Quantitative mass spectrometry in proteomics: a critical ... · identically during chromatographic...

REVIEW

Quantitative mass spectrometry in proteomics:a critical review

Marcus Bantscheff & Markus Schirle &

Gavain Sweetman & Jens Rick & Bernhard Kuster

Received: 30 April 2007 /Revised: 25 June 2007 /Accepted: 29 June 2007 /Published online: 1 August 2007# Springer-Verlag 2007

Abstract The quantification of differences between two ormore physiological states of a biological system is amongthe most important but also most challenging technicaltasks in proteomics. In addition to the classical methods ofdifferential protein gel or blot staining by dyes andfluorophores, mass-spectrometry-based quantificationmethods have gained increasing popularity over the pastfive years. Most of these methods employ differential stableisotope labeling to create a specific mass tag that can berecognized by a mass spectrometer and at the same timeprovide the basis for quantification. These mass tags can beintroduced into proteins or peptides (i) metabolically, (ii) bychemical means, (iii) enzymatically, or (iv) provided byspiked synthetic peptide standards. In contrast, label-freequantification approaches aim to correlate the mass spec-trometric signal of intact proteolytic peptides or the numberof peptide sequencing events with the relative or absoluteprotein quantity directly. In this review, we criticallyexamine the more commonly used quantitative massspectrometry methods for their individual merits and discusschallenges in arriving at meaningful interpretations ofquantitative proteomic data.

Keywords Quantitative proteomics . Mass spectrometry .

Stable isotope labeling

Introduction

There is a clear trend in the life sciences towards the studyof biological entities at the system level. This requiresanalytical tools that can identify the component parts of thesystem and measure their responses to a changing environ-ment. Towards this end, a multitude of transcriptomic,proteomic, and metabolomic profiling technologies havebeen developed, and proteomics in particular is continuingto evolve rapidly. Still, out of the many thousand proteomicstudies published to date, only a small minority hasattempted to provide a comprehensive quantitative descrip-tion of the biological system under investigation. Despitethe phenomenal impact of mass spectrometry and peptideseparation techniques on proteomics, the identification andquantification of all of the proteins in a biological system isstill an unmet technical challenge (Fig. 1). While forunicellular organisms proteomic coverage of the genomehas been occasionally achieved beyond 50%, coverage forhigher organisms rarely exceeds 10%. For protein quanti-fication, these figures are significantly smaller due to thefact that the data quality, in terms of information content,required for quantification by far exceeds that for proteinidentification.

The classical proteomic quantification methods utilizingdyes, fluorophores, or radioactivity have provided verygood sensitivity, linearity, and dynamic range, but theysuffer from two important shortcomings: first, they requirehigh-resolution protein separation typically provided by 2Dgels, which limits their applicability to abundant and solubleproteins; and second, they do not reveal the identity of theunderlying protein. Both of these problems are overcome bymodern LC-MS/MS techniques. However, mass spectrom-etry is not inherently quantitative because proteolyticpeptides exhibit a wide range of physicochemical properties

Anal Bioanal Chem (2007) 389:1017–1031DOI 10.1007/s00216-007-1486-6

M. Bantscheff :M. Schirle :G. Sweetman : J. Rick :B. Kuster (*)Cellzome AG,Meyerhofstrasse 1,69254 Heidelberg, Germanye-mail: [email protected]

such as size, charge, hydrophobicity, etc. which lead to largedifferences in mass spectrometric response. For accuratequantification, it is therefore generally required to compareeach individual peptide between experiments. In most

proteomic workflows, this can technically be achieved in anumber of ways (Fig. 2). One major approach is based onstable isotope dilution theory which states that a stableisotope-labeled peptide is chemically identical to its nativecounterpart and therefore the two peptides also behaveidentically during chromatographic and/or mass spectro-metric analysis. Given that a mass spectrometer canrecognize the mass difference between the labeled andunlabeled forms of a peptide, quantification is achieved bycomparing their respective signal intensities. Stable isotopelabeling was introduced into proteomics in 1999 by threeindependent laboratories [1–3] and has since been adoptedwidely in the field (for earlier reviews see, e.g., Refs. [4–11]). Isotope labels can be introduced as an internalstandard into amino acids (i) metabolically, (ii) chemically,or (iii) enzymatically or, alternatively, as an externalstandard using spiked synthetic peptides [11]. Morerecently, alternative strategies—often referred to as label-free quantification—have emerged. Label-free methods aimto compare two or more experiments by (i) comparing thedirect mass spectrometric signal intensity for any givenpeptide or (ii) using the number of acquired spectra

Fig. 2 Common quantitative mass spectrometry workflows. Boxes inblue and yellow represent two experimental conditions. Horizontallines indicate when samples are combined. Dashed lines indicate

points at which experimental variation and thus quantification errorscan occur (adapted with permission from Ref. [11])

Fig. 1 Schematic representation of the fraction of a proteome that canby identified or quantified by mass-spectrometry-based proteomics.Cellular proteins span a wide range of expression and current massspectrometric technologies typically sample only a fraction of all theproteins present in a sample. Due to limited data quality, only afraction of all identified proteins can also be reliably quantified

1018 Anal Bioanal Chem (2007) 389:1017–1031

matching to a peptide/protein as an indicator for theirrespective amounts in a given sample. As we will discuss inthe following sections, all of the mass-spectrometry-basedquantification methods have their particular strengths andweaknesses (Table 1) but they are beginning to mature to anextent that they can be meaningfully applied to the study ofbiological systems on a proteomic scale. In contrast, thestatistical treatment and subsequent interpretation of quan-titative proteomic data are still in their infancy, as the fieldis only beginning to experience the particular challengesassociated with transforming qualitative protein identifica-tion and post-translational modification data into reliablequantitative information.

