TOF−MS Analysis Using

Dec 23, 2006 - Automation of nanoflow liquid chromatography-tandem mass spectrometry for proteome and peptide profiling analysis by using a monolithic...
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A New Fast Method for nanoLC-MALDI-TOF/TOF-MS Analysis Using Monolithic Columns for Peptide Preconcentration and Separation in Proteomic Studies Katrin Marcus,*,† Heike Scha1 fer,† Stefan Klaus,† Christian Bunse,† Remco Swart,‡ and Helmut E. Meyer† Medizinisches Proteom-Center, Ruhr University Bochum, Bochum, Germany, and LC Packings and Dionex Benelux, Amsterdam, The Netherlands Received August 11, 2006

A new fast method for identification and characterization of proteolytic digests of proteins by monolithic liquid chromatography coupled with mass spectrometry has been developed. The advantages of the monolithic columns are a high-pressure stability and low back pressure resulting in higher flow rates for capillary or nanosize columns simplifying the system handling. As was shown in several publications, such monolithic stationary phases are highly qualified for the analysis of peptides and proteins, but so far, only small volumes could be injected into the system, which might hamper the sample preparation leading to protein precipitation and partial loss of sample. To overcome the problem of small injection volumes, we established a system including a short monolithic trap column to allow preconcentration of the peptides. The injected sample is flushed at higher flow rates onto the trap column, bound to the stationary phase, and in this way concentrated in a few nanoliters before starting the separation. The expanded system was optimized and tested using different reference protein samples. Eluting peptides were detected by MALDI-TOF/TOF-MS and identified by database searching. The system is now a permanent part for proteome analysis in our lab, and as such, it was successfully applied for the detection of post-translational modifications and the analysis of membrane proteins. One example for these analyses is also included in this paper. Keywords: nanoRP-HPLC • MALDI-TOF/TOF-MS • peptide preconcentration • monolithic columns

Introduction Over the past years nanoliquid chromatography tandem mass spectrometry (nanoLC-MS/MS) proved to be a workhorse in the field of proteomics. Frequently, proteins of a very complex sample, such as special cells or tissues, are separated by 1D- or 2D-PAGE initially. Protein spots of interest are excised from the gel, tryptically digested, and extracted in several microliters previous to mass spectrometric analysis.1 Eluting peptides are either directly detected by online coupling with an electrospray ionization mass spectrometer (ESI-MS) or offline coupling with a matrix-assisted laser desorption/ionization mass spectrometer (MALDI-MS). Subsequent protein identification is done by automatic database searching. A limitation of nanoLC is the small volume (maximum around 1 µL) that can be injected to the system to get a feasible separation and analysis time. Especially for the analysis of proteins after gel electrophoresis, the volume of the sample is predetermined by the extraction procedure, where typically * To whom correspondence should be addressed. Dr. Katrin Marcus, Medizinisches Proteom-Center, Ruhr-Universita¨t Bochum, ZKF 2.051, Universitaetsstrasse 150, 44780 Bochum, Germany. Phone: +49 234 32-29275. Fax: +49 234 32-14554. E-mail: [email protected]. † Ruhr University Bochum. ‡ LC Packings and Dionex Benelux.

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volumes in the range of 5-10 µL are used. First, the development of small trap columns for sample concentration and washing in front of the separation column enables the injection of larger volumes into such nanosystems: the sample can be bound and concentrated on the trap column using higher flow rates. Subsequent elution of the sample occurs directly by switching the trap column into the nanoflow separation system. The sensitivity of these systems is very high, but accurate handling is required to get the nanoLC system running due to the minimal flow rates of only a few hundred nanoliters. Therefore, it is highly important to prevent all void volumes, otherwise the separation and detection of peptides is hampered.2 In these applications, mostly silica-based stationary phases with reversed-phase characteristics are used for peptide separation. Monolithic columns prove to be a potential alternative to microparticular columns for comprehensive peptide/ protein analysis. The stationary phase consists of a continuous rod of a rigid, porous material which has only internal porosity composed of meso- and macropores.3 The macropores form a dense network of pores around the silica skeleton allowing rapid transit of the eluent through the stationary phase, and the mesopores form a large surface area for selective adsorption of the analyte molecules.4 A number of different types of monolithic materials based on organic polymers5-7 or silica 10.1021/pr060406w CCC: $37.00

