Advances in Proteome Analysis by Mass Spectrometry

Timothy J. Griffin and Ruedi Aebersold

Institute for Systems Biology, 4225 Roosevelt Way N. Suite 200, Seattle, WA 98105

Tim Griffin:Tel: 206-732-1359Fax: 206-732-1299tgriffin@systemsbiology.org

Ruedi Aebersold:

Tel: 206-732-1204Fax: 206-732-1255raebersold@systemsbiology.org

Keywords: proteomics, mass spectrometry, chromatography, gene expression analysis

Abbreviations used: 2DE, two-dimensional electrophoresis; RP-µLC, reverse-phasemicrocapillary liquid chromatography; MS, mass spectrometry; MS/MS, tandem massspectrometry; ESI, electrospray ionization; MALDI, matrix-assisted laserdesorption/ionization; TOF, time-of-flight; CID, collision-induced dissociation; ICAT,isotope-coded affinity tag; SCX, strong cation exchange; HPLC, high performance liquidchromatography.

Copyright 2001 by The American Society for Biochemistry and Molecular Biology, Inc.

JBC Papers in Press. Published on October 3, 2001 as Manuscript R100014200 by guest on M

ay 20, 2018http://w

ww

.jbc.org/D

ownloaded from

The interpretation of the information contained in the genomic sequence of a species

with respect to the structure, function and control of biological processes is a technical

and conceptual challenge for current research methods. Systematic and quantitative

analysis of gene expression is emerging as a valuable tool to diagnostically distinguish

between cell types (1-5) and to differentiate between states (metabolic, activation,

pathological) of a particular cell type (5-7). More elaborate strategies such as the

combination of systematic, quantitative gene expression analysis with targeted,

hypothesis-guided perturbations of cells are being explored for the comprehensive

mechanistic analysis of cellular pathways and processes (5,7-10)

Measuring gene expression at the protein level is potentially more informative than the

corresponding measurement at the mRNA level. Proteins, the major catalysts of

biological function, contain several dimensions of information that collectively indicate

the actual, rather than the potential functional state as indicated by mRNA analysis. These

include the abundance, state of modification, sub-cellular location, and 3D structure of

proteins, and their association with each other and/or with biomolecules of different

types. The goal of proteomics is to measure these types of information systematically

and, where applicable, quantitatively on all the proteins expressed by a cell. Mass

spectrometry has become the analytical technology of choice for many of the aspects of

proteome analyses that are reflected by the covalent structure of proteins. Recent

advances in instrumentation and methods have improved the sensitivity and throughput of

mass spectrometry-based approaches so that truly proteome-wide analyses are now

2

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

becoming feasible.

This review describes recent, innovative advances in mass spectrometry-based

proteome analysis. Several technical developments have been converging into a generic

new approach to proteomics. It’s performance and versatility promises to surpass those

of the initial proteome analysis platform, the combination of high resolution two-

dimensional gel electrophoresis and mass spectrometry. The specific advances include

high-throughput protein identification by multidimensional chromatography, automated

tandem mass spectrometry and sequence database searching, accurate quantification by

the application of stable isotope dilution theory to protein analysis, and the targeted

isolation of selected analytes by the use of highly selective chemistries. Selected

applications of these methods, along with speculations about future prospects and

directions of proteomics research are also included.

The Emergence of Proteomics: The First Generation Technology

The development of methods to separate complex protein mixtures at high resolution

by two-dimensional gel electrophoresis (2DE) into reproducible patterns (11,12)

presented the opportunity to diagnose quantitative and qualitative differences in the

protein composition of two or more cell- or tissue samples long before gene array

techniques to measure gene expression were conceived. Unfortunately, 2DE by itself was

an essentially descriptive technique and, without the availability of reliable tools for the

identification of the separated protein species, of limited utility as a molecular biology

research tool.

This changed in the early 1990’s when two revolutionary techniques, matrix-assisted

3

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

laser desorption ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS)

(13,14) and electrospray ionization (ESI) (15,16) MS and tandem mass spectrometry

(MS/MS) replaced the slower and less sensitive chemical degradation methods (17,18) as

the methods of choice for the identification of proteins separated by 2DE (19-24).

