ORIGINAL PAPER

A 2D reversed-phase × ion-pair reversed-phaseHPLC-MALDI TOF/TOF-MS approachfor shotgun proteome analysis

Maria Lasaosa & Nathanaël Delmotte &

Christian G. Huber & Katja Melchior & Elmar Heinzle &

Andreas Tholey

Received: 1 September 2008 /Revised: 13 November 2008 /Accepted: 21 November 2008 / Published online: 9 December 2008# Springer-Verlag 2008

Abstract The separation of complex peptide mixtures inshotgun proteome analysis using a 2D separation schemeencompassing reversed-phase × ion-pair reversed-phase(IP-RP) liquid chromatography coupled online to electro-spray ion trap mass spectrometry (MS) has been shownearlier to be superior in terms of separation efficiency andtechnical robustness compared to the classically usedseparation scheme encompassing strong cation exchange ×IP-RP-chromatography in shotgun proteome analysis. Inthe present study, this novel separation scheme was coupledoffline to matrix-assisted laser desorption/ionization(MALDI) time-of-flight (TOF)/TOF-MS for the analysisof the same sample, a tryptic digest of the cytosolicproteome of the bacterium Corynebacterium glutamicum.Compared to the earlier study, the MALDI-based platformled to a significantly increased number of peptides (7,416vs. 2,709) and proteins (1,208 vs. 468, without single

peptide-based identifications), respectively. This representsthe majority of all predicted cytosolic proteins in C.glutamicum. The high proteome coverage, as well as thelarge number of low-abundant proteins identified with thisimproved analytical platform, pave the way for newbiological studies.

Keywords Corynebacterium glutamicum .

ElectrosprayMS . Ion-pair reversed-phase chromatography .

LC-MALDI . Orthogonality . Protein identification

Introduction

Proteomic analysis involves the high-throughput identifica-tion, characterization, and quantification of proteins withincellular structures or biological fluids. The complexity ofproteomic samples, comprising thousands of individualcompounds present in concentration ranges spanningseveral orders of magnitude, prevents their direct analysisand requires highly sophisticated strategies for the separa-tion of the individual components prior to their character-ization. Two general strategies have been developed forproteome analysis. The top–down approach is based uponthe separation of intact proteins using either chromato-graphic or electrophoretic methods. In the bottom–up orshotgun approach, the proteins are cleaved followed bychromatographic separation of the resulting peptide mix-ture. In both platforms, identification and characterizationof the proteins are performed by means of mass spectro-metric methods [1].

The digestion of whole proteomes leads to very complexpeptide mixtures, the separation of which by far exceeds thecapabilities of 1D chromatography. Therefore, multidimen-

Anal Bioanal Chem (2009) 393:1245–1256DOI 10.1007/s00216-008-2539-1

Electronic supplementary material The online version of this article(doi:10.1007/s00216-008-2539-1) contains supplementary material,which is available to authorized users.

M. Lasaosa : E. Heinzle :A. Tholey (*)Technische Biochemie, Functional Proteomics Group,Saarland University,66123 Saarbrücken, Germanye-mail: a.tholey@mx.uni-saarland.de

N. Delmotte : C. G. Huber :K. MelchiorInstrumentelle Analytik und Bioanalytik, Saarland University,66123 Saarbrücken, Germany

C. G. HuberDepartment of Molecular Biology, Division of Chemistry,University of Salzburg,5020 Salzburg, Austria

sional separation schemes are needed for adequate frac-tionation of proteomic samples. A commonly appliedmultidimensional separation strategy for shotgun proteomeanalysis encompasses the online [2] or offline coupling oforthogonal separation methods [3, 4] like strong cation-exchange chromatography (SCX) and ion-pair reversed-phase (IP-RP) high-performance liquid chromatography(HPLC). Recently, an alternative 2D separation schemehas been elaborated for shotgun proteome analysis employ-ing high-pH reversed-phase HPLC in the first and low-pHion-pair reversed-phase HPLC coupled to electrosprayionization (ESI) ion trap (IT) tandem mass spectrometry(MS/MS) in the second dimension (RP × IP-RP-HPLC-ESI-IT-MS/MS) [5]. Compared to the classical strongcation-exchange × ion-pair reversed-phase (SCX × IP-RP-HPLC) approach, the RP × IP-RP-HPLC system wascharacterized by a lower degree of orthogonality, whichwas, however, more than counterbalanced by higherseparation efficiency, more homogenous distribution ofpeptide elution, and easier experimental handling [5].Together with the prevention of high salt loads hamper-ing the transfer of sample from the first to the seconddimension, the approach facilitated more efficient sepa-ration, which is mainly caused by the avoidance ofcharge clustering commonly observed in cation-exchangechromatography. This allowed the collection of lesschromatographic fractions from first dimension andfinally led to a significant reduction in overall measure-ment time while yielding significantly more peptide andprotein identifications.

Despite its widespread use and the ease of interfacing ofliquid chromatography with electrospray ionization MS, theonline coupling of separation and detection is associatedwith some inherent limitations. First, the peptides of interestare only available for mass spectrometric analysis duringthe limited time span of their elution from the column.Since usually several peptides coelute, this results in a lossof information caused by interference as well as peaksuppression effects. Furthermore, because of the timerequired for precursor selection and fragmentation in themass spectrometer, only a limited number of peptides canbe identified in a given time window. These limitations canbe partially overcome by offline coupling of the chroma-tography with matrix-assisted laser desorption/ionization(MALDI) MS [6]. In this setup, the peptides eluting fromthe chromatographic column are spotted onto a MALDItarget. The subsequent offline mass spectrometric investi-gation of the spotted fractions can then be performedwithout the chromatographic time constraints occurring inonline coupling. The use of LC-MALDI-MS/MS in thesecond dimension of multidimensional separations hasbeen proven to be a useful tool in large proteomic studies[1, 7].

