N-Terminal Isotope Tagging Strategy for Quantitative Proteomics

Comparing the relative abundance of each protein present in two or more complex samples can be accomplished using isotope-coded tags incorporated at t...
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Anal. Chem. 2004, 76, 6618-6627

N-Terminal Isotope Tagging Strategy for Quantitative Proteomics: Results-Driven Analysis of Protein Abundance Changes Francesca Zappacosta and Roland S. Annan*

Proteomics and Biological Mass Spectrometry, Department of Computational, Analytical and Structural Sciences, GlaxoSmithKline, King of Prussia, Pennsylvania 19406

Comparing the relative abundance of each protein present in two or more complex samples can be accomplished using isotope-coded tags incorporated at the peptide level. Here we describe a chemical labeling strategy for the incorporation of a single isotope label per peptide, which is completely sequence-independent so that it potentially labels every peptide from a protein including those containing posttranslational modifications. It is based on a gentle chemical labeling strategy that specifically labels the N-terminus of all peptides in a digested sample with either a d5- or d0-propionyl group. Lysine side chains are blocked by guanidination prior to N-terminal labeling to prevent the incorporation of multiple labels. In this paper, we describe the optimization of this N-terminal isotopic tagging strategy and validate its use for peptide-based protein abundance measurements with a 10-protein standard mixture. Using a results-driven strategy, which targets proteins for identification based on MALDI TOFMS analysis of isotopically labeled peptide pairs, we also show that this labeling strategy can detect a small number of differentially expressed proteins in a mixture as complex as a yeast cell lysate. Only peptides that show a difference in relative abundance are targeted for identification by tandem MS. Despite the fact that many peptides are quantitated, only those few showing a difference in abundance are targeted for protein identification. Proteins are identified by either targeted LC-ES MS/MS or MALDI TOF/TOF. Identifications can be accomplished equally well by either technique on the basis of multiple peptides. This increases the confidence level for both identification and quantitation. The merits of ES MS/MS or MALDI MS/MS for protein identification in a resultsdriven strategy are discussed. Global protein expression analysis or quantitative proteomics has historically been carried out using 2D gels.1 In the past few years, however, for reasons of sensitivity and automation, there has been a move toward liquid chromatography-mass spectrometry (LC-MS) for peptide-based protein expression analysis. From * Corresponding author: (e-mail) [email protected]. (1) Jungblut, P. R.; Bumann, D.; Haas, G.; Zimny-Arndt, U.; Holland, P.; Lamer, S.; Siejak, F.; Aebischer, A.; Meyer, T. F. Mol. Microbiol. 2000, 6, 710725.

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a quantitative perspective, this was made practical when Aebersold and co-workers2 introduced the isotopically coded peptide affinity tag (ICAT). By modifying the side chain of cysteine residues with either a light- or heavy-isotope-labeled biotin tag, these reagents served the dual purpose of enriching a sample for a selective subset of peptides (cysteine containing) and providing a relative measurement of the amount of protein present in two different samples.3-5 The measurement of protein expression is complicated by the fact that any given protein can exist as several or many unique isoforms and be located in one or more cellular compartments. The overall pool of any given protein and the abundance of any member of the various subsets of the overall pool is regulated at the transcriptional, translational, and posttranslational level. The translational machinery provides newly synthesized proteins to the overall pool. Posttranslational events trigger translocation of proteins to one or more cellular locations, initiate the assembly of protein complexes, and activate enzyme activity.6-10 Finally, protein degradation by the cell’s proteolytic machinery is regulated in large part through posttranslational modification of proteins by ubiquitin or ubiquitin-like molecules.11-13 The abundance of any of these regulatory modifications is a measure of the protein functional state relative to the overall protein pool. Thus, it would be advantageous to be able to detect and measure not only the total amount of a particular protein in a given sample but also the relative abundance of any posttranslational isoforms. Unfortunately, in the current implementation of the isotope-coded affinity (2) Gygi, S. P; Rist, B.; Gerber, S. A.; Turecek, F.; Gelb, M. H.; Aebersold, R. Nat. Biotechnol. 1999, 17, 994-999. (3) Hardwidge, P. R.; Rodriguez-Escudero, I.; Goode, D.; Donohoe, S.; Eng, J.; Goodlett, D. R.; Aebersold, R.; Finlay, B. B. J. Biol. Chem. 2004, 279, 2012720136. (4) Martin, D. B.; Gifford, D. R.; Wright, M. E.; Keller, A.; Yi, E.; Goodlett, D. R.; Aebersold, R.; Nelson, P. S. Cancer Res. 2004, 64, 347-355. (5) Wright, M. E.; Eng, J.; Sherman, J.; Hockenbery, D. M.; Nelson, P. S.; Galitski, T.; Aebersold, R. Genome Biol. 2003, 5, R4. (6) Geymonat, M.; Spanos, A.; Wells, G. P.; Smerdon, S. J.; Sedgwick, S. G. Mol. Cell. Biol. 2004, 6, 2277-2285. (7) Komeili, A., O’Shea, E. K. Science 1999, 284, 977-980. (8) Wood, L. D.; Irvin, B. J.; Nucifora, G.; Luce, K. S.; Hiebert, S. W. Proc. Natl. Acad. Sci. U.S.A. 2003, 100, 3257-3262. (9) Gay, F.; Calvo, D.; Lo, M. C.; Ceron, J.; Maduro, M.; Lin, R.; Shi, Y. Genes Dev. 2003, 17, 717-722. (10) Pawson, T. Cell 2004, 116, 191-203. (11) Deshaies, R. J. Trends Cell Biol. 1995, 5, 428-434. (12) Lipford, J. R.; Deshaies, R. J. Nat. Cell. Biol. 2003, 5, 845-850. (13) Liu, Y. C. Annu. Rev. Immunol. 2004, 22, 81-127. 10.1021/ac049169b CCC: $27.50

