quantitation in mass-spectrometry-based proteomics€¦ · quantitation in mass-spectrometry-based...

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Quantitation inMass-Spectrometry-BasedProteomicsWaltraud X. Schulze and Bjorn UsadelMax Planck Institute for Molecular Plant Physiology, 14476 Golm, Germany;email: [email protected], [email protected]

Annu. Rev. Plant Biol. 2010. 61:491–516

First published online as a Review in Advance onFebruary 3, 2010

The Annual Review of Plant Biology is online atplant.annualreviews.org

This article’s doi:10.1146/annurev-arplant-042809-112132

Copyright c© 2010 by Annual Reviews.All rights reserved

1543-5008/10/0602-0491$20.00

Key Words

label-free quantitation, stable isotope labeling, reciprocal labeling,statistical anaylsis, mass spectrometry, proteomics

AbstractMass-spectrometry-based proteomics, the large-scale analysis of pro-teins by mass spectrometry, has emerged as a new technology over thelast decade and become routine in many plant biology laboratories.While early work consisted merely of listing proteins identified in agiven organ or under different conditions of interest, there is a grow-ing need to apply comparative and quantitative proteomics strategiestoward gaining novel insights into functional aspects of plant proteinsand their dynamics. However, during the transition from qualitative toquantitative protein analysis, the potential and challenges will be tightlycoupled. Several strategies for differential proteomics that involve sta-ble isotopes or label-free comparisons and their statistical assessmentare possible, each having specific strengths and limitations. Further-more, incomplete proteome coverage and restricted dynamic range stillimpose the strongest limitations to data throughput and precise quanti-tative analysis. This review gives an overview of the current state of theart in differential proteomics and possible strategies in data processing.

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MS: massspectrometry, massspectrometer

Contents

INTRODUCTION . . . . . . . . . . . . . . . . . . 492STRATEGIES FOR DIFFERENTIAL

PROTEOMICS . . . . . . . . . . . . . . . . . . . 493Gel-Based Quantitation . . . . . . . . . . . . 493Mass-Spectrometry-Based

Quantitation . . . . . . . . . . . . . . . . . . . . 496LABEL-FREE TECHNIQUES . . . . . . 496

Protein-Based Methods: SpectrumCount and Derived Indices . . . . . . 496

Peptide-Based Methods:Ion Intensities and ProteinCorrelation Profiling . . . . . . . . . . . . 497

STABLE ISOTOPE LABELING . . . . . 499CHEMICAL LABELING . . . . . . . . . . . . 502ISOBARIC MASS TAGS . . . . . . . . . . . . . 502STANDARD PEPTIDES. . . . . . . . . . . . . 502CHALLENGES IN

DATA PROCESSING . . . . . . . . . . . . . 503From Spectra to

Quantitative Information . . . . . . . . 504Statistical Analysis . . . . . . . . . . . . . . . . . 507

TREATING CONTINUOUSDATA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507

TREATING DISCRETE DATA . . . . . . 507GENERAL ISSUES . . . . . . . . . . . . . . . . . . 508INTERPRETATION OF

THE RESULTS. . . . . . . . . . . . . . . . . . . 508PERSPECTIVES . . . . . . . . . . . . . . . . . . . . 509

INTRODUCTION

Proteomics is the science of large-scale analysisof proteins. In a general sense, it applies to anylarge-scale analysis of protein mixtures, oftenwithout a priori knowledge of the identity ofproteins in the samples. In the past decades,mass-spectrometry (MS)-based proteomics hasemerged as a potent technology that allows theanalysis and identification of proteins in highthroughput. With the completion of genomesequencing projects of a variety of organismsacross all kingdoms of life, the identificationof proteins based on their peptide fragmen-tation patterns in tandem-mass spectrometricexperiments has become an almost automated

task. The success of protein mass spectrometry(MS) has been boosted by the developmentof soft protein ionization methods, such aselectrospray ionization (ESI) or matrix-assistedlaser desorption ionization (MALDI). Thisachievement was acknowledged in 2002 withthe award of the Nobel Prize in chemistryto John Fenn and Koichi Tanaka. However,without the information derived from variousfull-genome sequencing projects, and withoutefficient algorithms for peptide sequencedetermination from fragmentation spectra(21, 91), proteomic experiments would be agreat deal more difficult today. Recent andongoing improvements of the mass analyzerand fragmentation technology (43, 66, 128)have further contributed to making mass-spectrometry-based proteomics a now widelyused technology.

Until about a decade ago, proteomicswas largely a qualitative discipline. Typicalproteomic experiments resulted in lists ofproteins identified in a given tissue or proteincomplex without any further information aboutabundance, distributions, or stoichiometry. Incontrast, quantitative strategies have becomewidely used for the analysis of gene expressionby microarrays or quantitative PCR, and inplants especially, the power of large-scalegenomics has produced novel insights intomany aspects of development and physiology.However, enzymatic reactions and signalingpathways ultimately depend on the activityof proteins. Protein amount is regulatedby protein synthesis and degradation, andthis may be independent of transcriptionalcontrol (92). In addition, posttranslationalmodifications, isoforms, and splice variants arenot captured by the mere analysis of transcriptabundances. Modern proteomic tools providethe technical framework for the analysis ofthe proteome complexity. Protein mixturestoday can routinely be characterized in termsof proteins present in the sample, but in orderto allow biological interpretation, quantitativeanalyses are necessary.

The field of quantitative mass-spectrometry-based proteomics is still under

492 Schulze · Usadel

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fast development; new and better instrumenta-tion is being developed almost on a yearly basis.Most of the early developments in applicationsof quantitative mass-spectrometry-based pro-teomics were driven by research on yeast and inmammalian cell lines (8, 9, 103). Quantitativeproteomic approaches have also helped in thecharacterization of protein complexes (2, 3,98) and in defining true interaction partnersto a given bait protein over backgroundproteins (8, 104). In plant physiology, though,mass-spectrometry-based proteomics is nolonger used only as a descriptive tool. Instead,well-designed quantitative proteomics havebeen applied to various aspects of organellebiology and growth regulation and signaling.Encouraging pioneer studies of specific sub-proteomes in plants have revealed candidateproteins that are phosphorylated specificallyunder different stress conditions (7, 73, 75) orduring a light–dark cycle (96). Protein abun-dance changes were monitored in responseto heat shock (86) or during leaf senescence(38), and protein turnover of photosyntheticproteins was monitored using pulse-chaselabeling in combination with protein massspectrometry (74). Organelle proteomes werecharacterized either by analysis of fractionsof separated proteins in a sucrose densitygradient (19, 20, 101), or by focused analysisof specific purified subproteomes, such aschloroplasts (53, 63, 89, 90, 96), or plasmamembranes and their microdomains (50, 71).Ultimately, quantitative proteomics methodsnow contribute to the framework of techniquesavailable to study regulatory processes in plantsin relation to the whole plant parameters, suchas growth or development (92, 94, 108).