 2007 American Chemical Society

Monolithic Columns for Proteomic Studies

gel8-10 are used for chromatographic separations today. Especially monolithic capillary columns consisting of poly-(styrenedivinylbenzene) (PS-DVB) possess an excellent chromatographic efficiency as there is no intra particular volume and the mobile phase is forced to flow through the pores of the stationary phase.11 The polymers are chemically stable even at elevated column temperatures, which is desirable for rapid and high-resolution separations.12 Moreover, higher flow rates can be applied as the porous structure of the monolith significantly reduces the column backpressure and leads to sharp chromatographic peaks with high relative concentration of the eluting peptides, reduction in separation time, and significantly increased sample throughput. No special frits or sieves are necessary as the monolith can be immobilized onto the wall of the tubing. Because of its highly hydrophobic nature, the synthetic polymer in certain cases can be used directly as a reversed-phase (RP) stationary phase without the need for further derivatization.13 Previous results showed that PS-DVB phases exhibit a hydrophobicity comparable to a particular C4 or C8 RP column; as a result they do not bind analytes as strong as microparticular C18 media (Irina Dragan and Steffen Liedtke, LC Packings, personal communication). Summarized, the monolithic columns are as sensitive as the microparticular columns, but they can be run with higher flow rates, simplifying the handling of the system. Also, in the field of proteomics, monolithic column systems are now applied to answer different questions for the analysis of peptide and protein. When monolithic column systems for peptide and protein identification were used coupled either offline or online with a mass spectrometer, different groups obtained very convincing and promising results.3,14 Because of the higher flow rates and faster separation of the analyte, the requirements of the coupled mass spectrometer become more sophisticated, especially in the case of online coupling where the scan speed of the MS instrument has to be very high. A very promising alternative is the offline coupling with a MALDI-MS: in this setup, the eluting peptides are first spotted automatically onto a MALDI-target, preferentially an anchor chip target, for further concentration of the peptides. The higher flow rates of the separation using monolithic columns are beneficial for fractionation, as it is possible to collect samples in a few seconds. The aim of the present study was to establish an alternative method to nanoLC-MS approaches with microparticular nanoRP C18 columns for proteome analysis. Therefore, we launched a nanoLC system consisting of a monolithic precolumn and separation column to get a more robust system for protein analysis in the low femtomole range without the limitations of microparticular nanoLC-MS. This new monolithic system enables the injection of larger sample volumes and provides shorter analysis times resulting in a higher number of analyses in the same time. The separation system is coupled offline with a MALDI-TOF/TOF mass spectrometer by automatic fractionation and direct spotting of the peptides onto an anchor chip target. The optimization and evaluation of this monolithic nanoLC-MS system coupled with MALDITOF/TOF-MS (LCmono-MALDI-MS), as well as its application for the detection of post-translational modifications, are presented in this work.

research articles hydroxycinnamic acid (HCCA) were purchased from Fluka (Buchs, Swizerland). The HCCA matrix was recrystallized before use. Acetone, ethanole, 3-[N-(cholamidopropyl)dimethylammonio]-1-propansulfat (CHAPS), and sodium citrate were obtained from Sigma Aldrich (Deisenhofen, Germany). Acetonitrile (ACN) was from Biosolve BV (Valkenswaardt, The Netherlands). Acrylamide, urea, iodoacetamide, sodium dodecylsulfate, glycine, and dithiothreitol (DTT) were purchased from Applichem GmbH (Darmstadt, Germany). Formic acid (FA) was purchased from J.T. Baker B.V. (Deventer, The Netherlands). All reagents were purchased in the highest quality available. Cytochrome C (horse), glucose oxidase (Aspergillus niger), and bacterial lipase were obtained from Serva Electrophoresis GmbH (Heidelberg, Germany). Trifluoroacetic acid (TFA) was from Merck KgaA (Darmstadt, Germany). Modified sequencing grade trypsin was obtained from Promega GmbH (Madison, WI). Sample Preparation. 1. Reference Proteins. Cytochrome C, glucose oxidase, and lipase, were mixed in amounts of 18 µg each. The protein amount was determined by amino acid analysis. Standard proteins were separated by 1D-SDS-PAGE with only one large pocket resulting in broad protein bands. Visualization of proteins was done by colloidal coomassie staining. One band of each protein was portioned in 32 spots each to ensure highest reproducibility for subsequent analysis. As a result, each of the singular spots contained about 0.5 µg of the respective protein. 2. Mouse Lens Proteins, Proteins were extracted from eye lenses of 70-week-old mice (C57BL6) as described in detail by Jungblut et al.15 Separation of the protein mixture was done by IPG-based 2D-PAGE.16 In brief, the samples were loaded on a 18 cm IPG strip (pH 4-7, GE Healthcare, Munich, Germany) via cup-loading at the anodic end of the strip. Focusing was done on a Multiphor-II-IEF system (GE Healthcare, Munich, Germany) for 80 kVh (30 min, 250 V; 60 min, 500 V; 60 min, 1000 V; 60 min, 2000 V; 22 h, 3500 V). SDS-PAGE (21 cm × 25 cm × 1 mm, 13% T) was run in an ETTAN-Dalt 12 system (GE Healthcare) with 2 W/gel for 45 min and additional 30 W/gel (maximum 180 W) for about 3 h. Proteins were visualized by imidazole-zinc staining.17 The protein bands/spots were excised from the gel, washed, and tryptically digested as previously described.16,18 Peptide extraction was done by sonication for 2 × 15 min using different extraction solutions (each 15 µL) depending on the following mass spectrometric analyses: for MS/MS analysis, using the LCQ Deca XP (ThermoElectron Finnigan, San Jose, CA) 5% FA + ACN 1:1; for MALDI-PMF, using the Ultraflex II (Bruker Daltonik, Bremen, Germany) analyses 0.1% TFA/100% ACN; and for LC-MALDI-MS(/MS) analyses, Ultraflex II) (Bruker Daltonik) 0.1% TFA was used. Peptide Separation, Fractionation, and Mass Spectrometry. Peptide analysis was performed using different techniques: (a) microparticular C18 nanoreversed-phase HPLC (LCC18) either offline coupled with a MALDI-TOF/TOF mass spectrometer (Ultraflex II, Bruker Daltonik) (LCC18-MALDI-MS) or online coupled with a ESI-IT mass spectrometer (LCQ XP, Thermo Electron)