Metabolic labeling

The earliest possible point for introducing a stable isotopesignature into proteins is by metabolic labeling during cellgrowth and division. Initially described for total labeling ofbacteria using 15N-enriched cell culture medium [2], it hasgained wider popularity in the form of the stable isotopelabeling by amino acids in cell culture (SILAC) approachintroduced by Mann and co-workers in 2002 [12]. In the

most commonly used implementation of the method, themedium contains 13C6-arginine and 13C6-lysine whichensures that all tryptic cleavage products of a protein(except for the very C-terminal peptide) carry at least onelabeled amino acid resulting in a constant mass incrementover the non-labeled counterpart. Protein identification isbased on fragmentation spectra of at least one of the co-eluting ‘heavy’ and ‘light’ peptides and relative quantitationis performed by comparing the intensities of isotopeclusters of the intact peptide in the survey spectrum. Incontrast to full metabolic protein labeling by 15N, thenumber of incorporated labels in SILAC is defined and notdependent on the peptide sequence thus facilitating dataanalysis. The main advantage of all metabolic labelingstrategies is that the differentially treated samples can becombined at the level of intact cells. This excludes allsources of quantification error introduced by biochemicaland mass spectrometric procedures as these will affect bothprotein populations in the same way. Despite a number ofcases that demonstrate the feasibility of total 15N metabolicprotein labeling of higher organisms in vivo such as C.elegans, Drosophila melanogaster [13], rat [14], or plants[15], it is neither possible nor practical to apply this strategyroutinely. The cost and time required for creating and

Table 1 Characteristics and applications of quantitative mass spectrometry methods

Application Accuracy(process)

Quantitativeproteomecoverage

Lineardynamicrangea

Metabolic protein labeling Complex biochemical workflows +++ ++ 1–2 logsComparison of 2–3 statesCell culture systems only

Chemical protein labeling(MS)

Medium to complex biochemicalworkflows

+++ ++ 1–2 logs

Comparison of 2–3 statesChemical peptide labeling(MS)

Medium complexity biochemicalworkflows

++ ++ 2 logs

Comparison of 2–3 statesChemical peptide labeling(MS/MS)

Medium complexity biochemicalworkflows

++ ++ 2 logs

Comparison of 2–8 statesEnzymatic labeling (MS) Medium complexity biochemical

workflows++ ++ 1–2 logs

Comparison of 2 statesSpiked peptides Medium complexity biochemical

workflows++ + 2 logs

Targeted analysis of few proteinsLabel free(ion intensity)

Simple biochemical workflows + +++ 2–3 logsWhole proteome analysisComparison of multiple states

Label free(spectrum counting)

Simple biochemical workflows + +++ 2–3 logsWhole proteome analysisComparison of multiple states

a In MRM mode, dynamic range may be extended to 4–5 logs [65]

Anal Bioanal Chem (2007) 389:1017–1031 1019

maintaining these systems is often incommensurate with thevalue of the information provided. As a result, the mainapplication of metabolic labeling in higher eukaryotes todate is SILAC in immortalized cell lines. Protein labeling inexcess of 90% is often achieved by 6–8 passages inmedium supplemented with heavy amino acids [12]. Whilemany cell lines can be converted quite readily, some dorequire special attention. For example, some cell linesrequire careful titration of the amount of arginine in themedium in order to prevent metabolic conversion of excessarginine into proline which in turn complicates dataanalysis [16]. Cell lines that are sensitive to changes inmedia composition or are otherwise difficult to grow ormaintain in culture may not be amenable to metaboliclabeling at all. A further limitation of metabolic labeling isthe restricted number of available labels. For SILAC, amaximum of three conditions can be compared in oneexperiment (unlabeled, 13C6, and

13C615N4-labeled amino

acids) which, albeit possible, complicates the analysis of,e.g., time-course experiments. Because of the early combi-nation of samples, metabolic labeling and SILAC inparticular is probably the most accurate quantitative MSmethod in terms of overall experimental process. Thismakes it particularly suitable for assessing relatively smallchanges in protein levels or those of post-translationalmodifications [17–19]. For the latter, it should be notedthough, that quantification on the peptide level is far fromtrivial because all information is derived from a single or afew observations.

Protein and peptide labeling

Post-biosynthetic labeling of proteins and peptides isperformed by chemical or enzymatic derivatization in vitro.An elegant and specific way to introduce an isotope labelinto peptides is the use of trypsin- or Glu-C-catalyzedincorporation of 18O during protein digestion [20, 21]. Thishas originally been employed to aid de novo sequencing ofpeptides by mass spectrometry [22] but has recently alsobeen applied to quantitative proteomic applications (for arecent review see Ref. [23]). Enzymatic labeling can beperformed either during proteolytic digestion or, morecommonly, after proteolysis in a second incubation stepwith the protease. Incorporation of 18O into C-termini ofpeptides results in a mass shift of 2 Da per 18O atom.While trypsin and Glu-C introduce two oxygen atoms re-sulting in a 4 Da mass shift which is generally sufficientfor differentiation of isotopomers, Lys-N and otherenzymes incorporate only one 18O molecule and shouldtherefore be avoided [24]. Acid- and base-catalyzed back-exchange with concomitant loss of the isotope label canoccur at extreme pH values [25], but under the mild acidic

conditions typically employed for ESI- and MALDI-MS18O-containing carboxyl groups of peptides are sufficientlystable. Because peptides are enzymatically labeled, arti-facts (i.e., side reactions) common to chemical labelingcan be avoided. A practical disadvantage is that fulllabeling is rarely achieved and that different peptidesincorporate the label at different rates which complicatesdata analysis [26, 27].

In principle, every reactive amino acid side chain can beused to incorporate an isotope-coded mass tag by chemicalmeans (reviewed by Ong and Mann [11]). In practice,however, side chains of lysine and cysteine are primarilyused for this purpose. In their pioneering work Gygi et al.[1] developed the isotope-coded affinity tag (ICAT)approach in which cysteine residues are specificallyderivatized with a reagent containing either zero or eightdeuterium atoms as well as a biotin group for affinitypurification of cystein-derivatized peptides and subsequentMS analysis. Following the initial success of the ICATapproach, several variations on this chemical reagent classemerged to improve, e.g., recovery of labeled peptides orchromatographic properties [28–31]. Other thiol-specificreagents typically contain halogen-substituted carboxylicacids or amides [32–35] or employ the Michael-typeaddition reaction to carbonyl groups (e.g., maleiimideesters and vinylpyridine) [36, 37]. As cysteine is a rareamino acid, ICAT and related methods significantlyreduce the complexity of the peptide mixture which canbe advantageous when highly complex samples areanalyzed. However, ICAT is obviously not suitable forquantifying the significant number of proteins that do notcontain any (or a few) cysteine residues and is of limiteduse for analysis of post-translational modifications andsplice isoforms. Despite these drawbacks, ICAT andsim ilar approaches will continue to be useful in a numberof broad (e.g., body fluid) or targeted (e.g., cysteineprotease) analyses.