Typically, these methods involved excision of gel bands of interest, in-gel digestion of

the proteins contained in the band using the enzyme trypsin (25), and finally mass

spectrometric analysis of the peptides produced. Protein identification was accomplished

using either peptide-mass fingerprinting by MALDI-TOF MS, as initially described by

Henzel et al. (26) and independently by others (reviewed in reference 23), nanoESI

tandem mass spectrometry (MS/MS) (27,28), or by reverse-phase (RP) microcapillary

liquid chromatography (µLC) ESI MS/MS (29-34) using automated, data-dependent

scanning and dynamic exclusion of peptide ions already analyzed in the same experiment

(29,35-37). In the latter of these methods, which is also the most highly automated and

detailed in Figure 1, the mass spectrometer first scans the full mass range to measure the

masses of the peptides eluting from the RP-µLC column at a given point in time. A

specific peptide ion is then selected by the software based on its mass-to-charge ratio to

undergo collision-induced dissociation (CID) (38,39) in the collision cell of the mass

spectrometer, producing fragment ions that are detected in a second scan. The CID mass

spectra for each peptide are then searched against the theoretical CID mass spectra of

peptides derived from protein sequences contained in a protein database, or alternatively,

against all protein sequences predicted from the translation of a DNA sequence database.

A variety of database search algorithms have been developed (40), the pioneering

4

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

program being the Sequest algorithm developed by Eng and Yates (41). The peptide

sequence obtained from the database search of the CID mass spectrum is then used to

identify the protein, based on the assumption that a continuous peptide sequence of

several (eight or more) amino acids uniquely identifies the protein from which it is

derived. The most highly advanced implementations of these procedures allow the

identification of hundreds of proteins per week in a highly automated manner. The

specific techniques and instruments have been reviewed in detail and are not further

discussed here (42,43).

Apparent Limitations of the 2DE-MS Approach: An Outline of a Second Generation

Technology

It has become apparent that the 2DE-MS method as most frequently practiced has

significant, inherent limitations. First, the combination of limited sample capacity and

limited detection sensitivity of 2DE restricts the detection of low-abundance proteins. If

total yeast cell lysates are separated and detected by silver staining, proteins present at

less than 1000 copies per cell are not detected (44). As proteins expressed at low-

abundance may make up a large portion of a given proteome (44), it is apparent that the

proteins detected by 2DE do not give a true representation of all the expressed proteins.

Second, in spite of substantial recent advances (45,46), the separation of transmembrane

proteins by 2DE remains challenging. Third, a substantial fraction of spots contain more

than one protein, and/or differentially modified or processed forms of a protein which

migrate to different positions in the gel, thus complicating quantification (44). Fourth,

the method is based on the sequential identification of individually processed protein

5

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

spots, which limits its throughput, and fifth, the method is inherently labor-intensive and

requires a high skill level, which limits the potential for full automation. Collectively,

these limitations indicate the need for the development of improved or alternative

technologies if routine proteome analysis is to become a reality. To address these

limitations, incremental improvements of the 2DE-MS approach have been made that

include sample pre-fractionation prior to 2DE (47), the use of fluorescent protein dyes

with enhanced detection sensitivity (48) and the use of gels with expanded separation

range (zoom gels) (46,49) to improve the detection sensitivity of low abundance proteins,

the search for new detergent systems to maximize solubility of membrane proteins (45),

and the development of robotic and software systems to increase the level of automation

of the process (42,50).

Concurrently, an alternative technique has been emerging that has the potential to

systematically identify and quantify all the proteins in a cell or tissue type. It is based on

three principles. The first is rapid protein identification by automated tandem mass

spectrometry and sequence database searching. Essentially the same methods developed

for the identification of gel-separated proteins are applied to identify the components of

un-separated protein mixtures. The second is the determination of the ratio of abundance

(relative quantification) for proteins present in different protein samples by stable isotope

dilution. Stable isotope dilution theory (51) states that the relative signal intensity in a

mass spectrometer of two analytes that are chemically identical but of different stable

isotope composition (and thus distinguishable in a mass analyzer) are a true

representation of the relative abundance of the two analytes in the sample. The third

6

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

principle is the targeted isolation of selected peptide analytes from complex peptide

mixtures via specific chemical reactions. Collectively, the three components permit the

relative measurement of abundance and the identification of the components of very

complex protein mixtures rapidly and with a high degree of automation, without the need

to separate protein mixtures prior to analysis. Variations of this technology also have the

potential to systematically and quantitatively determine properties of proteins that reflect

their functional state. These include the phosphorylation state and the activity of some

classes of enzymes. The evolution and early applications of this second generation

proteomics technology are described below.

The Emergence and Initial Applications of a Second Generation Proteomics Technology

The challenge facing any comprehensive proteomics approach is one of separating and

simplifying very complex mixtures of proteins in which individual components differ in

abundance by six or more orders of magnitude, while retaining enough information to

allow for comprehensive characterization of expressed proteins. To this end, the

combination of selective labeling of proteins with stable-isotope containing affinity

reagents and multidimensional liquid chromatography in conjunction with automated,

data-dependent tandem mass spectrometry and sequence database searching has proven

effective and been shown to overcome at least some of the critical limitations of the

2DE-MS based approach to proteomics.