© 2004 American Chemical Society Published on Web 10/09/2004

tag only cysteine-containing peptides can be quantitated. Since most proteins contain only relatively few cysteine residues (only tryptophan is rarer in eukaryotic proteomes), the limitation of this approach for measuring posttranslational modifications is obvious. As an alternative to ICAT and similar chemical labeling strategies, several protocols have recently been described that metabolically label proteins in growing cells with stable isotope tags either through the use of 15N- or 13C-enriched media14,15 or stable-isotope-labeled amino acids.16-18 All of these methods enjoy a substantial advantage over any chemical labeling method in terms of sample handling and ease of use. However, the choice of the label can be somewhat problematic. Less specific labeling methods that use enriched media or abundant amino acids (Gly or Leu for instance) target the largest possible number of peptides and thus would be suitable for the analysis of posttranslational modifications, but introduce a complex pattern of variable mass shifts between isotopically labeled peptides due to variable multiple labeling.15,16,18 This makes data analysis extremely challenging. When used in combination with trypsin digestion, labeling with the amino acids lysine19 or arginine20 predominantly incorporated one label per peptide, but based on the relative amounts of lysine and arginine in eukaryotic genomes, either one will incorporate into only roughly 50% of all peptides.21 It would of course be possible to double label, but this would be extremely expensive. Thus, neither of these approaches would seem to be the ideal candidate for a universal method to quantitate both protein expression and posttranslational modifications, though both would seem to enjoy a significant advantage over the current ICAT techniques, which target only cysteine-containing peptides. Neither of the metabolic labeling approaches is suitable for human or tissue samples or proteins from fluids. The ideal strategy to introduce a stable isotope label for quantitative proteomics should be able to label proteins from any source: cell culture, tissue, or biological fluid. It should be able to label every peptide from a protein so as to include posttranslational modifications and would introduce only one label per peptide to simplify data analysis. It should be relatively inexpensive so that most researchers can have access to it, be simple to perform, and go to completion under mild reaction conditions. Recently we described an N-terminal labeling method that satisfies all of the criteria listed above.22-24 It is based on a gentle (14) Oda, Y.; Huang, K.; Cross, F. R.; Cowburn, D.; Chait, B. T. Proc. Natl. Acad. Sci. U.S.A. 1999, 96, 6591-6596. (15) Veenstra, T. D.; Martinovic, S.; Anderson, G. A.; Pasa-Tolic, L.; Smith, R. D. J. Am. Soc. Mass. Spectrom. 2000, 11, 78-82. (16) Chen, X.; Smith, L. M.; Bradbury, E. M. Anal. Chem. 2000, 72, 11341143. (17) Hunter, T. C.; Yang, L.; Zhu, H.; Majidi, V.; Bradbury, E. M.; Chen, X. Anal. Chem. 2001, 73, 4891-4902. (18) Ong, S. E.; Blagoev, B.; Kratchmarova, I.; Kristensen, D. B.; Steen, H.; Pandey, A.; Mann, M. Mol. Cell. Proteomics. 2002, 1, 376-386. (19) Gu, S.; Pan, S.; Bradbury, E. M.; Chen, X. J. Am. Soc. Mass Spectrom. 2003, 14, 1-7. (20) Blagoev, B.; Kratchmarova, I.; Ong, S. E.; Nielsen, M.; Foster, L. J.; Mann, M. Nat. Biotechnol. 2003, 21, 315-318. (21) Pe’er, I.; Felder, C. E.; Man, O.; Silman, I.; Sussman, J. L.; Beckmann, J. S. Proteins 2004, 54, 20-40. (22) Zhang, X.; Jin, Q. K.; Carr, S. A.; Annan, R. S. Proceedings of the 49th Conference on Mass Spectrometry and Allied Topics, Chicago, IL, 2001. (23) Zhang, X.; Jin, Q. K.; Carr, S. A.; Annan. R. S. Rapid Commun. Mass Spectrom. 2002, 16, 2325-2332. (24) Zappacosta, F. Annan, R. S. Proceedings of the 51th Conference on Mass Spectrometry and Allied Topics, Montreal, Canada, 2003.

Table 1. Protein Composition of the 10-Protein Standard Mixture Used To Validate the NIT Strategy for Protein Abundance Analysis protein

sample A (pmol)

sample B (pmol)

A/A ratio

A/B ratio

phosphorylase B lactoperoxidase transferrin BSA glutamate dehydrogenase R-amylase peroxidase conalbumin chymotrypsinogen hemoglobin

5 10 5 20 15 20 15 5 10 5

5 10 10 5 15 20 15 40 10 5

1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

1.0 1.0 0.5 4.0 1.0 1.0 1.0 0.125 1.0 1.0

chemical labeling strategy that specifically and differentially labels the N-terminus of all peptides in a sample with either a d5- or d0-propionyl group. Lysine side chains are blocked by guanidination prior to N-terminal labeling to prevent the incorporation of multiple labels. In our first report, we showed how this labeling strategy, together with the enzymatic removal of phosphate, could be used to determine absolute site-specific phosphorylation stoichiometry in isolated proteins.22-23 In this paper, we describe the optimization of our previously reported N-terminal isotopic labeling strategy (NIT)23 and validate its use for peptide-based protein abundance analysis of complex samples. We use a results-driven strategy, based on quantitative analysis of isotopically labeled peptide pairs by MALDI TOF-MS, to target peptides that show a difference in relative abundance for identification by tandem MS. Proteins are identified by either targeted LC-ES MS/MS or MALDI TOF/TOF. The merits of each identification approach in a results-driven strategy are discussed. We also show that this technique can identify differentially expressed proteins in a complex mixture such as a fraction from a yeast cell lysate. EXPERIMENTAL SECTION Reagents. O-Methylisourea sulfate salt was purchased from ICN Biomedicals, Inc (Aurora, OH). [d10]-Propionic anhydride was synthesized by Cambridge Isotope Laboratories, Inc. (Boston, MA) with 98% atom purity. [d0]-Propionic anhydride was purchased from Aldrich. Protein Samples. Carboxymethylated tryptic digests of rabbit phosphorylase B, bovine lactoperoxidase, bovine transferrin, bovine serum albumin, bovine glutamic dehydrogenase, subtilis R-amylase, horseradish peroxidase, chicken conalbumin, bovine chymotrypsinogen, and chicken hemoglobin (R+β chain)) were purchased from Michrom BioResources, Inc. (Auburn, CA). Two protein standard mixtures were prepared (A and B). Each sample contained the same 10 proteins, with 7 proteins being present in different amounts but the same in both samples, and 3 being present in different amounts in the two samples, as is shown in Table 1. Two equal amounts of sample A were labeled independently with d0 and d5 and then combined to make sample A+A. Equal amounts of sample A and sample B were labeled independently with d0 and d5, respectively, and combined to make sample A+B. Analytical Chemistry, Vol. 76, No. 22, November 15, 2004