Another group of labeling reagents targets the peptideN-terminus and the epsilon-amino group of lysine resi-dues. Most of the time, this is realized via the veryspecific N-hydroxysuccinimide (NHS) chemistry or otheractive esters and acid anhydrides as in, e.g., the isotope-coded protein label (ICPL) [38], isotope tags for relativeand absolute quantification (iTRAQ) [39], tandem masstags (TMT) [40], and acetic/succinic anhydride [41–44].Isocyanates or isothiocyanates have also been employed,albeit to a lesser extent [45, 46]. In recent studies,formaldehyde has been used for methylation of lysineresidues via Schiff base formation and subsequent reduc-tion by cyanoborohydride [47–49]. This reaction is veryfast, very specific, and very cheap. However, a sufficientlylarge mass shift between ‘heavy’ and ‘light’ labeledpeptides can only be achieved with deuterated formalde-


hyde which in turn leads to partial LC separation of labeledand non-labeled peptides, thus complicating data analysis(discussed below).

In most of the aforementioned chemical modificationtechniques, relative quantification is achieved by integra-tion of MS signal over isotopomers of ‘heavy’ and ’light’labeled peptides in survey spectra. Isobaric mass tagginginitially introduced by Thompson and co-workers [40]differs from this concept by introducing tags that initiallyproduce isobaric labeled peptides which precisely co-migrate in liquid chromatography separations. Only uponpeptide fragmentation are the different tags distinguishedby the mass spectrometer. This permits the simultaneousdetermination of both identity and relative abundance ofpeptide pairs in tandem-mass spectra. The commerciallyavailable iTRAQ reagent [39] provides a further refinementof this approach, allowing multiplexed quantitation of up toeight samples. This has turned out to be particularly usefulfor following biological systems over multiple time pointsor, more generally, for comparing multiple treatments in thesame experiment.

Carboxylic acids in side chains of glutamic and asparticacid residues as well as the C-termini of polypeptide chainscan be isotopically labeled by esterification using deuterat-ed alcohols [50, 51]. This reaction is particularly attractivefor the quantification of phospho-peptides because esterifi-cation has been shown to reduce binding of acidic peptidesto ion metal chelate affinity chromatography (IMAC)columns, thus improving the specificity of this enrichmentprocedure [52]. Other, more tailored labeling techniqueshave been developed, e.g., for quantification of phosphor-ylated and glycosylated peptides. For the former, b-elimination of phosphoric acid followed by Michaeladdition using, e.g., ethanedithiol derivatives is typicallyemployed [53–56]. For glycopeptides, hydrazide chemistryreplaces the carbohydrate moiety with a labeled chemicalgroup [57].

Broadly speaking, the chemical properties of amino acidside chains of proteins and peptides chains are rathersimilar. Consequently, almost all chemical labeling methodsmay also be applied to intact proteins. For example, theICPL reagent [38] has been employed for N-terminalpeptide labeling as well as lysine side chain labeling ofintact proteins. A similar protocol has been described foriTRAQ [58]. In most cases, full protein denaturationimproves labeling results but care has to be taken to avoidprotein precipitation (by, e.g., the use of charged reagents).Labeling of intact proteins can be quite advantageous sinceit allows for further protein separation steps on thecombined samples. This may facilitate characterization ofprotein isoforms by, e.g., 2D gel electrophoresis [38].However, there are two important caveats to protein label-ing: one is that trypsin does not cleave modified lysine

residues, which leads to significantly longer peptides thatgenerally are more difficult to identify by MS; second,very high labeling efficiencies are required in case furtherprotein separation is desired prior to MS analysis, sinceincomplete labeling impairs resolving power achievablewith, e.g., 1D and 2D gel electrophoresis. A general drawback with all chemical labeling approaches is that they areprone to side reactions that can lead to unexpectedproducts and which may adversely influence quantificationresults.

Absolute quantification using internal standards

The use of isotope-labeled synthetic standards has a longhistory in quantitative mass spectrometry. Originally de-scribed in the early 1980s [59], it is now becoming morebroadly applied as a method commonly known as AQUA(absolute quantification of proteins) [60]. In the simplestcase, absolute quantification can be achieved by theaddition of a known quantity of a stable isotope-labeledstandard peptide to a protein digest and subsequent com-parison of the mass spectrometric signal to the endogenouspeptide in the sample. Unlike in metabolic labeling, whererelative quantitative information is acquired for a largenumber of the proteins present in a mixture, the addition ofsynthetic peptides to a proteome digest focuses on thedetermination of the quantity of one or a few particularproteins of interest. This approach is attractive for studiesaimed at, e.g., the analysis and validation of potentialbiomarkers in a large number of clinical samples [61] or atmeasuring the levels of particular peptide modificationssuch as ubiquitinylation [62].

The approach has been refined by constructing syntheticgenes that express concatenated standard peptides whichupon tryptic digestion either provide multiple peptides ofthe same protein for quantification or quantification stand-ards for a group of proteins of interest [63]. Not only doesthe provision of multiple peptides increase confidence inquantification, the synthetic protein can also be addedearlier in the process than individual peptides, thuscontrolling any potential bias encountered during proteindigestion. One notable example of following the syntheticgene strategy is the determination of the stoichiometry ofthe eight-membered eIF2B-eIF2 protein complex [64].

