Four Stage Liquid Chromatographic Selection of ... - ACS Publications

Four Stage Liquid Chromatographic Selection of Methionyl Peptides for Peptide-Centric Proteome Analysis: The Proteome of Human Multipotent Adult Progenitor Cells Kris Gevaert,* Jef Pinxteren, Hans Demol, Koen Hugelier, An Staes, Jozef Van Damme, Lennart Martens, and Joe1 l Vandekerckhove Department of Biochemistry and Medical Protein Research, Faculty of Medicine and Health Sciences, Ghent University, A. Baertsoenkaai 3, B9000 Ghent, Belgium Received January 24, 2006

Serial application of strong cation-exchange and diagonal reversed-phase chromatography selecting methionyl peptides by stepwise shifting them from their reduced to their sulfoxide and sulfone forms generates a four-stage fractionation system, allowing high coverage analysis of complex proteome digests by LC-MALDI-MS/MS. Application to the proteome of a human multipotent adult progenitor cell line (MAPC) identified 2151 proteins with high confidence as on average four MS/MS-spectra were linked to each protein. Our dataset contains several novel, potential marker proteins that may be evaluated as affinity-anchors for isolating different adult stem cells in further studies. Furthermore, at least 2 tyrosine kinases that were previously linked to the self-renewal potential of stem cells were identified, validating the stemness of the analyzed cells. We also present data hinting at possible involvement of the ubiquitin/proteasome machinery in steering proliferation and/or differentiation of MAPC. Finally, following comparison of the MAPC proteome with proteomes of four human differentiated cell lines reveals differential usage of chromosomal information: compared to differentiated cells, MAPC do not appear to hold any preference for expressing genes located on specific chromosomes. Keywords: diagonal chromatography • gel-free proteomics • COFRADIC • multidimensional chromatography

Introduction Contemporary proteomics methods either rely on gel-based, protein-centric technologies or on LC-based peptide-centric approaches. While the former concentrate on protein separation prior to their individual identification by peptide mass fingerprinting or LC-MS/MS analysis,1-3 the latter start from complex protein mixtures, such as total cell lysates, which are then digested into peptides. This mixture is fractionated and analyzed by LC-MS/MS, leading to qualitative and/or semiquantitative information on their parent proteins. Peptidebased approaches have gained a lot of attention over the last years, primarily because of their potential superior proteome coverage and efficient connection with modern mass spectrometers. These methods however show a serious limitation because not all the peptides present in the mixture are identified due to the extreme complexity of such mixtures. In the most successful examples, several thousands of different peptides and proteins were identified after single analyses of eukaryotic lysates.4-7 This probably still represents only a minor percentage of the actual proteins present in cells and thus leads to a partial and quite random picture of the proteome composition, a phenomenon well-known as under-sampling.8 * To whom correspondence should be addressed. Tel.: +32/92649274. Fax: +32/92649496. E-mail: [email protected]. 10.1021/pr060026a CCC: $33.50

 2006 American Chemical Society

Under-sampling is of major concern when very similar samples are repetitively analyzed in order to detect common features or profiles in sets of samples; a situation typical of biomarker discovery. This is manifested by good global coverage of the major proteins and, although these are often well characterized, they may be variably covered when individual analyses are considered.9 The reason for this is largely due to poor separation of peptides delivered to the mass spectrometers. When too many different peptides are entering simultaneously, there is imperative ionization suppression and random sampling, resulting in under-representation of peptides.10,11 In the case of MALDI-TOF-MS analysis, too many peptides present on the same target may override the capacity of the timed ion-selector, making fragmentation analysis of several peptides impossible. The cures for this problem should be situated at two levels. First, to further develop mass spectrometers, able to generate high quality fragmentation spectra in shorter scanning times, and second, to put efforts in more powerful peptide separation techniques. While the former is mainly an MS-technical issue that will be addressed by the next generation of machines, the peptide separation problem becomes an important laboratory challenge for the future. Indeed, so far insufficient attention has been paid to the development of chromatographic systems for peptide separation. Since the early 1980s, reversed-phase liquid chromatography has been the method of choice because Journal of Proteome Research 2006, 5, 1415-1428

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research articles of its very high resolution, ease of use and its compatibility with on-line mass spectrometry. Other powerful separation technologies such as capillary electrophoresis were so far only rarely used.12,13 With the sharp rise in complexity and the emerging problem of under-sampling, peptide-centric proteomics has re-boosted the need for introduction of alternative peptide separation protocols. Multidimensional separation technologies are now making use of combinations of ionexchange and RP-HPLC.14,15 However, the fractionation power is still insufficient in reaching reasonable peptide and protein coverage in a single analysis. It is therefore mandatory to explore additional differentiating parameters not only relying on the net-charge or hydrophobicity. One strategy, which in this context has not been fully exploited, is diagonal chromatography. This method consists of two consecutive identical separations, carried out on the same peptide mixture, with a chemical or enzymatic alteration of a subset of peptides between the two runs. Thus, altered peptides will elute differently in the primary versus the secondary run, and can be isolated.16 This technique has recently been adapted to sort subsets of peptides from highly complex mixtures and is referred to as COmbined FRActional DIagonal Chromatography (COFRADIC).17-20 In this paper, we have integrated different separation strategies consisting of a cation-exchange chromatographic step, a sorting step for methionine-containing peptides, discarding the majority of nonmethionine peptides and the differential elution properties of methioninyl peptides in their non-oxidized, sulfoxide and sulfone forms during reversed-phase chromatography. The entire procedure thus consists of four consecutive steps, is selective for Met-peptides, and results in a total of 10 368 2-µL fractions in which peptides are uniformly distributed. The procedure was applied to provide a proteomic view as exhaustive as possible of human multipotent adult progenitor cells (MAPC), leading to the unambiguous identification of 2151 different proteins in a single experiment. The sample-to-sample overlap in repeated runs typically amounts to about 90% at the peptide level. This study demonstrates that peptide-centric, MALDI-MS based proteomics can lead to a high proteome coverage, however at the expense of separation time and MSanalysis time; an important balance of cost and coverage, which has to be seriously considered, particularly during future biomarker discovery. To our knowledge, our study is the first that has revealed a detailed picture of the proteins expressed in these adult pluripotent stem cells and several interesting findings are here discussed in detail. For example, comparison with the protein expression profiles of differentiated human cells (Jurkat Tlymphocytes, SH-SY5Y cells, DLD1 cells, and A549 cells) reveals a quite specialized system and predicts novel mechanisms which may be activated, once the progenitor cells enter a differentiation pathway.

Experimental Section MAPC Cell Culture and Proteome Preparation. Human bone marrow (BM) was obtained from patients under informed consent at the University Hospital of Ghent. The BM mononuclear cells (BMMNCs) were obtained by Lymphoprep density gradient centrifugation (Axis-Shield, Oslo, Norway). MAPC were generated as previously described.21 BMMNCs were plated on a FN-coated 25 cm2 culture flask at approximately 106 BMMNCs/cm2. The next day the medium was 1416