Experimental Section

(b) monolithic nanoreversed-phase HPLC (LCmono) offline coupled with a MALDI-TOF/TOF mass spectrometer (Ultraflex II, Bruker Daltonik) (LCmono-MALDI-MS).

Chemicals and Reagents. Ammonium bicarbonate, ammoniumpersulfate, coomassie G-250, thiourea, and R-cyano-4-

In all cases, tryptic peptides were preconcentrated and separated on the Ultimate HPLC System consisting of Famos, Journal of Proteome Research • Vol. 6, No. 2, 2007 637

research articles Switchos, and Ultimate equipped with a 3 nL UV flow cell (all Dionex LC Packings, Idstein, Germany). For LCC18, peptide separation was performed as follows: preconcentration was done on a µ-C18-trap column (0.3 mm i.d. × 5 mm, PepMap, Dionex LC Packings) (0.1% TFA, flow: 30 µL/min, 6 min) followed by peptide separation on a 75 µm i.d. × 150 mm PepMap column (Dionex LC Packings) (flow, 250 nL/min; solvent A, 0.1% FA; solvent B, 0.1% FA/84% ACN) with a 70 min gradient of 5-50% solution B in 50 min and 5095% B in 5 min (at room temperature). LCmono of the tryptic peptides was done using a monolithic trap column (200 µm i.d. × 5 mm (PS-DVB, Dionex LC Packings) (0.1% TFA, flow: 20 µL/min, 3 min) for preconcentration. Peptide separation was performed at 50 °C on a monolithic separation column (200 µm I.D. × 5 cm (PS-DVB, Dionex LC Packings) (flow, 3 µL/ min; solvent A, 0.05% TFA; solvent B, 50% ACN/0.04% TFA) with a 30 min gradient of 2-17% B in 4 min, 17-40% B in 13 min, 40-60% B in 5.5 min, 60-90% B in 0.1 min, and holding 90% B for 1 min. The parameters used for ESI-MS/MS were described in detail elsewhere.18 For offline RPnanoLC-MALDI-TOF/TOF-MS analyses, fractions were collected on a MALDI-anchor chip target (AnchorChip 600/384, Bruker Daltonik) prepared as follows: the matrix solution (R-cyano-4-hydroxycinnamic acid (HCCA) (saturated in 97% acetone, 3% TFA (0.1%)) was pre-spotted on the anchor chip target. Fractionation was done using the Probot fractionation robotor (Dionex LC Packings). For LCC18, fractions were collected (from 24 min after start of separation) every 10 s (42 nL/fraction), whereas for LCmono, the fractionation time was 4 s (200 nL/fraction) (from 3 min after separation starts). In total, for every separation run, 180 fractions were collected. MALDI-TOF/TOF-MS analyses were performed using the following parameters: target voltage, 25 kV; acceleration voltage, 21.8 kV; reflector voltage, 26.3 kV; detector voltage, 13.9 kV; lens voltage, 10.35 kV; laser-offset, 68% + 15%. MS spectra were recorded automatically in a mass range of 600-3000 Da resulting from 400 laser shots of constant intensity. Data were collected using ProteinScape19-21 (Bruker Daltonik) allowing for an automatic selection of peptide masses (5 per fraction) for subsequent MS/MS experiments. The MS/MS spectra each acquired using 1000 laser shots were further processed using ProteinScape. Data Analysis. MS data were interpreted using the Mascot search algorithm (version 2.0.04)22 (Matrix Science Ltd., London, U.K.) starting the database searches (parameters: NCBInr, 2 missed cleavages, tryptic digest, modification of methionine (oxidation, M + 16 Da) and cystein (propionamide after 1DPAGE or carbamidomethyl addition after 2D-PAGE, C+71 Da/ + 57 Da), and mass accuracy of 100 ppm) directly from ProteinScape. MS/MS data were analyzed with both the Mascot and the SEQUEST algorithm (TurboSEQUEST v.27)23,24 in parallel, using the following parameters: either tryptic or unspecific cleavage, variable modification of methionine (oxidation, M + 16 Da) and cystein (propionamide after 1D-PAGE or carbamidomethyl addition after 2D-PAGE, C + 71 Da/ + 57 Da), and mass accuracy of 0.5 Da for both precursor and fragment ions. For the phosphorylation studies, serine and threonine residues were additionally assumed to be phosphorylated (S/T + 79 Da, variable modification). Additional validation of the data from phosphorylation studies was done using PTM Explorer (Bruker, Daltonik).19 638