Given that tryptic digests of entire proteomes are verycomplex mixtures, and that most mass spectrometers have arather limited dynamic detection range, there are a numberof limitations to the AQUA approach. One practicaldrawback is that one has to ‘guess’ how much of thelabeled standard should be added to a sample. This amountmay be different for all proteins of interest as theirexpression levels (used here in the sense of protein


abundance rather than protein synthesis) may differ greatlywithin a sample. Another limitation is the specificity of thespiked standard as there are likely multiple isobaricpeptides present in the mixture. Both of these issues canbe greatly improved by a method called multiple reactionmonitoring (MRM) [62] in which the (triple quadrupole)mass spectrometer monitors both the intact peptide massand one or more specific fragment ions of that peptide overthe course of an LC-MS experiment. The combination ofretention time, peptide mass, and fragment mass practicallyeliminates ambiguities in peptide assignments and extendsthe quantification range to 4–5 orders of magnitude [65].Obviously, the choice of synthetic peptide standard isimportant and is mostly determined empirically. However,recent data suggest that it is possible to predict which of aprotein’s tryptic peptides will be most frequently observedfor a given proteomic platform and thus would be a suitablequantification standard [66]. Despite the ability to calculateprotein amounts from an AQUA experiment, there are stillquestion marks as to how absolute these values are as anysample manipulation prior to adding the synthetic standardmay bias the results (losses or enrichment). Consequently,the amount of a protein in an experiment determined byAQUA may not reflect the true expression levels of thisprotein in a cell.

LC-MS/MS analysis of stable isotope labeled peptides

As described above, quantitation based on stable isotopelabeling can be achieved by signal integration in survey MSspectra (e.g., SILAC) or tandem MS spectra (e.g., iTRAQ).For both approaches, several points have to be consideredin the design and analysis of an experiment. Although theassumption that stable isotope labeling does not alter thephysicochemical properties of a peptide is generally valid,it has been observed that deuterated peptides show smallbut significant retention time differences in reversed-phase

HPLC compared to their non-deuterated counterparts [67].This complicates data analysis because the relative quantitiesof the two peptide species cannot be determined accuratelyfrom one spectrum but requires integration across thechromatographic time scale. Retention time shifts are farless pronounced for labels such as 13C, 15N, or 18O isotopes[68], so that the additional signal integration step overretention time can generally be omitted.

Another requirement for any stable isotope labelingapproach is that the heavy label can be clearly distinguishedfrom the unlabeled peptide or any other unrelated ion species(Fig. 3a). For quantification in survey MS spectra, it isessential that the mass shift introduced by the label is at least4 Da in order to distinguish the isotopomer clusters of thelabeled and unlabeled forms of the peptide. As isotopomerclusters increase in width with increasing peptide mass, theapplication of labeling methods such as methylation andenzymatic 18O labeling becomes limited for larger peptides.Reporter ions used for quantification in tandem MS spectrashould be designed such that interference by ordinary peptidefragments is minimal. For the iTRAQ label, the m/z region of114–117 was chosen for this reason. Still, some interferenceshave been identified (notably the 116.1 Da y(1) fragment ionof peptides containing a C-terminal proline residue [69]) andthese data points have to be carefully removed in the dataanalysis process.

A further parameter impacting accuracy and dynamicrange of quantification is the mass spectrometric detectionsystem itself. In survey MS spectra, the definition of verylow and very strong signals can be problematic. At verylow signal, peptide ions are often difficult to distinguishfrom background noise (Fig. 3b) and for very strongsignals, the detector may become saturated (Fig. 3c). Inpractice, saturation is more often observed for quadrupoleTOF instruments than ion traps because these latter devicescan control the number of ions before detection [70]. In anycase, the relatively recent introduction of high-resolution/high mass accuracy mass spectrometers in proteomics has

Fig. 3 Examples illustrating mass spectral features relevant forquantification. a Example of a SILAC-labeled peptide pair suitablefor quantification. The spectra displays the characteristic 6 Da (3 m/z)mass difference between light and heavy forms of the peptide, goodsignal to noise ratio and no interfering signals. Signal intensities

indicate a 1:1 abundance ratio. b Example of a peptide and otherinterfering signals with signal to noise ratios too low for reliablequantification. c Example of a peptide signal saturating the detectorand thus distorting the isotope pattern to a degree that the spectrum isnot suitable for quantification


greatly facilitated the ability to quantify proteins in complexproteomes because the increased instrument performanceenables the exact discrimination of peptide isotope clustersfrom interfering signals caused by, e.g., co-eluting and near-isobaric peptides and other chemical entities [71–73]. Forquantification in tandem MS spectra, saturation effects arerarely a problem. Instead, low-intensity spectra are fre-quently obtained and may result in less robust quantitationvalues due to poor ion statistics. Unlike for quantification insurvey spectra, the contribution of peptidic or chemicalbackground noise to quantification does not depend on themass resolution of the mass spectrometer but on the size ofthe m/z window chosen for isolation of peptides forsequencing (typically 2–6 m/z). All ions present in thiswindow will contribute to the signal of the, e.g., iTRAQreporter ions. As a result, it is not always clear to whatextent quantification was contributed by the peptide ofinterest or by background. This can sometimes lead to alarge underestimation of true changes, especially for veryweak peptide signals.

Taken together, the limits to quantification of complexproteomes by stable isotopes is first and foremost an issueof signal interference caused by co-eluting components ofsimilar mass. Therefore, the most straightforward way foroptimizing quantitative analyses is to decrease samplecomplexity by increasing HPLC gradient times or bybiochemical fractionation prior to LC-MS analysis.

Label-free quantification

Currently, two widely used but fundamentally differentlabel-free quantification strategies can be distinguished: (a)measuring and comparing the mass spectrometric signalintensity of peptide precursor ions belonging to a particularprotein and (b) counting and comparing the number offragment spectra identifying peptides of a given protein. Inthe former approach, the ion chromatograms for everypeptide are extracted from an LC-MS/MS run and theirmass spectrometric peak areas are integrated over thechromatographic time scale. For low-resolution massspectra this is typically done by creating extracted ionchromatograms (XICs) for the mass to charge ratiosdetermined for each peptide [74]. More recently, thisconcept has been extended to high-resolution data toinclude contributions of 13C isotopes to the overall signalintensities [75]. The intensity value for each peptide in oneexperiment can then be compared to the respective signalsin one or more other experiments to yield relativequantitative information [74, 76–80]. For proteomic analy-sis of very complex peptide mixtures, three importantexperimental parameters affect the analytical accuracy ofquantification by ion intensities. (i) It is advantageous to