The goal of the present investigation was to establish ananalytical platform for shotgun proteome analysis bycombination of the advantages of 2D HPLC using high-and low-pH elution with those offered by offline HPLC-MALDI-MS/MS coupling. The method was applied for theanalysis of the cytosolic proteome of Corynebacteriumglutamicum. The results obtained were compared withthose obtained in an earlier study [5] applying the sameseparation strategy coupled online to ESI-MS/MS.

Materials and methods

Chemicals and preparation of C. glutamicum protein digest

Deionized water (18.2 MΩ cm) was prepared with a PurelabUltra Genetic System (Elga, Griesheim, Germany). Triethyl-amine (>99%) was obtained from Merck KGaA (Darmstadt,Germany). Acetonitrile (E Chromasolv), α-cyano-4-hydrox-icinnamic acid (CHCA, ≥98%), and glu-fibrinopeptide Bwere purchased from Sigma-Aldrich (Steinheim, Germany).Heptafluorobutyric acid (HFBA, ≥99.0%) and trifluoroaceticacid (TFA, ≥99.5%) were purchased from Fluka (Buchs,Switzerland). Cultivation of C. glutamicum wild-type strainATCC 13032 (American Type and Culture Collection),extraction of the cytosolic proteins, denaturation, reduc-tion, alkylation, and trypsin digestion were performed asdescribed in detail in an earlier study [5].

First-dimension separation at high pH by reversed-phaseHPLC

Peptide separation at high pH was performed on a GeminiC18 column (Phenomenex, Aschaffenburg, Germany,150 mm×2 mm i.d., 3 μm, 110 Å). Eluent A was 72 mmolL−1 aqueous triethylamine, pH 10, adjusted with aceticacid; eluent B was 72 mmol L−1 triethylamine, 52 mmolL−1 acetic acid in acetonitrile. Peptide elution was accom-plished with a gradient of 0–55% B in 55 min with a flowrate of 200 μL min−1 using a low-pressure gradient pump(Rheos 2000, Flux Instruments, Basel, Switzerland)equipped with a degasser (Knauer GmbH, Berlin, Ger-many) and an injection system (Model 7725, Rheodyne,Rohnert Park, CA, USA). The digest of 280 μg total proteinwas injected into a 400 μL sample loop. Detection ofeluting peptides was performed at 280 nm. Fractions werecollected every minute (approximately 200 μL). Subse-quently, acetonitrile was removed by evaporation to 1/10 ofthe original volume using a vacuum concentrator (Model5301, Eppendorf AG, Hamburg, Germany). Fractionseluting between 15 and 45 min of the gradient were usedfor separation in second dimension (in the following namedfractions 1–31).

1246 M. Lasaosa et al.

Second-dimension separation at low pH by ion-pairreversed-phase HPLC

Fractions collected in first dimension were reconstitutedwith 105 μL of 0.10% heptafluorobutyric acid. Eachfraction was separated and spotted onto MALDI targets intriplicate. A 10-μL aliquot of each selected fraction wasinjected with an autosampler (Model Famos, LC Packings,Dionex, Amsterdam, The Netherlands) equipped with a 20-μL sample loop and loaded onto a polystyrene-divinylben-zene (PS-DVB) trap column (10×0.2 mm i.d.) for peptidepreconcentration. The trap column was washed for 2.5 minwith 10% aqueous heptafluorobutyric acid at a flow rate of10 μL min−1 with an HPLC pump (WellChrom HPLCpump K-1001, Knauer, Berlin, Germany). Peptides wereeluted onto an monolithic PS-DVB column (60×0.1 mmi.d.), and separation was performed using a capillary-HPLCsystem (Model Ultimate, LC Packings, Dionex) with a 60-min gradient from 0.05% aqueous TFA (eluent A) to 20%acetonitrile/0.05% aqueous TFA (eluent B) followed by a3-min washing step at 100% B at a flow rate of 0.7 μLmin−1. The oven temperature was set to 25 °C, and UVdetection was performed at 214 nm. The column effluentmixed with the MALDI matrix solution (3 mg mL−1 CHCAand 17 nmol L−1 glu-fibrinopeptide B in 70% acetonitrile/0.1% aqueous TFA solution) in a 1: 4 (v/v) ratio using amicrofraction collector (Model Probot, LC Packings,Dionex) leading to a matrix concentration of 2.4 mg mL−1

in the spotted droplets. The resulting matrix–analytemixture was deposited every 5 s onto MALDI targets(Opti-TOF™ LC MALDI Insert, Applied Biosystems,Darmstadt, Germany).

MALDI TOF/TOF mass spectrometry

A MALDI time-of-flight (TOF)/TOF mass spectrometer(Model 4800, Applied Biosystems) was used for theanalysis of the peptides. MS data from the spots selectedfor analysis were acquired in positive ion mode in the massrange of 800–4,000 m/z by accumulation of 1,000 lasershots. In addition to the default calibration performed usingstandard peptides covering the measured m/z range (4700Proteomics Analyzer Mass Standards Kit, Applied Bio-systems), MS spectra were internally calibrated with glu-fibrinopeptide B spiked to the matrix.

The list of precursors for MS/MS analysis was automat-ically generated by the instrument software (4000 SeriesExplorer Software, Applied Biosystems) according to thefollowing selection criteria: minimum signal-to-noise ratio,35; precursor mass tolerance between spots, ±200 ppm;minimum chromatogram peak of two spots; maximumprecursors per spot, six. MS/MS spectra were generated by1 kV collisions with ambient air and accumulation of

maximally 3,000 laser shots. Stop conditions for MS/MSwere defined as a minimal number of ten fragment peakswith S/N>35 with at least 12 subspectra accumulated.

The 2D LC-MALDI approach presented here wascompared with the results of an earlier study employingthe same separation strategy but online coupling of thesecond dimension separation to an ion-trap mass spectrom-eter (Model esquire HCT, Bruker Daltonics, Bremen,Germany) equipped with a modified ESI-ion source (spraycapillary: fused silica capillary, 0.090 mm o.d., 0.020 mmi.d.) [5]. The following mass spectrometric parameters wereapplied for automated peptide identifications; a moredetailed description of measurement conditions can befound in reference [5]: data-dependent tandem massspectrometry: mass range mode, ultra scan 50–3,000 m/z;scan speed, 26,000 m/z s−1; full scan, 450–1,500 m/z; ionpolarity, positive; maximum accumulation time, 200 ms;precursor ions auto MS(n), 3; MS averages, five spectra;MS/MS scan range, 200–2,000 m/z; active exclusion, aftertwo spectra for 0.50 min; MS/MS fragmentation amplitude,1.5 V; smart fragmentation, on (30–200%); absolutethreshold MS/MS, 4,500.