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refreshed to remove the cells that did not adhere. After 48 h, growth was seen and after another 48 h, the cells were subcultured in FN-coated 96-well culture plates at a density of 1 cell/well. The MAPC culture medium consisted of 57.5% low glucose DMEM (Cambrex, Verviers, Belgium), 39% MCDB-201 (Sigma), 1% insulin-transferrin selenium (Sigma), 0.5% linoleic acid-bovine serum albumin (Sigma), 100 µM ascorbic acid 2-phosphate (Sigma), 100 U/mL penicillin (Cambrex), 100 U/mL streptomycin (Cambrex), 10 ng/mL PDGF-BB (R&D, Minneapolis, MN), 10 ng/mL EGF (Sigma), 2% FCS (Serum Supreme, Cambrex) and 50 nM dexamethasone (Sigma). When the cells were 50% confluent, they were detached with trypsin/ EDTA (Cambrex) and seeded in FN-coated 24-well plates in a well per well fashion. This was repeated when passing the cells to 6-well plates. From then on, every time the cell density reached 3000 cells/cm2 they were split. The cells were counted and reseeded at 500-1000 cells/cm2 in FN-coated cell culture flasks. For proteome preparation, a large culture consisting of 72 culture flasks and totalling 21.6 million cells (at a cell density of about 3000 cells/cm2) from a representative clone from one single patient was harvested. The flasks were rinsed twice with PBS and then a buffered EDTA solution (Versene, Cambrex) was applied to detach the cells. Cells were collected by centrifugation and rinsed twice with PBS. The pellets were dissolved in 2 mL lysis buffer (50 mM Hepes pH 7.4, 100 mM NaCl, 0.7% CHAPS, 0.5 mM EDTA) supplemented with a protease inhibitor cocktail (Complete, Roche). The cells were lysed for 30 min on ice and debris was removed by centrifugation. Proteome Digestion and SCX Fractionation of the MAPC Proteome Digest (First Stage LC). The four-stage LC-based peptide enrichment scheme is illustrated by the sorting of methionyl peptides from a tryptic digest of a MAPC proteome (see below) in Figure 1. A 6-mg portion of protein material was desalted over a PD10 column (Amersham Biosciences, Uppsala, Sweden) in 3.5 mL of freshly prepared 50 mM ammonium bicarbonate (pH 7.9), concentrated by vacuum-drying to 1.5 mL, boiled for 10 min and after a 5 min incubation on ice followed by reheating to 37 °C, 100 µg of sequencing grade, modified trypsin (Promega Corporation, Madison, WI) was added. The digestion proceeded for 16 h, after which the pH of the peptide solution was lowered to pH 3 by adding 15 µL of 6 M HCl and the peptide mixture was cleared by centrifugation. The complete peptide mixture was loaded on a SCX column (Zorbax-SCX2, 2.1 mm (I. D.) × 150 mm, Agilent technologies, Waldbronn, Germany) equilibrated with solvent A (10 mM sodium phosphate (pH 3) in 20% acetonitrile). Following a 40 min wash with solvent A at a constant flow rate of 80 µL/min, a gradient with solvent B (10 mM sodium phosphate (pH 3) and 2 M NaCl in 20% acetonitrile) was applied using an Agilent 1100 Series HPLC system. The concentration of solvent B was first linearly increased to 10% 45 min after sample loading and then to 50% (1 M NaCl) over another 45 min. The column was run in 50% of solvent B for 5 min after which the concentration of solvent B was increased within 2 min to 100% (2 M NaCl) and the column was ran with this solvent for another 15 min. A total of 47 peptide fractions of 2 min (or 160 µL) each were collected between 16 and 110 min and these were then pooled into 6 main SXC fractions such that each fraction contained about the same amount of peptide material (measured by cumulated UV absorbance). SCX fraction 1 contained the

Proteome of Human Multipotent Adult Progenitor Cells

Figure 1. Four-stage LC isolation of methionyl peptides for gelfree proteomics. The MAPC proteome was digested with trypsin and 6 mg of digested material was first fractionated on a SCX column (stage 1) in 6 different fractions (A). The peptide mixture of SCX-fraction 5 is further fractionated on a RP-column (stage 2) in 60 primary COFRADIC fractions (B). For each secondary COFRADIC separation (stage 3, C), 5 primary peptide fractions that are each separated by 12 min (fractions 5, 17, 29, 41, and 53 in chromatogram (B)) are pooled, dried and treated with hydrogen peroxide which converts methionine to the more hydrophilic methionine-sulfoxide. Peptides carrying methionine-sulfoxide display a hydrophilic shift and are now collected in an interval 9 to 4 min prior to their original, primary collection interval into 6 secondary fractions per primary fraction. Identically indexed secondary COFRADIC fractions (54, 174, 294, 414, and 534) are dried, oxidized with performic acid, separated by capillary RPHPLC and automatically collected onto MALDI-targets in 24 fractions for further analysis (D). In all chromatograms shown the UV absorbance was measured at 214 nm.

peptide material elution between 16 and 46 min (i.e., mainly the flow-through), while pools 2 to 6 contained combined fractions eluting between 46 and 52 min, 52 and 58 min, 58 and 64 min, 64 and 74 min, and 74 and 110 min, respectively.

research articles Isolation of Methionyl Peptides from the SCX Fractions (Second and Third Stage LC). Methionyl peptides were isolated from each main SCX fraction using a diagonal reverse-phase liquid chromatographic setup (COFRADIC) that was previously described17 and modified for this study. Material of each SCX fraction was dried and redissolved in 1.5 mL of solvent A′ (0.1% TFA in acetonitrile/water (2/98, v/v)) and fractionated a first time by RP-HPLC on a 2.1 mm internal diameter (I. D.) × 150 mm 300SB-C18 Zorbax column (Agilent technologies) using an Agilent 1100 Series HPLC system running at a constant flow rate of 80 µL/min (the primary COFRADIC run). Following a 50 min wash with solvent A′, a linear gradient to 100% solvent B′ (0.1% TFA in acetonitrile/ water (70/30, v/v)) was applied over 100 min. Per main SCX fraction, a total of 60 primary RP-HPLC fractions were collected of 1 min (80 µL) each, between 60 (10% solvent B′) and 120 (70% solvent B′) min during the primary run. To decrease the number of secondary HPLC separations, primary peptide fractions that were separated by 12 min were pooled and dried. Prior to the secondary run, methionyl peptides in each peptide pool were oxidized to their sulfoxide derivatives in the auto-injector compartment of the HPLC system by incubation for 30 min at 30 °C with 70 µL of a freshly prepared solution of 0.6% H2O2 in 0.1% TFA. To avoid overoxidation of methionines to sulfones or oxidation of cysteine and tryptophane,17 the oxidized peptide mixture was immediately injected onto the RP-HPLC column and separated using the same solvent gradient as before (i.e., the secondary COFRADIC run). As oxidation of methionine to methioninesulfoxide leads to weaker retention on RP-columns, peptides carrying such oxidized amino acids undergo a hydrophilic shift. Therefore, during each secondary run methionine-sulfoxide peptides were collected in a time interval between 9 and 4 min prior to the collection of each primary fraction. Per primary fraction, 6 secondary fractions were collected, implying that each SCX fraction was finally split up into 360 secondary fractions and thus the whole proteome digest (now mainly consisting of methionyl peptides) was divided into 2160 different methionine-sulfoxide peptides enriched secondary fractions. Performic Acid Oxidation of Methionyl Peptides. To reduce the number of final analyses, here again peptide fractions were pooled. The pooling scheme was such that per secondary COFRADIC run, identically indexed secondary fractions were mixed: for example, secondary fraction 1 of primary fraction X was pooled with the analogous secondary fractions of the primary fractions X+12, X+24, X+36 and X+48. On this way per secondary run, 72 peptide pools were obtained for each original SCX fraction (432 peptide pools in total). These peptide pools were dried and oxidized with performic acid. A performic acid stock solution was made by mixing 900 µL of formic acid and 100 µL of 30% H2O2 and incubating this mixture for 2 h at room temperature. To each dried peptide mixture, 10 µL of the freshly prepared performic acid solution was added and the peptides were incubated on ice for 45 min. Oxidation was stopped by adding 200 µL water and subsequent freezing and lyophilization. LC-MALDI Analysis (Fourth Stage LC and Peptide Analysis). Peptides were redissolved in 75 µL 0.1% heptafluorobutyric acid (HFBA) and centrifuged to remove insolubilities before RPHPLC analysis on an Agilent 1100 Series HPLC system. The peptide mixture was first trapped on a C18 PepMap100 column (300 µm (I. D.) × 5 mm (length), LC Packings BV, Amsterdam, Journal of Proteome Research • Vol. 5, No. 6, 2006 1417