Selective Protein Labeling and Automated Tandem Mass Spectrometric Analysis

The labeling of proteins at specific sites in a complex mixture followed by proteolysis

and selective purification of the labeled peptide fragments has proven to be an effective

7

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

method for the analysis of un-separated protein samples. The nucleophilic thiol group

contained in the side chain of reduced cysteine residues is a commonly targeted site for

the modification and labeling of proteins and peptides (52,53). The frequency of cysteine

residues in protein sequences makes it an attractive amino acid to target for the reduction

of the complexity of peptide mixtures (~10% of all possible tryptic peptides in the yeast

Saccharomyces cerevisiae contain a cysteine (54)). However, as approximately 92% of the

total proteins in the Saccharomyces cerevisiae genome contain at least one cysteine (54),

selection and identification of only the cysteine-containing peptides still enables the

comprehensive identification of expressed proteins, while the complexity of the sample is

significantly decreased. Reduction of the complexity of peptide mixtures prior to mass

spectrometric analysis is advantageous for several reasons. First, the selection of a subset

of the peptides generated by proteolysis of a protein mixture greatly increases the

representation of the selected peptides in the sample that is loaded onto the µLC column.

The reduction of the sample complexity achieved by selective tagging is therefore

essential for detecting and identifying low-abundance proteins. Second, a larger

proportion of the available peptides are identified if the automated, data-dependent

scanning methods using dynamic exclusion are employed that have become a cornerstone

of RP-µLC-ESI tandem mass spectrometric approaches to high-throughput protein

analyses (29,35-37). The data-dependent scanning routine that is employed in these

analyses selects eluting peptides in descending order of mass spectral signal intensity. If

the number of peptides eluting from the column at a specific time exceeds the number of

peptides that can be analyzed by the mass spectrometer within the available window of

8

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

chromatographic elution time, some peptides (particularly peptides of lower abundance)

will be excluded from MS analysis. This apparent compression of the dynamic range of

MS sensitivity is reduced or eliminated by the reduction of the complexity of the sample

prior to analysis by RP-µLC-ESI MS/MS. Third, the presence of the relatively rare

amino acid cysteine that is indicated by the specific reaction between the alkylating group

and the thiol side chain provides a significant constraint for sequence database searching.

The effective use of selective labeling of cysteine residues for the simplification of the

peptide samples generated by proteolysis of protein mixtures prior to mass spectrometric

analysis has been demonstrated by Spahr et al. (55). The authors labeled cysteine side

chains using a cleavable, biotinylated reagent, and these peptides were then affinity

purified using immobilized avidin, and identified by LC-ESI MS/MS. This experiment

was part of a study in which a total of 108 soluble intermembrane mitochondria proteins

from mouse liver samples were identified. Selective labeling and capture of cysteine

containing peptides is the also the basis of the isotope-coded affinity tag (ICAT)

approach that was recently developed in our laboratory (56). In this approach the

proteins present in two samples (e.g. the proteins expressed by a cell under two different

physiological conditions) are labeled separately on the side chains of their reduced

cysteine residues using one of two isotopically different, but chemically identical

sulfhydryl reactive ICAT reagents (Figure 2A). One of the ICAT reagents is an

isotopically normal reagent containing hydrogen atoms on the carbon backbone (referred

to as the d(0) reagent), and the other is an isotopically heavy (d(8)) reagent, where the

9

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

hydrogen atoms have been replaced with deuterium atoms. The labeled protein mixtures

are then combined and enzymatically digested, and the labeled, biotinylated peptides are

isolated by affinity chromatography. The purified peptides are then analyzed by RP-

µLC MS. As the pairs of peptide labeled with the d(0) and d(8) versions of the ICAT reagent

are chemically identical, according to stable isotope dilution theory (51) they serve as

mutual internal standards for accurate protein quantification. The relative quantity of

each protein present in the two biological samples is therefore determined by measuring

the relative signal intensities of pairs of isotopically labeled, concurrently eluting peptides

using an initial mass spectral scan. The identification of the proteins is accomplished by

switching the instrument to MS/MS mode in which it selects peptides for CID.

Alternative methodologies for quantitative protein analysis using selective peptide

labeling and MS have also been recently developed (57,58). Munchbach et al. (57)

labeled the N-termini of peptides derived from 2DE separated proteins using a stable-

isotope containing reagent to profile the effects of carbon source restriction on protein

expression in E. Coli. CID of these N-terminal labeled peptides generates unique mass

signatures that facilitate de novo peptide sequencing by MS/MS analysis (i.e. sequence

determination from interpretation of raw MS/MS spectra without the need for database

searching). A similar approach involves the labeling of carboxylic acid residues on

peptides with isotopically normal or heavy methanol which allows for both relative

quantification of protein expression as well as de novo peptide sequencing (D. Goodlett,

personal communication). Another alternative approach to quantitative protein analysis

employs metabolic incorporation of stable-isotope containing amino acids into

10

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

differentially expressed proteins isolated from cell cultures grown on either normal media

or media enriched or depleted in stable isotope containing amino elements (59,60). The

isotopically labeled peptide pairs that are detected and identified in the mass spectrometer

are then used to quantify the protein expression levels between the two cell states.