Protein identification by database searching

For generation of Mascot generic files, the peak lists of theprecursor and fragment ions were extracted from raw datafiles using the Peak to Mascot tool (4000 Series ExplorerSoftware). The settings were the following: maximal peakdensity of 20 peaks per 200 Da; mass range from 60 Da tothe precursor mass minus 35 Da; minimal peak area 200;minimal S/N=10; maximum of 65 peaks per precursor. Thefiltered files were submitted for database search using theMascot search engine [8] (version 2.1, Matrix Science,London, UK). The search was performed against C.glutamicum ATCC 13032 Kitasato database (2,993 proteincoding genes), downloaded from the ComprehensiveMicrobial Research (CMR) public database of the Institutefor Genomic Research (TIGR, http://cmr.tigr.org/tigr-scripts/CMR/CmrHomePage.cgi, version 8.0, August 28,2002).

The following search parameters were used: enzyme,trypsin; allowed missed cleavages, 1; fixed modifica-tions, carboxymethylation of cysteine; variable modifica-tion, oxidation of methionine; mass tolerance forprecursors was set to ±50 ppm and for MS/MS fragmentions to ±0.2 Da. The confidence interval for proteinidentification was set to ≥95% (p<0.05), and onlypeptides with an individual ion score above the identitythreshold were considered.

Estimation of the false positive rate was calculated byMascot search against a composite database created byappending a random database to the normal database thus

Reversed-phase × ion-pair reversed-phase HPLC MALDI TOF/TOF MS 1247

generating a database twice the size of the original [9].Only peptides with ion scores above the identity thresholdwere considered for calculation of the false positive rate.

Results and discussion

Analytical strategy

Successful strategies for proteome analysis are character-ized by five key issues: (1) the method should provide thehighest proteome coverage possible and cover the entirerange of protein expression levels; (2) the sequencecoverage per protein should be maximal in order to enableunambiguous protein identification and allow for thedistinction of protein variants as well as for the detectionof posttranslational modifications; (3) the method shouldavoid the induction of unwanted artificial protein modifi-cations; (4) together with low sample consumption, themeasurement time should be minimal in order to allowmore replicates in shorter time scales; (5) the methodshould be robust both in terms of repeatability and from apractical point of view. Employing an efficient multidimen-sional separation strategy is important because peaksuppression effects severely hamper the mass spectrometricdetection of the compounds in a complex sample mixture,thus also strongly reducing the sensitivity of the detectionmethod. In this study, we applied an offline couplingscheme of reversed-phase chromatography encompassingelution at pH 10.0 in the first dimension and ion-pairreversed-phase chromatography using an acidic eluent(pH 2.1) in the second dimension [5, 10] (Fig. 1).

The elution profile in the first dimension (Fig. 1) of thetryptic digest from the cytosolic proteome extracted from C.glutamicum was characterized by a high number ofincompletely resolved peaks. Fifty five 1-min fractionswere collected and concentrated by evaporation of thesolvent. The pH of the fractions was lowered by dilutionwith 0.1% HFBA before HPLC-MALDI analysis. HFBAwas used instead of the less hydrophobic ion-pairing TFAto improve the retention of the peptides during the trappingand preconcentration step performed on a short precolumnprior to the separation in second dimension [11]. Thefractions were then aliquoted and separated in the seconddimension by ion-pair reversed-phase chromatographyusing an acetonitrile gradient with TFA as ion-pair reagentat pH 2.1 on a PS-DVB-monolithic column and analyzedeither by offline MALDI TOF/TOF-MS (this manuscript)or online by ESI-MS as published before [5]. Preliminaryexperiments revealed that the first fractions up to 15 mincontained only a minor number of peptides; therefore, weonly selected fractions starting from 15 to 45 min forseparation and analysis in the second dimension.

For the coupling of second dimension separation withMALDI-MS, peptides eluting from the column were mixedonline with MALDI matrix and deposited every 5 s ontothe MALDI target. This spotting frequency is a compromisebetween the preservation of the chromatographic separationand the measurement time needed for the overall analysis.One chromatographic run yielded 740 spots on the MALDItarget. Every fraction from the first dimension wasseparated and analyzed in three independent technicalreplicates, leading to a total of 93 LC-MALDI-MS andMS/MS runs, respectively. In a first step, survey MSspectra were measured in positive ion mode. The instru-ment software then automatically selected the six mostintensive precursors per spot for MS/MS experiments,taking into account and neglecting peaks which occurredin more than one spot thus avoiding unnecessary repetitivemeasurements of peptides. This principle was only appliedwithin single LC-MALDI runs; an inter-run exclusion ofpreviously measured peaks was not performed.

Peptide separation and identification

The measurement of the 93 LC-MALDI-MS and MS/MSruns took an overall measurement time of 375 h (withoutthe time for chromatographic separation/spotting) andyielded a total of 75,262 MS/MS queries for database

Fig. 1 Scheme of analytical strategy applied in this study. Addition-ally shown is an alternative strategy applied in an earlier study. Theresults of both platforms are compared in this study. The elutionprofile of the separation in first dimension is equal to that publishedearlier [5]

1248 M. Lasaosa et al.

searching. Out of these MS/MS spectra, 20,552 peptidesequences could be identified, from which finally 7,416unique peptides (nonredundant peptides) were extracted.The peptide identifications were distributed within the threetechnical replicate analyses as follows: 2,596 peptides wereidentified once, 1,928 peptides two times, 1,631 three time,and 1,261 peptides were identified four or more times. Themajority of the peptides identified four or more times wereidentified in neighboring fractions of the first dimension. Adetailed analysis of these peptides showed that most ofthem derived from high-abundant proteins.