employ a high mass accuracy mass spectrometer becausethe influence of interfering signals of similar but distinctmass can be minimized. (ii) The peptide chromatographicprofile should be optimized for reproducibility to easefinding corresponding peptides between different experi-ments. This is not a trivial task and special software hasbeen developed to align LC-runs prior to identifyingcorresponding peptides [81–84]. (iii) The right balancebetween acquisition of survey and fragment spectra has tobe found. While extensive peptide sequencing by tandemMS is required to identify as many proteins as possible incomplex mixtures, a robust quantitative reading by ionintensities requires multiple sampling of the chromato-graphic peak by survey mass spectra. Typically, multiplefragment spectra are acquired for every survey spectrum atacquisition rates ranging from 0.2 s/spectrum (ion traps) to1–3 s/spectrum (quadrupole-TOF instruments). Given thatchromatographic peak widths are in the order of 10–30 s fornano-LC separations, ion traps have an inherent advantageover QTOFs because many more MS to MS/MS cycles canbe performed within the available chromatographic time.Still, even for fast sampling instruments, better quantifica-tion accuracy will inevitably mean poorer proteomecoverage and vice versa. This dilemma has led somelaboratories to conduct two separate experiments for eachsample: one which focuses on identifying as many peptidesas possible by MS/MS and a second performed in MS-onlymode in order to optimize sampling of intact peptidesignals. In these approaches, matching of integrated peakintensities to identified peptides is performed by using acombination of accurate mass and retention time [84–86].An alternative has been proposed in which the massspectrometer no longer cycles between MS and MS/MSmode but aims to detect and fragment all peptides in achromatographic window simultaneously by rapidly alter-nating between high- and low-energy conditions in themass spectrometer [87–90]. Obviously, there are challengeswith analyzing such data from complex samples as manyfragmentation spectra will be populated with sequence ionsfrom multiple peptides each contributing differently to theoverall spectral content.

The peptide or more recently introduced spectralcounting approach [91–93] is based on the empiricalobservation that the more of a particular protein is presentin a sample, the more tandem MS spectra are collected forpeptides of that protein. Hence, relative quantification canbe achieved by comparing the number of such spectrabetween a set of experiments. In contrast to quantificationby peptide ion intensities, spectral counting benefits fromextensive MS/MS data acquisition across the chromato-graphic time scale both for protein identification as well asprotein quantification. However, the commonly employeddynamic exclusion of ions that have already been selected


for fragmentation is detrimental for accurate quantification[94]. Although very intuitive and attractive in practicalterms, the spectrum counting approach is still controversialbecause it does not measure any direct physical property ofa peptide. It further assumes that the linearity of response isthe same for every protein. In fact, the spectrum countresponse is different for every peptide because, e.g., thechromatographic behavior (retention time, peak width)varies for every peptide. Therefore, even reasonablequantification requires the observation of many spectra fora given protein. Old et al. [94] have shown that although itis possible to detect threefold protein changes with as fewas four spectra; this number increases exponentially forsmaller changes (ca.15 spectra for twofold). At the sametime, saturation effects will be observed at higher spectralcounts and saturation levels will be different for all proteinswhich renders the assessment of the dynamic range ofobserved changes difficult.

Nevertheless, the correlation between amount of proteinand number of tandem mass spectra does hold and has ledresearchers to extend the concept to the estimation ofabsolute protein expression levels. In the first of a series ofpapers, Rappsilber et al. [95] computed a protein abundanceindex (PAI) by dividing the number of observed peptidesby the number of all possible tryptic peptides from aparticular protein that are within the mass range of theemployed mass spectrometer. In a subsequent refinement,the same group transformed the PAI into an exponentiallymodified form (emPAI) [96] which showed a bettercorrelation to known protein amounts. Further advanceshave been made by using computational models that predictwhich peptides of a given protein are likely to be detectedby the mass spectrometer in the first place and thus wouldform a better basis for quantification [97–99, 66]. Forexample, results obtained by the absolute protein expres-sion profiling (APEX) method [99] suggest that absoluteprotein expression can be determined to within the correctorder of magnitude.

Label-free approaches are certainly the least accurateamong the mass spectrometric quantification techniqueswhen considering the overall experimental process becauseall the systematic and non-systematic variations betweenexperiments are reflected in the obtained data (Fig. 2).Consequently, the number of experimental steps should bekept to a minimum and every effort should be made tocontrol reproducibility at each step. Nonetheless, label-freequantification is worth considering for a number of reasons.In simple practical terms, the time-consuming steps ofintroducing a label into proteins or peptides can be omittedand there are no costs for labeling reagents. In terms ofanalytical strategy, the following points may also beimportant: (i) there is no principle limit to the number ofexperiments that can be compared. This is certainly an

advantage over stable isotope labeling techniques that aretypically limited to 2–8 experiments that can be directlycompared. (ii) Unlike for most stable isotope labelingtechniques, mass spectral complexity (in terms of detectedpeptide species within a particular chromatographic timewindow) is not increased which, in turn, might provide formore analytical depth (i.e., number of detected peptides/proteins in an experiment) because the mass spectrometer isnot occupied with fragmenting all forms of the labeledpeptide. (iii) There is evidence that label-free methodsprovide higher dynamic range of quantification than stableisotope labeling (Table 1) and therefore may be advanta-geous when large and global protein changes betweenexperiments are observed. However, particularly for spec-tral counting, this comes at the cost of unclear linearity andrelatively poor accuracy [94].

Analysis of quantitative MS data

When contemplating a data analysis strategy for proteomicdata generated by quantitative mass spectrometry, it isworth reconsidering a couple of principle points. Quantita-tive proteomic data are typically very complex, and often ofvariable quality. This is in part because the data areincomplete: even the most advanced mass spectrometers,which can acquire several tandem MS spectra per second,are often overwhelmed by the number of peptides presentin a sample. As a consequence, only a subset of all proteinspresent can be identified in any one analysis [100]. Forprotein quantification, it is further mandatory to detect aprotein in all experiments that should be compared. As aresult, often only a subset of identified proteins can actuallybe quantified (Fig. 1) [92]. Identification and quantificationrates are direct functions of sample complexity. While alarge fraction of proteins present in, e.g., affinity purifica-tions can be identified and quantified using a reasonablenumber of acquired spectra, a much smaller fraction of thecontent of whole proteome shotgun experiments will becovered and with fewer spectra for each protein. This clearlylimits the confidence in quantification results.