Multidimensional Separation Strategies

Along with selective labeling and purification of proteins, promising approaches that

employ multiple, orthogonal liquid chromatography steps in conjunction with automated

ESI tandem mass spectrometric analysis have been recently developed as an alternative

strategy to 2DE for analyzing complex protein mixtures (61-63). Most notably, Link et

al. (63) described an integrated system that employed a biphasic two-dimensional µLC

column packed with strong-cation exchange (SCX) and RP materials. Peptides loaded

onto this biphasic column were serially displaced, first using a step gradient of increasing

salt concentration to separate the peptides by charge on the SCX column and to pass them

onto the in-line RP-µLC column. The peptides were then eluted from the RP-µLC

column using a linear gradient of increasing organic solvent and analyzed by ESI-

MS/MS. This method has proven effective for the comprehensive analysis of protein

complexes (63), and more recently it has been applied to the identification of nearly 1500

proteins, including low-abundance proteins, from a whole-cell yeast lysate (64). Gygi et

al. (44) have used a similar approach that employs SCX high performance liquid

chromatography (HPLC) in conjunction with off-line RP-µLC-ESI MS/MS, and shown

that this method enables the analysis of low-abundance proteins in Saccharomyces

cerevisiae that 2DE-based approaches are not sensitive enough to detect.

11

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

We have also coupled this multidimensional LC methodology with the ICAT strategy

described above to form an integrated approach to the quantitative analysis of protein

expression (Figure 2B). Enzymatically digested, ICAT labeled samples are first loaded

onto a SCX HPLC column, and peptides are eluted using a linear gradient of increasing

salt concentration, with automated fraction collection of the eluting peptides. Each of

these SCX fractions is then run over an avidin affinity column to isolate the ICAT

labeled, biotinylated peptides, which are subsequently quantified and identified by RP-

µLC ESI-MS/MS. We have applied this strategy to the investigation of changes in the

protein expression profile after metabolic perturbation in yeast cells (56), to detect

differentially induced changes in the membrane protein composition in UL-60 cells (D.

Han et al., submitted for publication), as well as to detect differences in protein profiles

isolated from simulated and non-stimulated androgen-dependent human prostate cell

lines (manuscript in preparation). Additionally, by correlating protein expression data

with cDNA array data measuring the corresponding mRNA expression levels, we now

have the tools necessary for the global analysis of gene expression on a system-wide

level. In an initial study we have applied these two complementary gene expression

profiling approaches to the systematic analysis of metabolic pathways in yeast (10).

Future Prospects

It can be anticipated that the traditional 2DE-MS based approach as well as

alternative, second generation proteomics technologies will continue to rapidly evolve

and diversify over the next few years. These advances will be accelerated by exciting

hardware and software developments related to mass spectrometry. Described below is

12

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

anticipated progress related to second generation proteomics approaches.

Development of additional labeling chemistries

The combination of group-specific chemistries, stable isotope dilution and automated

MS/MS can also be used to quantitatively and systematically assess properties of

proteomes other than their composition. For example, we have recently developed a

method for the quantitative analysis of protein phosphorylation on a proteome-wide scale

(65). It involves the specific labeling of phosphate groups on peptides, enabling the

isolation of these phosphopeptides for subsequent mass spectrometric analysis using a

solid-phase purification strategy. This method also includes a labeling of the carboxylic

acid groups of the peptides with stable-isotope tags, thus facilitating the quantitative

analysis of phosphorylated proteins by RP-µLC ESI-MS/MS in a manner similar to the

ICAT strategy. This approach applied to the proteins contained in a total yeast cell lysate

identified in a single RP-µLC ESI-MS/MS experiment numerous phosphorylation sites

on 13 phosphoproteins, many of which were not previously known to be phosphorylated

proteins. The development of chemistries (66-68) that select proteins based on their state

of activity rather than simply on the presence of specific amino acid residue represents an

exciting opportunity to analyze proteomes functionally (69). This is achieved by the

synthesis of chemical reagents that selectively bind to the active site of specific enzymes

in an activity-dependent manner. Liu et al. (66) have developed a chemistry that

specifically targets the active site of catalytically active serine hydrolases and Greenbaum

et al. (68) have developed an analogous chemistry for the selective labeling of

catalytically active cysteine proteases. Similar chemistries, coupled with stable isotope

13

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

tagging, could enable the quantitative, proteome-wide characterization of the function

and activity of selected classes of proteins by MS.