In Fig. 2, the number of peptides identified by MS/MSand the repeatability of the analysis within three replicateruns of second-dimension LC-MALDI-MS/MS are plottedagainst the fraction number of the first dimension. Theaverage standard deviation of the average number of peptidesidentified in the three runs was 50; the elevated values forfractions 26 and 29 (standard deviation above 150) arecaused by an experimental error affecting the mass accuracyof the acquisition during one of the replicate measurementsleading to less identified peptides. For all fractions measured,the merging of the data of the three replicates followed by asingle database search led to an increased number of peptideidentifications compared to the combination of the results ofthe data of three replicate analyses.

A relatively uniform, trapezoid distribution of the peptidehits was observed with a maximum of peptides found infractions 9–23. In each of these fractions, between 448 and664 different peptides (average 526) were identified insecond-dimension MALDI-MS/MS. In fractions 1–8 and24–31, 2,480 additional peptides could be identified.

The 2D separation of tryptic peptides is based on thedifferent charge states at high and low pH, resulting insignificant complementarity of the separation modes,although hydrophobic stationary phases were applied inboth dimensions. In order to visualize the charge distribu-tion of the tryptic peptides found in the different fractionsof the first dimension, we calculated the charge states forpeptides generated upon in-silico digestion of all predictedgene products at pH 10.0 and 2.1, respectively. In total,94,973 peptides were generated, taking into account themass range between 800 and 4,000 Da and one missedcleavage site according to the parameters also used fordatabase searching. As shown in Fig. 3, a broaderdistribution of charge states in solution can be observed atpH 10.0 as compared to pH 2.1.

The trapezoid distribution of peptides observed (Fig. 2)in the first dimension is a typical feature of the separationscheme applied here [5]. The broader charge distribution atpH 10.0 leads to a broadening of the elution window for thepeptides as compared to conventionally employed SCX-separations [10]. Peptide separation in SCX is usuallyperformed at low pH (e.g. pH 3), where most of thepeptides carry charges between +2 and +4. This narrowrange of charges in SCX hampers efficient separation andlimits the peak capacity of the separation [12]. In reversed-phase chromatography at pH 10, the charges themselves arenot involved in the separation but strongly contribute to thetotal hydrophilicity of the peptides. Previous investigationshave revealed that triethylamine added to the mobile phasedoes not act significantly as an ion-pairing reagent atpH 10. As shown in Fig. 4, even peptides of the samecharge are distributed over a wide range of elution timesdue to separation according to their hydrophobicity [5]. Adetailed analysis of the identified peptides revealed thathighly negatively charged peptides (charges −12 to −4)eluted in the first dimension before lower negativelycharged (charges −3 to −1) and neutral or positivelycharged (charges +1 to +3) ones (Fig. 4). This observation

Fig. 2 Replicate analysis by LC-MALDI-MS/MS of the fractionscollected during the first dimension. The average number of differentpeptides obtained by triplicate measurements of a fraction is showntogether with the standard deviation. The total number of differentpeptides identified per fraction is also shown

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

<-6 -6 -5 -4 -3 -2 -1 0 1 2 3 4 >4

net charge

no

. of

tryp

tic

pep

tid

es

Fig. 3 Net-charge distribution of 94,973 tryptic peptides generated byin silico digest (one missed cleavage site allowed, m/z 800–4,000) ofall theoretical gene products in C. glutamicum strain ATCC 13032 atpH 10.0 (black bars) and pH 2.1 (grey shaded bars)

Reversed-phase × ion-pair reversed-phase HPLC MALDI TOF/TOF MS 1249

demonstrates that charging has a significant influence onthe overall hydrophilicity of the peptides [13].

In the second-dimension separation performed at pH 2.1,the basic and acidic functionalities are protonated, and thusthe peptides are mainly positively charged. The majorityof the peptides carries charge +2 (56% of all identifiedpeptides) and +3 (33%), while only a small fraction carriescharge +4 or more (Fig. 3). The ion-pairing reagenttrifluoroacetic acid is significantly adsorbed to the hydro-phobic surface of the stationary phase and entails asignificant contribution of electrostatic interactions toretention. In this case, a mixed mechanism of ion-pairingand reversed-phase chromatography leads to the separationof the peptides [14].

Figure 5 illustrates the distribution of the peptides overthe entire separation space in the 2D separation scheme.The peptides are distributed over the entire separation-space. However, there is a tendency of the peptides in laterfractions of the first dimension to elute also later in thesecond dimension. This reflects the nonperfect orthogonal-ity (quasi-orthogonality) of the separation scheme due tohydrophobic interactions playing a role in the separationmechanisms of both dimensions. Nevertheless, it allows foran efficient separation of the complex mixture of peptides.A distinct advantage of the new separation scheme is theavoidance of high salt loads, which minimizes the danger of

column clogging thus increasing the robustness of theanalytical platform. Furthermore, no desalting of the sampleis necessary, and sample preconcentration is easily per-formed by evaporation of the acetonitrile-water eluent.

Efficient separation strategies are characterized byminimal carryover of peptides over different fractions. Wechose a fractionation interval of 1 min for the firstdimension, which resulted in the identification of 80% ofthe peptides in only a single fraction (Fig. 6). Thirteenpercent of the peptides were identified in two neighboringfractions, and only a minority of about 7% of the peptideswas found in three or more fractions. Thus, compared toSCX-separations, where carryover is prevalent because ofnonspecific hydrophobic interactions of the peptides withthe stationary phase [4, 15, 16], the application of reversed-phase separation in the first dimension shows a stronglyreduced carryover of peptides. A detailed analysis of thepeptides identified showed that 13% of the identifiedpeptides were deamidated. This high deamidation rate isprobably caused by the experimental conditions employed,