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Results and Discussion Optimization of the LCmono System. Presently, RPnanoLCMS/MS analysis of peptides for protein identification using microparticular C18 phases is state-of-the-art in combination with trap column enrichment of the peptides allowing online sample cleanup and higher loading capacities.2 Therefore, we use LCC18 routinely for peptide separation prior to either MALDI-MS or ESI-MS.16,25 To increase the separation efficiency and to reduce the overall analysis time, a nanoLC system with the same setup but including monolithic trap and separation columns (LCmono) was established. Only a few approaches are published in the field of proteomics for RPnanoLC-MS/MS using monolithic columns (LCmono-MALDIMS).3,14,26 Most performed a direct injection of the digest without any preconcentration step, whereas Rieux et al. describe in their study the successful introduction of a microparticular C18 trap column. Initially, we performed direct injection (1 µL injection volume, 500 fmol/µL) without the application of a trap column within the system (Figure 1A). For this purpose, a standard peptide mixture generated by insolution tryptic digestion of three proteins, glucose oxidase (GluOx), lipase (LIP), and cytochrome C (CytC) or RA-crystallin, respectively, was analyzed using a gradient (gradient I) under the following conditions: flow rate, 3 µL/min; solvent A, 0.05% TFA; solvent B, 50% ACN/0.04% TFA, 21 min gradient of 0-70% B in 13 min, 70-90% B in 1 min. The injection volume was proven to be of major importance for the quality of the separation: the larger the injection volume the wider the detected peaks. Thus, it is important to get the analyte solved in the smallest volume possible. The UV-chromatogram (Figure 1A) shows a very short effective separation window for the peptides (about 5-10 min) and very narrow and sharp signals. Indeed, the applicability of this system is restricted by the volumes that can be injected. As a result, extensive evaluation of a complete monolithic system, including a monolithic trap column to allow the injection of larger volumes for the analyses, was performed. In a next step, 15 µL of sample (500 fmol/15 µL) was injected into the trap column followed by a 6 min washing step and peptide separation using gradient I (Figure 1B). Some of the signals in the chromatogram could be assigned to those detected in the first experiments. Indeed, the separation of the peptides, even in this sample of low complexity, was far from optimal: only a limited degree of separation combined with a narrow elution window of the peptides was achieved with this setup. Several optimization steps, including reduction of initial washing time from 6 to 3 min, increase of the initial ACN concentration from 0 to 2%, parallel start of the gradient already with sample washing (resulting in 17% B when the trap column is switched inline with the separation column), spreading of the effective separation interval, and increase of the total separation time from 21 to 30 min, resulted in the establishment of an optimized gradient II (flow, 3 µL/min; solvent A, 0.05% TFA; solvent B. 50% ACN/0.04% TFA; gradient already starting with sample preconcentration of 2-17% B in 4 min, 17-40% B in 13 min, 40-60% B in 5.5 min, 60-90% B in 0.1 min, holding 90% B for 1 min) (Figure 1C). These results were compared to those from separations of the same sample (500 fmol/15 µL) separated by LCC18 using a 0.3 mm i.d. × 5 mm trap column followed by a 75 µm i.d. × 150 mm separation column (both PepMap, Dionex LC Packings) (Figure 1D). This separation was done with our optimized standard conditons (gradient III) (flow rate, 250 nL/min; solvent A, 0.1% FA; solvent