Advances in Mass Spectrometric Instrumentation

It can be anticipated that parameters critical to the performance of mass spectrometers,

including detection sensitivity, sample throughput, mass resolution and mass accuracy,

will continue to improve, although not necessarily all on the same instrument. The recent

introduction of a mass spectrometer that combines a MALDI source with a selection

quadrupole (Q), a collision cell (q) and a time-of-flight (TOF) fragment ion analyzer

(MALDI QqTOF) (70,71) offers the previously unavailable ability to combine the

sensitivity, amenability to automation, and mass accuracy of MALDI-TOF MS with the

capabilities of MS/MS. We have shown the MALDI QqTOF instrument to be effective

for the analysis of ICAT labeled proteins (72) with the main advantage of the approach

being that those proteins showing significant differential expression between the two

biological conditions can be selectively identified by MS/MS analysis, while those peaks

showing little or no differences in expression can be omitted. The option to selectively

analyze only the differentially expressed proteins could lead to a dramatic increase in

throughput for proteome-wide protein expression profiling studies. Another recent

advance in instrumentation is the development of a MALDI TOF-TOF mass

spectrometer (73). This instrument combines the same advantages of the MALDI

QqTOF instrument with the added potential for extremely fast analysis times and thus the

potential to significantly increase sample throughput, making the identification of

thousands of proteins per day possible. Another type of instrument that holds great

14

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

potential for proteomic studies is the Fourier Transform (FT) mass spectrometer, which

gives significantly increased sensitivity, resolution, and mass accuracy relative to other

mass spectrometers (60,74,75). It has been demonstrated in yeast that with added

constraints, the mass accuracy obtained by FT-MS analysis of peptides is sufficient to

uniquely identify the peptides (and thus the proteins from which they are derived) from a

database without the need for tandem mass spectrometric analysis (76,77). This allows

for the detection of low-abundance proteins that are many times missed when sequencing

peptides by standard MS/MS methods, and thus this approach has great promise for

improved sensitivity and increased throughput in proteome-wide analyses. Collectively,

these advances are expected to dramatically change the performance of proteomic

technology, with respect to sensitivity, level of automation, sample throughput and

accuracy. Furthermore, it can be anticipated that the ability to measure additional

properties of proteins will move proteomics ever closer to the comprehensive analysis of

biological function.

Acknowledgements

The authors thank David Goodlett for his helpful comments on this manuscript. T.J.G.

was funded by an NIH Postdoctoral Genome Training Grant fellowship. This work was

also supported by a grant from the Merck Genome Research Institute (MGRI) and a grant

from the National (USA) Cancer Institute (1R33CA84698).

15

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

References

1. Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.

P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., Bloomfield, C. D.,

and Lander, E. S. (1999) Science 286(5439), 531-7

2. Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A.,

Boldrick, J. C., Sabet, H., Tran, T., Yu, X., Powell, J. I., Yang, L., Marti, G. E.,

Moore, T., Hudson, J., Jr., Lu, L., Lewis, D. B., Tibshirani, R., Sherlock, G.,

Chan, W. C., Greiner, T. C., Weisenburger, D. D., Armitage, J. O., Warnke, R.,

Staudt, L. M., and et al. (2000) Nature 403(6769), 503-11

3. Scherf, U., Ross, D. T., Waltham, M., Smith, L. H., Lee, J. K., Tanabe, L., Kohn,

K. W., Reinhold, W. C., Myers, T. G., Andrews, D. T., Scudiero, D. A., Eisen, M.

B., Sausville, E. A., Pommier, Y., Botstein, D., Brown, P. O., and Weinstein, J. N.

(2000) Nat Genet 24(3), 236-44

4. Ross, D. T., Scherf, U., Eisen, M. B., Perou, C. M., Rees, C., Spellman, P., Iyer,

V., Jeffrey, S. S., Van de Rijn, M., Waltham, M., Pergamenschikov, A., Lee, J. C.,

Lashkari, D., Shalon, D., Myers, T. G., Weinstein, J. N., Botstein, D., and Brown,

P. O. (2000) Nat Genet 24(3), 227-35

5. Lockhart, D. J., and Winzeler, E. A. (2000) Nature 405(6788), 827-36

6. Lashkari, D. A., DeRisi, J. L., McCusker, J. H., Namath, A. F., Gentile, C.,

Hwang, S. Y., Brown, P. O., and Davis, R. W. (1997) Proc Natl Acad Sci U S A

94(24), 13057-62

7. Marton, M. J., DeRisi, J. L., Bennett, H. A., Iyer, V. R., Meyer, M. R., Roberts, C.

16

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

J., Stoughton, R., Burchard, J., Slade, D., Dai, H., Bassett, D. E., Jr., Hartwell, L.