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A yeast cell lysate was prepared as described,25 and 200 µg each was loaded into several lanes of a 10% SDS-PAGE minigel. Each lane was cut into 10 equal sections, and each section was reduced, alkylated, and digested with trypsin as described previously.26 Tryptic digests of lactoperoxidase (68 and 34 ng), transferrin (38 and 192 ng), and serum albumin (166 and 55 ng) were added in different amounts to the tryptic digests from two adjacent SDS-PAGE sections of the yeast lysate (i.e., 272 ng of total protein weight in fraction 8A and 229 ng of total protein weight in fraction 8B). Fractions 8A and 8B were labeled with d0 and d5 separately and combined to make sample 8A+B. Another fraction 8A was divided into two equal portions, which were labeled with d0 and d5 separately and combined to make sample 8A+A. NIT Labeling. The  amino group of all lysines was blocked by adding an equal volume of 2 M O-methylisourea in 100 mM NaHCO3 (pH 11.0) to a mixture of tryptic peptides in 100 mM NH4HCO3 (pH 8.5) and incubating the sample for 2 h at 37 °C. Acylation with either d0-propionic or d10-propionic anhydride was carried out by adding of 1 µL of reagent per 30 pmol of protein to the above mixture and reacting for 30 min at 37 °C. Following this, the d0- and d5-labeled samples were pooled, and the excess reagent was removed by desalting on a C18 MicroTrap (Michrom BioResources, Inc.) cartridge. Peptides were eluted in 30 µL of 70% acetonitrile (ACN) in 0.1% trifluoroacetic acid (TFA). Volume was reduced to 5 µL in a Speed-Vac. To promote hydrolysis of propionyltyrosine residues, the samples were diluted in 90 µL of 1 M NH4OH (pH 11.0) and incubated for 1 h at 37 °C. The samples were acidified by addition of an appropriate volume of 10% TFA and stored at -20 °C until further analysis. LC Fractionation. NIT labeled protein digests were fractionated by off-line RP-HPLC using either a Dionex-LC Packings PepMap C18 capillary column (300 µm) or a Michrom BioResources Magic C18 capillary column (100 µm). Peptides were eluted with an ACN/water-acid gradient from 0 to 50% B in 30 min. HLPC mobile phases contained 0.1% formic acid and 0.02% TFA. Flow rates were 4 µL/min and 200 nL/min, respectively. Fractions from the 300-µm-i.d. column were manually collected into PCR tubes at 1-min intervals. Fractions from the 100-µm-i.d. column were automatically spotted every 20 s onto MALDI targets using a Probot (Dionex-LC Packings). MALDI matrix (6 mg/mL R-cyano-4-hydroxycinnamic acid in 75% acetonitrile with the addition of 0.5 mM ammonium citrate) was added postcolumn via a syringe pump through a tee at a flow rate of 200 nL/min. Quantitation by MALDI Analysis. Sample fractions that were collected into tubes were manually spotted on a MALDI target by mixing a 0.3-µL aliquot from each HPLC fraction with 0.3 µL of matrix (10 mg/mL R-cyano-4-hydroxycinnamic acid in acetonitrile/ethanol 50:50) and spotting 0.3 µL on the target. These samples were analyzed either on a Micromass M@LDI or an ABI 4700 proteomic analyzer. Samples from the yeast lysate were automatically spotted using a Probot as described above and analyzed by using an ABI 4700 proteomic analyzer. Spectra generated with either MALDI instrument were externally calibrated using peptide standards. (25) Washburn, M. P.; Wolters, D.; Yates, J. R., 3rd. Nat. Biotechnol. 2001, 19, 242-247. (26) Joyal, J. L.; Annan, R. S.; Ho, Y. D.; Huddleston, M. J.; Carr, S. A.; Hart M. J.; Sacks, D. B. J. Biol. Chem. 1997, 272, 15419-15425.

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For spectra recorded on the Micromass M@LDI, peak intensities were determined by the Masslynx software for each peptide in every fraction.The d0/d5 ratio for doublets spaced by 5.03 was calculated manually using the intensity of the 12C peaks for each peptide in a pair. Because the d0- and d5-labeled forms of the same peptide do not exactly coelute, d0/d5 pairs whose ratios differed from 1.0 by greater than 2σ (as determined from the control sample A+A) were examined in the preceding and following fractions. In cases where the peptides overlapped fractions, the abundance of each label was summed prior to determining the d0/d5 ratio. Spectra recorded on a ABI 4700 were quantitated automatically by the GPS (Global Proteomic Server) Explorer software (Applied Biosystems). All peptides whose ratios were determined by the GPS Explorer to differ from 1.0 by greater than 2σ were checked manually. To ensure accurate quantitation, we examined the corresponding peptides in two fractions prior to and two fractions after the fraction where the altered ratio was determined and summed the abundance of each label when appropriate. Protein Identification. NIT labeled peptides that showed a ratio different from 1:1 by greater than 2σ were targeted for protein identification either by LC-ES MS/MS on an Agilent LC-MSD ion trap or by MALDI MS/MS on an AB 4700 proteomic analyzer. For identification by LC-ES MS/MS, fractions containing the targeted peptide precursors were grouped such that fractions in each group were separated from one another by a retention time of 5 min (for example, fractions 20 + 25 + 30 were combined). For each of the pooled samples, an include list that contained the doubly charged m/z value for the more abundant peptide in each differentially regulated pair was created. After pooling the fractions in each group, one-fifth of the total pool was used for protein identification. Peptide pools were loaded on a trap cartridge, washed, and back-flushed onto a 75-µm-i.d. C18 Zorbax analytical column (15 cm) at 300 nL/min using an acetonitrile/water/0.1% formic acid gradient. Uninterpreted MS/MS data from each pool were combined into a single file and searched using Mascot.27 To take into account the modifications introduced by the NIT chemistry, acetyllysine, which has the same mass as guanidinyllysine, was set as a fixed modification and both d0- or d5propionylation were set as variable modifications on the peptide N-terminus (the latter modifications were specifically added to the Mascot configuration file on an in-house Mascot server). Protein identification by MALDI MS/MS was carried out in an automated mode on a list of precursors determined by the ABI GPS Explorer software and manually validated. Data were searched using Mascot as described above. RESULTS AND DISCUSSION NIT Labeling Strategy. We have developed a stable-isotopebased tagging method that seeks to tag every peptide in a sample by targeting the peptide N-terminus.23 This approach removes any sequence dependence associated with the incorporation of the tag. There are a number of advantages to having a tag on every peptide. First, when the NIT strategy is used for protein abundance measurements, all proteins are amenable to quantitation and the likelihood that several peptides per protein can be (27) Perkins, D. N.; Pappin, D. J.; Creasy, D. M.; Cottrell, J. S. Electrophoresis 1999, 20, 3551-3567.