Fig. 5 Orthogonality of the two separation principles. Peptidesidentified in three replicate measurements are plotted, redundantpeptides are included. Each point represents one peptide identified byMALDI TOF/TOF analysis, overlapping points are displaced. x-axis31 fractions from reversed-phase separation at pH 10.0 (firstdimension), y-axis separation by IP-RP chromatography at pH 2.1(second dimension)

Peptides sequenced acrossmore than 3 fractions (5%)

Peptides sequenced in 3consecutive fractions (1%)

Peptides sequenced in 2consecutive fractions (13%)

Peptides sequencedwithin 1 fraction (80%)

Fig. 6 Analysis of peptide carry-over across different fractions.Eighty percent of the peptides were identified by MALDI TOF/TOFMS/MS in one single fraction of first dimension; the residual 20%were found in more than one fraction

Fig. 4 Net charges of the peptides eluting in the first dimension atpH 10.0. The y-axis represents the number of different peptides

1250 M. Lasaosa et al.

e.g. the basic pH applied in first dimension separation [17].On the other hand, it cannot be ruled out that biologicaldeamidation events also contribute here [18].

Protein identification

After HPLC-MALDI TOF/TOF analysis, the raw data ofeach single run were converted into a peak list. Proteinidentification was done by merging all generated peak lists,including all fractions generated in the first dimension andthe three replicate runs in second dimension and performinga single Mascot search against a C. glutamicum database. Astatistical significance ≥95% was selected as threshold forprotein identification, and only peptides with individualion scores above the identity threshold (p<0.05) wereconsidered.

The database search resulted in the identification of1,644 proteins. The majority of these proteins (1,208proteins, 74%) was identified by two (17%), three (12%),or more than three (44%) different peptide hits (see alsoTable S1 in Electronic Supplementary Material). Wecalculated a false positive rate of 2.4% performing a searchin a composite database [9]. This false positive rate is ingood accordance with other shotgun-based proteomeanalyses [19].

The number of so called one-hit wonders, representingproteins identified by a single peptide only, was 436,corresponding to 26% of the identified proteins. It is agenerally accepted rule for the presentation of proteomedata that proteins identified by a single peptide only (one-hit wonders) should be excluded or at least be separatelyhandled [20]. A detailed analysis of these proteins reveals,that 182 proteins (See Table S2, Electronic SupplementaryMaterial) of this class are characterized by one or more ofthe following characteristics: (1) The peptide was repeat-edly identified in several technical replicates of theexperiment (163 proteins). (2) The peptide showed a highion score in MS/MS experiments. Thus from a massspectrometric point of view, nearly complete or at leasthigh sequence coverage per peptide in MS/MS experimentswas achieved, guaranteeing a high confidence of peptideidentification (14 proteins). (3) The peptide was identifiedboth in modified and unmodified form (five proteins). Inthis subclass, the oxidation of methionine residues and thedeamidation of Asn and Gln residues, respectively, areincluded. Further, 61 (14%) of the one-hit wonders hitscorrespond to proteins with molecular masses below 15 kDa.These small proteins do only deliver a restricted number ofpeptides after digestion. In some cases, the single peptideidentified accounted for a protein coverage higher than 10%(43 proteins) or even higher than 20% (ten proteins).

Peptides, and consequently proteins identified from thesepeptides, analyzed by one or more of these characteristics

can be regarded as unambiguously identified, at least asborderline identifications [21]. Nevertheless, for moredetailed, biologically oriented studies, the confirmation ofthese proteins has to be done by additional experiments,e.g., by Western blot analysis.

Protein sequence and proteome coverage

The genome of the C. glutamicum strain investigatedcontains 2,993 open reading frames. The number ofidentified proteins corresponds to a proteome coverage of40.4% of the theoretical gene products. Two facts have tobe taken into account in the discussion of proteomecoverage: first, it is not to be expected that all proteins areexpressed at every point of time in the life cycle of thebacterium. Second, we investigated solely the cytosolicfraction of the proteome; the membrane proteome,corresponding to about 22% of the genome (about 659proteins) [22], was not investigated here. Thus, the realproteome coverage achieved is in fact higher than 40.4%.

Besides the achievement of a high coverage of theproteome, the achievement of high sequence coverage ofthe individual proteins identified is an important goalboth for confident protein identification and for theidentification of posttranslational modifications. For themajority of the proteins (701 proteins excluding one-hitwonders) identified in this study, a sequence coveragebelow 25% was obtained; for 434 proteins, the sequencecoverage was between 25% and 50%, 70 proteins wereidentified with coverages between 50% and 75%, andonly three proteins were identified with a coverage above75%.

A deeper investigation of the proteins identified showedthat the analytical approach delivered a representative viewof the proteins expected in this organism. A comparison ofthe virtual gel, created by plotting the theoretical pI valuesagainst the molecular mass of the proteins potentiallyexpressed and a corresponding plot of the proteinsidentified, showed that there was no obvious discriminationof the proteins identified with respect to one of these twoparameters (See Fig. S1, Electronic Supplementary Mate-rial). The calculated bimodal distribution of proteins wasmirrored by the proteins identified in this study. Aninteresting fact is the high number of proteins (102 proteins,corresponding to 13.7% of expected) with pI values above7.5 were identified (31 of these proteins have pI valuesabove 10). This demonstrates a certain advantage of thechromatography-based shotgun analysis compared to 2Dgel-based approaches, which have been shown only todeliver a lower number of basic proteins [23]. The sameholds true for the 21 (10.5% of expected) proteins withmasses below 10 kDa, and 28 (85% of expected) proteinswith masses above 120 kDa were identified here.