research articles The Netherlands) for 10 min with 0.1% HFBA at a flow rate of 20 µL/min. The trapping column was then back-flushed and the peptides were separated on a capillary C18 polymeric reversed-phase column (3 µm particle diameter, 150 µm (I. D.) × 100 mm (length), Grace Vydac, Hesperia, CA) using an acetonitrile gradient (solvent A′′ was 0.1% formic acid in acetonitrile/water (2/98) and solvent B′′ was 0.1% formic acid in acetonitrile/water (70/30)). The concentration of solvent B′′ was linearly increased to 60% within 30 min during which the peptides eluted. Subsequently, the column was briefly washed with solvent B′′ and reequilibrated with solvent A′′ until the next sample injection. During the peptide separation run, the flow rate was kept constant at 2 µL/min. Peptides eluting between 18 and 34 min were automatically collected as 24 distinct spots on MALDI targets (MTP AnchorChip 600/384 T F, Bruker Daltonics, Bremen, Germany) using Agilent’s 1100 Series Micro Fraction Collector. Following sample collection, the dried peptides were incorporated into MALDImatrix by adding 0.6 µL of a freshly prepared MALDI-matrix solution (0.1 mg/mL R-cyano-4-hydroxycinnamic acid in ethanol/acetone/0.1% TFA (6/4/1)) on each spot. Once the MALDImatrix solution was dried, the whole plate was briefly submerged in 10 mM ammonium phosphate and excess of this wash solution was removed under a gentle nitrogen stream. Next, the spotted peptides were automatically measured in MS mode on a Bruker ultraflex II TOF/TOF MALDI mass spectrometer under the fuzzy logic control of Bruker’s WarpLC software (version 1.0, Build 98.5). To achieve high mass measurement accuracies a “nearest neighbor” external calibration was performed in the center of four peptide spots using Bruker’s peptide calibration standard (version II). After measuring all 384 spots, a so-called MALDI compound list was automatically generated by the WarpLC software in which (peptide) ions with a signal-to-noise ratio of more than 60 were ranked by decreasing spectral quality of their measured isotopic envelope. This list served as input for automated MS/MS (LIFT) measurements and these measurements were scheduled such that peptide ions with decreasing spectral quality were used for MS/MS analyses. Peptide Identification by the Mascot Algorithm. The generated MS/MS spectra were automatically converted into peak lists containing the mass and the intensity of each fragment and the precursor ion and were stored in the Mascot generic file format (mgf). The Mascot daemon software was used such that six consecutive searches were done. Because of the difference of Mascot’s identity threshold score (and general differences in overall searching time), the first three searches were done in the SwissProt database, version 47.4 (de dato 5th of July, 2005) (ftp://ftp.ebi.ac.uk/pub/databases/uniprot/ knowledgebase/) containing only human sequences and the last three searches were done in the IPI human protein database,22 version 3.08 (de dato 3rd of June, 2005) (ftp:// ftp.ebi.ac.uk/pub/databases/IPI/current/ipi.HUMAN.dat.gz). First, the Swiss-Prot database was searched and Mascot’s parameters were set as follows: 0.1 Da parent mass tolerance, 0.5 Da fragment mass tolerance, enzyme: trypsin, maximum number of missed cleavages: 1, fixed modifications: cysteic acid (C) and sulfone (M) and variable modifications: N-acetyl (protein) and pyro-Glu (N-ter Q). The two follow-up searches in Swiss-Prot were somewhat more “relaxed” as for both the parent mass tolerance was increased to 0.15 Da and the fragment mass tolerance to 0.8 Da and for the third search the following variable modifications were also allowed: kynurenine 1418


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(W), 3-hydroxykynurenine (W), oxidation of H and hydroxylation of W (both + 16 Da), oxidation of W (+32 or +48 Da) and deamidation (NQ). Mascot daemon was used such that only spectra not identified in the previous search space were considered for analysis in the next search. All MS/MS spectra not identified in the Swiss-Prot database were then automatically fed to the human IPI database and the same Mascot searching order was applied as before. Using in-house developed software tools (see http://genesis.ugent.be/ms_lims/), the DAT result files of Mascot were queried and only those MS/MS-spectra that were identified by a score exceeding Mascot’s identity threshold score at the 95% confidence level were retained. Such spectra were then manually validated and only those that held a significant number of peptide fragment ions and thus largely covered the peptide’s sequence were considered as positively identified (typically about 50% of b- and y-ions were present). The identified peptides were then automatically stored in a MySQL relational database (http://genesis.ugent.be/ms_lims/) in which links were made to their MS/MS-spectra and precursor proteins. All peptide identifications done in this study are made publicly available via the PRIDE database23 (http://www.ebi.ac.uk/pride/) under the experiment number 1642.

Results Four-Stage LC Enrichment and MALDI-TOF/TOF Analysis of Methionyl Peptides. The peptide analysis scheme used in this study combines strong cation exchange (SCX) chromatography, COFRADIC isolation of methionyl peptides17 and offline coupling of capillary RP-HPLC to MALDI-TOF(/TOF)MS. SCX was used during the first stage of peptide separation because of two reasons: (1) SCX columns allow loading large sample amounts (here 6 mg was loaded) and, given the intrinsic enormous differences in protein cellular concentrations, should thus allow a more efficient recovery of low abundant peptides and thus their corresponding proteins and (2) tryptic peptides hold at least one positively charged residue and have therefore been widely used in orthogonal/multidimensional LC separations involving SCX heading MS(/MS) analysis.24 In the study described here, a tryptic digest of proteome preparation from MAPC cells was first fractionated in 6 SCX-fractions (Figure 1A). The second and third stages of our peptide isolation scheme consist of the primary and secondary RP-HPLC COFRADIC separations leading to the specific enrichment of methionyl peptides in their sulfoxide form. Peptides from one SCXfraction were fractionated into 60 primary COFRADIC fractions by RP-HPLC (as shown for SCX-fraction 5 in Figure 1B). Twelve pools of 5 primary peptide fractions were then assembled, dried, and methionyl peptides in each peptide pool were converted to their more hydrophilic sulfoxide forms using a mild oxidation with hydrogen peroxide as previously described.17 When this oxidized peptide pool is re-separated under identical conditions, methionyl-sulfoxide peptides migrate out of their original, primary collection intervals and shift to earlier elution times. At this stage, it is important to note that the hydrophilic shifts are not identical for all methioninepeptides from the same primary fraction. Indeed, they depend on the number of methionines and on the position of the altered residue within the peptide sequences. Consequently, methionyl peptides that are originally contained within a given primary collection interval, upon oxidation typically spread over a secondary elution interval which is 2 to 7 times broader as


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Figure 2. Performic acid oxidation of COFRADIC methionyl sulfoxide peptides leads to improved peptide identification by the Mascot algorithm. Methionyl peptides from a proteome digest of human Jurkat cells were once isolated in their sulfoxide form (grey bars) and once further oxidized by performic acid prior to MALDI-MS/MS analysis (black bars). Sample preparation, MALDI-MS/MS analysis and Mascot database searching was identical as indicated for the MAPC sample in the Experimental section. Clearly, the difference between the actual Mascot score of a MS/MS spectrum and its corresponding Mascot threshold score for identity gets bigger when methionine-sulfone peptides are analyzed. This hints to the fact that methionine-sulfone peptides lead to less ambiguous peptide identification by Mascot.