quantitated is improved. This latter point increases confidence that an observed change in abundance is real. Second, the elimination of the sequence dependency means that quantitative analysis of any and all posttranslational modification should be feasible. By choosing a chemical labeling strategy, the approach is amenable to samples from any source, including tissues, biological fluids, and cell culture. Labeling the N-terminus of a peptide is a relatively straightforward matter. The primary amino group of the N-terminus is easily modified under mild conditions by a variety of reagents that are commercially available at affordable prices. Several strategies to introduce N-terminal labels have been described.29-30 Unfortunately, it is not possible to reliably control the selectivity of the reaction, because the -amino group of lysine is readily and stably modified by reagents that target the N-terminus. We felt that it was critical for data analysis reasons that our protocol introduces only a single isotope tag to each peptide. Previously we showed that this could be accomplished by guanidination of lysine residues with O-methylisourea after enzymatic digestion of the sample with trypsin and prior to acylation of the Nterminus.23 The guanidination reaction has been well characterized previously.30-31 It goes easily to completion under mild conditions and is highly specific for the -amino group of lysine. We and others32 have noted that there is some reactivity at the N-terminus of peptides that have glycine as the N-terminal residue. This side reaction can be minimized by keeping the reaction temperature at 37 °C or lower. Blocking the lysine side chain to facilitate specific labeling of the N-terminus can be done either prior to28 or after enzymatic digestion of the sample. By blocking the lysines after digestion, we retain the ability to use trypsin as our primary enzyme for proteolytic cleavage. We felt this was important since trypsin generates peptides of an ideal size for mass spectrometry and all peptides have a basic residue at the C-terminus, which facilitates sequencing by tandem MS. With a strategy designed to block the lysine side chain after enzymatic digestion, a highly specific blocking agent was imperative in order to leave the primary amino group of the peptide N-terminus free for incorporation of an isotopic tag. Guanidination seems to fit this need perfectly. In addition to blocking the reactivity of the lysine side chain, guanidination has additional advantages related to the MS-based analysis of the peptides. Guanidination converts lysine residues to homoarginine, making them more basic. This preserves the situation where all peptides have a strongly basic residue at the C-terminus to facilitate MS/MS-based sequencing. Acetylation or succinylation of lysine residues prior to derivatization of the N-terminus with an isotope-encoded acyl group leaves lysineterminating peptides in the possible situation of not having any strong charge-carrying function. Furthermore, it had been reported that lysine-terminating peptides tend to be underrepresented in MALDI spectra, and guanidination has been shown to be useful for improving their ionization efficiency.30,31 (28) Munchbach, M.; Quadroni, M.; Miotto, G.; James, P. Anal. Chem. 2000, 72, 4047-4057. (29) Geng, M.; Ji, J.; Regnier, F. E. J. Chromatogr., A 2000, 870, 295-313. (30) Brancia, F. L.; Oliver, S. G.; Gaskell, S. J. Rapid Commun. Mass Spectrom. 2000, 14, 2070-2073. (31) Beardsley, R. L.; Karty, J. A.; Reilly, J. P. Rapid Commun. Mass Spectrom. 2000, 14, 2147-2153. (32) Beardsley, R. L.; Reilly, J. P. Anal. Chem. 2002, 74, 1884-1890.

After guanidination, peptides are labeled at their N-terminus via acylation with propionic anhydride that encodes either five hydrogens (d0) or five deuteriums (d5), adding 56 or 61 Da, respectively, to each peptide. The choice of the tag was driven mainly by the desire to have an easily available reagent that used simple and mild reaction conditions and that provided a mass shift of at least 5 Da in the isotopically labeled pairs. A mass shift of less than 5 Da leads to a significant overlap of the isotope envelopes for peptide pairs with a mass greater than 1500 Da (about 10% at 1500 Da, 25% at 2000 Da, etc.). Though this can be corrected via an appropriate software algorithm, the additional complication can be eliminated by the appropriate choice of the mass-encoded tag. The d10 propionic anhydride reagent encodes five deuteriums and was synthesized by a commercial source at a very reasonable cost. The only disadvantage of this reagent, as per all deuterium-encoded reagents, is that the d0 and d5 peptides do not exactly coelute on RP-HPLC. For this derivatization scheme we found that the average peak top separation of the d0- and d5labeled peptides was ∼4.0 s on peaks that were 20-25 s wide at half-height (data not shown). Thus, it is critical for accurate quantitation that ion abundances be summed across the entire elution profile of the d0/d5 pair. Many tyrosine residues are also susceptible to acylation under the reaction conditions employed here. To eliminate this additional labeling, the d0- and d5-labeled samples are combined, desalted to remove the excess anhydride, and treated with base to hydrolyze the O-propionyltyrosine groups. Under these conditions, a single label is introduced and the sample can then be analyzed by MALDI or LC-ES MS. The only peptides not amenable to this protocol are the N-terminal peptides from N-terminally blocked proteins. Optimization of the NIT Labeling Procedure. In optimizing the NIT labeling protocol for both MALDI and ES MS analysis, we first substituted a pH 11.0 NaHCO3 solution for the pH 11.0 CAPS buffer used in the original protocol23 for the guanidination and acylation reactions. We found that even freshly prepared CAPS can polymerize under the conditions employed here and that the amino group on the CAPS monomer and the various CAPS multimers is labeled by the anhydride, giving rise to a series of highly abundant products that elute throughout the RP-HPLC gradient. The pH of the NaHCO3 solution was adjusted to pH 11.0, because we and others have found that at pH lower than 10.0 incomplete guanidination of the Lys residues will occur due to partial protonation of the lysine -amino group. Attempts to limit the extent of O-acylation on the tyrosine residues were unsuccessful with the primary outcome being incomplete reaction at the N-terminus. Instead, we chose to drive the N-terminal reaction to completion and hydrolyze the Opropionyltyrosyl residues with base. Under these conditions, for standard protein digests, the overall reaction yield was greater than 98%. Finally, since the deacylation of the tyrosine residues is inhibited by the excess anhydride present in the reaction mixture, a desalting step was introduced after the incubation with anhydride and prior to treatment with base. Validation of the NIT Strategy for Determining Protein Abundance. To validate the overall accuracy and reproducibility of the labeling chemistry for relative quantitation, we analyzed Analytical Chemistry, Vol. 76, No. 22, November 15, 2004