These general considerations aside, practitioners ofproteomics will soon face a number of practical challengesin analyzing quantitative mass spectrometric data: (i)quantitative readings must be extracted from MS or MS/MS spectra; (ii) peptide and protein identification must beperformed; (iii) the two types of information must bemerged and quality controlled; (iv) the applicable statisticalmethods have to be identified; and (v) the individual stepshave to be combined into a workflow which bridges gapsbetween commercially available software and custom-builttools and which ideally also allows for automating most ofthe tasks (Fig. 4).


For protein quantification based on spectrum counting,the data processing steps are basically identical to thegeneral protein identification workflow in proteomicswhich is one of the reasons why this approach has becomeso popular. Researchers can choose from a variety ofmethods available for automated protein identification andsubsequent (probabilistic) validation of spectrum-to-peptidematches (for a recent review see Ref. [101]). It should beemphasized that for any quantification method it ismandatory to consider only those spectrum-to-peptidematches that are unique for a particular protein [11].

Extracting quantitative information from MSand MS/MS spectra

Quantification methods based on ion intensities, regardlessof whether employing stable isotope labeling or not, requirea number of additional steps prior to protein quantification(boxed area in Fig. 4). Two particular elements areimportant to mention here: intensity integration (i) withinthe mass spectrum (centroiding) and (ii) across thechromatographic peak. For low-resolution MS data, bothaspects are carried out in one operation by extracting the ionchromatograms from the LC-MS data. For high-resolutionMS data, the procedure is more complex and typicallyperformed in two steps. Signal intensity integration withinthe mass spectrum can either utilize the intensity/area of themonoisotopic peak or the sum of the intensities/areas of all

isotopomers of a peptide. Each method has its merits anddetractions: monoisotopic peak integration is relativelystraightforward to implement but not very sensitive partic-ularly for larger peptides for which the monoisotopic peaksonly constitute a minority of the total signal intensity. Inaddition, the use of heavy isotopes distorts the relativeisotope distribution of peptides which leads to inaccuracies.In contrast, the summed area of the entire isotope cluster isthe most sensitive and accurate method [102] as it utilizesall of the data but is more difficult to implementcomputationally. As discussed in a previous section, signalintensity integration over the chromatographic time scale isprimarily required for label-free quantification as well asthose stable isotope reagents that lead to significant differ-ences in chromatographic behavior. For methods which donot suffer from this shortcoming, time integration can beperformed but is not required. Instead, collection of severalspectra for each peptide is generally useful in order toobtain several quantitative readings.

Quality control of raw MS data

There are several sources of potential error in the massspectrometric readout of an LC-MS experiment that cannegatively affect the results of peptide quantification.Spectra for which these errors are detected should befiltered out prior to computing quantification values. Thefirst of these issues is the presence and variability of

Fig. 4 Generic data processingand analysis workflow forquantitative mass spectrometry.Yellow icons indicate stepscommon to all quantificationapproaches with or without theuse of stable isotopes. Blueicons in the boxed area refer toextra steps required when usingmass spectrometric signal inten-sity values for quantification


spectral background noise (Fig. 3b) which can be filteredout by most if not all available commercial and academicdata processing packages. A second common issue is thepresence of interfering signals other than background noise(Fig. 3b). For very complex peptide mixtures, these oftenconstitute co-eluting peptides of very similar m/z valueswhich in turn will render the correct assignment of signalintensities to particular peptide ions difficult. This is truefor quantification in both MS and MS/MS spectra and suchspectra should be removed from the analysis. Third, strongsignal intensities can lead to detector saturation for somemass spectrometers (particularly quadrupole TOF instru-ments, Fig. 3c) which distorts the natural isotope intensitydistribution and thus leads to false quantitative readings.

For stable isotope labeling, further quality criteria mustbe considered. One very simple and often incurred problemis systematic bias introduced by imperfections in mixingthe two protein populations. Mixing errors can most of thetime be determined experimentally and apply uniformly toall protein quantification values and are thus easilycorrected for. A second systematic error is represented bythe isotope purity of the employed labeling reagent whichrarely exceeds 95–98%. Although this may not appear to bea significant source of uncertainty and, again, can be easily

corrected for, isotope impurities lead to increased spectralinterferences and, more importantly, limit the dynamicrange of detectable differences between samples. A similarargument applies to incomplete incorporation of the isotopelabel into proteins and peptides. Again, while isotopeincorporation can be measured and correction factors canbe applied, the combination of the above items limits thedynamic range of detectable differences between samples toapproximately 20–30:1. Consequently, determined changesare often smaller than their true values. It is important tokeep in mind that this effect can be much more pronouncedwhen spectral background contributes significantly tooverall spectral intensity.

From spectra to relative protein quantification

For the spectrum counting approach, relative proteinquantification between two or more samples is simplyperformed by comparing the respective numbers. If tenspectra are observed for a protein under condition 1 and 15spectra under condition 2, the change between the twoconditions is 1.5-fold. In contrast, for all approaches thatmeasure signal intensities of peptide spectra, a quantitativereading is obtained for each spectrum. Obviously the accuracy