the primary one.17 This leads to a reduction of the flux density and to a better fractionation. As shown in Figure 1C per primary fraction the shifting peptides are collected in 6 secondary fractions (labeled for instance 51 to 56 for primary fraction 5). The fourth and final LC stage was LC-MALDI analysis (Figure 1D). One important topic to consider is that methionyl-sulfoxide peptides undergo facile and dominant neutral loss of methane sulfenic acid25 (CH3SOH) particularly during MALDI-based MS/ MS analysis.26,27 Thereby, efficient fragmentation of the peptide backbone is hampered,28 interfering with successful spectral interpretation and peptide identification. For this reason, we introduced a second oxidation step, now with performic acid, prior to the LC-MALDI runs. Performic acid converts methionine-sulfoxide to methionine sulfone and cysteine to cystic acid29 and besides oxidation of tryptophan,30 no other amino acid modifications and protein/peptide degradation reactions have been described.31 Importantly, methionine sulfones are not prone to neutral losses in MS/MS mode and therefore fragmentation mainly occurs at peptide bonds thus facilitating peptide identification. This is illustrated in Figure 2 in which the difference between the actual spectrum (MS/MS) score and its corresponding Mascot’s threshold score for identity is indicated for MS/MS spectra from sulfoxide (grey bars) and sulfone (black bars) peptides. MS/MS scores calculated by Mascot are based on the absolute probability (P) that a match between the experimental MS/MS spectrum and a peptide sequence is a random event. Within a selected search base and given a confidence interval, Mascot calculates a threshold score for identity for each spectrum and spectra obtaining scores above this threshold have a high probability of being derived from the actual

sequence stored in the database (see also http://www.matrixscience.com). About 60% of all MS/MS spectra from sulfoxide peptides (1971 spectra identified) obtain scores that are either identical or 20 points higher than the threshold score for that particular spectrum, whereas this is only the case for 33% of the MS/MS spectra from sulfone peptides (3336 spectra identified). Thus, on average, MS/MS spectra from sulfone peptides receive higher Mascot scores and since the reported score equals -10*log(P), this indicates that the probability that peptide identification was a random event (i.e., a false positive identification) is on average lower for such peptides as compared to sulfoxide peptides. To circumvent suppression of peptide ionization/desorption and at the same time reduce the number of LC-MALDI analyses, we combined 5 secondary COFRADIC fractions that were separated by 12 min (fractions 54, 174, 294, 414, and 534 as illustrated in Figure 1D) for each fourth stage separation. During each of these final peptide separation steps, 24 peptide fractions were collected automatically on MALDI-target plates. The number of fourth stage separations per proteome digest was 432 (see Experimental Section), which implies that 432 times 24, thus 10 368 spotted peptide fractions were analyzed by MALDI-MS to cover one proteome. Interestingly, because of the substantial distribution of representative peptides (methionyl peptides), peptide separation/resolution was typically very high. This is evident in Figure 3 showing 10 consecutive MALDI-MS spectra obtained from the peptides fractionated and spotted in Figure 1D. While some peptides are present in one fraction (e.g., the peptides with masses of 1052.462, 1555.832, and 1245.744 Da in fractions 16, 18, and 19 respectively, Figure 3) we found that most peptides are on average distributed over only two consecutive fractions. Journal of Proteome Research • Vol. 5, No. 6, 2006 1419

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Figure 3. Four-stage LC isolation of methionyl peptides leads to less crowded peptide mixtures. Ten consecutive MALDI-MS spectra obtained from fractions 15 to 24 shown in Figure 1D are shown. On first sight, consecutive peptide mass patterns are clearly different, and it becomes clear that most peptides either pop up in one fraction or are present in two consecutive LCMALDI fractions. Ultimately, such less crowded, methionylpeptide enriched fractions should lead to less under-sampling and thus overall improved proteome coverage.

This results in two clear advantages: (1) the number of observed peptide ions is very high and (2) as timed ion-gates in MALDI-based mass spectrometers tend to have a rather low selective power, this increases the change of effectively selecting a peptide ion for further fragmentation. Reproducibility of the Peptide Sorting Procedure. The potential of MALDI-MS for peptide-centric proteomics lies in the high speed by which peptide samples are screened: in our current setup it typically takes about 15 s to screen one spotted peptide mixture in MS mode. This opens up possibilities to 1420


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use the technique for more routine and fast proteomic profiling based on peptide mass and COFRADIC sorting features. However, this implies that the peptide separation is done as reproducibly as possible. To evaluate this, two identical lysates of human Jurkat T-lymphocytes32 were treated in parallel as described in the Experimental Section for MAPC and the proteome digests were passed through the four-stage LC isolation procedure. Supporting Information Figure 1 shows the MALDI-MS profiles of 6 secondary LC-MALDI spots from the two separate peptide preparations. As can be seen, the obtained mass spectra are extremely similar. In fact, we picked 20 secondary, methionine-sulfoxide containing fractions (cfr. Figure 1C) and analyzed a total of 576 LC-MALDI fractions for each preparation. More than 1500 ions were observed in both analyses and the overlap between these was 70% when considering ions with a signal-to-noise (S/N) ratio above 25. Furthermore, this number raised to about 90% when ions with a S/N of 100 or moresions that normally give rise to interpretable PSD spectras were withheld. These numbers are high, considering the intrinsic reproducibility matters when running MALDI-MS in automated mode. For example, when analyzing exactly the same spots in duplicate, 81% or 98% of identical ions with an S/N ratio higher than 25 or 100 respectively were counted. These findings thus indicate that the four-stage HPLC setup is highly reproducible and might thus be considered for future peptide-centric proteome profiling studies. MAPC Proteome Analysis by Four-Stage LC Analysis of Methionyl Peptides. During the MAPC-proteome analysis a total of 27 251 peptide fragmentation spectra were acquired in automated mode and of these, 8480 (corresponding to 31.1%) were identified by Mascot. The complete list of identified peptides and their corresponding proteins may be accessed via PRIDE (http://www.ebi.ac.uk/pride/) and their distribution over the different SCX fractions is shown in Supporting Information Figure 2. The efficacy of methionyl peptide selection is evident from the fact that 7493 peptides (or 88.4%) contained at least one methionine-sulfone. Of the 987 nonmethionyl peptides, 237 (or 24%) either start with a pyrrolidone carboxylic acid or contain a deamidated asparagine or glutamine residue. We believe partial cyclization reactions at N-terminal glutamine or infrequent deamidation of the amide containing side chains may occur between the primary and secondary runs of the COFRADIC procedure, resulting in mobility shifts with concomitant peptide selection as artifacts of methionyl peptides. The remaining 750 nonmethionyl peptides are invariably from abundant proteins and are best explained by their expected, inefficient sorting by time-based (and not peak shape-based) peptide collection in diagonal chromatographic separation of complex peptide mixtures.33 In total, 3864 unique peptide sequences were identified; thus, on average each peptide was linked to about two fragmentation spectra. Not considering the possibility that a peptide is coupled to multiple protein isoforms stored in a database, these peptides were finally linked to 2151 proteins, which should thus be considered as the minimal MAPC proteome characterized in this study. The identified proteins were searched with the Multi-Protein Survey System34 (MPSS) at http://www.scbit.org/mpss/which, by integrating several databases, allows an in-depth analysis of physicochemical protein parameters and provides relational, biological protein data for complex protein mixtures. After


Figure 4. Characterization of the physicochemical parameters of the identified MAPC proteome. The theoretical protein molecular weight, the isoelectric point and GRAVY value were calculated using MPSS. The upper panel is a virtual 2D map of the proteins identified in this gel-free study. Proteins that are quite problematic to visualize by 2D-PAGE are indicated as black diamonds (proteins smaller than 10 kDa) and as open circles (proteins bigger than 200 kDa). The y-axis is scaled logarithmically. The distribution of calculated GRAVY values is indicated in the lower panel. Proteins with GRAVY values equal to or higher than zero (thus indicating hydrophobic proteins) are given as black bars.