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Figure 1. Optimization of the LCmono system. (A) A tryptic digest of three reference proteins, glucose oxidase (GluOx), lipase (LIP), and cytochrome C (Cyt C), was separated using four different column systems and gradients. In a first approach, 1 µL (500 fmol peptides/ µL) was injected directly on a LCmono system without a trap column. This LCmono setup resulted in a fast separation with a narrow peptide elution window but was hampered by only a restricted sample load. (B) In a next step, a monolithic precolumn was included in the system, and 15 µL of the same sample (500 fmol peptides/15 µL) was separated without any gradient optimization. (C) After extensive optimization of different parameters, a highly effective peptide separation in a narrow time window (only 26 min) could be obtained. (D) Compared to those results from an optimized LCC18 separation (a, whole chromatogram; b, section marked in panel a illustrating only the area where peptides elute) (generally performed in our lab, 0.3 mm i.d. × 5 mm trap column, 75 µm i.d. × 150 mm separation column, 15 µL injection with 500 fmol peptides/15 µL), this new setup proved to be very effective and highly suited for an offline combination with a MALDI-TOF/TOF mass spectrometer. The reproducibility of this method is demonstrated in panel E showing five independent separations on two different days (I-III, day 1; IV and V, day 2).

B, 0.1% FA/84% ACN; with 5-15% B in 3 min, 15-50% B in 32 min, 50-95% B in 5 min, and holding 95% B for 1 min). In comparison, both separations proved to be efficient; indeed, a direct assignment of the peak pattern was not feasible. In the case of LCmono, the signal intensity was decreased by about a factor of 2 due to dilution effects resulting from the higher flow rate. The separation time was reduced drastically (half of the time) providing the opportunity to analyze twice the samples within the same time. From our results, the LCmono setup

including a monolithic trap column clearly showed a better degree of separation compared to the LCC18 separation; that is, 28 signals could be detected after LCmono compared to 21 in the case of LCC18. Moreover, the peaks in LCmono were smaller (averaged peak width 0.13 s) than in LCC18 (averaged peak width of 0.4 s) resulting in shorter elution time per peptide and therewith higher concentration of the related analyte being analyzed by MS. Repeated analyses showed very reproducible separations/peak patterns (Figure 1E). Journal of Proteome Research • Vol. 6, No. 2, 2007 639

research articles This optimized LCmono setup was applied for offline coupling to a MALDI-TOF/TOF mass spectrometer. Optimization of Fractionation and LCmono-MALDI Analyses. For comprehensive peptide detection by MALDI-MS, the separation as well as the fractionation of the eluate after HPLC must be optimized. The individual fractions may contain only a limited number of peptides in terms of subsequent MS and MS/MS analyses, whereas the fraction number should be manageable to avoid dramatic increase in the follow-up analysis time. The size (volume) of the collected fractions is limited by the fractionation capabilities of the fractionation devicesin our case, the Probot fractionation robot (Dionex LC Packings)sapplied. For the LCC18 system, the fractionation time was the limiting factor; as for reduced times (down to 5 s), the droplets were too small to be displaced on the target reliably. We found 40-50 nL to be the optimal volume. Therefore, fractions could be collected every 10 s resulting in fraction volumes of about 42 nL. In case of higher flow rates like those seen in the LCmono system, the moving time of the robot was the limiting factor. As a certain time is needed to move from one position on the target to the next, for LCmono, fractions were collected every 4 s (200 nL/fraction). For LCC18 fractions, this was the case 30 min after injection, whereas for LCmono, the first peptides eluted at 6 min after injection. The fraction number was limited to 180 fractions per run for the abovementioned practical reasons. Fraction collection started when the first peptides eluted from the column (LCmono starting 3 min after separation, LCC18 starting 24 min after separation). For effective subsequent MALDI-TOF/TOF-MS analysis, two factors have to be balanced: (i) The separation of peptides has to be very efficient resulting in only a limited number of peptides per fraction. This increases the chance to comprehensively analyze and fragment a high number of peptides for protein identification. As every laser shot removes material from the target, the available amount of sample is limited and the chance to gain high-quality fragmentation data for protein identification decreases. (ii) The number of fractions should be restricted to a sufficient quantity to prevent an increase in total analysis time. The used fractionation settings described above allowed an effective fragmentation of a suitable number of peptides by subsequent MALDI-TOF/TOF-MS. In addition, the parameters for automatic fragmentation analysis had to be optimized due to the same reasons described above: only a restricted number of laser shots and with this, only a limited quantity of MS and fragmentation analyses could be performed for one fraction. In consequence, an optimal number of selected peptides for fragmentation analysis was evaluated. MS spectra for each fraction were collected by 400 laser shots avoiding excessive sample loss from the target. Resulting MS data were processed by ProteinScape (Bruker Daltonik)20 allowing an automatic selection of peptide masses for subsequent fragmentation experiments. From several analyses using the standard peptide mixture, 5 fragmention spectra for the individual fraction (acquired using 1000 laser shots each) were found to be the optimal number in terms of comprehensive protein identification and overall time needed for the analysis. Further processing of MS/MS spectra and database searches with SEQUEST and Mascot were automatically performed using ProteinScape. Commparison of Different LC-MS/MS Techniques (LCC18ESI-MS, LCC18-MALDI-MS, and LCmono-MALDI-MS). The comparison and evaluation of the techniques was performed 640