H., Brown, P. O., and Friend, S. H. (1998) Nat Med 4(11), 1293-301

8. Holstege, F. C., Jennings, E. G., Wyrick, J. J., Lee, T. I., Hengartner, C. J., Green,

M. R., Golub, T. R., Lander, E. S., and Young, R. A. (1998) Cell 95(5), 717-28

9. Roberts, C. J., Nelson, B., Marton, M. J., Stoughton, R., Meyer, M. R., Bennett,

H. A., He, Y. D., Dai, H., Walker, W. L., Hughes, T. R., Tyers, M., Boone, C.,

and Friend, S. H. (2000) Science 287(5454), 873-80

10. Ideker, T., Thorsson, V., Ranish, J. A., Christmas, R., Buhler, J., Bumgarner, R.,

Aebersold, R., and Hood, L. (2001) Science, In press

11. O’Farrell, P. H. (1975) J Biol Chem 250(10), 4007-21

12. Klose, J. (1975) Humangenetik 26(3), 231-43

13. Karas, M., and Hillenkamp, F. (1988) Anal Chem 60(20), 2299-301

14. Hillenkamp, F., Karas, M., Beavis, R. C., and Chait, B. T. (1991) Anal Chem

63(24), 1193A-1203A

15. Fenn, J. B., Mann, M., Meng, C. K., Wong, S. F., and Whitehouse, C. M. (1989)

Science 246(4926), 64-71

16. Banks, J. F., Jr., and Whitehouse, C. M. (1996) Methods Enzymol 270, 486-519

17. Aebersold, R. H., Leavitt, J., Saavedra, R. A., Hood, L. E., and Kent, S. B. (1987)

Proc Natl Acad Sci U S A 84(20), 6970-4

18. Lin, C. S., Aebersold, R. H., Kent, S. B., Varma, M., and Leavitt, J. (1988) Mol

Cell Biol 8(11), 4659-68

19. Humphery-Smith, I., Cordwell, S. J., and Blackstock, W. P. (1997)

17

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

Electrophoresis 18(8), 1217-42

20. Dunn, M. J., and Corbett, J. M. (1996) Methods Enzymol 271, 177-203

21. Shevchenko, A., Jensen, O. N., Podtelejnikov, A. V., Sagliocco, F., Wilm, M.,

Vorm, O., Mortensen, P., Boucherie, H., and Mann, M. (1996) Proc Natl Acad Sci

U S A 93(25), 14440-5

22. Clauser, K. R., Hall, S. C., Smith, D. M., Webb, J. W., Andrews, L. E., Tran, H.

M., Epstein, L. B., and Burlingame, A. L. (1995) Proc Natl Acad Sci U S A

92(11), 5072-6

23. Pandey, A., and Mann, M. (2000) Nature 405(6788), 837-46

24. Patterson, S. D., and Aebersold, R. (1995) Electrophoresis 16(10), 1791-814

25. Shevchenko, A., Wilm, M., Vorm, O., and Mann, M. (1996) Anal Chem 68(5),

850-8

26. Henzel, W. J., Billeci, T. M., Stults, J. T., Wong, S. C., Grimley, C., and

Watanabe, C. (1993) Proc Natl Acad Sci U S A 90(11), 5011-5

27. Wilm, M., Shevchenko, A., Houthaeve, T., Breit, S., Schweigerer, L., Fotsis, T.,

and Mann, M. (1996) Nature 379(6564), 466-9

28. Wilm, M., and Mann, M. (1996) Anal Chem 68(1), 1-8

29. Shabanowitz, J., Settlage, R. E., Marto, J. A., Christian, R. E., White, F. M.,

Russo, P. S., Martin, S. E., and Hunt, D. F. (2000) in Mass Spectrometry in

Biology and Medicine (Burlingame, A. L., Carr, S. A., and Baldwin, M. A., eds),

pp. 163-177, Humana Press, Totowa, N.J.