STRATEGIES FORDIFFERENTIAL PROTEOMICS

Most experimental designs in quantitativeproteomics aim at the comparison of a stressedor disturbed status to an undisturbed referencesample. Over the last decade, several technolo-gies and workflows have been presented thatleave the biologist with an often confusing

choice of strategies that all have particularadvantages and disadvantages within theircontexts. However, the choice of quantitationmethod is less important than the practicalexperience with the method resulting in goodtechnical reproducibility (111). In addition,biological variation must be considered in asufficiently replicated experimental design, andthis in combination with cost considerationsmay well influence the choice of strategy.In proteomic experiments, sample amountis often the limiting factor since there is noamplification step like the polymerase chainreaction in microarray experiments. Thus,enrichment and purification of subproteomesoften are key steps before actual comparisonof protein abundances is carried out. Further,the difficulty of obtaining sufficient amountsof sample may also influence the choicefor specific quantitative strategies. Table 1summarizes the characteristic features of thedifferent quantitative methods discussed below.

Gel-Based Quantitation

Two-dimensional (2D) gels were initially con-sidered the most suitable method to visualizethe differences between protein samples de-rived from different conditions or tissues. Com-plex protein mixtures could be resolved effi-ciently, and detection of differences in bandor spot intensity seemed intuitive. Today, it ispossible to visualize over 10,000 spots corre-sponding to over 1000 proteins on single 2Dgels (29). However, in traditional 2D gel exper-iments, protein separation and differential anal-ysis are not coupled directly with identificationof the protein underlying a given spot. In manycases, individual spots may still consist of morethan one protein, but this can be recognizedonly if differential spots are actually excised andanalyzed by mass spectrometry (35). With thedevelopment of peptide sequencing technologyby mass spectrometry in the mid-1990s (123),separation and quantitative analysis by 2D gelscould be coupled with protein identification. Inthis combination, 2D-gel-based quantitation isstill attractive and has been applied in the past to

www.annualreviews.org • Differential Proteomics 493

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a variety of questions in plant biology, rangingfrom stress response analysis (97) to character-ization of cell types or organelles (53, 64, 89).

In quantitative studies, accurate repro-ducibility of 2D gels is often a limitation. Per-haps as a consequence, the major advancesin gel-based quantitation have occurred when

DIGE: difference ingel electrophoresis

using fluorescent dyes to label different proteinsamples that were then separated on the samegel (112). This so-called DIGE technology hasimproved quantitative accuracy of 2D gels sig-nificantly (Figure 1a). The DIGE approachhas been successfully applied to study phospho-rylation responses in plant plasma membrane

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Figure 1Overview of different strategies in quantitative proteomics. (a) Gel-based quantitative approaches. In classictwo-dimensional gel experiments, spots with differential intensities from different gels are analyzed by massspectrometry. The DIGE (difference in gel electrophoresis) approach allows separation of proteins from twodifferent samples on the same gel. (b) Mass-spectrometry-based approaches. Metabolic labeling allows veryearly mixing of samples. Chemical labeling introduces a label on either protein or peptide level, andquantification can be done on survey scans or on fragment scans. Synthetic standard peptides are spiked intothe sample at known concentrations. Label-free quantitation allows comparisons of multiple samplesanalyzed in parallel.


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LC-MS/MS: liquidchromatographycoupled tandem massspectrometry

Mass-to-charge ratio(m/z): two particleswith the same m/zmove in the same pathin a vacuum whensubjected to the sameelectric and magneticfields. Massspectrometers detectnot the real mass of anion but its m/z. Theunit of mass-to-chargeratio is the Thomson(Th)

Survey scan: massspectrometricdetection of all ionswithin a selectedmass-to-charge range.The survey scan is alsocalled a MS full scan

rsd: relative standarddeviation

Extracted ionchromatogram: theion intensity of a givenion integrated over thechromatographictimescale. Thus, thisvalue represents thepeak volume

Fragment ion scan:mass spectrometricdetection of all ionsthat were producedupon fragmentation ofa peptide ion. Thefragment ion scan isalso called an MS2scan or MS/MS scan

proteins upon brassinosteroid treatment (17)or to compare light and dark adapted pro-teomes of chloroplast thylakoid lumen (31).Recent advances in using the DIGE technol-ogy in combination with blue native gel elec-trophoresis (rather than isoelectric focusing) asa first-dimension separation offer new perspec-tives and applications, especially with regard tothe comparative and structural study of proteincomplexes or the assignment of complexes tosubcellular fractions (39).

Mass-Spectrometry-BasedQuantitation

Protein mass spectrometry using LC-MS/MSis not quantitative as such becasue of differentphysical and chemical properties of differenttryptic peptides. Differences in charge state,peptide length, amino acid composition, orposttranslational modifications result in greatdifferences in ion intensities for the peptides,even when they belong to the same protein.Thus, for accurate quantitation using ionintensities, comparisons between differentsamples can only be done based on the samepeptide mass-to-charge ratios (m/z) that wereacquired under the same general conditionsduring LC-MS/MS experiments. Thus, allmass-spectrometry-based quantitative meth-ods are necessarily relative comparisonsbetween two or more samples, and compara-tive quantitation is feasible only when carefulexperimental design and suitable data analysisstrategies are employed (42). A number ofsuch comparative strategies have becomeincreasingly popular and can be categorizedas either stable-isotope-labeling approaches orlabel-free approaches (Figure 1b).

Stable-isotope-labeling strategies that relyon survey scan quantitation typically providerelative standard deviations (rsd) below 10%(36, 82) (Table 1). Quantitation without sta-ble isotopes, but instead based on peak intensi-ties or extracted ion chromatograms in separateLC-MS/MS runs, can usually provide quantita-tive accuracies within 30% (3). In contrast, thequantitative precision of label-free approaches

based on spectral counting or derived indicescan be as low as 50% rsd (77).

LABEL-FREE TECHNIQUES

Differential proteomic studies employing label-free quantitation compare two or more samplesbased on the ion intensities of identical pep-tides or based on the number of acquired spectrafor each protein. Ideally, samples for label-freecomparisons are run consecutively on the sameLC-MS/MS setup to avoid variations in ion in-tensities due to differences in the system setup(column properties, temperatures), and therebyallow precise reproduction of retention times.

Label-free approaches are inexpensive; theycan be applied to any biological material; andthe proteome coverage of quantified proteinsis high because basically every protein that isidentified by one or more peptide spectra canbe quantified. In addition to these advantages,the complexity of the sample is not increased bythe mixing of different proteomes. Label-freemethods therefore usually have a high analyticaldepth and dynamic range, giving this method anadvantage when large, global protein changesbetween treatments are expected. However, es-pecially with spectrum count, the advantage ofhigh proteome coverage may come at the costof relatively poor precision (77).