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using one single in-gel-digested protein, GlucOx, LIP, and Cyt C, respectively. For the analysis, the single reference proteins (18 µg each) were separated by one-pocket 1D-SDS-PAGE. In total, 32 spots were excised from the broad protein band resulting in a protein amount of about 0.5 µg per spot. After tryptic in-gel digestion and peptide extraction, the extract was subdivided for both nanoLC-MS/MS analyses (LCC18-MALDI-MS and LCmonoMALDI-MS). All experiments were run four times independently; that is, for every experiment, another gel spot was used. In LCC18-MALDI-MS and LCmono-MALDI-MS analyses, 180 fractions were collected in total, meaning a collection time of 10 or 4 s, respectively. For each fraction, 5 fragmention spectra were recorded automatically, data were stored in ProteinScape, and the database searches using Mascot and SEQUEST in parallel were started automatically. For LCC18-MALDI-MS, a mean value of 533 fragmention spectra was recorded, 114 of which could be assigned to GlucOx covering 16.7% total sequence on average using only MS/MS data (with identification of 12 peptides on average) and 27.5% including both MS and MS/MS data (with identification of 15 peptides on average). Averaged, 593 fragmention spectra were obtained in the LCmono-MALDI-MS experiments. Of these, 74 were matched to GlucOx, denoting 26.2% average sequence coverage considering only MS/MS data for protein identification (detection of 14 different peptides on average) and 33.8% adding MS and MS/MS data files (identification of 17 different peptides on average). Those results based on only the number of spectra matching to GlucOx showed LCC18-MALDI-MS to be the most effective method for this type of analysis, as, in total, 21.4% of all spectra recorded could be successfully assigned to GlucOx. In contrast, for LCmono-MALDI-MS, 12.5% of all spectra could be matched. Indeed, in total higher sequence coverages with less spectra were obtained with LCmono-MALDI-MS which could be explained by a bias of this method toward larger peptides (all these data are summarized in Figure 2A). For comparison of both methods, only those peptides identified by MS/MS analyses at least three times in four analyses were included in further data interpretation and calculation of the total sequence coverage (Figure 2B). Here, with LCC18-MALDI-MS, 16.2% of the total sequence was covered. LCmono-MALDI-MS investigations had 23.8% complete sequence coverage. Additionally, the respective average Mascot scores calculated in ProteinScape were explicitly higher for LCmono-MALDI-MS (875.7) than for LCC18-MALDI-MS (650.9) analyses, demonstrating higher spectra quality (data are summarized in Figure 2B). On the level of sequence coverage and also regarding the quality of the fragmention spectra expressed in the respective Mascot or SEQUEST score, LCmono-MALDI-MS proved to be the method of choice for the analysis of a single protein. Summarized, a combination of the results from both methods resulted in an increase of sequence coverage for GlucOx to 28.4%. The same experiments were also performed with Lipase and Cyt C confirming the above-described results (not shown here). Application of the Developed Method. Presently, the developed method is an inherent part in our lab for the analysis of single proteins, especially those separated by gel electrophoresis. Next, one application, the localization of posttranslational modifications in the protein sequence, is presented in more detail. The analysis of post-translational