extracting the calculated molecular weight and isoelectric point of each protein, a “virtual” protein 2D-gel was built and a quite characteristic “butterfly pattern” was observed with two dense areas: an acidic one between pI 4 and 7 and a basic one between pI 8 and 10 (Figure 4, upper panel). Such “two-wing patterns” are generally not obtained using 2D-gel proteomic approaches which often lack representatives of the “basic wing”. This suggests that we have not specifically lost entire classes of proteins, arguing for a good coverage of the abundant MAPC-proteome. This is further illustrated by pointing to small sized proteins such as thymosin beta-10 (4894 Da, 17 identified spectra), thymosin beta-4 (4921 Da, 18 spectra), the copper transport protein ATOX1 (7402 Da, 2 spectra), the transcription factor BTF3 homologue 2 (7606 Da, 1 spectrum) and the gamma subunit of the protein transport protein SEC61 (7741 Da, 26 spectra). On the opposite virtual 2D-gel side we detected 100 proteins with a calculated molecular weight well over 200 kDa. Such proteins are quite challenging to study by 2D-PAGE as they do not enter the gel quantitatively (or not at all). Selected examples of these proteins include nebulin (773 kDa, 1 identified spectrum), the microtubule-actin cross-linking

research articles factor 1 (670 kDa, 14 spectra), the piccolo protein (592 kDa, 1 spectrum), dystonin (590 kDa, 8 spectra) and the ryanodine receptor 3 (552 kDa, 1 spectrum). When classified according to their grand average of hydropathicity (GRAVY) index,35 129 proteins (about 6% of all proteins) had positive values indicating that such proteins tend to be hydrophobic and might thus be difficult to extract out of cells or keep soluble during IEF (Figure 4, lower panel). Examples include HSPC163 (GRAVY index of 0.83), HSPC039 (GRAVY index of 0.83), the myeloid-associated differentiation marker (GRAVY index of 0.77), CD63 (GRAVY index of 0.76), and the cytochrome b561 domain containing protein 1 (GRAVY index of 0.73). Potential Role of Ubiquitin in the MAPC Phenotype? Recently, spectral features such as the number of spectra or peptides associated to a given protein, (the sum of) the intensities of peptide ions as well as taking the expected number of identifiable peptides per protein into account were used to estimate relative copy numbers of proteins.8,36-40 When ranking the list of identified MAPC proteins according to the number of MS/MS spectra coupled to an identified protein (Table 1), as expected, the proteins predicted to be most abundant in the MAPC proteome are structural proteins including vimentin, myosin, actin, plectin, and talin next to typically abundant products of “household genes” like stress proteins and enzymes. Quite unexpectedly however, ubiquitin was ranked as the 17th most abundant protein with 45 different MS/MS spectra linked to 2 nonmethionyl peptides. In other proteomes of human cell lines that were studied so far with COFRADIC techniques,18,19,41 we never noticed ubiquitin to be this abundant, and thus believe this finding to be important in the context of MAPC biology. Next to this quite surprisingly high amount of ubiquitin identified in the MAPC proteome several enzymes involved both in protein ubiquitination and de-ubiquitination were present in MAPC (see Supporting Information Table 1). However, only 73 spectra were linked to these 30 different enzymes thus possibly indicating that these proteins are not highly abundant in MAPC. Nevertheless, these findings hint that the whole machinery for ubiquitin-steered protein degradation is at least already present, active, or dormant, in MAPC. Furthermore, following a targeted reanalysis of the generated data using Mascot now allowing for a digycline motif on Lys (i.e., the remnant of ubiquitination following tryptic proteome digestion), 5 peptides were identified (see Supporting Information Table 2). Retrieving ubiquitinated peptides in our study is however quite unexpected as the stem cells were not preincubated with a proteasome inhibitor prior to harvesting the proteome, and thus most of the poly-ubiquitinated proteins are expected to have entered into the proteasome degradation pathway and hence disappear out of the proteome. Furthermore, the peptide isolation protocol mainly targeted methionyl peptides. Hence, the chance that a peptide is picked up carrying both a methionine-sulfone and a diglycine motif on an internal lysine was selected is low. As we now identified 5 peptides indicative of ubiquitination one may expect that the original proteome digest contained at least 5 times more ubiquitinated peptides (on average, 20% of all tryptic peptides carry at least one methionine residue). Taken together, these data hint to an important control of the physiology of MAPC by ubiquitination. Whether, this is mono or poly-ubiquitination as well the type of ubiquitin branches cannot be extracted from the current data and may thus be tackled in further studies. Journal of Proteome Research • Vol. 5, No. 6, 2006 1421

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Table 1. List of the Most Abundant MAPC Proteins Based on the Number of MS/MS Spectra Linked to Protein Entriesa accession no.

protein description

spectra

unique peptides

P08670 P35579 P60709 Q15149 Q01995 P12814 Q9Y490 P21333 Q09666 P11021 O43707 P19105 P37802 P60842 P26038 P04406 P62988 P06733 P06576 Q14204 Q01082 P04350 P07355 Q9P2E9 P55072 P02751 Q14847 Q96AY3 P38646 P60660 P18206 P52272 P07737 P07437 P55209 P39023 P11142 P14625 P60059 Q9BUF5 O60814 Q9NZM1 P14174 P00338 P68363 O75369 P13667 P23246 P04075 P09382 P30101 Q13813 O75396 P10809 Q14974

Vimentin Myosin heavy chain, nonmuscle type A Actin, cytoplasmic 1 Plectin 1 Transgelin Alpha-actinin 1 Talin 1 Filamin A Neuroblast differentiation associated protein AHNAK 78 kDa glucose-regulated protein precursor Alpha-actinin 4 Myosin regulatory light chain 2, nonsarcomeric Transgelin-2 Eukaryotic initiation factor 4A-I Moesin Glyceraldehyde-3-phosphate dehydrogenase, liver Ubiquitin Alpha enolase ATP synthase beta chain, mitochondrial precursor Dynein heavy chain, cytosolic Spectrin beta chain, brain 1 Tubulin beta-4 chain Annexin A2 Ribosome-binding protein 1 Transitional endoplasmic reticulum ATPase Fibronectin precursor LIM and SH3 domain protein 1 FK506 binding protein 10 precursor Stress-70 protein, mitochondrial precursor Myosin light polypeptide 6 Vinculin Heterogeneous nuclear ribonucleoprotein M Profilin-1 Tubulin beta-2 chain Nucleosome assembly protein 1-like 1 60S ribosomal protein L3 Heat shock cognate 71 kDa protein Endoplasmin precursor Protein transport protein SEC61 gamma subunit Tubulin beta-6 chain Histone H2B K Myoferlin Macrophage migration inhibitory factor L-lactate dehydrogenase A chain Tubulin alpha-ubiquitous chain Filamin B Protein disulfide-isomerase A4 precursor Splicing factor, proline-and glutamine-rich Fructose-bisphosphate aldolase A Galectin-1 Protein disulfide-isomerase A3 precursor Spectrin alpha chain, brain Vesicle trafficking protein SEC22b 60 kDa heat shock protein, mitochondrial precursor Importin beta-1 subunit

299 235 138 94 93 83 76 75 67 59 56 50 48 47 46 45 45 44 43 40 37 37 36 35 35 34 34 33 32 30 30 29 29 29 28 27 27 26 26 26 25 25 24 23 23 22 22 22 21 21 21 21 21 20 20

30 43 10 32 12 16 18 15 28 10 14 5 5 8 14 8 2 4 9 24 13 5 8 7 6 5 3 6 11 5 11 10 4 4 3 8 10 7 2 8 2 9 1 4 5 10 7 8 4 3 2 10 3 10 7

a Proteins are ranked according to the number of MS/MS spectra linked to their sequence. The number of unique peptide sequences hereby identified is indicated in the last column. According to this classification, vimentin is the most abundant MAPC protein and clearly and as expected, most products of household genes are quite abundant as well, with one marked example: ubiquitin is present in the top twenty of abundant proteins but was never predicted this abundant in other COFRADIC-based proteome studies.