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the relative abundance of 10 different proteins in two samples. Tryptic digests of 10 purified proteins were combined in different amounts and different ratios to give two samples, A and B as shown in Table 1. Each sample contained the same 10 proteins, with 7 being present in different amounts but the same in both A and B. The remaining three proteins were present in different amounts between the two samples (see Table 1). After labeling sample A with the d0 tag and sample B with the d5 tag, the samples were combined (A+B) and the peptides fractionated by RP-HPLC. As a control, two equivalent portions of sample A were labeled with d0 and d5, combined (A+A), and analyzed in the same way as A+B. We used the A+A sample to establish a level of confidence in the quantitative analysis and to detect peptides with aberrant labeling. In the case of A+A, every peptide pair should be present at a 1:1 ratio. In the case of A+B, peptides from three proteins should be present at ratios differing from 1:1. MS-based quantitation of protein abundance via stable isotope labeling of peptides from enzymatic digests can be carried out on a number of different types of instruments or combination of instruments. Initial attempts at peptide-based quantitative proteomics were carried out by performing quantitation and shotgun protein identification in either a single LC-MS2 or in back-toback LC-MS experiments33 using full-scan MS and data-dependent LC-MS/MS. The major limitation of this approach is that the mass spectrometer spends most of its time identifying proteins that are not differentially expressed. To improve the efficiency and sensitivity for finding differentially abundant proteins, it seems reasonable to perform the quantitation in one experiment and then target peptides for protein identification in a second experiment. This approach has been described as abundance ratio-dependent34 or results driven,35 in that only those peptides which show a difference in relative abundance are targeted for identification. We have chosen to use MALDI MS for the quantitative analysis for several reasons. MALDI TOF MS instruments have very good sensitivity and relatively high resolution and mass accuracy and MALDI MS spectra are quite simple relative to ES MS spectra, being composed entirely of singly charged ions (for peptides). The reduced complexity of the MALDI spectra makes it much easier to identify peptide pairs and extract quantitative data from the experiment either manually or via simple software routines. Furthermore, the results-driven follow-up experiments described below are facilitated by this approach. Finally, in a high-throughput mode, it would be much less expensive to multiplex preparative HPLC instrumentation for use on a single MALDI-TOF instrument than to multiplex LC-ES MS systems. The experimental work flow for the determination of protein abundance changes in the 10-protein standard mix sample using the NIT strategy is shown in Figure 1. After fractionation by RPHPLC, an aliquot of each fraction (1/10) was analyzed by MALDITOF. For the control (A+A), an experimental ratio of 1.04 with a standard deviation (σ) of 15% was determined using 225 peptide pairs. The overall distribution of the 225 peptide pairs is shown (33) Conrads, T. P.; Alving, K.; Veenstra, T. D.; Belov, M. E.; Anderson, G. A.; Anderson, D. J.; Lipton, M. S.; Pasa-Tolic, L.; Udseth, H. R.; Chrisler, W. B.; Thrall, B. D.; Smith, R. D. Anal. Chem. 2001, 73, 2132-2139. (34) Griffin, T. J.; Lock, C. M.; Li, X. J.; Patel, A.; Chervetsova, I.; Lee, H.; Wright, M. E.; Ranish, J. A.; Chen, S. S.; Aebersold, R. Anal. Chem. 2003, 75, 867874. (35) Graber, A.; Juhasz, P. S.; Khainovski, N.; Parker, K. C.; Patterson, D. H.; Martin, S. A. Proteomics 2004, 4, 474-489.

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Figure 1. Scheme for the results-driven protein abundance analysis of the 10-protein mixture.

Figure 2. Distribution of d0/d5 ratios in the 10-protein mixture control sample A+A. Ratios were calculated using the intensity of the 12C peak for each peptide in a pair. Using 225 peptide pairs, an experimental ratio of 1.04 ( 0.16 (15.4%) was determined.

in Figure 2. Using a value of 2σ as a confidence threshold, we next determined which peptide pairs showed altered abundance levels in A+B and compared them to the corresponding pairs in A+A. For example, in A+B fraction 27, we found 10 peptide pairs with a ratio smaller than 0.7 or greater than 1.3 (1.0 ( 2σ) (indicated by arrows in Figure 3B and boldface type in Figure 3A). However, when compared to the ratios for the corresponding pairs in A+A (Figure 3B and C), only six peptides were found to be truly altered (indicated with an asterisk in Figure 3A and B). There are a variety of reasons why peptide pairs may show aberrant ratios. Certainly one of the most likely reasons is a difference in peptide bond cleavage during enzymatic digestion of the two samples. The presence of overlapping peptides certainly also contributes to incorrect d0/d5 ratio. Finally, in an analysis of peptides with incorrect ratios in an A+A sample, we identified a number of keratin peptides contaminating the sample. Singles can come from proteins with blocked N-termini and from the 1-2% of any given peptide that might not have incorporated a label. A shotgun style sequencing analysis of a d0 only labeled yeast lysate fraction found less than 0.5% of the identified peptides as being unlabeled. The majority of false singles are likely to result from ions originating from non-peptide species and from peptides whose N-terminus has been blocked after tryptic digestion by unwanted chemical reactions. Comparing potential targets from A+B with the corresponding ions in A+A provided an efficient method for eliminating erroneous peptide pairs and false singles.

Figure 3. Verification of peptides with altered abundance in the 10-protein mixture by comparing sample A+B with A+A. (A) Ten out of the 27 peptides pairs quantitated in the MALDI spectrum of fraction 27 from sample A+B were found to be singlets (S) or to have a d0/d5 ratio smaller than 0.7 or greater than 1.3 (indicated by boldface type in the table). However, when compared to the ratio for the corresponding pair in A+A, only six peptides (labeled with an asterisk in the table) were found to be truly altered. (B, C) Partial MALDI TOF mass spectrum of fraction 27 from sample A+B (panel B) and A+A (panel C). Peptides marked with an arrow in panel B showed altered d0/d5 ratios or were found to be singlets (S) in sample A+B. Those peptides found to have truly altered ratios after comparison with A+A (panel B) are marked with an asterisk.