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Figure 2. Analysis of GlucOx using three different methods. LCmono-MALDI-MS and LCC18-MALDI-MS are compared with respect to their suitability for nanoLC-MS/MS analyses of a single protein after tryptic digestion. In total, four independent analyses were performed with each method. A summary is given in panel A, and the results are additionally visualized in panels B-H. In LCC18MALDI-MS, analyses (vertically brindled bar) were performed with the following results: 533 fragment ion spectra were obtained, 114 spectra were matched to GlucOx (I (only MS/MS data included), 12 identified peptides resulting in 16.7% sequence coverage; II (MS and MS/MS data included), including MS data 15 peptides were identified leading to 27.5% sequence coverage). In total, 593 fragment ion spectra were acquired in LCmono-MALDI-MS (diagonally brindled bar) analyses, 114 spectra were assigned to GlucOx, 14 peptides were identified with 26.2% sequence coverage (I, MS/MS data), whereas in total, (II, MS and MS/MS data) 17 peptides with 33.8% sequence coverage were identified including also MS data. (H) Considering only peptides that were identified by MS/MS and in at least three of four analyses, the sequence coverages obtained were 19.5% for LCC18-MALDI-MS and 23.8% for LCmono-MALDI-MS (all peptides identified are highlighted in boldface). A combination of the results from all three methods resulted in a total sequence coverage of 28.4%. (SD, standard deviation).

modification is a very important field in proteomics today as especially phosphorylation controls many cellular functions. The established method was successfully used to localize phosphorylation sites of lenticular RA-crystallin.16,18 The number of post-translational modifications in RA-crystallin increases drastically during aging; as the fiber cells of the lens do not contain DNA or RNA, no significant turnover of its densely packed proteins is observed.27 In our previous studies, eight phosphorylation sites and the acetylation of the Nterminus could be localized within the protein sequence of RAcrystallin after protein separation by 2D-PAGE and LCC18-ESIMS analysis.16,18 Several more modification sites are described further in the literature,28,29 but the function of these modifications is not fully avowed yet. Phosphorylation, deamidation, acetylation, oxidation of methionine, and cleavage of the C-terminal residues are reported to increase with aging due to the limited turnover28,30,31 resulting in a change of conformation,

wrong protein-protein interactions, and aggregations.32 In the present study, the 2D gel spots previously identified as RAcrystallin isoforms were reanalyzed using LCmono-MALDI-MS to confirm the results of the LCC18-ESI-MS16 and possibly to amend the number of localized modification sites. Lenticular proteins were separated by 2D-PAGE and visualized by imidazole zinc staining. Different spots of interest were excised from the gel, and tryptic digestion was performed. Peptide separation was carried out exactly the way described for the LCmono-MALDI-MS analyses of GlucOx (see above). Here, for example, the results of four different spots (1, 4, 15, and 20, Figure 3A) were shown: two of which (1 and 4) were known to contain phosphorylated forms of RA-crystallin. Previous LCC18-ESI-MS analyses showed that spot 14 contains the primary form of this protein already detectable in lenticular fiber cells of embryonic mice, and spot 19 detected at lower Journal of Proteome Research • Vol. 6, No. 2, 2007 641

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Marcus et al.

Figure 3. Analysis of lenticular RA-crystallin. (A) Gel region of interest. Spots 1, 4, 14, and 19 were used for a detailed data analysis. (B) Chromatographic profile of tryptic-digested protein of spot 14. Sample fractionation started 6 min after sample injection. In many cases, baseline separation of the peptides was achieved leading to an optimal peptide separation and a minimal number of peptides in the single fraction. (C) LCmono-MALDI-MS, spectrum of the singly charged phosphorylated tryptic peptide LPpSNVDQSALSC*SLSADGM*LTFSGPK (additionally, the cysteine is carbamidomethylated and the methionine is oxidized) (m/z ) 2778.24 Da). Most ions of the y-ion series were identified. The serine 122 could be unequivocally identified as the phosphorylation site within the protein.

molecular weight in the gel contains a C-terminal truncated form of the RA-crystallin. An optimal peptide separation over the complete separation time could be obtained with the above-described optimized approach in all analyses (Figure 3B). Generally, in all four spots, the acetylation of the N-terminal methionine (N-terminal peptide: Ac-MDVTIQHPWFKR) was identified, confirming the results of our previous studies. Most fragment ions of the band y-ion series were detected, whereas in contrast to the earlier LCC18-ESI-MS, the singly charged peptide was identified in this LCmono-MALDI-MS analysis. RA-Crystallin in spot 14 was identified with high sequence coverage (76%), comparable with those obtained in the previous LCC18-ESI-MS analyses.16 Both the N- and C-terminal peptides of the protein were identified confirming the existing knowledge that the mainly intact unmodified protein is contained in this spot. Spot 19 was identified with 31% sequence coveage; no modifications except N-terminal acetylation were identified. The C-terminus of the protein was not found, validating that this spot contains a C-terminal truncated form of the RA-crystallin. In spot 4, another RA-crystallin isoform was identified with a sequence coverage of 62%. Both the acetylated N-terminal peptide and the C-terminal tryptic peptide were identified. Furthermore, the phosphorylation of serine 122 (Ser-122) in the large tryptic peptide LPpSNVDQSALSC*SLSADGM*LTFSGPK (the cysteine is carbamidomethylated and the methionine is oxidized) (m/z 642