One protein targeted by ubiquitination is a regulatory subunit of the 26S proteasome, and it was previously already shown that several members of this protein complex were affinity purified using tagged ubiquitin,42 although it was not directly clear whether this proteins were in vivo ubiquitinated or not. Next to this protein, MCM7 has also been identified as a substrate for ubiquitination,43 hence supporting the identification of these two ubiquitinated proteins. Putative MAPC-Surface Proteins. A panel of previously used marker proteins for distinguishing between MAPC, MIAMI 1422


(multipotent MSC isolated under low oxygen tension), and MSC cells21,44-46 was compared to the proteins found in our MAPC (Table 2). Note that for the MAPC proteome dataset only a positive selection could be made: in other words, if a potential marker protein (e.g., CD90, Flk-1, and Flt-1) was not identified in the proteome study one may not state that this protein was absent in the analyzed cells. Its absence might be due to undersampling, suppression of both desorption and ionization, lack of efficient PSD fragmentation or lack of a marker tryptic methionyl peptide analyzable in the system used. Several adult

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Proteome of Human Multipotent Adult Progenitor Cells Table 2. Comparison of Known and Putative Markers for Adult Stem Cells with the MAPC Proteomea FACS MARKER

MAPC(1)

b2-Mic BMPR1B CD1a CD10 CD11a CD11b (Mac-1) CD13 CD133 CD14 CD27 CD29 CD31 CD34 CD36 CD38 CD43 CD44 CD45 CD49a CD49b CD49c CD49d CD49e CD50 CD51 CD53 CD54 CD56 CD59 CD61 CD62E

low

a

MIAMI

+ low +

low -

-

-

+

-

MSC

MAPC(2)

MAPC

+

-

-

proteome

low

+ + + low/low + + + + + + +

+ +

+

+ -

low

+ + + + + +

+ + -

FACS MARKER

MAPC(1)

CD62L CD62P CD63 CD71 CD81 CD90 CD104 CD105 CD106 (VCAM) CD109 CD114 CD117 CD120a CD122 CD124 CD144 (VE-Cad) CD146 (Muc18) CD164 CD166 cMet Flk-1 Flt-1 GlyA HLA-DR HLA-I NTRK3 SH2 (CD105) SH3 (CD73) SH4 (CD73) STRO-1 vWF

MIAMI

MSC

MAPC(2)

MAPC

+ -

low + low -

proteome

-

+ + + + + ( +

+ + + +

-

+

+ + -

+ low

+

+

+ low low -

+

-

-

-

low/+

low

low + + +/low -

+ + -

studies.44-46

The list of protein markers was compiled from previous The presence of a given marker on a stem cell line was mainly addressed following FACS analysis and this is indicated in this table with a plus sign, its absence with a minus sign and if the protein was found to be only lowly expressed this is indicated. As indicated in the column entitled “proteome, MAPC”, several previously characterized stem cell markers are present in our proteome (see text).

stem cell markers were identified in our study. Examples include CD13, CD29, CD44, CD49b, and CD63. On the other hand, 3 proteins previously not identified in nonproteomics studies of human MAPC were here identified: CD10, CD117, and CD146. One plausible explanation might be that their presence indicates a subtype of MAPC with MSC characteristics, however, we do not yet hold conclusive data for this. CD271, also known as low-affinity nerve growth factor receptor (LNGFR), was recently shown to be the best marker to isolate MSC directly from bone marrow49 however, since this protein was not identified in our study the cells analyzed were truly MAPC and not MSC. Another explanation might be that the proteome study presented here is much more sensitive, identifying proteins with low(er) copy numbers as compared to the FACSbased studies,21,44-46 thus enabling identification of “unanticipated stem cell marker proteins”. An important issue in stem cell research is the identification of surface proteins that could be used as a (potential exclusive) marker for the pluripotent state of the cells. For that reason, the TMHMM Server version 2.0 at http://www.cbs.dtu.dk/ services/TMHMM-2.0/47,48 was used for predicting those proteins in the MAPC proteome that contain at least one transmembrane helix. The returned list of 296 different proteins entries was further reduced to 163 components by removing known or predicted proteins that not do surpass the plasma membrane (Supporting Information Table 3). Clearly, numerous membrane proteins, including both expected, “household” proteins and unexpected ones (see below), that have not been tested before on adult stem cells were identified in the MAPC

proteome and these may be used in follow-up studies as potential markers for MAPC. MAPC Protein Kinases. An important class of proteins well represented in our MAPC proteome is that of the protein kinases. In total, 49 different kinases were identified, the majority being Ser/Thr kinases next to 5 receptor Tyr kinases and 3 nonreceptor Tyr kinases (Table 3). Although the acquired data does not allow us to draw major conclusions concerning the fact whether these kinases were active (only their presence in MAPC can be noted), some interesting comments hinting to the self-renewal potential of the MAPC studied in this work may be made. The presence of CD117 (stem cell factor receptor, c-kit, steel factor) points to the possibility of hematopoietic differentiation from these cells. Stem cell factor receptor is found on hematopoietic stem cells and a number of lineages thereof and bone marrow stromal cells are considered as the main producer of stem cell factor. Furthermore, one of the Src family of Tyr kinases, Lyn, binding to activated c-Kit is also present in our MAPC proteome and essential roles for Lyn in preserving the LIF-induced self-renewal properties of murine stem cells have been described.50 Although human LIF can induce STAT3 phosphorylation and nuclear translocation in hESCs, human LIF is unable to maintain the pluripotent state of hESCs.51 Nevertheless, the Src family of Tyr kinases is important for self-renewal in all known systems. GO-Based Comparison of the MAPC Proteome to COFRADIC-Proteomes of Differentiated Cells. Using GO-terms52 the proteome composition of undifferentiated MAPC stem cells was compared to that of four different human cell lines: Jurkat Journal of Proteome Research • Vol. 5, No. 6, 2006 1423

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Table 3. Protein Kinases Identified in the MAPC Proteomea Serine/threonine kinases Calcium/calmodulin-dependent protein kinase type II delta chain (Q13557) cAMP-dependent protein kinase, gamma-catalytic subunit (P22612)

RAC-alpha serine/threonine-protein kinase (P31749) Receptor-interacting serine/threonine-protein kinase 2 (O43353) Rho-associated protein kinase 2 (O75116) Ribosomal protein S6 kinase alpha 3 (P51812) Serine/threonine-protein kinase 10 (O94804) Serine/threonine-protein kinase 19 (P49842) Serine/threonine-protein kinase 24, splice isoform A (IPI00335281) Serine/threonine-protein kinase 3 (Q13188) Serine/threonine-protein kinase 4 (Q13043)

Cell division protein kinase 8 (P49336) cGMP-dependent protein kinase 1, beta isozyme (P14619) Citron Rho-interacting kinase (O14578) DNA-dependent protein kinase catalytic subunit (P78527) Dual-specificity tyrosine-phosphorylation regulated kinase 4 (Q9NR20) G protein-coupled receptor kinase 4 (P32298) Inhibitor of nuclear factor kappa-B kinase alpha subunit (O15111) Inhibitor of nuclear factor kappa-B kinase epsilon subunit (Q14164) Integrin-linked protein kinase 2 (P57043)

Serine/threonine-protein kinase 6 (O14965) Serine/threonine-protein kinase MRCK gamma (IPI00454910) Serine/threonine-protein kinase Nek7 (Q8TDX7) Serine/threonine-protein kinase PAK 3 (O75914) Serine/threonine-protein kinase PAK 4 (O96013)

Mitogen-activated protein kinase 1 (P28482) Mitogen-activated protein kinase 3 (P27361) Mitogen-activated protein kinase kinase kinase kinase 4 (O95819) Mitogen-activated protein kinase kinase kinase kinase 5 (Q9Y4K4) Mitotic checkpoint serine/threonine-protein kinase BUB1 beta (O60566) Myosin light chain kinase, smooth muscle and nonmuscle isozymes (Q15746) Protein kinase C, D2 type (Q9BZL6) Protein kinase C, delta type (Q05655) Protein kinase N2 (Q16513) Proto-oncogene serine/threonine-protein kinase Pim-1 (P11309)

Serine/threonine-protein kinase PLK2 (Q9NYY3) Serine/threonine-protein kinase SNF1-like kinase 2 (Q9H0K1) Serine/threonine-protein kinase SPRK1 (Q96SB4) Serine/threonine-protein kinase TAO3 (IPI00410485) Serine/threonine-protein kinase WNK4 (Q96J92) Serine-protein kinase ATR (Q13535)

Tyrosine kinases Focal adhesion kinase 1 (Q05397) Tyrosine-protein kinase Lyn (P07948) Tyrosine-protein kinase-like 7 precursor (Q13308) Receptor tyrosine kinases Angiopoietin 1 receptor precursor (Q02763) Ephrin type-A receptor 2 precursor (P29317) Ephrin type-B receptor 2 precursor (P29323) Ephrin type-B receptor 4 precursor (P54760) Mast/stem cell growth factor receptor precursor (c-Kit) (P10721) a The kinases are catalogued according to three classes: Ser/Thr, Tyr, and receptor Tyr kinases (data extracted from Swiss-Prot and IPI). The protein description with the corresponding accession number in the sequence databases is indicated.