Fig. 5 a Distribution of measured changes from peptide spectra as afunction of spectrum intensity for a single protein mixed in a 2:1 ratio.Diamonds represent intensity readings from individual spectra. Thered line indicates the expected ratio of 2. It is evident that variations inchange determination are much larger for low-intensity spectra thanfor medium- or high-intensity spectra. b Protein change determinationby linear regression analysis. Diamonds represent intensity readingsfrom individual spectra for samples 1 and 2 (same data as in a). Theslope of the two-sided regression line approximates the expectedtwofold difference in protein quantity between the two samples. cHistogram showing the relationship between precision of quantifica-

tion (expressed as relative standard deviation, RSD) and the number ofobserved peptide spectra for a given protein from replicate experi-ments. Not surprisingly, precision increases with increasing number ofspectra. d Change distribution for approximately 1,000 proteinsidentified and quantified between two experimental conditions in asingle experiment. Diamonds represent individual protein foldchanges in ascending order. In the absence of replicate experiments,data points between yellow lines (arbitrarily set at 2σ) are typically notconsidered to change significantly. However, these data points maycontain many false negatives (small but significant changes)


of the protein quantification is determined by the accuracyof each peptide (spectrum) determination. The resultingdata are spectrum-related quantity measures of varyingprecision. As an experiment typically produces a number ofspectra per protein, these measurements have to beaggregated in a way that returns the best (i.e., most precise)protein quantification measure. Most publications to daterely on simple averaging of ratios [103], but as exemplifiedin Fig. 5a, variation of change determination is a functionof signal intensity. Thus, low-intensity or noisy data mayeasily distort the mean value of computed ratios [104]. Toovercome this problem, intensity thresholds have beenemployed [65]. However, these mostly arbitrary thresholdsmay also lead to arbitrary reduction of proteins that can bequantified. As an alternative, results can be improved eitherby calculation of an intensity weighted average, bysumming up of all measured quantities followed bycalculation the protein ratio [103, 75], or by calculating alinear regression (allowing for two dimensions of freedom)to determine the protein ratio (Fig. 5b) [105]. Apart frommass spectrometric signal strength, accuracy of quantifica-tion also benefits from the availability of multiple spectrafor a given protein (Fig. 5c).

Statistical analysis of experimental data

Proteomic experiments comparing a number of states of abiological system typically generate complex data. Anunderstanding of the experimental setup and the natureand quality of the obtained data are required to deviseappropriate statistical methods. Experiments typically fallinto two distinct categories: either the interrelation betweena protein’s abundance (or another property) and a certainsample condition is examined or the interaction betweenproteins is analyzed. Table 2 lists examples of suchquestions and some appropriate statistical strategies thathave been applied to answer them. The detection of proteinabundance changes is discussed in more detail below as itrepresents one of the major applications of proteomics.Most of the available statistical methods have previously

been applied to gene expression analysis but can often alsobe applied to quantitative MS data. However, the requireddata preparation steps such as normalization might besignificantly different.

Data preparation

Raw data from quantitative MS experiments are generallynot suitable for statistical analysis, thus a number ofpreparative steps are required. First, raw data are typicallynot normally distributed, an assumption made by manystatistical tests. Therefore, data are frequently log-trans-formed assuming that the data are lognormal-distributed.This operation typically also harmonizes the variance ofdata (otherwise high values would have large variances andvice versa). If replicates of the experiment have beengenerated, normalization of their data is mandatory becausetechnical bias may overshadow the underlying biologicaleffects (for details on normalization techniques, see Refs.[106–108]). As discussed above, technical effects includesample mixing errors, incomplete isotope incorporation, orisotope impurity. In many cases, systematic technical biascan be measured directly but in some cases requiresdedicated experimentation (e.g., by a label swap experiment[109, 110]) to determine its source. The resulting informa-tion is used to build correction functions that are consec-utively applied to the data. It should be noted that it is verylikely that not all manifesting sources of systematic errorhave been described yet or that these are not readilyamenable to determination (e.g., background contributionin iTRAQ experiments). It can be expected though that withthe rapid evolution of proteomic technologies, many ofthese yet unknown sources of error will be uncovered andthe learnings subsequently used to sharpen the data which,in turn, increases data quality.

Another challenge to a statistical treatment of proteomicdata is the mostly random sequencing of peptides by themass spectrometer. As a result, not every available peptideis identified in every experiment. This effect is more pro-nounced for peptides of low abundance and poor detectabil-ity, resulting in many missing values in an experiment.

Table 2 Statistical methods for proteomics

Category Question Analysis suggestions

Protein change betweenconditions

Does a protein behave significantly differentbetween two samples?

Multiple hypothesis testing

Does a protein exhibit time-dependent change? Analysis of variance (ANOVA)Is the sample a member of a defined class ofsamples?

Classification methods (e.g., linear discriminant analysis,support vector machines)

Dependencies betweenproteins

Which proteins behave similarly in theexperiment?

Cluster analysis


However, statistical methods often require complete data. Insuch cases, missing values may be estimated by, e.g.,averaging available values of the protein from otherreplicates or using related values from other proteins fromthe same experiment. It should be noted though thatestimating values inevitably results in decreased statisticalpower [111, 112].

Values that are grossly different from comparableobservations (outliers) require special attention. They caneither indicate a true observation of a particular peptidespecies, e.g., a regulated post-translational modification, ora false reading. In both cases, these data points shouldinitially be excluded from the calculation of proteinquantities but not categorically rejected. A common wayto spot outliers is visual inspection by the investigator,leaving considerable room for subjective judgement. Dur-ing calculation of protein values from individual spectra bylinear regression (see above) outlier detection on thespectrum level is possible using established methods [113,114] but may result in loss of valuable data. For datacorrection at the protein level, methods for multivariate datacan also be adapted [115, 116].

Detection of differential protein expression

It is not uncommon that publications reporting results ofproteomic experiments using quantitative mass spectrome-try base conclusions on measurements generated in one ortwo experiments. This is understandable given the oftenlimited availability of specimen as well as the cost and timerequired to perform and analyze these samples. However, inlight of the often considerable experimental variation, it islikely that those studies will not realise their full potential.For example, the graph shown in Fig. 5d represents a rankorder list of the observed changes between two experimentsfor approximately 1,000 proteins. Proteins at the extremesof the distribution change the most and are therefore oftenconsidered to be the most interesting. While this mightoften be true when these observations are backed by many