Protein-Based Methods: SpectrumCount and Derived Indices

The protein-based methods allow relativequantitation of protein amounts within thesame sample as well as between samples, andthey are very fast and easy to perform. In ad-dition, basically unlimited numbers of samplescan be compared. The spectrum count methodsuse the number of peptide-identifying spectraassigned to each protein as a quantitative mea-sure (61). The rationale behind this quantita-tion method is that more abundant peptidesand proteins are sampled more often in frag-ment ion scans than are low abundance pep-tides or proteins. Obviously, the outcome ofspectrum counting depends on the settings of


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data-dependent acquisition on the mass spec-trometer. In particular, the linear range forquantitation and the number of proteins tobe quantified are influenced by different set-tings for dynamic exclusion (116); the opti-mal settings will depend on sample complexity.The most significant disadvantage of spectrumcounting is that it behaves very poorly with pro-teins of low abundance and few spectra. Theaccuracy of the spectrum count method, espe-cially for low abundance proteins, suffers fromthe fact that each spectrum is scored with thevalue “1” independently of its ion intensities. Toovercome this problem, an approach has beensuggested that uses the average of the total ioncount from all fragment spectra that identify aprotein as a quantitative measure. Thereby, thelinear dynamic range for quantitation can bevastly exceeded (4).

The empirical relationship between thenumber of spectra or peptides identified for agiven protein and overall protein abundance inthe sample has been used as a basis to calcu-late the absolute concentration of each proteinwithin the sample. The exponentially modifiedprotein abundance index (emPAI) is calculatedfrom the number of observed spectra for eachprotein divided by the number of possibly ob-servable peptides, a fraction that has been de-scribed as a protein abundance index (PAI) (95).PAI is then used as an exponent to the base 10,resulting in an exponentially modified value,the so-called emPAI index (46). A very simi-lar approach was taken to calculate the abso-lute protein expression (APEX) index (62). Theestimated protein concentrations calculated byemPAI indices have correlated very well withthe protein concentrations calculated from en-zymatic activities (92). The indices emPAI andAPEX are thus derived measures of absoluteprotein abundance in a given sample based onthe analytical features in mass spectrometricanalysis. Their predictive value is certainly atleast as good as quantitation based on standardprotein staining.

The APEX index has been used to gener-ate a protein abundance index of the Arabidop-sis proteome (5) and a protein abundance map

of chloroplast proteins (131). The emPAI in-dex has been applied to calculate protein abun-dances of metabolic enzymes and of ribosomalproteins in a day–night cycle (92). Spectrumcount has been used to study drought stress re-sponse in root nodules of Medicago (58) and,among other methods, in comparison of pro-tein abundance in mesophyll and bundle sheathchloroplasts (63, 64). In combination with highmass accuracy precursor alignment, spectrumcount helped to identify variant-specific pro-teome changes among potato varieties (41).

Peptide-Based Methods:Ion Intensities and ProteinCorrelation Profiling

Peptide-based approaches use averaged, nor-malized ion intensities of each identified pep-tide ion species as a basis for quantitation. Theheight or volume of a peak with a given m/z isa measure of the number of ions of that par-ticular mass detected within a given time inter-val. This process of determining the peak vol-ume is referred to as ion extraction and resultsin a so-called extracted ion chromatogram ofa given ion species. Such extracted ion chro-matograms can be produced for each m/z acrossall LC-MS/MS runs within an experiment, andthe resulting peak volumes can then be com-pared quantitatively.

Due to differences in ionization efficiencyof different peptide species, only the same ionspecies can be compared between different sam-ples. This fact significantly complicates thecomputational effort necessary to use total ionabundance as a comparative measure. One ma-jor problem in typical MS/MS experiments isthat the parent ion survey scans are interruptedby the fragment ion scan events (MS/MS),which results in discontinuous coverage of thepeptide ion peaks. Depending on the fragmen-tation duty cycle time of the instrument, thisresults in more or fewer data points acquiredacross the elution time of each ion peak. There-fore, for each instrument and for different sam-ple complexities, the right balance between ac-quisition of survey and fragment spectra has to


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be experimentally determined. While frequentfragment ion scans (MS2) are necessary to allowextensive peptide sequencing and identificationof as many peptides and proteins as possible incomplex mixtures, multiple sampling points ofthe chromatographic peak by survey scans (MS)are required for a robust quantitative reading ofion intensities. Better quantitative accuracy willinevitably come at the cost of poorer proteomecoverage, and vice versa.

Since ion intensities can be compared be-tween two samples only if the exact same ionspecies are being used, and since two differ-ent LC-MS/MS experiments of the same com-plex protein extract sample usually have anoverlap of approximately 60% on the peptidelevel, this method may result in very poorcoverage of common ions between two sam-ples. This problem is even more apparent ifmore than two samples are being compared.To overcome these drawbacks, a method calledprotein correlation profiling has been devel-oped that aligns the total ion chromatogramsof different samples (Figure 2a). Typically,ion species for which fragment spectra (andthus peptide sequence identification) have beenobtained are correlated based on their chro-matographic retention times (Figure 2b). Us-ing the information from the retention timecorrelation, peaks that were fragmented inonly one of the samples (Figure 2c) can beidentified in the survey scan spectra of theother samples, though no fragment spectrum(Figure 2d ) is available. Thus, based on re-tention time and accurate mass, extracted ionchromatograms from both samples can be usedfor quantitative comparisons (Figure 2e). Thismethod increases the number of proteins forquantification by up to 40%. The main dis-advantage of the protein correlation profil-ing method is that the computational processof ion chromatogram extraction, alignment ofdifferent chromatographic profiles, and inser-tion of respective “missing” ion chromatogramsis significantly more complicated and usuallyrequires a high degree of manual inspectionand verification compared to simple spectrumcounting.

With the development of modern high-precision mass spectrometers, the label-freequantitation has become a very appealing al-ternative as better mass accuracy increases thereliability of mapping peptides across samplesdue to the more narrow mass-to-charge win-dow that defines each peptide peak (41). How-ever, good reproducibility of the retention timevalues over different LC-MS/MS runs remainscrucial for precision in label-free quantitationusing peptide ion intensities.

A thorough evaluation of the different label-free peptide-based methods revealed that if atleast four spectra per protein were used forquantitation, sample-to-sample variation wasless than twofold and protein ratios were wellwithin 95% confidence limits (77). Removalof very abundant proteins from the sample in-creased the reproducibility and linearity (115).In general, comparisons of spectral count meth-ods to quantitation methods based on pep-tide ion intensity reveal that both methodsare well suitable to distinguish protein abun-dance changes of approximately twofold. Spec-tral counting was able to detect more proteinsthat undergo changes in protein abundance(greater coverage), while quantitation based onpeptide ion intensities resulted in higher accu-racy of the estimated protein ratios (77). Severalrecent studies have shown that both methodsof label-free quantitation are complementary(120), thus the choice between the two methodsmay be based on the experience of the experi-menting group (124).

Profiling methods based on ion intensitywere first applied to define the proteome ofthe human centrosome (3), then extended tocarry out whole-organelle protein profiles onmouse liver (24), and used recently to charac-terize mouse liver peroxisomes (122). Label-free peptide quantitation in combination withstatistical profiling methods made it possibleto define protein-protein interactions in pull-down experiments over background proteins(98). In plant biology, protein correlation pro-filing has been applied to study phosphorylationtime profiles in Arabidopsis seedlings in responseto sucrose treatment (73).