T-lymphocytes (dataset available in PRIDE; accession numbers 1632-1635), type II alveolar epithelial cells-like A549 lung carcinoma cells, neuroblastoma SY-SY5Y cells and a colon cancer cell line (DLD1 cells). The proteomes of these differentiated cells were isolated similarly as was done for MAPC and COFRADIC techniques18 were also used for peptide/protein identification thereby “legalizing” the proteome composition comparisons. MPSS was now used for linking GO-terms to the proteins identified in all five proteomes. The frequency of each identified term from each of the three general GO categories (cellular compartment, biological process and molecular function) was normalized for each proteome and finally the normalized frequencies of GO-terms identified in the MAPC proteome were compared with those identified in the four other proteomes (see columns entitled “ratio” in Supporting Information Tables 5-7). Following a statistical analysis of the base-2 logarithm values of these frequency ratios, it was determined that values higher than 2.24 indicated a significant (within a 95% confidence interval) increase of proteins linked 1424


to a particular GO-term in the MAPC proteome (data not shown). Similarly, ratios lower than 0.15 indicate a significant reduction of the number of MAPC proteins belonging to a given GO-term. Only considering GO-terms holding 10 or more MAPC proteins, we found several protein classes to be preferentially present in the MAPC proteome as compared to the proteomes of differentiated cells. For instance, proteins penetrating at least one layer of a biological membrane, extracellular and lysosomal proteins appear to be more represented in the MAPC proteome (Supporting Information Table 4). As can be judged from Supporting Information Table 4, several biological processes may be more active in MAPC cells. These include processes of protein/peptide degradation, processes evoking an immune response, developmental processes, G-protein coupled and cell-cell signaling and processes of membrane fusion. When looking at a more individual protein level, it is tempting to assume that Ser/Thr-kinases (see also Table 3), receptors and their docking molecules are more manifested in MAPC cells

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Proteome of Human Multipotent Adult Progenitor Cells Table 4. Chromosomal Distribution of Identified MAPC Gene Productsa

Chromosome

Genes (BUILD 35.1) (%)

MAPC

SH-SY5Y

DLD1

Jurkat

A549

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Y

9.74 6.52 5.15 3.82 4.44 5.20 5.14 3.46 4.01 3.67 6.31 4.73 1.85 4.38 3.38 3.85 5.20 1.49 5.94 2.65 1.26 2.61 4.26 0.95

1.05 1.18 1.18 0.82 1.02 0.79 0.90 0.94 0.96 1.08 1.02 1.23 0.95 0.72 0.98 0.98 1.23 1.02 1.07 1.15 0.83 1.04 0.77 0.05

1.02 0.92 0.88 0.84 0.82 0.91 1.21 0.75 1.23 1.06 0.99 0.86 0.93 0.88 0.64 1.34 1.16 0.72 1.34 1.54 1.03 0.90 0.96 0.23

0.95 1.11 1.19 0.86 1.04 0.95 0.77 0.92 0.99 1.11 1.01 1.25 0.65 0.75 0.85 1.26 1.23 0.66 1.26 1.04 0.44 1.18 0.83 0.12

0.97 1.00 0.86 1.15 0.75 0.87 0.73 1.00 1.03 1.02 0.95 1.02 0.79 0.79 1.19 1.60 1.36 0.81 1.08 1.46 0.96 0.92 0.94 0.14

0.97 1.05 0.96 0.73 1.22 0.63 1.03 0.94 1.43 0.97 0.96 1.41 0.42 0.89 0.78 1.13 1.40 0.73 1.04 1.40 0.37 1.01 0.84 0.16

a Following assignment of the identified proteins to their corresponding chromosomal location, the generated chromosomal distribution was compared to that of the complete human genome (build 35.1, data available via http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db)genomeprj&cmd) Retrieve&dopt)Overview&list_uids)9558). Similar comparisons were made for the identified gene products in 4 differentiated cell lines. Following a statistical analysis on the log-2 values on all calculated ratiosseach ratio represented the relative number of gene products present on a given chromosome divided by the expected relative number of genes present in the whole humane genomesit was determined that values outside the interval 0.56 to 1.43 significantly (with 95% confidence) differed from the expected ratio and thus hint to decreased (lower values) or increased (higher values) presence of gene products of the corresponding chromosome in the final proteome. Such values are indicated in bold. For the sake of completeness, the distributions of genes located on the sex chromosomes are indicated in italics though these distributions were not used for calculating the significance interval.

(Supporting Information Table 4). Since only a fraction of all identified proteins is classifiable using GO-terms (e.g., for the MAPC proteome 4763 GO terms were linked to 1397 proteins, which constitute only 65% of all identified proteins) care must be taken not to over-interpret the results of these comparative GO-analyses. However, given the fact that the majority of the classified proteins are abundant and well-characterized proteins and given the abovementioned ratio limits we consider the compositional proteomic differences between the undifferentiated MAPC cell and the cell lines indicative for their phenotypic differences; i.e., the potential to differentiate to different cell types. Chromosomal Distribution of Translated MAPC Genes. In another set of experiments, we determined the relative chromosomal distribution of the identified MAPC gene products and compared it to the expected distribution based upon the latest prediction of human genes and their distribution among the 24 chromosomes (see Table 4). Not taking into account genes located on sex chromosomes, there is a clear trend that the chromosomal distribution of the identified MAPC gene products follows that of the total genome (ratios close to 1). In other words, it appears that genes on all chromosomes are quite uniformly transcribed and translated thus forming the MAPC proteome. Interestingly, when similar distributions were

calculated for the identified gene products in the proteomes of differentiated cells a different trend becomes apparent. For example, lung epithelial A549 cells have a reduced expression of genes located on chromosomes 13 and 21, whereas Jurkat T-cells show an increased expression of genes located on chromosomes 16 and 20. Hence, in conclusion, it is alluring to state that nondifferentiated adult stem cells express a patchwork of genes which is uniformly distributed among the 22 autosomal chromosomes. And, when cells differentiate they tend do drop or increase the expression of genes located on specific chromosomes. Whether this phenomenon would explain part of cellular differentiation processes remains to be analyzed with more sets of proteomics data.

Discussion The four-stage LC enrichment procedure for methionyl peptides holds two important features for peptide-centric proteome studies. First of all, peptides are distributed over 10 348 MALDI-MS samples and most of them are present in only two consecutive LC-MALDI fractions (Figure 3). In fact, our peptide isolation scheme could be termed “four-dimensional” because peptides are first separated based upon their net charge at pH 3 (first dimension). Using COFRADIC, methionyl peptides are then isolated from each SCX-fraction first in their non-oxidized form and then in their sulfoxide from undergoing a hydrophilic shift leading to their isolation (second and third dimension). Prior to the last LC step, these methionine-sulfoxide peptides are further oxidized to their sulfone forms, which undergo a (less extensive) hydrophobic shift (fourth dimension). Since the extent of these shifts is not uniform (as illustrated in Supporting Information Figure 3), such “back-and-forth peptide shuffling” results in simpler peptide mixtures spotted onto MALDI-targets (Figure 3). Therefore, one can generally expect to observe relatively more peptide ions as suppression of peptide ionization and desorption drops with decreasing sample complexity and thus finally more peptides and thus proteins are expected to be identified. The second important advantage is that the final peptide mixture is highly enriched in methionine content and it has been shown that methionyl peptides are efficient signature peptides (almost all proteins carry at least one identifiable methionyl peptide53,54). This clearly certifies our approach for specifically targeting methionyl peptides. Furthermore, as such peptides are converted to their MS more stable sulfone counterparts, as shown in Figure 2 peptide identification becomes less ambiguous. As indicated above, 88.4% of all identified peptides contained at least one methionine residue present in the sulfone form. We checked if performic acid oxidation of methionines was quantitative by including the possibility that this amino acid was present as a sulfoxide. However, we failed to identify this type of modification, indicating that the final oxidation step was quantitative for methionines. On the other hand, of the 259 tryptophan-containing peptides identified, 31 were not oxidized whereas the rest was found in different oxidation states: 25 peptides contained one or more kynurenines, 112 contained one or more 3-hydroxykynurenines, and 91 contained at least one hydroxylated tryptophan. The same “base peptide” was several times identified with different types of oxidized tryptophans. This might thus point to a potential disadvantage of our technique: one Journal of Proteome Research • Vol. 5, No. 6, 2006 1425