spectra indicating this change, there are two importantcaveats. In this representation, small but potentiallysignificant changes go unnoticed (false negatives) and, inthe absence of repeating the experiment, there is no way ofassessing if the observed large protein changes that arebacked by few spectral observations can be reproduced(false positive). Even small numbers of repetitions canincrease confidence in the results considerably. In addition,the use of statistical testing methods adds options todetermine the probability of false decisions. A typicalsituation is the comparison of protein levels between twodifferent samples with the goal to detect those proteins thatare significantly changed between conditions. This biolog-ical question can be formulated as a problem in multiplehypotheses testing that describes a simultaneous test foreach protein on the null hypothesis of no change in proteinmeasure between the two conditions. A standard approachto such a multiple testing problem consists of two aspects:(i) computing a test statistics and (ii) applying a multipletesting procedure to determine which hypothesis to reject(change or no change) while controlling a defined falsepositive error rate [117]. Computing the test statistic for eachprotein can be carried out, e.g., by employing the frequentlyused t-test. This test expects the data to be normallydistributed, an assumption that is not always justified andrequires a significant number of replicates in order to returnreliable results (Table 3). For lower replication numbers (2–3) the so-called local-pooled-error test (LPE) has been foundto be useful provided that protein changes are not too small[118–120]. For data with unknown distribution character-istics, non-parametric tests can also be used that are agnostictowards the data’s distribution but come at the expense ofstatistical power [121].

In the proteomics case where many proteins are testedsimultaneously, the probability of committing an errorincreases often dramatically. For example, when consider-ing a list of hundreds of proteins at a defined error rate of,e.g., 0.01, it is likely that several false positives will occurby chance. However, when setting the thresholds too

Table 3 Characteristics and applications of statistical tests

Test Requirements Statistical power Application

Tests for experiments with replicatest-test Replications, n>3 +++ All quantitation methods

Data normally distributedLPE-test Replications, n>1 ++ All quantitation methods

2–3 replicatesStrong changes

Tests for experiments without replicatesG-test (Very) large number of peptide spectra + Spectrum countingFischer’s exact test (Very) large number of peptide spectra + Spectrum countingAC-test (Very) large number of peptide spectra + Spectrum counting


conservatively to minimize false positive rate (i.e., the ratethat truly null features are called significant), this oftenleads to an unacceptable increase in the false negative rate(i.e., the rate that truly significant features are called null).Commonly used alternative measures of error rates inmultiple testing procedures are the family wise error rate(FWER; i.e., the rate that one truly null feature is calledsignificant among all tests) and the false discovery rate(FDR; i.e., the rate that features called significant are trulynull) which break up the direct dependency between falsepositive and false negative rates. Instead of simply report-ing rejection or acceptance of the specified hypothesisusing these methods, a p-value connected to the test can bedefined which describes the significance of a test as thesmallest possible significance level at which the nullhypothesis would be rejected. Various procedures forderiving adjusted p-values for multiple hypothesis testinghave been suggested, e.g., the Bonferroni adjusted p-valuefor FWER and the q-value for FDR [122]. q-Values havesince also been adopted in proteomics research [123, 124].A detailed overview of multiple hypothesis testing has beengiven by Dudoit and co-workers [125].

Sampling statistics

For a number of proteomic applications, sampling statistics(e.g., spectrum count, peptide count, sequence coverage)shows increasing potential. Zhang and co-workers [120]recently compared the aforementioned three approachesand found that the spectrum counting approach offered thegreatest reproducibility. This is probably not surprisinggiven that this approach generates many more data pointsthan peptide counting or measuring sequence coverage. Inaddition this paper explores a number of statistical methodsfor data analysis. For experiments that feature three or morereplicates of each condition, statistical difference can beassessed by the t-test as described above. However, ifrepetitions are not available, other statistical options have tobe considered. To that end, tests may be applicable thatattempt to mimic replicates by pooling certain features. Forexample, for each detected protein, spectral counts from apair-wise experiment can be arranged in a two-way table(proteins vs. conditions). A protein is then called differen-tially expressed if its proportion of spectrum counts to thetotal spectrum count in the experiment is significantlydifferent between both conditions. There are a number ofpossible statistical tests using different hypotheses for thisapproach (Table 3, bottom). The authors of the aforemen-tioned paper conclude that Fisher’s exact test, the AC-test,and the G-test return comparable results. However the G-test is computationally simpler and can be generalized formulti-condition experiments and thus may be the moreversatile approach. Results typically improve with in-

creased sampling (total number of spectrum counts in anexperiment). Despite the fact that the commonly useddynamic exclusion option during LC-MS analysis violatesrandom sampling, Zhang et al. showed that the approachcan be generally useful [120].

In contrast to statistical estimation, the performance of achosen statistical test can often also be assessed experi-mentally by means other than multiple repetitions. One wayof measuring errors directly and under the same analyticalconditions is to offset the measurement of a particularsample to a dilution of the very same sample [126]. Also,spiked proteins have been used to generate reference datafor a set of proteins with known behavior that can beutilized for ‘calibrating’ an experiment type [92]. Once thestatistical parameters have been learned, these may beapplied to subsequent experiments without the need forrepetition. Although the statistical power of such approachesis lower than those based on multiple repetitions of the sameexperiment, the former may be sufficient particularly forsamples of low protein complexity (e.g., affinity purifica-tions). Further assessment of data significance may beprovided by curve fitting methods (e.g., the LOWESS fit)which can reveal regions of random experimental error in theobserved dataset [123].

Concluding remarks

A multitude of methods has emerged for the analysis ofsimple and complex (sub-)proteomes using quantitativemass spectrometry, and the field is beginning to learn forwhich type of study these methods can be meaningfullyapplied. However, significant further improvements toexperimental strategies are required particularly for thequantitative analysis of post-translational modifications. Itis probably fair to say that the field is still far from beingable to generate quantitative proteomic data at a scalewhich would allow the comprehensive investigation of abiological phenomenon. At the same time, the recentexponential increase in data volume and complexitydemands the development of appropriate statisticalapproaches in order to arrive at meaningful interpretationsof the results. This can only be achieved if the influence ofthe employed technologies on the results obtained is wellunderstood and by ensuring that experimental designfollows the biological context so that the ‘right statistics’can be developed for the problem at hand in order togenerate scientific insight.

Acknowledgements The authors wish to thank David Simmons andUlrich Kruse for critically reading the manuscript and FrankWeisbrodt for help with preparing the figures. We are grateful toNature Publishing Group for granting permission to reproduce andadapt previously published material.


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