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Base peak chromatogram Sample 1

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Figure 2Protein correlation profiling. (a) Alignment of base peak chromatograms of two or more samples. (b) Pairwise correlation of retentiontimes of common peptide ions with MS/MS fragment spectra between two samples. For comparison of multiple samples, one sample isthe common base for pairwise comparisons. (c) Extracted ion chromatograms (XICs) from an ion with fragment spectrum (left) andbased on retention time correlation (right). The peak volumes are used for quantitation. (d ) Fragment spectrum of the peptide ion fromsample 1 used for peptide identification. (e) Survey scan ions from both samples. Abbreviation: Th, Thomson (i.e., the unit of mass-to-charge ratio).

STABLE ISOTOPE LABELING

Stable isotope labeling approaches are based onthe fact that a peptide labeled with stable iso-topes differs from the unlabeled peptide only interms of its mass but exhibits the same chemi-cal properties during chromatography. The la-bel can be introduced at various steps duringsample preparation. In metabolic labeling, the

label is introduced to the whole cell or organismthrough the growth medium, while in chemicallabeling it is added to proteins or tryptic pep-tides through a chemical reaction (Figure 1b).Synthetic labeled standard peptides are addedto the extract after tryptic digest. The limita-tion of protein quantification in complex sam-ples by stable-isotope-based methods mainlylies in signal interferences caused by co-eluting


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SILAC: stable isotopelabeling in cell cultures

components of similar mass. Therefore, themost efficient way to optimize the quantita-tive analysis is to decrease sample complexityby increasing chromatrographic gradient timesor through biochemical fractionation prior toLC-MS/MS analysis.

In proteomics, [15N]-labelling was first usedin bacteria to study protein phosphorylation(76). The high-throughput quantitative pro-tein analysis based on metabolic labeling wasestablished using stable-isotope-labeled essen-tial amino acids in mammalian cell cultures(SILAC) (81). This method has since been ap-plied to various aspects of mammalian signalingbiology, protein-protein interactions (8, 104,105), protein dynamics (9, 78), the effect ofmicro-RNA expression on global protein levels(106), and the interaction of small moleculeswith proteins (84). SILAC also works well inbacteria or specific yeast strains in which aux-otrophy for the labeled amino acid has been cre-ated (15, 33). In plants, SILAC has only yieldedlabel incorporation of approximately 70% (34),which is not satisfying for many global pro-teomics applications. The only organism of theplant kingdom that has been efficiently SILAClabeled using auxotrophic mutants is Chlamy-domonas (70).

However, as autotrophic organisms allplants can very easily be labeled metabol-ically through feeding of labeled inorganiccompounds in the form of [15N]-nitrogen-containing salts, as first demonstrated in NMRstudies (45). For proteomics applications, full[15N]-labeling in multicellular organisms wasfirst done in Drosophila and Caenorhabditis ele-gans by feeding the animals with [15N]-labeledyeast or bacteria (55). The labeling of plantcell cultures with [15N]-nitrogen for large-scaleproteomic analysis was demonstrated indepen-dently (22, 57), and the general data analysisworkflow has been established (85).

The process of quantification in stable-isotope methods is based on extracted ion chro-matograms of survey scans containing the pairof labeled (heavy) and unlabeled (light) pep-tide isoforms (Figure 3a,b). Since the physic-ochemical properties of the peptides do not

change due to the isotope label, heavy and lightforms usually co-elute off the chromatographicgradient. Typically, a peak is 10 to 30 secondswide (250 nL min−1, 75 μm ID, 10 cm C18column). Depending on the operating param-eters of the mass spectrometer and instrumenttype, one survey scan may be collected every 1to 5 seconds, allowing the sampling of severalMS full scan spectra across the time course ofthe eluting peak. Each of these MS spectra is asingle observation of the peptide pair, and aver-aging the ratios determined from several suchMS spectra gives the ratio and standard devia-tion for each peptide. There are two ways of cal-culating an abundance ratio between heavy andlight forms based on the respective extractedion chromatograms. In one method, the ratiobetween heavy and light forms is calculated ateach of the survey scan events across the ionpeak, and the individual ratios are then averagedto a peptide ratio (Figure 3a). In this case, eachmeasured ion will have an average ratio and astandard deviation. In the alternative method,the ratio is formed between the peak volumesacross the total extracted ion chromatogram forthe heavy and the light form of the peptide(Figure 3b). This method of calculation is lessaffected by slight shifts in chromatographic elu-tion between heavy and light peaks.

Due to a large number of incorporated la-beled atoms in the peptide sequence, more iso-baric amino acid forms are generated, lead-ing to higher ambiguities in the sequencematching process (72). Furthermore, isotopeclusters of [15N]-labeled peptides are broader,and this is especially true for longer pep-tides. Since most quantitation algorithms usethe first isotope for quantitation, the accu-racy with which the peptides are quantifiedalso depends on peptide length and amino acidcomposition (30). However, approaches thattake into account differences in isotope en-velopes of heavy and light peptide isoformshave been described (118). In general, the dy-namic range across which accurate quantita-tion is possible using [15N]-labeling covers oneto two orders of magnitude, depending on in-strumentation (114). This means that changes


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LVSWYDNEWGYSSR

Extracted ion chromatogramaAverage of ratios: 1.22

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Figure 3Metabolic labeling using [15N]. (a) Extracted ion chromatogram (XIC) of co-eluting heavy and light peptide forms. Calculation of theaverage peptide [15N]/[14N] ratio from the average of ratios of different survey scans across the eluting peak. (b) Extracted ionchromatogram of co-eluting heavy and light peptide forms. Calculation of the average peptide [15N]/[14N] ratio from the ratio of peakvolumes. (c) MS survey scan at the peak apex, including light and heavy form of the peptide ion. Mass difference between heavy andlight form depends on number of nitrogen atoms in the peptide sequence. (d ). Fragment spectra of the heavy and light form of thepeptide. Note that in all cases both forms are fragmented. Although rather inexpensive and efficient to implement in plants, the labelingstrategy with [15N] has several drawbacks: The number of atoms incorporated in each tryptic peptide depends on peptide length andamino acid composition, which makes the matching process more difficult. Usually, only those pairs of labeled and unlabeled isoformscan be used for quantitation, for which a peptide sequence was assigned at least to one of the pairs (c). The nonfragmented heavy or lightpartner must then be found according to the predicted mass difference, which depends on the number of nitrogen atoms and thus thepeptide sequence of the fragmented peak (22). In only a few cases are both heavy and light forms identified by tandem mass spectra (d ).

smaller or greater than tenfold are often beyondthe linear range and will be over- or underes-timated. A thorough assessment of the use offull versus partial labeling revealed that in gen-eral both strategies are comparable with regard

to dynamic range and accuracy. While partiallabeling is more challenging with respect to au-tomated identification of labeled and unlabeledpeptide pairs, it allowed quantification of morepeptides across the whole dynamic range (44).