30. Hunt, D. F., Henderson, R. A., Shabanowitz, J., Sakaguchi, K., Michel, H.,

18

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

Sevilir, N., Cox, A. L., Appella, E., and Engelhard, V. H. (1992) Science

255(5049), 1261-3

31. McCormack, A. L., Schieltz, D. M., Goode, B., Yang, S., Barnes, G., Drubin, D.,

and Yates, J. R., 3rd. (1997) Anal Chem 69(4), 767-76

32. Davis, M. T., Stahl, D. C., Hefta, S. A., and Lee, T. D. (1995) Anal Chem 67(24),

4549-56

33. Ducret, A., Van Oostveen, I., Eng, J. K., Yates, J. R., 3rd, and Aebersold, R.

(1998) Protein Sci 7(3), 706-19

34. Haynes, P. A., Fripp, N., and Aebersold, R. (1998) Electrophoresis 19(6), 939-45

35. Gatlin, C. L., Eng, J. K., Cross, S. T., Detter, J. C., and Yates, J. R., 3rd. (2000)

Anal Chem 72(4), 757-63

36. Figeys, D., and Aebersold, R. (1997) Electrophoresis 18(3-4), 360-8

37. Courchesne, P. L., Jones, M. D., Robinson, J. H., Spahr, C. S., McCracken, S.,

Bentley, D. L., Luethy, R., and Patterson, S. D. (1998) Electrophoresis 19(6),

956-67

38. Hunt, D. F., Yates, J. R. d., Shabanowitz, J., Winston, S., and Hauer, C. R. (1986)

Proc Natl Acad Sci U S A 83(17), 6233-7

39. Papayannopoupos, I. A. (1995) Mass Spectrom Reviews 14, 49-73

40. Beavis, R. C., and Fenyo, D. (2000) in Proteomics: A Trends Guide (Mann, M.,

and Blackstock, W., eds), pp. 22-27, Elsevier, London

41. Eng, J., McCormack, A. L., and Yates, J. R., 3rd. (1994) J Am Soc Mass

Spectrom 5, 976-989

19

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

42. Blackstock, W. (2000) in Proteomics: A Trends Guide (Mann, M., and

Blackstock, W., eds), pp. 12-17, Elsevier, London

43. Yates, J. R., 3rd. (2000) Trends Genet 16(1), 5-8

44. Gygi, S. P., Corthals, G. L., Zhang, Y., Rochon, Y., and Aebersold, R. (2000)

Proc Natl Acad Sci U S A 97(17), 9390-5

45. Rabilloud, T., Adessi, C., Giraudel, A., and Lunardi, J. (1997) Electrophoresis

18(3-4), 307-16

46. Gorg, A., Obermaier, C., Boguth, G., Harder, A., Scheibe, B., Wildgruber, R., and

Weiss, W. (2000) Electrophoresis 21(6), 1037-53

47. Corthals, G. L., Molloy, M. P., Herbert, B. R., Williams, K. L., and Gooley, A. A.

(1997) Electrophoresis 18(3-4), 317-23

48. Steinberg, T. H., Chernokalskaya, E., Berggren, K., Lopez, M. F., Diwu, Z.,

Haugland, R. P., and Patton, W. F. (2000) Electrophoresis 21(3), 486-96

49. Wildgruber, R., Harder, A., Obermaier, C., Boguth, G., Weiss, W., Fey, S. J.,

Larsen, P. M., and Gorg, A. (2000) Electrophoresis 21(13), 2610-6

50. Lopez, M. F. (2000) Electrophoresis 21(6), 1082-93

51. De Leenheer, A. P., and Thienpont, L. M. (1992) Mass Spectrom Rev 11, 249-

307

52. Kenyon, G. L., and Bruice, T. W. (1977) Methods Enzymol 47, 407-30

53. Sechi, S., and Chait, B. T. (1998) Anal Chem 70(24), 5150-8

54. Fenyo, D., Qin, J., and Chait, B. T. (1998) Electrophoresis 19(6), 998-1005

55. Spahr, C. S., Susin, S. A., Bures, E. J., Robinson, J. H., Davis, M. T., McGinley,

20

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

M. D., Kroemer, G., and Patterson, S. D. (2000) Electrophoresis 21(9), 1635-50

56. Gygi, S. P., Rist, B., Gerber, S. A., Turecek, F., Gelb, M. H., and Aebersold, R.

(1999) Nat Biotechnol 17(10), 994-9

57. Munchbach, M., Quadroni, M., Miotto, G., and James, P. (2000) Anal Chem

72(17), 4047-57

58. Ji, J., Chakraborty, A., Geng, M., Zhang, X., Amini, A., Bina, M., and Regnier, F.

(2000) J Chromatogr B Biomed Sci Appl 745(1), 197-210

59. Oda, Y., Huang, K., Cross, F. R., Cowburn, D., and Chait, B. T. (1999) Proc Natl

Acad Sci U S A 96(12), 6591-6

60. Pasa-Tolic, L., Jensen, P., Anderson, G., Lipton, M., Peden, K., Martinovic, S.,

Tolic, N., Bruce, J., and Smith, R. (1999) J Am Chem Soc 121(34), 7949-7950

61. Washburn, M. P., and Yates, J. R. (2000) in Proteomics: A Trends Guide (Mann,

M., and Blackstock, W., eds), pp. 27-30, Elsevier, London

62. Opiteck, G. J., Lewis, K. C., Jorgenson, J. W., and Anderegg, R. J. (1997) Anal