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Dynamic range of protein identifications

An important factor defining the usefulness of proteomeanalysis to investigate biological problems is the dynamicrange of proteins which can be covered. Previous studiesdemonstrated a good correlation between the degree of thecodon bias and the level of gene expression in bacteria [24],with large codon bias levels corresponding to highexpression levels. The codon adaption index (CAI) [25]therefore allows a rough estimate of protein expressionlevels, with CAI values close to one indicating high, CAIvalues close to zero indicating low expression levels. CAIvalues were calculated (www.jcat.de) according to aprocedure previously defined [26] for all predicted proteinsin the C. glutamicum database as well as for the proteinsidentified by the 2D HPLC-MALDI approach. Figure8 illustrates the number of identified proteins within adefined CAI value interval; additionally, the percentage ofidentified proteins belonging to this interval is assigned. Itis observed that, independently of the total number ofproteins in each interval, the percentage of identifiedproteins increases for proteins with higher expressionlevels. The sensitivity of our method decreases to 50%identifications for proteins with CAI values between 0.3and 0.4. Nevertheless, 20% of the proteins encoded bygenes presenting the lowest CAI values (0–0.2) wereidentified. Despite the commonly observed preference ofhigh-abundant proteins being analyzed in proteome analy-sis, a significant part of low-abundant proteins could alsobe detected with the method presented here.

Comparison of the 2D LC approach coupled eitherto MALDI TOF/TOF or ESI ion trap MS

Together with the separation strategy applied, the selectionof the mass spectrometric system coupled for the detectionand the identification of the separated peptides has asignificant influence on the results of the overall proteomeanalysis. We therefore compared the results of the 2Dseparation strategy coupled to offline MALDI-MS/MSpresented here with those obtained in an earlier studywhere the separation in second dimension was coupledonline to electrospray ionization ion trap MS/MS [5].

In both studies, identical fractions of the first dimensionseparation of 280 μg of proteome digest were used forsecond-dimension separation. For second dimension, equalsample volumes (10 μL) were injected, and peptideseparation was carried out under the same chromatographicconditions in both approaches. Three technical replicateswere performed in both experiments.

The results obtained by both platforms showed a numberof significant differences (Table 1). The number ofunambiguously identified peptides (without redundant hits)

in the MALDI platform was 7,416, whereas 2,709 peptideswere identified in the ESI-based approach. This discrepan-cy can be explained by several inherent method-specificfactors. First, in online coupling with ESI-MS, the timescale for the performance of the measurements is restricted(1) by the peak width in the chromatographic separationand by (2) the necessity for switching between MS and MS/MS experiments within a short time interval. Thus, acomplete analysis cycle in ESI-IT-MS allowed only themeasurement of three MS/MS spectra per cycle [5].

Both factors are practically not relevant when thechromatographic separation and the mass spectrometricanalysis are decoupled from each other as realized in offlineHPLC-MALDI-MS, where the chromatographic separationis conserved on the MALDI target. Additionally, MSanalysis and MS/MS experiments are performed indepen-dently from each other. MS analysis is performed toproduce a survey of all precursors contained over the entirechromatographic run, followed by the generation of a list ofprecursors, which also prevents the unnecessary repeatedmeasurements of redundant precursors. After selection ofthe precursors, a maximum of six MS/MS experiments wascarried out on each spot. MALDI limiting factors areconsumption of the amount of sample on the plate andsuppression effects affecting the ionization process. Despitethese drawbacks, the number of precursors selected for MS/MS in LC-MALDI-MS approach was double than thatpossible in the LC-ESI approach applied in this compara-tive study. It has to be mentioned at this point that, aftercompletion of the measurement on a number of samplespots on the target, still sufficient amounts of analytes could

Table 1 Comparison of the 2D HPLC MALDI TOF/TOF and ESI-ITapproach (data from Delmotte et al. [5])

MALDITOF/TOF

ESI-IT

Acquired MS/MS spectra 75,262 25,209Successfully assigned MS/MS spectra 20,553 10,882Unique peptides identified 7,416 2,709Total number of proteins identified 1,644 745Number of proteins without one hitwonders/% of total

1,208/74% 468/63%

Number of protein identifications byonly a single peptide/% of total

436/26% 277/37%

Average mass accuracy (MS) 20 ppm 218 ppmMeasurement time HPLC + MS + MS/MS 492 h 124 hNumber of proteins identified per hour ofmeasurement

3.34 6.00

False positive rate (%) 2.4 2.3Proteome coverage (with/withoutone-hit wonders)

54.9%/40.4%

24.9%/15.6%

Average sequence coverageof proteins (%)

19.8 16.8

1252 M. Lasaosa et al.

be found (data not shown). This allows for additional MS/MS experiments, e.g., after creation of exclusions lists withstill measured precursors and hence for a deeper coverageof the proteome.

In the online HPLC-ESI-MS approach, in three replicatesof the 31 fractions from first dimension, a total of 25,209 MS/MS experiments were performed, whereas in off-line LC-MALDI-MS, 75,262 precursors were analyzed. Interestingly,the portion of successfully assigned MS/MS spectra washigher for the ESI-IT-MS/MS approach: 43% successfulpeptide identification vs. 27% in MALDI TOF/TOF MS/MS.The higher yield of successful MS/MS experiments observedin the ESI-based approach is potentially caused by the factthat higher charged precursors undergo more efficientcollision induced decomposition compared to the singlycharged precursors mainly formed in MALDI-MS. Althoughthe total number of unique peptides identified by ESI-IT-MSwas significantly lower (2,709 vs. 7,416 peptides in MALDITOF/TOF), the efficiency of correctly assigned MS/MSspectra was better for ion trap MS.

For three technical replicate measurements of the 31fractions obtained in first dimension separation, a totalmeasurement time of 124 h was needed in online LC-ESI-MS. In case of LC-MALDI-MS/MS, the total measurementtime including spotting, MS, and MS/MS experiments was492 h for the three replicates. Hence an increase in thenumber of protein identifications by a factor of more than 2(or a factor of three without one hit wonders) has to becounterbalanced by four-fold analysis time. It can bespeculated that a further optimization of the online ESI-IT-MS approach based on longer analysis times (e.g.,higher degrees of fractionation in the first dimension,longer gradients in the second dimension, more MS/MSacquisition per MS spectrum, which requires more recentmass spectrometry technologies) would yield more identi-fications. As outlined above, in future LC-MALDI-basedexperiments, the information content still could be in-creased by application of dynamic exclusion of redundantprecursors as well as by the use of more efficientinterpretation algorithms for the MS/MS spectra. Further-more, the analysis time could be reduced by merging thosefractions showing low peptide content. These modificationsof the analytical setups could potentially lead to aconvergence of the overall measurement times in bothapproaches.