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ICAT: istotope-codedaffinity tag

LC: liquidchromatography

Fragment ion: an ionthat occurs afterfragmentation of apeptide ion. Fragmentions are detectedduring the tandemmass spectometry scan(MS/MS or MS2)

Precursor ion: theion of a peptide thatwas selected forfragmentation in themass spectrometer.Precursor ions aredetected during thesurvey scan (MS)

AQUA: absolutequantification usingsynthetic standardpeptides

CHEMICAL LABELING

Chemical labeling essentially works the sameway as described for the metabolic [15N]-labeling, except that the label is introduced toisolated proteins or peptides by a chemical re-action, e.g., with sulfhydryl groups or aminegroups, or through acetylation or esterificationof amino acid residues (83). The isotope labelcan also be introduced to the peptide chain dur-ing the enzymatic reaction of the tryptic diges-tion by the addition of H2[18O] to the peptidecleavage sites (126). Quantitation is based onthe full scans, and in contrast to [15N] labeling,the mass shift between heavy and light peptidesis constant. However, it should be noted thatfor unambiguous quantitation, the mass differ-ence between heavy and light form should be atleast 4 Da in order to clearly distinguish the iso-topomer clusters of the heavy and light formsof the peptide. Since the isotopomer cluster be-comes larger with increasing mass of the pep-tide, small labels, e.g., [18O] introduced by atryptic digest in H2[18O], become limiting forlarger peptides.

A commonly used chemical isotope label isthe isotope-coded affinity tag (ICAT), whichbinds to sulfhydryl groups of cysteine residues(36). It is a useful tool to study oxidation or re-duction status of proteins, but since cysteine isnot a very abundant amino acid, the number ofpeptides that can be tagged and quantified bysulfhydryl labels is rather low.

ISOBARIC MASS TAGS

Isobaric mass tagging (110) differs from thestrategy described above in that addition of themass tags initially produces labeled peptides ofthe same total mass that co-elute in liquid chro-matography. Only upon peptide fragmentationcan the different mass tags be distinguished.As each tag adds the same total mass to a givenpeptide, each peptide species produces onlya single peak during liquid chromatography,even when two or more samples are mixed(Figure 4a). Thus, there also will be onlyone peak in the survey MS scan, and only a

single m/z will be isolated for fragmentation(Figure 4b). The different mass tags separateupon fragmentation (Figure 4c). The frag-ments are in the low mass range, which usuallyis not covered by typical peptide fragment ions.The intensity ratio of the different reporter ionsis used as a quantitative readout (Figure 4d ).

Quantitation for isobaric mass tagging isbased on the fragment spectra rather than onthe survey scans. Thus, quantitative accuracydepends on the isolation width of precursor ionsfor fragmentation, since all ions isolated in thatwindow will contribute to fragments in the re-porter ion mass ranges. It is also significant thatoften in fragment scans only a single fragmentspectrum per peptide is available, whereas inquantitation based on survey scans, usually sev-eral data points across the eluting peptide peakare sampled.

iTRAQ (99) and TMT (110) are commer-cially available isobaric mass tags that are intro-duced to the proteins of interest at the level oftryptic peptides. iTRAQ has been widely usedin plant proteomics to study phosphoproteomicresponses of elicitor treatment by comparingseveral time points post-treatment (48, 75).Protein degradation in chloroplasts (100) anddevelopmentally induced changes in chloro-plast proteomes (53) were studied. Chloroplastproteomes in different cell types were com-pared in maize (64) and Brassica (129). In anelegant study, organelle proteomes (19) and theproteomes of different endomembranes (101)were defined by differential mass tagging ofsequential fractions across continuous sucrosegradients and assignment of protein profiles toprofiles of known marker proteins.

STANDARD PEPTIDES

The use of stable-isotope-labeled standard pep-tides was first described in 1983 (18). However,only recently has increasing analytical through-put on modern tandem mass spectra madepossible the large-scale use of synthetic isotope-labeled peptides as a standard for absolutequantification (AQUA) (26). In combinationwith multiple selected reaction monitoring


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(SRM) on a triplequad mass spectrometer, suchtargeted analyses of specific proteins across awide range of samples are very efficient. Thecombination of information on retention time,peptide mass, and fragment ion mass giveshigh specificity to the particular target peptide,and due to very low noise levels in the SRMspectra, the linear range for quantitation isextended up to five orders of magnitude (52).

If known concentrations of the labeled stan-dard peptide are added to the sample, the con-centration of the native peptide in the samplecan be calculated. However, the amount of pro-tein in an experiment determined by AQUAmay not reflect the true expression levels of thisprotein in the tissue, because sample prepara-tion steps may lead to losses or enrichments thatare not addressed by the AQUA technique.

In plant proteomics, the standard peptideshave been used to monitor abundance changesof different isoforms of sucrose phosphate syn-thase in different tissues of Arabidopsis (59)and in Medicago root nodules during droughtstress (119). Target sites for phosphorylationin isoforms of trehalose phosphate synthasewere studied using standard peptides in in vitrokinase assays (28).

CHALLENGES INDATA PROCESSING

No matter the choice of quantitative method,quantitative proteomic data are typically verycomplex and often of variable quality. The main

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−→Figure 4Isobaric mass tags. (a) Survey scan of the parentpeptide ion. (b) Extracted ion chromatogram (XIC)of the precursor peptide ion that combines alldifferent tagged forms in one single mass.(c) Fragment spectrum of the parent peptide ion thatallows sequence identification and that producescharacteristic reporter ions for quantification in thelow mass region ( gray field ). (d ) Close-up of the lowmass region of the fragment spectrum. Intensities ofthe reporter ion of different mass are indicative ofthe relative abundance of the peptide in the differentsamples.

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Proteins in sample Identified

10–11 10–10 10–9 10–8

QuantifiedProtein concentration(mol/gFW)

Figure 5Proteome coverage for quantitation. Of all the proteins in the sample, only asubset is being identified in LC-MS/MS (liquid-chromatography coupledtandem mass spectrometry) experiments. Out of the identified proteins,another subset is suitable for quantitation. Usually, the higher abundanceproteins are covered by identification and quantification.

challenge stems from incomplete data, sinceeven today’s most advanced mass spectrome-ters cannot sample and fragment every peptideion present in complex samples (Figure 5). Asa consequence, only a subset of peptides andproteins present in a sample can be identified(1). Furthermore, due to several quality issues(see below), the fraction of identified peptidesand proteins that can actually be used for quan-titation is even smaller (Figure 5). Therefore,careful experimental design involving steps ofprotein separation, enrichment, and purifica-tion is essential for successful interpretation ofproteomic datasets.

From Spectra toQuantitative Information

The workflow in quantitative approaches basedon mass spectrometry requires extraction ofquantitative information either from surveyscan or fragment spectra, as well as qualita-tive information for peptide identification fromfragment spectra. Most important, manual val-idation of peptide identity and quantitation isrequired. During manual validation, it is pos-sible to review sequence assignment to spectraand to assess the quality of the quantitative datawith regard to signal-to-noise level, presenceof interfering peaks, or isotope label incorpora-tion. In many cases, the identification processand the quantitation process are carried out in-dependently and are linked later at the level ofindividual spectra.

While advanced algorithms for proteinidentification have been available for quite sometime [Sequest (21), Mascot (91), X!Tandem(14), Omssa (25), InsPect (109)], the develop-ment of robust workflows and algorithms toextract quantitative information from multi-dimensional proteomics experiments based onmass spectrometry has just begun (Table 2).Generic formats such as mzData, mzXML, orpepXML allow independent use of softwarealgorithms to manipulate data without compat-ibility constraints. A thorough review of dif-ferent freely available software for analysis ofmass spectrometry data can be found elsewhere(68).