Journal of Proteome Research • Vol. 6, No. 2, 2007

) 2778.24 Da) was identified. The broad mass range of 6003000 Da enabled the identification of the singly charged modified peptide. The phosphorylation site could be localized unequivocally, as nearly the complete y-series was detected including several ions showing loss of H3PO4 (-98 Da), showing that the described method is suitable for identification of phosphorylation sites within a protein. Indeed, the method possesses some limitations, as demonstrated in the analyses of spot 1. Here, the RA-crystallin was identified with 36% sequence coverage, but no phosphorylation sites could be localized within this protein isoform so far.

Conclusion The application of monolithic columns has become more attractive also in the field of proteomics due to their special characteristics such as high chromatographic efficiency, robustness, and reduced backpressure. Miniaturization of these columns provides excellent possibilities for protein/peptide separation. Columns (200 µm i.d.) packed with monolithic stationary phases are, in combination with mass spectrometry, extremely suitable for peptide identification due to the occurrence of sharp chromatographic peaks and, with this, high relative concentration of the eluting peptides. Indeed, the use of the monoliths so far is limited by the restricted volumes that could be injected. We therefore developed a new setup using a PS-DVB monolithic separation column in combination with

research articles

Monolithic Columns for Proteomic Studies

a trap column of the same material with special emphasis on applications in proteome research. Our results demonstrate that the use of monolithic trap and separation columns does not negatively influence chromatographic performance and protein identification. On the contrary, in combination with MALDI-TOF/TOF-MS, this separation method proved to be very fast and highly efficient for protein characterization and identification, especially after separation of the proteins by 1Dor 2D-PAGE. For the analysis of higher complex samples (e.g., digest of a whole protein lysate without any further prefractionation), the method showed first promising results. Indeed, the separation conditions have to be optimized; for example, the gradient has to be extended and fractionation must be adapted. The method is now a permanent part of the proteome analysis workflow in our lab, enhancing the results gained from other nanoLC-MS/MS setups, for example, with regard to the localization of post-translational modificatons or the analysis of membrane proteins.

Acknowledgment. This work was supported by the Federal Ministry of Education and Research (BMBF) (Fo¨rderkennzeichen 01GR0440) in the framework of the National Genome Research Network (NGFN). Special thanks go to Mrs. Irina Dragan and Dr. Steffen Liedke (Dionex LC Packings) for fruitful discussions and helpful advice. References (1) Schafer, H.; Nau, K.; Sickmann, A.; Erdmann, R.; Meyer, H. E. Electrophoresis 2001, 22 (14), 2955-2968. (2) Mitulovic, G.; Smoluch, M.; Chervet, J.-P.; Steichmacher, I.; Kungl, A.; Mechtler, K. Anal. Biol. Anal. Chem. 2003, 376, 946-951. (3) Toll, H.; Wintringer, R.; Schweiger-Hufnagel, U.; Huber, C. G. J. Sep. Sci. 2005, 28 (14), 1666-1674. (4) Hennessy, T. P.; Boysen, R. I.; Huber, M. I.; Unger, K. K.; Hearn, M. T. W. J. Chromatogr., A. 2003, 1009, 15-28. (5) Hjerten, S.; Liao, J. L.; Zhang, R. J. Chromatogr. 1989, 473, 273275. (6) Svec, F.; Frechet, J. M. J. Anal. Chem. 1992, 64, 820-822. (7) Gusev, I.; Huang, X.; Horvath, C. J. Chromatogr., A. 1999, 855, 273-290. (8) Minakuchi, H.; Nakanishi, K.; Soga, N.; Ishizuka, N.; Tanaka, N. Anal. Chem. 1996, 68, 3498-3501. (9) Ishizuka, N.; Kobayashi, H.; Minakuchi, H.; Nakanishi, K.; Hirao, K.; Hosoya, K.; Ikegami, T.; Tanaka, N. J. Chromatogr., A. 2002, 960, 85-96. (10) Cabrera, K. J. Sep. Sci. 2004, 27, 843-852.

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