commercially available and differs from lysine only by the presence of one additional methyl group, which makes it 14 amu (atomic mass unit) heavier than lysine. Arginine is also commercially available and its molecular weight is 28 amu (i.e., 2×14) heavier than lysine. This part of the reagent could be replaced with any other amino acid or similar molecule that provided an attachment site for the iodoacetamide group. Finally, the integral difference of 14 amu could be further enhanced by the choice of two amino acids differing by 14 amu (e.g., valine and leucine) in the Z portion of the peptide labeling moiety.  
      In addition to the above methods, the methods of the invention may be used to determine the proteomic differences in an organism or cell based on the change in the cell&#39;s environmental condition. Thus, for example, one may compare the proteome of the cells of two plants of the same species, one having encountered high salt concentrations and the other low salt concentrations, thereby determining the effect of salt concentration on the plant&#39;s proteome.  
      It is also within the scope of the present invention that the two modes of analysis discussed herein, i.e., the qualitative and quantitative proteome analyses, are exercised in conjunction with each other. Thus, by way of example only, one may compare the proteome of the cells of two plants of the same species, one having encountered higher temperatures than the other, thereby not only determining the effect of heat on the proteome in terms of which proteins are expressed, but also determining the effect of heat on the level of expression of each protein of interest.  
      In practicing the present invention to achieve the above end, one may use a number of different compounds of the present invention, having different masses (yet all within an integer multiple of 14 from each other), and mark different proteins of the cells with the different reagents. By applying the multidimensional LC/MS techniques described herein, one is able to determine which proteins, and to what extent, are expressed in the cells.  
      V. Fusion Protein Preparation  
      Another aspect of the invention relates to a process for preparing a fusion protein of Formula IV or V: 
 
Protein-Acyl-N—X-[Epitope Tag Site] A -Y-[Protease Cleavage Site]-Z-[Lys-6-N-iodoacetamide]  (IV) 
 
Protein-Acyl-NH—X-alk-O-Ph-CH 2 -Z-Link  (V) 
 
 where A, X, Y, Z, alk, Ph, Link, Epitope Tag Site, and Protease Cleavage Site are as defined herein comprising, 
      a) preparing a fusion protein sample of Formula II or m from cells 
 
Protein-Acyl-NH—X-[Epitope Tag Site] A -Y-[Protease Cleavage Site]-Z-Orn-δ-NHCOCH 2   (II) 
 
Acyl-NH—X-alk-O-Ph-CH 2 -Z—NHCOCH 2   (III) 
    b) reacting the protein sample with a Link or with iodoacetamide.    

      In another aspect, the invention relates to a process for preparing a fusion protein of Formula VI: 
 
Protein-Acyl-N—X-[Epitope Tag Site] A -Y-[Protease Cleavage Site]-Z-[Lys-δ-N-iodoacetamide]  (VI) 
 
 where A, X, Y, Z, alk, Ph, Link, Epitope Tag Site, and Protease Cleavage Site are as defined herein 
 
 comprising, 
      a) preparing a fusion protein sample of Formula VII from cells 
 
Protein-Acyl-NH—X-[Epitope Tag Site] A -Y-[Protease Cleavage Site]-Z-Lys-δ-NHCOCH 2   (VII) 
    b) reacting the protein sample with iodoacetamide.    

      Markers that are useful in plant breeding, genetics, and diagnostics are disclosed in U.S. Provisional Patent Application No. 60/264,226, entitled “Cereal Simple Sequence Repeat Markers,” filed on Jan. 26, 2001 (Attorney Docket No. NADII.026PR), which is hereby incorporated by reference in its entirety.  
      F. Conclusion  
      Briefly summarizing one embodiment of the present invention, upon receiving the results of the quantitation of the resolved peptides  146 , the data analysis system  200  compares the relative peptide expression levels for the analogous peptides with different markers  122 ,  124 . Using the quantitation module  230 , the system  200  then identifies each recognizable peak or intensity curve  407  and associates any differentially tagged partner peptides (analogs). These tagged partner peptides can be recognized as peaks or intensity curves  407  that are present at a predicted mass displacement distance, based on the mass differential created by the markers  122 ,  124 . If a potential partner peak or intensity curve  407  is found, the peptide-correlated output files  260  may be used to confirm or deny the sequences of the peptides to establish if peptides being compared are partners. This process is repeated until all possible pairs of peptide partners have been identified in the data set. The data processing module  225  then integrates the area contained by each peak or intensity curve  407  and calculates the ratio of the quantitated peaks to identify differences in peptide expression.  
      In a subsequent analysis stage, the data output comprising the identified differences in peptide expression can be sorted and presented to the investigator in the form of one or more reports. These reports may be categorized by identification of the peptide constituents of the mixed-peptide population, ratios of peptides containing different markers  122 ,  124 , names of the peptides identified by the data analysis system  200 , or other user-defined criteria. Additionally, the identification reports may list any unpaired peaks in the mass spectrum ordered by confidence level, peptide name, or other user-defined criteria.  
      The data analysis system  200  and related methods feature a significantly improved means of identifying proteomic differences between two or more biological samples. The use of markers  122 ,  124  with similar chemical and physical properties further serves as a basis for selective identification of peptides originating from each biological sample and permits the samples to be mixed for simultaneous mass analysis. Analysis in this manner not only improves the throughput of identification but also provides an ideal mutual internal standard for quantification which helps to increase identification accuracy and sensitivity.  
      Although the foregoing description of the invention has shown, described and pointed out novel features of the invention, it will be understood that various omissions, substitutions, and changes in the form of the detail of the apparatus as illustrated, as well as the uses thereof, may be made by those skilled in the art without departing from the spirit of the present invention. Consequently the scope of the invention should not be limited to the foregoing discussion but should be defined by the appended claims.