Intensity ratios of good-quality spectra areusually averaged to yield abundance ratiosfor a peptide, and peptide abundance ratiosare averaged to obtain protein abundance ra-tios. The assignment of peptides to individualproteins requires special care, since many pep-tide sequences can match more than one pro-tein. For accurate quantitation, it is thereforeimportant to consider only those peptides inthe quantitation that are unique to a particularprotein, so-called proteotypic peptides. This isimportant since different protein isoforms canbe differentially regulated, and this can resultin peptide ratios of conserved peptides deviat-ing from peptide ratios of proteotypic peptides(83).

At the stage of experimental planning, aswell as during extraction of primary data, thefollowing points must be considered in relationto the nature of the biological question askedand the quantitation method used.

(a) In general, the quantitative accuracyof peptide ratios is affected by severalfactors. On the technical side, instrumentresolution, sensitivity, and scan speed,as well as liquid chromatography (peakwidth), have significant influence on thequality of the quantitation. The use ofhigh resolution and high mass accuracyinstruments certainly will increase theconfidence in protein identification,and it will also increase quantitative


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Table 2 Quantitation software that is free to use but may require specific input formats

Software Website Features Format requirements ReferenceASAP Ratio http://tools.proteomecenter.org/wiki/

index.php?title=Software:ASAPRatio

15N labeling, ICAT,label-free; possibility toself-define mass tags

mzXML, pepXML,DTAselect

60

AYUMS www.csml.org/ayums/ SILAC Mascot output, convertedfile from Waters

102

Census http://fields.scripps.edu/census/index.php 15N labeling, ICAT,ITRAQ, label-free;possibility to self-definemass tags

mzXML, pepXML,DTAselect

87

i-Tracker www.cranfield.ac.uk/health/researchareas/bioinformatics/page6801.jsp

iTRAQ peak list files (∗.dta; ∗.mgf) 107

jTraqX http://sourceforge.net/projects/protms/ iTRAQ Mascot output 69MaxQUANT www.maxquant.org SILAC Mascot output, instrument

raw files (Thermo)12, 13

MRMer http://proteomics.fhcrc.org/CPL/MRMer.html

mSRM mzXML 65

MSQuant http://msquant.sourceforge.net 15N labeling, ICAT,label-free; possibility toself-define mass tags

Mascot output in htmformat, instrument rawfiles (Thermo, ABI,Waters)

67

XPRESS http://tools.proteomecenter.org/wiki/index.php?title=Software:XPRESS

ICAT, 15N labeling mzXML, pepXML,DTAselect

37

confidence by facilitating more nar-row isolation widths and less peakinterferences on full scans (79, 80, 130).

(b) Quantitative accuracy at the peptidelevel depends on the number of datapoints available across the eluting peak.More mass spectrometry spectra provideincreased confidence in the quantitativevalues obtained. This applies to allquantitation methods based on peptideion intensities.

(c) Ion intensities greatly influence thequantitative accuracy of single peptides(Figure 6a). Peptides with high ion in-tensities are more accurately quantified,while peptides with low ion intensitiesshow a much greater variation of ratios.Although the majority of ratios fromall quantified peptides is within theexpected range in a sample 1:1 mixture,the outliers in a real biological experi-ment are usually considered to be the

interesting candidates and must thereforebe carefully validated.

(d ) Further problems arise from overlap ofunrelated signals with the isotope clustersof either member of stable-isotope pep-tide pairs, or with peaks to be comparedin label-free protein correlation profiling.This can lead to the contribution of theunrelated peak to the peptide ratio. Thisproblem is less apparent if instrumentswith high resolution can be used for quan-titation of complex samples.

(e) Isotopic envelopes of the unlabeled andlabeled peptide forms may overlap de-pending on the size of the peptide. Thisproblem depends on the mass differenceof the incorporated stable-isotope labelused. Larger peptides tend to have greateroverlap, and smaller mass differences be-tween heavy and light labels tend to creategreater overlap. Label-free quantitationis not affected.


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a b8

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Figure 6Quantitative accuracy in peptide ratios and protein ratios in a 1:1 mixture of labeled and unlabeled proteins. (a) Magnitude versusamplitude plot of log2 values of ratios versus log2 values of average ion intensities. (b) Protein ratios depend on the number of peptidescontributing to the average ratio. Single peptide proteins require careful interpretation.

( f ) Chromatographic separation of the heavyand light peptide pairs in stable-isotope-labeling approaches can lead to distinctionization conditions for each member ofthe peptide pair, meaning that ion inten-sities cannot be compared from the samesurvey scan spectrum. Rather, peak ar-eas must be determined for each part-ner separately, and the ratio of thesesummed peak areas of extracted ionchromatograms must be calculated. Iso-baric mass tags, for which quantita-tion is done on the fragment ion scan,are not affected by chromatographicseparation.

( g) Using spectrum count data can lead tozero counts of a given protein in one sam-ple, but it might be detected in anothersample. This makes calculation of a foldchange impossible and leads to datasetswith missing values. Similarly, in stable-isotope labeling, if only one partner of thepeptide pair is detected (i.e., only the la-beled form or only the unlabeled form),no ratio can be calculated and the actualabundance level of the single partner can-not be accurately determined.

( h) The accuracy of the protein ratio is pri-marily dependent on good quantitationat the peptide level but is also influencedby the number of quantified peptides perprotein and the number of proteotypicpeptides for each protein. Protein ra-tios calculated from more than one con-tributing peptide are more accurate thanprotein ratios based on the presence ofonly one peptide (Figure 6b). Althoughthe majority of proteins are not affectedby this problem, the proteins quantifiedbased on a single peptide must be val-idated especially thoroughly, as chancesof incorrect quantitation are considerablyhigh.

To overcome some of these problems,especially in the case of stable-isotope la-beling, reciprocal experimental designs havebeen proposed that distinugish treatment ef-fects from both labeling effects and biologi-cal variation. Reciprocal experiments involv-ing metabolic [15N]-labeling were first used toidentify differentially regulated candidate pro-teins, prior to sequence assignment, throughdatabase search (117). In addition, a detailedstatistical procedure describing identification of


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treatment-responsive proteins versus biologicalvariance of two proteomes has been developed(51). Reciprocal design of [15N]-labeling exper-iments has been implemented in phosphopro-teomic studies of elicitor treatment (7), in defi-nition of plant sterol-rich domains (50), and instudies of proteome changes in leaf senescence(38).

Statistical Analysis

Experiments involving quantitative proteomicstypically aim at either the analysis of changes inprotein abundance between a set of treated sam-ples compared to a control condition or at thedefinition of protein complexes or specific sub-proteomes compared to background proteins.The aim of the data analysis procedures is, inmost cases, the definition of “deviating” pro-teins that ultimately are considered as “respon-sive candidates” in the biological context underinvestigation. When protein complexes or or-ganelle fractions are characterized, large num-bers of proteins with similar quantitative behav-ior are considered as members of the same com-plex or subproteome compared to other pro-teins with different quantitative behavior (3, 19,20, 24).