After a complete comparison of A+B with A+A, we found 56 peptide pairs that differed from a ratio of 1.0 by greater than 2σ ((30%). Fifty of these peptides could be clustered into three groups with ratios of 3.78 ( 0.37 (14 peptides), 0.50 ( 0.04 (15 peptides), and 0.12 ( 0.07 (21 peptides). The remaining six peptides had ratios between 1.5 and 2.7. The peptides in each cluster were subjected to a mass fingerprint search, which identified BSA (10 peptides), transferrin (7 peptides), and conalbumin (17 peptides), respectively (data not shown). To obtain a rigorous identification of the proteins that showed altered abundance between the two samples, we sequenced the most abundant peptide from 48 of the 56 pairs by tandem mass spectrometry. The remaining eight had intensities that were considered too low to attempt sequencing. The 15 fractions that contained the selected precursors were combined into 5 pools which were analyzed in 5 separate LC/MSMS runs. Based on the size of the peptides targeted for identification, the m/z value for the doubly charged peptide was selected as the precursor for sequencing. The MS/ MS spectra from all five runs were combined and searched as a single file using Mascot. Twenty-four precursors led to a significant peptide match allowing the identification of transferrin with 5 peptides, BSA with 10 peptides, and conalbumin with 9 peptides (see Figure 4). Of the six peptides that did not cluster into any of the three main groups, none were identified. Quantitation based

only on those peptides identified gave ratios of 0.47, 3.80, and 0.18 for transferrin, BSA, and conalbumin, respectively (see Figure 4). One of the precursors that showed a ratio of 0.51 in the A+B sample was matched with a high Mascot score to bovine lactoperoxidase. This protein is present in the 10-component mixture but is in equal amounts in both samples. No other differentially expressed peptides from this protein were identified. We ultimately determined that the lactoperoxidase assignment was correct but that it was not the differentially labeled peptide we set out to identify. The mass of the lactoperoxidase peptide differed from the target peptide by 1 Da and was identified as the heavy tag, while the target precursor was for a peptide containing a light tag. However, the precursor for the lactoperoxidase peptide (m/z 762.9) was within the selection window of the LC-MS/MS experiment for the targeted precursor (m/z 763.4). The detection and sequencing of this peptide resulted from pooling the fractions for sequencing. The MALDI spectrum of a fraction containing a peptide of the correct mass for the lactoperoxidase peptide showed the expected d0/d5 ratio of 1.0. We also determined that the peptide showing a ratio of 0.51 was in fact a transferrin peptide, but Mascot could not assign the sequence because the peptide N-terminus was generated via cleavage between a tryptophan and a glutamic acid. We made this identification based on manual Analytical Chemistry, Vol. 76, No. 22, November 15, 2004

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Figure 4. Identification of proteins with altered abundance in the 10-protein mixture using a results-driven strategy. (A) Peptide pairs that had verified abundance changes were targeted for protein identification either by LC-ES MS/MS or by MALDI MS/MS. The d0/ d5 ratios for the identified peptides were averaged to determine the final protein abundance ratios. (B) The overall success rate for precursor identification was equivalent for the two techniques. Among the 31 precursors in common between the two MS techniques, 21 were identified by LC-ES MSMS and 18 by MALDI MSMS with an overlap of 16 peptides. (a) The number of precursors targeted for identification is indicated in parentheses. (b) Values in parentheses correspond to the total Mascot score for that protein.

interpretation of the MS/MS spectrum, which showed a nearly complete y ion series. Obviously it would not be possible to apply this level of scrutiny to a very large data set where no a priori knowledge of the protein

abundance values existed. Therefore, we believe that any protein abundance data generated on a single peptide should be viewed as highly suspect and probably ignored. An advantage of global labeling strategies such as the NIT protocol described here is that by labeling all peptides from a protein there exists a much better chance that protein quantitation can be performed using more than one peptide per protein. Figure 5 shows an example that highlights several features of the NIT labeling, results-driven strategy we have employed for quantitative proteomics. Based on a d0/d5 ratio of 3.8 observed in the MALDI MS spectrum of fraction 24 for the A+B sample (Figure 5B), a peptide corresponding to the pair MH+ ) 1283.1/ 1288.1 is found to be less abundant in sample B (compare to Figure 5A). Targeting the doubly charged ion for this peptide (m/z 642.1) in an LC-ES MS/MS include list produced a spectrum that Mascot identified as the d0-tagged sequence KQTALVELLK (Figure 5C). Although we were able to quantitate more than 40 easily distinguished peptide pairs in this fraction, the results-driven strategy targeted only 5 peptides for identification (4 positive identifications resulted). The example shown in Figure 5 also demonstrates how the particular labeling strategy we have described facilitates the quantitative analysis of the peptide pairs. Global chemical labeling protocols that target the peptide Nterminus and do not protect lysine residues produce a variable mass difference between isotope-tagged peptide pairs. Given the specificity for trypsin and the relative abundance of lysine and arginine,21 approximately half of all peptides will terminate with lysine. When one then considers trypsin inability to cleave at KP sequences and its variable activity at KE, KD, KK, and KR sequences, more than half of the peptides from a tryptic digest

Figure 5. NIT labeling for results-driven identification of protein abundance changes. (A) Partial MALDI MS spectrum from sample A+A fraction 24 and (B) sample A+B fraction 24. More than 40 peptide pairs were detected and quantitated in fraction 24. However the resultsdriven strategy targeted only five peptides for identification. The peptide at M + H m/z 1283 was targeted for identification based on having a verified d0/d5 ratio of 3.81. (C) Targeted LC-ES MS/MS sequencing of the d0-labeled doubly charged precursor at m/z 642. For the sake of clarity, not all ions are labeled. Peptide fragment ion nomenclature is that of Biemann (37); K* indicates homoarginine. (D) Targeted MALDI MS/MS sequencing of the singly charged precursor m/z 1283.1. All identified fragment ions are labeled. -K* indicated a neutral loss of homoarginine from the precursor. 6624 Analytical Chemistry, Vol. 76, No. 22, November 15, 2004

Figure 6. Product ion mass spectra of NIT labeled peptides targeted for identification. The d0-labeled peptide HLVDEPQNLIK sequenced by (A) LC-ES MS/MS from the doubly charged precursor and by (B) MALDI MS/MS from the singly charged precursor. K* indicates homoarginine. The d5-labeled peptide FDEFFSAGCAPGSPR sequenced by (C) LC-ES MS/MS from the doubly charged precursor and by (D) MALDI MS/MS from the singly charged precursor. The cysteine residue is modified as carboxymethyl cysteine.