Out of the 7,416 peptides identified in MALDI-MS/MS,2,035 peptides (26% of the sum of peptides in bothapproaches) were also identified in the ESI-MS/MS study;5,378 (69%) peptides were only identified in MALDI-MS/MS, 395 peptides (5%) only in ESI-MS/MS experiments,respectively. In the MALDI-based approach, the percentageof peptides with masses below 1,400 Da was highercompared to the ESI-based approach (Fig. 7a), whereas

Fig. 7 Comparison of physicochemical parameters of peptidesidentified either by 2D LC-MALDI TOF/TOF or by 2D LC ESI-IT-MS/MS. a molecular mass, b pI values, c hydrophobicity [13]. y-axispercentage of all peptides identified

Reversed-phase × ion-pair reversed-phase HPLC MALDI TOF/TOF MS 1253

with the latter approach, higher percentages of peptideswith masses above 1,400 Da were identified. In bothplatforms, peptides with masses below 1,000 Da wereunderrepresented. Due to the potential overlap with matrixsignals in case of MALDI-MS, the lower limit ofmeasurement range was set to m/z 800, whereas in ESI-MS, the lower m/z limit was set to 500 in order to detectmultiply charged peptide ions. The more efficient fragmen-tation of multiply charged precursor ions explains thepreference for higher mass peptides to be identified inESI-MS/MS. The majority of theoretical peptides with massabove 3,000 Da were not identified in both approaches.

The hydrophobicity of the peptides was calculated usingthe normalized consensus Eisenberg scale [13]. The relativenumber of identified peptides with both approachesaccording to their hydrophobic value is shown in Fig. 7b.A slight but not significant tendency can be observed forthe MALDI-based approach to identify more hydrophobicpeptides (positive values), whereas in the ESI-basedapproach, hydrophilic peptides are slightly preferred. Therelative number of identified peptides classified accordingto the calculated pI is given in Fig. 7c. The percentage ofpeptides identified with pI values below 5 was higher inESI-MS, while the percentage of identified peptides withhigher pI values was slightly elevated for MALDI-MS.Potentially, this behavior is related to the formation ofsingly charged ions in MALDI-MS, which may berestricted in case of acidic peptides.

Out of the 7,416 unique peptides identified in theMALDI based platform, 1,644 proteins where identified,whereas in the ESI-based approach, 2,709 unique peptidesallowed for the identification of 745 proteins. Amongstthese proteins, the number of one-hit wonders was 436(26%) in case of MALDI and 277 (37%) in case of the ESIbased platform. Among the proteins identified in bothplatforms, sequence coverages were generally higher in theMALDI-based platform. In the ESI-based approach, thepercentage of proteins with low (between 1% and 15%)sequence coverage was 6% higher compared to MALDI-MS; it was similar for proteins with sequence coveragebetween 15% and 25%, whereas MALDI delivered 6%more proteins with sequence coverage higher than 25%.The false positive rates determined for the proteinsidentified with more than one peptide were comparable inboth platforms (MALDI, 2.4%; ESI, 2.3%).

Of the 1,208 proteins identified with more than onepeptide by the MALDI-based strategy, only 461 proteinswere also identified by ESI-MS. Seven hundred forty-sevenproteins were identified only by MALDI-MS, whereas onlyseven proteins were exclusively identified by ESI-MS;these proteins were the fructose specific IIc component ofthe phosphotransferase system (NCgl1861/molecularweight 70,501.5 Da/theoretical pI 5.1), a hypothetical

protein (NCgl0385/17830.0/4.0), the ribosomal proteinL11 (NCgl0459/15329/10.5), the phosphoribosyl-ATP-pyrophosphohydrolase (NCgl1448/9790.0/4.4), a hypothet-ical protein (NCgl2493/8207.3/4.3), a hypothetical protein(NCgl0930/7507.2/4.4), and the ribosomal protein L30/L7E(NCgl0519/6863.2/11.6). None of these proteins showsobvious unusual properties justifying its identification byESI-MS only, therefore their identifications can be regardedas random events.

Identification of high-abundant proteins with CAI valuesbetween 0.6 and 0.9 was comparable on both platforms,covering between 75% and 90% of the proteins of this class(Fig. 8). At lower expression levels, the MALDI-basedapproach showed significantly increased positive identifi-cations. For example, for the CAI range between 0.2 and0.3, 396 (30.2%) of the proteins predicted were identifiedby MALDI, compared to 47 (3.9%) in ESI; for the lowestexpression level range between 0.1 and 0.2, in MALDI 107proteins (19.3%) compared to six proteins (1.1%) in ESIcould be identified.

Proteins identified in the cytosole of C. glutamicum

The classification of the proteins of C. glutamicum intocategories based on the gene information, as well as thepercentage of gene products identified in the present study,is given in Table 2. Almost all proteins involved inbiosynthetic functions in the cell were identified in thepresent work: biosynthesis of amino acids (88%), proteins(83%), nucleosides and nucleotides (75%), cofactors,

0

10

20

30

40

50

60

70

80

90

0.1-0.20.2-0.3

0.3-0.40.4-0.5

0.5-0.60.6-0.7

0.7-0.80.8-0.9

0

200

400

600

800

1000

1200

no. o

f pro

tein

s

CAI-value

ident.MALDI ident.ESI theoretical

rel.MALDI rel.ESI

% o

f the

oret

ical

Fig. 8 Calculated codon adaption indices (CAI values) for theproteins encoded in C. glutamicum wild-type ATCC 13032 (openrectangles) and identified by 2D LC MALDI TOF/TOF MS/MS (greyshaded rectangles) and by 2D LC ESI-IT-MS/MS (black rectangles,data taken from Delmotte et al. [5]). Squares show the percentage ofproteins identified by MALDI compared to theoretical, open trianglesthe percentage of proteins identified by ESI-MS. Only proteinsidentified by at least two peptides are included. Higher CAI valuescorrespond to higher expected expression levels of the proteins

1254 M. Lasaosa et al.

prosthetic groups, and carriers (71%). Besides, it isremarkable that over 50% of the predicted proteinsinvolved in cellular processes (61%), energy metabolism(58%), DNA metabolism (55%), transcription (57%), andcentral intermediary metabolism (51%) were identified. Incontrast, transport and binding proteins as well as proteinsfrom the cell envelope are underrepresented (34% and 32%identified, respectively) as expected in a fraction of theproteome containing mainly cytosolic proteins.