The first step in primary data processing isusually normalization, which is a very criticalstep in quantitative experiments. It correctsfor technical effects, such as sample mixingerrors, incomplete isotope incorporation, ordifferences in ionization between independentLC-MS/MS experiments, and thus influencesthe result. Total ion counts, total numberof spectra, or the average ratios of the mostabundant proteins are often used as basis fornormalization, relying on the assumption thatthe majority of proteins will remain unchangedand can thus be used for normalization. Ideally,in stable-isotope-labeling strategies, a controlmixture of labeled and unlabeled untreatedsamples is used to determine the biologicalvariation of ratios and mixing errors (51).Log-transformation of ratios is a common steptaken to harmonize the variances of ratios. Thelog transformed data are subsequently used in

exploratory plots showing the average log abun-dance on the x-axis and the log fold changebetween conditions on the y-axis (Figure 6a).Using such plots, linear and nonlinear bi-ases in the data can be identified, and thusnormalization procedures can be benchmarked.

TREATING CONTINUOUS DATA

Relatively simple algorithms, e.g., scaling theindividual log intensity values using the globalmedian value or the average obtained froma subset of proteins (55), sometimes provideenough normalization. Other potentially morepowerful methods, such as quantile normaliza-tion (10, 40), variance stabilization (54), or a“spectral index” combining different featuresof each data point, have been benchmarked forlabel-free proteomics (56, 32).

Alternatively, data can be normalized by ap-plying a locally weighted scatter plot smoothingprocedure (data plotted as in Figure 6a; 125),thus efficiently removing biases that are depen-dent on measurement values like ion intensities.However, systematic bias coming from anal-ysis order (56) or the data itself (49) shouldbe minimized by the experimental design(42).

When the data sets are sufficiently compa-rable after normalization, the next step is to de-tect changes between the different conditions.The data sets can be analyzed using statisticaltests such as the student’s t-test or the Wilcoxonrank sum test (7). Tests taking the experimen-tal confidence about the quantitation into ac-count should provide more statistical power forsmall sample sizes (51). In cases where thereare more than two groups to compare, it isusually preferable to use ANOVA type analy-ses, many of which can be performed using thefreely available R software.

TREATING DISCRETE DATA

Spectral count data is reminiscent of data aris-ing from the serial analysis of gene expres-sion (SAGE), and thus statistical methods andconcepts developed for SAGE can be used.


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However, proteomics-specific problems have tobe accounted for, e.g. that the number of ob-servable peptides is not the same for all pro-teins or that not all peptides may be detectedwith the same confidence. In the case of con-tinuous data, such factors can be accounted forby weighting the influence of peptide ions bytheir confidence (12, 60).

Methods that allow the direct assessmentof count data and have been used in the pro-teomics field include the goodness-of-fit (G-test), Fisher’s exact test, and the AC test, whichall performed quite similarly (127). The G-testwas extended to cover more than two condi-tions (127). Because these tests inherently takeinto account the number of spectra acquired intotal per run, no normalization is required forthis parameter.

Usually, extending beyond these count-based statistical approaches requires normal-ization that tries specifically to minimize thetechnical effects of spectral count data. Meth-ods that take into account the detection confi-dence, protein length, or the number of observ-able peptides (e.g., the emPAI and APEX valuesdiscussed above) are suitable. Using these de-rived indices yields values that, when contrastedbetween experiments, behave quite like the datashown in Figure 6a. The standard deviation isdependent on the protein abundance, and thisbehavior can be taken into account explicitly forstatistical analysis when using R (88).

Alternatively, more sophisticated modelsthat consider potentially biasing factors suchas protein length and general count abundancecan be applied (11).

GENERAL ISSUES

To date, the biggest challenge in comparativeproteomics is to account for missing values dueto incomplete proteome coverage in peptidefragmentation. The mostly random sequencingof peptides by the mass spectrometer resultsin not every peptide being fragmented in ev-ery sample. Especially low abundance peptidesand peptides with low ionization efficiency willmost likely be fragmented in a few out of several

samples compared, resulting in missing quanti-tative values. However, statistical methods of-ten require complete datasets. A common work-around is to estimate missing values, but thatinfluences statistical evaluation. Eliminating in-complete datasets from the analysis stronglyreduces the number of proteins that can bestatistically assessed. Improving peptide sepa-ration and fractionation and optimizing chro-matography influence the quantitative coverage(23).

Finally, no matter how the data is generated,modern proteomics techniques generate largeamounts of data from just a single experiment,and thus multiple statistical tests must be per-formed. This necessitates correcting p-valuesfor multiple testing using family-wise error ratemethods or false discovery rate approaches.The latter type of correction method is oftenpreferable when dealing with proteomics data.

INTERPRETATION OFTHE RESULTS

When multiple measurements have been ob-tained from biological samples, insights can begained by subjecting the data to more spe-cialized analysis and visualization procedures.Clustering the data by treatment often revealsglobal trends or may help in characterizing mu-tants. A plethora of clustering algorithms isavailable; however, either hierarchical (5, 42) ork-means clustering (98) is often used, perhapsbecause many software tools support these al-gorithms. Proteins can also be grouped by clus-tering according to their behavior in multipleexperiments or in a time course. Often, eitherthe groups of proteins in a cluster or all differ-entially expressed proteins are categorized (5,58, 71) and statistically assessed for the enrich-ment of biological categories. Many tools areavailable to the plant community for these anal-yses, including the GO slim categorization byTAIR; the Classification SuperViewer (93) forArabidopsis; or PageMan (113), which is avail-able for many plant species.

Commonly used tools for projecting datainto a 2D space include principal component


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analysis (47), which tries to preserve the max-imal variance in a low-dimensional space,and independent component analysis (58),which attempts to identify independent com-ponents in the dataset. The projected datafrequently provides visual separation of condi-tions such as stressed and unstressed plants, mu-tant genotypes, or different treatments (121).In cases where multiple replicates from severaldifferent plant samples are available, proteinsspecific for a certain condition/tissue can beidentified by inspection of the data or by us-ing machine learning (5). These methods helpin assessing new protein functions and aid intheir classification.

PERSPECTIVES

Although proteomics has been able for sometime to obtain a relatively complete view ofquantitative changes in subproteomes such asorganelles, the proteomics datasets are usu-ally much smaller than microarray datasets andtend to be biased against lower abundanceproteins. Global abundance measurements inSaccharomyces cerevisiae have revealed a bell-shaped distribution of proteins spanning ap-proximately six orders of magnitude in abun-dance (27), while only approximately three tofour orders of magnitude can be covered bymodern LC-MS/MS methods in complex sam-ples (16). Most notably, the robust and re-peated identification of low abundance proteins

across replicates is still far from being rou-tine. The major limitation in quantitative pro-teomics thus lies in the incomplete proteomecoverage between different samples, with theresult that many experiments result in datasetswith missing values.

These limitations pose significant chal-lenges for the researcher, as only with usefulproteome fractionation and intelligent strate-gies for enrichment of protein targets can thecoverage of the relevant proteome be maxi-mized. On the technical side, improvement ofmass spectrometer scan speed and sensitivity, aswell as new developments in chromatographicseparation, will likely contribute to deeper pro-teome coverage in future.