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Figure 5. Saturation of proteome coverage. The cumulative number of peptides identified over 432 blocks of LC-MALDI runs is indicated with a gray line (each open triangle represents 6 LC-MALDI runs and a linear trend line is indicated). The number of new proteins identified per 6 LC-MALDI runs drops exponentially (shown in black) with increasing number of identified peptides suggesting a near saturation of identifiable proteins using the current MS(/MS) instrumentation.

peptide can be distributed over several forms and the sensitivity for detecting these peptidessin casu, methionine peptides also containing tryptophansmight thereby drop. However, in view of the low frequency of such peptides (about 3% of all peptides identified) we do not consider this effect to have an important impact on the final results. A last type of modification observed was oxidation of histidine, which was again only minor since only 14 histidinyl peptides out of 2305 identified were affected. Therefore, we may conclude that the performic oxidation step mainly affects the expected amino acids (methionine and cysteine) and leads to additional oxidative modification of tryptophan which can be easily accounted for when searching databases with MS/MS data. Although the number of MS/MS spectra leading to peptide identification tends to increase linearly with the number of analyzed samples, the number of newly identified proteins per LC-MALDI run drops quite rapidly over the course of MS analysis (Figure 5). For example, 98 peptides identified in the first 6 LC-MALDI runs were linked to 67 different proteins (thus on average 1 new protein per 2 analyzed MALDI spots), whereas the 83 peptides present in the last 6 runs only identified 12 novel proteins (or on average 1 new protein per 12 analyzed MALDI spots). This phenomenon has actually two effects: more spectra and/or peptides are linked to proteins already identified thereby increasing the soundness of their presence while on the other hand, less novel proteins are identified at the “end of the analysis” indicating that with the instrumentation used “complete” proteome coverage was nearly reached. Thus, the issue of the tradeoff between analysis time and proteome coverage is clearly manifested. One possible way to override this proteome saturation is to separate intact proteins in blocks of physicochemical alike proteins by chromatographic means prior to their digestion and the four-stage LC peptide isolation described here. 1426


Since this is a peptide-centric approach focusing on methionyl peptides, many proteins were identified by a single spectrum (49%, Supporting Information Figure 4) and/or a single peptide (66%, Supporting Information Fgure 4) indicating that a high number of spectra needed manual confirmation of the suggested peptide hit. Simply stating that single-hit identifications are wrong cannot be considered as the sole criterion for not reporting such identifications.32 This requires quite some work since, at least in our experience, no well-performing algorithms are available that do this automatically. Nevertheless, an experienced experimentalist spends about one min analyzing one identification reported by Mascot, thus making this task realistic. Although the proteome map obtained in this study is descriptive it contains several interesting features that emerge because of the high proteome coverage that was reached. For instance, several novel MAPC surface proteins were present that may be pursued in follow-up studies aiming at the potential discriminative power of the these particular proteins between different stem cells (see Table 2). The high abundance of ubiquitin, the presence of numerous enzymes involved in ubiquitination and de-ubiquitination and the identification of ubiquitinated peptides (Table 1 and Supporting Information Tables 1 and 2) are all unexpected following comparison with the proteomic compositions of other, nonstem cells. However, this “ubiquitin phenomenon” might be explained in view of the stemness of the studied cells and changes therein. For instance, a rapid response of stem cells to external triggers driving them into a differentiation route might be the proteasome-mediated removal of specific proteins. This might explain the prominent, active, or dormant, proteolytic machinery found in MAPC and will be the subject of further functional proteome studies. Likewise, the apparent unbiased exploitation of autosomal genes by MAPC as compared to differentiated cells (Table 4) portrays the proteome of these adult stem cells as a

research articles


me´lange of gene products preventing differentiation though permitting proliferation while preserving their self-renewal properties.

Conclusions To our knowledge our study is one of the first in its kind where MALDI-MS/MS spectrashere obtained by post-source decay (PSD55) and not collision-induced dissociation (CID)s of signature peptides has been used to chart a cellular proteome. MALDI-PSD-spectra are traditionally regarded as inferior to CID-spectra of doubly or triply charged ESI peptide ions, primarily because of the rather poor and quite uncontrollable fragmentation behavior of singly charged MALD ions of unmodified peptides. However, we here demonstrated that such spectra can be efficiently used to identify peptides in a peptide-centric proteomics study: over 27 000 spectra were automatically generated and over one-third was linked to a peptide sequence using Mascot. These figures are very similar to those observed in our other studies using CID of tryptic peptides with an ESI-Q-TOF instrument.17-19 However, one of the advantages of using MALDI-MS in our hands is the fact that the instrument was run for long periods of time without human intervention as compared to direct LC-MS/MS analyses using ESI mass spectrometers. The latter instruments tend to need more frequent maintenance as spraying needles tend to get clogged quite often. One important aspect to take into account when “copying” our technology into a lab is the total time spent on analyzing a proteome. With the instrumentation used in this study, an average of 15 s is spent analyzing a spotted sample in MS mode and about 1 min is required to obtain one PSD spectrum. Hence, we needed about 3 weeks for analyzing one sample. At first glance, this might seem quite long however, given the fact that in our hands one LC-MS/MS analysis on an ESI-based mass spectrometer (genre Q-TOF or ion trap) takes about 1 h and since 432 peptide pools would need analysis (see above), the total analysis time is comparable between MALDI and ESIbased mass spectrometers. Interestingly, however, the fact that for differential proteome analysis or proteome profiling only MS scans are needed and using the MALDI mass spectrometer in our lab, a proteomic sample prepared as described is measured within 2 days (in fact about 9 times faster than when using LC-MS on an ESI mass spectrometer). Since for such analyses, the number of peptide ions needing further fragmentation is typically quite low (for instance only those peptides of which the measured ratio differs significantly from the expected value) such proteome analyses can be finished within 3 days, opening up the possibility for more than 100 quite indepth proteome analyses per year. Furthermore, we have shown that the whole procedure is reproducible since about 90% of the peptide ions were observed in similar proteome digests that were sent through the four LC stages in parallel. Clearly, this opens up possibilities for using such exhaustive peptide sorting techniques for routine analysis of similar proteomes as required for many peptide-centric biomarker studies. Finally, when applied to the proteome of MAPC we were able to identify the largest proteome set of human adult stem cell to date; a minimal set of 2151 proteins and as described above, holding interesting proteins and modification events that may be the subject of follow-up studies.

Acknowledgment. K.G. is a Postdoctoral Fellow and L.M. a Research Assistant of the Fund for Scientific Researchs

Flanders (Belgium) (F.W.O.sVlaanderen). The project was supported by research grants from the Fund for Scientific ResearchsFlanders (Belgium) (Project No. G.0008.03), the GBOU-research initiative (project number 20204) of the Flanders Institute of Science and Technology (IWT), the Concerted Research Actions (GOA) from the Ghent University and the European Union Interaction Proteome (6th Framework Program). The authors further wish to thank Dr. Luc Krols from Peakadilly N.V. for his help on determining the chromosomal distribution of identified gene products.

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