Especially interesting is the fact that 49% of the proteinsinvolved in regulatory functions in C. glutamicum wereidentified. The proteins involved in regulation of geneexpression have generally low CAI values which indicatelow expression in the cell [25]. They are hence thusdifficult to identify in a complex mixture of proteins. Mostof the regulatory proteins in C. glutamicum are DNA-binding transcriptional regulators [27] containing a helix-

turn-helix structural motif responsible for the protein–DNAinteraction [28]. Recently, a list of 56 transcriptionalregulators of known function was published based onexperimental characterization and prediction by bioinfor-matics tools [27]. Table S3 (Electronic SupplementaryMaterial) shows the 33 DNA-binding transcriptional regu-lators of known function identified in the present study.

In the genome of C. glutamicum, 13 two-componentsignal transduction systems have been annotated [29],which are integrated by two domains: a kinase and acorresponding response regulator. As the sensor kinases areintegral membrane proteins with at least one transmem-brane helix, they were not extracted with the cytosolicproteins analyzed in this work and therefore not identified.However, seven of the response regulators which form partof a two-component system were identified (see Table S4,Electronic Supplementary Material).

As C. glutamicum is an important organism for thebiotechnological production of amino acids, the knowledgeof the function and interrelations of the main metabolicpathways, especially the central metabolism deliveringprecursors and energy for biosynthesis and pathways andof amino-acid production, are of special interest for thedeeper understanding of regulative processes and hence forrational strain design [30]. Therefore, the identification ofthe proteins involved in these major pathways is of specialinterest. In the present study, all enzymes involved inglycolysis and gluconeogenesis were identified (Table S5,Electronic Supplementary Material). Further, all majorenzymes of the citrate cycle were identified; only a singlehypothetical membrane protein (locus tag NCgl0359) wasnot found, which was expected as only the cytosolicproteome was investigated here. From the enzymes partic-ipating in the pentose phosphate cycle, only gluconatekinase (NCgl 2399, MW 18.4 kDa) was not identified. Allenzymes involved in the lysine biosynthesis pathway excepta putative dihydrodipicolinate synthase were identified.

Conclusions

The strategy for shotgun proteome analysis presented herecombines both the advantages of a novel chromatographic2D separation scheme with the advantages offered byoffline coupling of chromatographic separation with latestMALDI TOF/TOF MS technology. Compared to onlinecoupling with electrospray ion trap MS, the application ofMALDI TOF/TOF technology is characterized by asignificantly increased number of identified proteins. Thisis mainly caused by the absence of time-limiting factorsinherently present in online methods, mainly those restrict-ing the number of measurable compounds. Further, theimproved technical performance, in particular in terms of

Table 2 Classification of proteins identified by MALDI-MSaccording to their biological function

Gene role category # ofgenesa

Identified by RP×IP-RP MALDITOF/TOF

Percent

Amino acid biosynthesis 100 88 88Biosynthesis of cofactors,prosthetic groups andcarriers

100 71 71

Cell envelope 297 94 32Cellular processes 95 58 61Central intermediarymetabolism

99 51 51

DNA metabolism 99 55 55Energy metabolism 234 137 58Fatty acid and phospholipidsmetabolism

33 10 30

Hypothetical proteins 139 19 14Hypothetical proteins-Conserved

541 246 45

Mobile andextrachromosomal elementfunctions

43 4 9

Protein fate 104 50 48Protein synthesis 121 100 83Purines, pyrimidines,nucleosides and nucleotides

71 53 75

Regulatory functions 174 86 49Transcription 35 20 57Transport and bindingproteins

292 100 34

Unclassified 580 372 64Unknown function 60 30 50

Assignment of gene role categories according to ComprehensiveMicrobial Resource (www.tigr.org). Identified proteins were classifiedaccording to their main role categorya Note that some genes are assigned to more than one role category

Reversed-phase × ion-pair reversed-phase HPLC MALDI TOF/TOF MS 1255

mass accuracy in MS and MS/MS mode and of sensitivity,of MALDI TOF/TOF MS compared to the ESI ion trapmass spectrometer used in this comparative study increasesthe significance and confidence of protein identifications.On the other hand, the major advantage of the onlineplatform is the elevated analytical productivity caused bythe reduced measurement time. It has to be mentioned thatthe use of an alternative mass analyzer technologyhyphenated to electrospray ionization clearly is expectedto deliver improved results in terms of mass accuracy,resolution, and sensitivity; therefore, the gap between theonline and the offline platform described here can benarrowed. Nevertheless, the ion trap technology used forthe comparison in this study is widely used in manylaboratories, which, in our opinion, legitimates the com-parison of these two platforms.

Upon application of this novel analytical platform,including borderline identifications, more than 50% of allproteins theoretically expressed in C. glutamicum could beidentified. Besides high-abundant proteins involved inmetabolic events, a number of low-abundant proteins, suchas regulatory proteins, were found. Thus, the analyticalplatform presented and evaluated here will be a valuabletool for proteome analysis in this and other organisms in thefuture. First results also indicate the suitability of themethod for quantitative measurements using the iTRAQstrategy [31], which are presently under investigation.

Note added in proof A detailed study of the repeatability of peptideidentifications in shotgun proteome analysis employing the off-linetwo-dimensional chromatographic separation presented here waspublished recently [32].

Acknowledgment We thank the Centre for Bioinformatics (ZBI) atSaarland University and the Deutsche Forschungsgemeinschaft (DFG)for funding of the MALDI TOF/TOF mass spectrometer.

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