will incorporate two or more isotope tags depending on the number of lysines present in the peptide sequence, giving rise to a complex pattern of mass differences between the peptide pairs. In the example shown in Figure 5, three isotope labels would be added to the peptide and the heavy label form would have overlapped with the light form of a peptide present at m/z 1298. The strategy we have described here adds only a single label to each peptide at the N-terminus. Since all of the peptide pairs produced by this labeling are separated by 5 Da (∆5), in most cases it is a trivial matter to establish the d0/d5 pairs. For instance, we can be confident that in the spectrum shown in Figure 5B the peptides at m/z 1283 and 1288 (∆5) are one pair and at m/z 1298 and 1303 (∆5) are another pair, rather than concern ourselves with the various multiples of 5 Da that could link these peptides as pairs. Although this ambiguity could be removed by using accurate mass, the incorporation of a single tag allows the analysis to be performed in much more straightforward manner. It would obviously be more convenient if the results-driven strategy could be carried out on a single mass spectrometry platform. Therefore, we analyzed the same A+B fractions by MALDI-MS on a 4700 TOF/TOF proteomic analyzer, which has the capability of performing true MS/MS from MALDI-derived ions. After recording MS spectra for each fraction, peptide pairs and their d0/d5 ratios were determined by the ABI GPS Explorer software. Those peptide pairs showing a ratio differing from 1:1 by at least 2σ (as determined above) were automatically targeted for MALDI MS/MS. The list of targeted precursors was checked by manual inspection of the actual d0/d5 ratios in the A+B spectra and by comparison with the same peptide pair in the A+A sample. This resulted in 43 precursors being sequenced by MALDI MS/ MS (For example, see Figures 5D, 6B, and 6D,). From this we

identified transferrin with eight peptides at a d0/d5 ratio of 0.48, BSA with eight peptides at a d0/d5 ratio of 3.4, and conalbumin with four peptides at a d0/d5 ratio of 0.18 (see Figure 4). As above, we found one false positive in this analysis. Chymotrypsinogen, which is present in the mixture at a 1:1 ratio, was identified from a single precursor with a Mascot score of 41 and experimentally determined ratio of 0.61. Since this identification was based on the quantitation of a single peptide, we would consider it invalid. MALDI TOF MS/MS versus LC-MS/MS for ResultsDriven Protein Identification. With respect to the overall result, we found little difference between the use of LC-MS/MS or MALDI TOF MS/MS for the results-driven protein identification. The inclusion of the stable isotope tag described here seems to have little if any effect on the overall ionization efficiency of peptides from tryptic digests analyzed by either MALDI or ES, though individual peptide abundances were seen to vary by a factor of 2-3. Interestingly, in a strictly shotgun style MALDI MS/ MS analysis of a yeast lysate fraction, we found no difference in the number of lysine-containing peptides identified when comparing neat and NIT labeled (with guanidination) samples. By ES, we observed overall no change in the charge-state distribution of individual peptides. The NIT derivatization also seems to have little if any effect on the database search scores generated from the various tandem MS spectra. Looking at 58 peptides from three different proteins, we found that the Mascot scores varied very little between the labeled and unlabeled peptides (data not shown). If anything, the scores of labeled smaller peptides appears to be somewhat higher in general. The N-terminal propionyl tag does not fragment to produce any tag-specific ions. Tagging the amino terminus of a peptide with the propionyl group does not eliminate bn series ions, regardless of whether there is a basic residue at Analytical Chemistry, Vol. 76, No. 22, November 15, 2004

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the N-terminus (see Figures 5 and 6). As might be expected, the b ions are more abundant in the ES MS/MS spectra from multiply charged precursors than from the MALDI MS/MS spectra, which are produced exclusively from singly charged precursors (see Figures 5 and 6). We have noticed that the y1 ion (m/z 189), produced when homoarginine (isomethylurea-modified lysine) is at the C-terminus of a peptide, is enhanced in both MALDI MS/ MS and ES MS/MS spectra (Figures 5 and 6) compared to the y1 ion from unmodified lysine (m/z 147). In some spectra we also observe a b1 ion (Figures 5B and 6D), which are quite rare in the spectra of unmodified peptides. Both methods were able to correctly identify, with multiple peptides, the three proteins that showed different abundances in the two samples (see figure 4). Furthermore, the overall success rate for precursor identification was equivalent for the two techniques, 52% for LC-MS/MS and 49% for MALDI TOF MS/ MS. Because the quantitative analysis of the MALDI MS spectra was done differently in the two experiments, the list of precursors chosen for identification by either LC-MS/MS or MALDI TOF MS/MS was not identical. Of the 31 peptides present in both target lists, 21 were identified by LC-MS/MS (67% success) and 18 by MALDI MS/MS (58% success). The overlap between the techniques was 16 peptides (see Figure 4, bottom). The five peptides that were identified only by targeted LC- ES MS/MS all yielded excellent spectra, whereas the MALDI MS/MS spectra of the same peptides were either very weak and noisy despite a robust precursor signal or contained significant numbers of unassigned fragment ions (see Figure 5D). The two peptides identified by MALDI MS/MS only had moderately low, but sufficient Mascot scores. The MS/MS spectra generally contained only a few highly diagnostic ions, such as those derived from cleavage at aspartic acid residues.36 Neither of these peptides yielded ES-MS/MS spectra that contained any recognizable fragment ions related to the predicted sequence. Indeed, there did not appear to be any precursors for these peptides in the survey scans. Given the very different ionization mechanisms at work in MALDI and ES, it is not wholly unexpected that an ion observed by MALDI does not yield a precursor by ES. From these results it is clear that both the described approaches for results-driven protein identification work well. Combining the LC-MS/MS and MALDI MS/MS results from the set of overlapping precursors yielded only one extra peptide each for BSA and transferrin. Others using much larger data sets have found greater advantage to using both techniques.35 The major advantages of using MALDI for both the quantitation and protein identification are simplicity, efficiency, and sample consumption. Using MALDI for both analyses requires only one aliquot of sample. The protein identification experiments are carried out on the same sample spot that was used to generate the quantitative data. On the other hand, the targeted LC-MS/ MS analysis uses an additional aliquot of each fraction for the protein identification. Another factor that may be important in some cases is time. After establishing the sample queue, the MALDI MS/MS experiments were carried out in ∼0.5 h, whereas the five LC-MS/MS runs required just under 5 h total time to complete. For less complicated samples, the element of time may (36) Yu, W.; Vath, J. E.; Huberty, M. C.; Martin, S. A. Anal. Chem. 1993, 65, 3015-3023.