Chem 69(8), 1518-24

63. Link, A. J., Eng, J., Schieltz, D. M., Carmack, E., Mize, G. J., Morris, D. R.,

Garvik, B. M., and Yates, J. R., 3rd. (1999) Nat Biotechnol 17(7), 676-82

64. Washburn, M. P., Wolters, D., and Yates, J. R., 3rd. (2001) Nat Biotechnol 19(3),

242-7.

65. Zhou, H., Watts, J. D., and Aebersold, R. (2001) Nat Biotechnol 19(4), 375-378.

66. Liu, Y., Patricelli, M. P., and Cravatt, B. F. (1999) Proc Natl Acad Sci U S A

96(26), 14694-9

21

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

67. Patricelli, M. P., Lovato, M. A., and Cravatt, B. F. (1999) Biochemistry 38(31),

9804-12

68. Greenbaum, D., Medzihradszky, K. F., Burlingame, A., and Bogyo, M. (2000)

Chem Biol 7(8), 569-81

69. Cravatt, B. F., and Sorensen, E. J. (2000) Curr Opin Chem Biol 4(6), 663-8.

70. Loboda, A. V., Krutchinsky, A. N., Bromirski, M., Ens, W., and Standing, K. G.

(2000) Rapid Commun Mass Spectrom 14(12), 1047-57

71. Shevchenko, A., Loboda, A., Shevchenko, A., Ens, W., and Standing, K. G.

(2000) Anal Chem 72(9), 2132-41

72. Griffin, T. J., Loboda, A., Gygi, S. P., Rist, B., Jilkine, A., Ens, W., Standing, K.

G., and Aebersold, R. (2001) Anal Chem 73(5), 978-86

73. Medzihradszky, K. F., Campbell, J. M., Baldwin, M. A., Falick, A. M., Juhasz, P.,

Vestal, M. L., and Burlingame, A. L. (2000) Anal Chem 72(3), 552-8

74. Martin, S. E., Shabanowitz, J., Hunt, D. F., and Marto, J. A. (2000) Anal Chem

72(18), 4266-74

75. Bruce, J. E., Anderson, G. A., Wen, J., Harkewicz, R., and Smith, R. D. (1999)

Anal Chem 71(14), 2595-9

76. Goodlett, D. R., Bruce, J. E., Anderson, G. A., Rist, B., Pasa-Tolic, L., Fiehn, O.,

Smith, R. D., and Aebersold, R. (2000) Anal Chem 72(6), 1112-8

77. Conrads, T. P., Anderson, G. A., Veenstra, T. D., Pasa-Tolic, L., and Smith, R.

D. (2000) Anal Chem 72(14), 3349-54

22

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

Figure Legends

Figure 1. Protein identification of gel separated proteins by RP-µLC ESI-MS/MS. In

this procedure, the protein contained in an excised gel band is digested with trypsin and

the resulting peptides are loaded onto a RP-µLC column. The peptides are eluted from

the column and introduced directly into the mass spectrometer by ESI. The mass

spectrometer then selects specific peptides for CID, and detects the fragments produced

to give a tandem mass spectrum. The peptide sequence is determined by automatically

matching this observed mass spectrum to a theoretical mass spectrum contained in a

sequence database, and this peptide sequence then identifies the protein from which it

was derived.

Figure 2. Global, quantitative mass spectrometric analysis of protein expression. A. The

structure of the ICAT reagent. B. Mass spectrometric analysis using selective protein

labeling with the ICAT reagent and multidimensional chromatography. Equal amounts

of total protein are isolated from cells existing in two different biological states and

labeled with the d(0) or d(8) versions of the ICAT reagent. The proteins are mixed,

enzymatically digested, separated by multidimensional chromatography and analyzed by

MS. Relative quantification of protein expression between the two states is accomplished

by comparison of peak intensities of the isotopically different peptides, and identification

is accomplished by selecting these peptides for MS/MS and subsequent sequence

database searching with the generated CID spectra.

23

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from

Timothy J. Griffin and Ruedi AebersoldAdvances in proteome analysis by mass spectrometry

published online October 3, 2001J. Biol. Chem. 

  10.1074/jbc.R100014200Access the most updated version of this article at doi:

 Alerts:

  When a correction for this article is posted• 

When this article is cited• 

to choose from all of JBC's e-mail alertsClick here

by guest on May 20, 2018

http://ww

w.jbc.org/

Dow

nloaded from