Despite these limitations, differential pro-teomics techniques are used increasinglythroughout plant biology. Currently, moststudies employ rather straightforward pair-wise comparisons of tissue types or treatments,but more complex experimental designs willemerge. The challenge in the large-scale,quantitative proteomics experiments will liein the application of data-mining strategiesto plant biology. Well-designed experimentsand focused hypotheses, in combination withhigh-quality mass spectrometry, are likely tocontribute significantly to our understand-ing of protein function in plant growth anddevelopment—both at the level of global pro-tein profiling and in-depth, targeted proteinanalysis.

SUMMARY POINTS

1. Comparative proteomic approaches involving stable-isotope-labeled quantitation havethe potential for the highest precision and lowest relative standard deviations. However,proteome coverage may be compromised due to higher sample complexity.

2. Label-free quantitative strategies allow the comparison of a large number of samples orconditions at rather low cost. This strategy is of particular interest for expected large dif-ferences, since quantitative precision is lower compared with stable-isotope approaches.

3. Quantitation based on full scan techniques allows precise statistics across multiple scansof the chromatographic peak of each peptide ion.

4. Quantitation based on fragment ions usually has low noise levels. Often, however, onlyone scan per peptide is available.


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5. Quantitation of a protein gains precision with the number of peptides contributing tothe quantitation. Each protein quantitation based on a single peptide requires thoroughverification.

6. Statistical treatment of the data is necessary and, despite the complexity of the data, manytools are available for data normalization and detection of differential expression.

FUTURE ISSUES

1. A need exists for improvement of proteome coverage due to faster instrumentation androutine implementation of peptide and protein fractionation techniques during samplepreparation. We will need to formalize quality standards for quantitative analysis withinthe proteomic community.

2. Good experimental design around specific biological hypotheses will be key to our func-tional understanding of protein function in regulatory processes of plant growth anddevelopment.

3. Further development of statistical methods and data-mining workflows in combinationwith modeling approaches are necessary for better biological interpretation of large-scaleproteomics datasets.

DISCLOSURE STATEMENT

The authors are not aware of any affiliations, memberships, funding, or financial holdings thatmight be perceived as affecting the objectivity of this review.

ACKNOWLEDGMENTS

Special thank goes to Annemarie Matthes, Borjana Arsova, Wolfgang Engelsberger, Lydia Brown,and Tony Bolger for their critical comments on the manuscript.

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Annual Review ofPlant Biology

Volume 61, 2010Contents

A Wandering Pathway in Plant Biology: From Wildflowers toPhototropins to Bacterial VirulenceWinslow R. Briggs � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 1

Structure and Function of Plant PhotoreceptorsAndreas Moglich, Xiaojing Yang, Rebecca A. Ayers, and Keith Moffat � � � � � � � � � � � � � � � � � � � � �21

Auxin Biosynthesis and Its Role in Plant DevelopmentYunde Zhao � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �49

Computational Morphodynamics: A Modeling Framework toUnderstand Plant GrowthVijay Chickarmane, Adrienne H.K. Roeder, Paul T. Tarr, Alexandre Cunha,

Cory Tobin, and Elliot M. Meyerowitz � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �65

Female Gametophyte Development in Flowering PlantsWei-Cai Yang, Dong-Qiao Shi, and Yan-Hong Chen � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �89

Doomed Lovers: Mechanisms of Isolation and Incompatibility in PlantsKirsten Bomblies � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 109

Chloroplast RNA MetabolismDavid B. Stern, Michel Goldschmidt-Clermont, and Maureen R. Hanson � � � � � � � � � � � � � � 125

Protein Transport into ChloroplastsHsou-min Li and Chi-Chou Chiu � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 157

The Regulation of Gene Expression Required for C4 PhotosynthesisJulian M. Hibberd and Sarah Covshoff � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 181

Starch: Its Metabolism, Evolution, and Biotechnological Modificationin PlantsSamuel C. Zeeman, Jens Kossmann, and Alison M. Smith � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 209

Improving Photosynthetic Efficiency for Greater YieldXin-Guang Zhu, Stephen P. Long, and Donald R. Ort � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 235

HemicellulosesHenrik Vibe Scheller and Peter Ulvskov � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 263

Diversification of P450 Genes During Land Plant EvolutionMasaharu Mizutani and Daisaku Ohta � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 291

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AR410-FM ARI 6 April 2010 15:25

Evolution in Action: Plants Resistant to HerbicidesStephen B. Powles and Qin Yu � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 317

Insights from the Comparison of Plant Genome SequencesAndrew H. Paterson, Michael Freeling, Haibao Tang, and Xiyin Wang � � � � � � � � � � � � � � � � 349

High-Throughput Characterization of Plant Gene Functions by UsingGain-of-Function TechnologyYouichi Kondou, Mieko Higuchi, and Minami Matsui � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 373

Histone Methylation in Higher PlantsChunyan Liu, Falong Lu, Xia Cui, and Xiaofeng Cao � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 395

Genetic and Molecular Basis of Rice YieldYongzhong Xing and Qifa Zhang � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 421

Genetic Engineering for Modern Agriculture: Challenges andPerspectivesRon Mittler and Eduardo Blumwald � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 443

Metabolomics for Functional Genomics, Systems Biology, andBiotechnologyKazuki Saito and Fumio Matsuda � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 463

Quantitation in Mass-Spectrometry-Based ProteomicsWaltraud X. Schulze and Bjorn Usadel � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 491

Metal Hyperaccumulation in PlantsUte Kramer � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 517

Arsenic as a Food Chain Contaminant: Mechanisms of Plant Uptakeand Metabolism and Mitigation StrategiesFang-Jie Zhao, Steve P. McGrath, and Andrew A. Meharg � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 535

Guard Cell Signal Transduction Network: Advances in UnderstandingAbscisic Acid, CO2, and Ca2+ SignalingTae-Houn Kim, Maik Bohmer, Honghong Hu, Noriyuki Nishimura,

and Julian I. Schroeder � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 561

The Language of Calcium SignalingAntony N. Dodd, Jorg Kudla, and Dale Sanders � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 593

Mitogen-Activated Protein Kinase Signaling in PlantsMaria Cristina Suarez Rodriguez, Morten Petersen, and John Mundy � � � � � � � � � � � � � � � � � 621

Abscisic Acid: Emergence of a Core Signaling NetworkSean R. Cutler, Pedro L. Rodriguez, Ruth R. Finkelstein, and Suzanne R. Abrams � � � � 651

Brassinosteroid Signal Transduction from Receptor Kinases toTranscription FactorsTae-Wuk Kim and Zhi-Yong Wang � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 681

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Directional Gravity Sensing in GravitropismMiyo Terao Morita � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 705

Indexes

Cumulative Index of Contributing Authors, Volumes 51–61 � � � � � � � � � � � � � � � � � � � � � � � � � � � 721

Cumulative Index of Chapter Titles, Volumes 51–61 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 726

Errata

An online log of corrections to Annual Review of Plant Biology articles may be found athttp://plant.annualreviews.org

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