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Table 2. Protein Standards Quantitated in Yeast Lysate Fraction protein

d0/d5 expected

d0/d5 measureda

peptides

transferrin lactoperoxidase serum albumin

5.0 0.5 0.33

4.55 ( 1.2 0.62 ( 0.15 0.32 ( 0.04

14 3 4

a Average value based on the number of peptides shown in the adjacent column.

not be an issue. Nevertheless, when one considers that a singlestage reflectron-MALDI TOF and some sort of LC-ES MS/MS instrument is a combination common to many protein analysis laboratories, it is an important point that targeted LC-ES MS/ MS is a valid means of protein identification, for a results-driven strategy that is driven by MALDI-based peptide quantitation. Determination of Relative Protein Abundance Changes in a Complex Mixture. The analysis of quantitative protein expression changes in an entire proteome using a universal tagging strategy such as the one that has been defined here or by others29 would be hampered by the tremendous complexity and the large dynamic range of protein expression in such a sample. For studies such as these, the enrichment of a select peptide pool such as are provided by the affinity capture step of the ICAT-based quantitation is a tremendous advantage. On the other hand, the NIT labeling strategy can conveniently be applied to any sample that has been enriched at the protein level (such as by immunoprecipitation). Complex samples might also be enriched or simplified at the peptide level after NIT labeling, using techniques such as metal affinity chromatography with Fe, Ga, or Ni, ion exchange, or peptide-based antibodies. To test whether the NIT labeling strategy could quantitate a relatively small number of abundance changes in a relatively complex sample, we excised a 40-60-kDa section from two adjacent SDS PAGE lanes containing total yeast lysate (200 µg/lane), digested these sections with trypsin, and spiked amounts ranging from 2.5 pmol to 400 fmol of tryptic digests from three nonyeast proteins into the two samples. After NIT labeling with d0 or d5, the samples were pooled, fractionated by RP-HPLC, and analyzed by MALDI MS and MALDI MS/MS as described above. The average ratio of the yeast proteins was determined to be 1.06 with a standard deviation of 22%. Peptides in the A+B sample whose d0/d5 abundance ratios were determined by the software to be less than 0.6 or greater than 1.4 were automatically targeted for identification. The d0/d5 ratios for the identified peptides were checked manually and compared to the d0/d5 ratio found in the corresponding fraction from 8A+A. The results are shown in Table 2. The NIT labeling strategy was able to determine the abundance ratios of the three target proteins in the sample with errors that ranged from 3 to 24% of the theoretical value. We believe the large spread in the errors is due to the complexity of the samples. Even after SDSPAGE and reversed-phase HPLC fractionation, it is clear from an examination of the MALDI spectra that there are still many obvious overlapping peptide signals. No doubt there are also many not readily obvious overlapping peptide signals that yield erroneous d0/d5 ratios and produce errors in the quantitation. Despite these limitations, these results suggest that this approach is

capable of quantitating protein abundance changes even in very complex samples. The different amounts of the standard proteins spiked into the yeast lysate approximate expression levels of between 100 000 and 500 000 copies per cell for these proteins, placing them among the more abundant proteins in the yeast but not the most abundant. This is supported by an analysis of the individual MALDI mass spectra of the various fractions, where peptides from the spiked proteins were among the more abundant peptides, but usually not the most abundant (data not shown). Protein expression in yeast varies from less than 50 copies/cell to more the 1 million copies/cell.38 Our results here suggest that the NIT labeling strategy applied in a results-driven manner to a sample as complex as a total yeast lysate should provide quantitative information on at least the 15% of the yeast genome whose expression is greater than 10 000 copies/cell.38 Lower abundance proteins could be analyzed if the initial cell number was increased or some form of enrichment was used. CONCLUSIONS The N-terminal isotope tagging strategy we have described here incorporates a single mass tag to any peptide that is not N-terminally blocked. The creation of d0/d5 isotope-labeled peptide pairs that all differ by 5 Da greatly simplifies the quantitative analysis since the analyst or software application need only look for a single fixed mass difference. Using a results-driven strategy, we have demonstrated that the NIT labeling protocol can quantitate protein abundance changes even in very complex samples such as a cell lysate. Although many peptides pairs are quantitated, only relatively few peptides are targeted for protein identification. Protein identification can be accomplished equally well by either MALDI MS/MS or LC-ES MSMS. The use of MALDI MS for both quantitation and protein identification, however, simplifies the overall process. We have found that the (37) Biemann, K. In Methods in Enzymology, Mass Spectrometry; McCloskey, J. A., Ed.; Academic Press: San Diego, 1990: Vol. 193, pp 886-887. (38) Ghaemmaghami, S.; Huh, W. K. Bower, K.; Howson, R. W.; Belle, A.; Dephoure, N.; O’Shea, E. K.; Weissman, J. S. Nature 2003, 425, 737-741.

inclusion of a d0/d5-labeled control is a tremendous advantage in eliminating false positives. False singlets and aberrant d0/d5 ratios are easily flagged by comparing peaks in the A+B sample with the corresponding peaks in the A+A sample. The enormous complexity of any given proteome is a daunting challenge for the current array of technologies used for quantitative proteomic analysis. The large dynamic range of protein expression particularly in eukaryotes mandates some type of enrichment strategy to be used if low copy number proteins and regulatory posttranslational events are to be detected and monitored. Global chemical labeling strategies such as NIT, which seek to label every peptide and thus potentially provide access to the entire proteome, can be used in combination with most any purification or enrichment method. The reduced complexity of the sample and the universal labeling inherent with NIT greatly increase the likelihood that multiple peptides per protein can be quantitated, providing increased confidence in the result. Additionally, because all peptides are tagged, the technique is amenable to the quantitation of posttranslational modification in addition to protein expression and abundance. Previously we have shown that this labeling strategy can be used to derive absolute, site-specific phosphorylation stoichiometry.23 A detailed study on the use of the NIT labeling strategy for measuring relative changes in site-specific protein phosphorylation without phosphopeptide enrichment will be reported elsewhere (Zappacosta and Annan, unpublished data). ACKNOWLEDGMENT The authors are grateful to Therese Sterner for providing the yeast lysate. We also acknowledge Fadi Abdi and Melanie Lin from ABI for assistance in acquiring the MALDI MS/MS data.

Received for review June 7, 2004. Accepted August 23, 2004. AC049169B

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