Comparison of a Protein-Level and Peptide-Level Labeling Strategy

Mar 10, 2010 - Psychiatry, King's College London, London, United Kingdom. Received May 22, 2009. Abstract: Quantitative proteomics using isobaric labe...
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Comparison of a Protein-Level and Peptide-Level Labeling Strategy for Quantitative Proteomics of Synaptosomes Using Isobaric Tags Olivia Engmann,† James Campbell,‡ Malcolm Ward,‡ K. Peter Giese,*,† and Andrew J. Thompson§ Centre for the Cellular Basis of Behaviour, Institute of Psychiatry, King’s College London, London, United Kingdom, Proteome Sciences plc, Institute of Psychiatry, King’s College London, London, United Kingdom, and MRC Centre for Neurodegeneration Research, Department of Neuroscience, Institute of Psychiatry, King’s College London, London, United Kingdom Received May 22, 2009

Abstract: Quantitative proteomics using isobaric labeling typically involves sample digestion, peptide-level labeling and 2D LC-MS/MS. Proteomic analysis of complex samples can potentially be performed more comprehensively with GeLC-MS/MS. However, combining this approach with peptide-level labeling of multiple in-gel digests from entirely sectioned gel lanes can introduce many points of variation and adversely affect the final quantitative accuracy. Alternatively, samples labeled with isobaric tags at the protein level can be combined and analyzed by GeLC-MS/MS as a single gel lane. A caveat to this strategy is that only lysine residues are labeled, which might limit protein digestion and quantitation of peptides. Here we have compared a protein-level labeling GeLC-MS/MS strategy with a peptide-level labeling 2D LC-MS/MS approach, using mouse hippocampus synaptosomes and isobaric tandem mass tags. Protein-level labeling enabled the identification of 3 times more proteins (697 versus 241) than did peptide-level labeling, and importantly for quantitation, twice as many proteins with labeled peptides (480 versus 232) were identified. Preliminary in silico analysis also suggested the alternative use of Asp-N to trypsin to circumvent the interference of lysine labeling on protein digestion. Use of Asp-N resulted in the effective analysis of fewer peptides than with trypsin for the protein-level approach (1677 versus 3131), but yielded a similar quantitative proteomic coverage in terms of both peptides (1150 versus 1181) and proteins (448 versus 480). Taken together, these experiments demonstrate that protein-level labeling combined with GeLC-MS/MS is an effective strategy for the multiplexed quantitation of synaptosomal preparations, and may also be applicable to samples of a similar proteomic complexity and dynamic range of protein abundance. * CORRESPONDING AUTHOR: Prof. K. Peter Giese, Centre for the Cellular Basis of Behaviour, James Black Centre, King’s College London, Institute of Psychiatry, 125 Coldharbour Lane, London SE5 9NU, United Kingdom, ph: +44 (0)20 7848 5402, fax: +44 (0)20 7848 3808, email: [email protected]. † Centre for the Cellular Basis of Behaviour, King’s College London. ‡ Proteome Sciences plc, King’s College London. § MRC Centre for Neurodegeneration Research, King’s College London. 10.1021/pr900627e

 2010 American Chemical Society

Keywords: Quantitation • mass spectrometry • isobaric tags • GeLC-MS/MS • 2D LC-MS/MS • hippocampus • synaptosome • endoproteinase Asp-N

1. Introduction Mass spectrometry (MS)-based proteomics enables the routine quantitation of hundreds of proteins in complex biological samples. Quantitation can be performed using labelfree approaches including the direct comparison of analyte peptide ion currents in different samples1 and semi-quantitation of proteins by spectral counting,2 or by label-based methods including AQUA,3 SILAC4 and differential chemical labeling.5 A popular extension of chemical labeling uses isobaric tags for relative and absolute quantification (iTRAQ) or tandem mass tag (TMT) reagents.6,7 These reagents employ elegant chemical design such that differentially labeled peptides have the same mass, but when selected for MS/MS, mass separated quantitative reporter ions representative of each sample state are equivalently generated. This approach enables the multiplexed analysis of up to eight samples in a single experiment, and quantitative information is generated simultaneously with peptide sequence information, and hence peptide and protein identification. iTRAQ and TMT workflows for analyzing complex proteomes typically involve in-solution digestion of the samples. The resulting peptides from each sample are then differentially labeled at the ε-amino group of lysine and/or the peptide N-terminus, combined and analyzed by 2D LC-MS/MS. 2D LCMS/MS has a disadvantage in that analyses are biased toward the more abundant proteins in a sample.2,8 This occurs because the proteins in each sample are co-digested shotgun-style without prior separation at the protein level, and peptides representative of most proteins, including the more abundant ones, are generally present in all fractions. Since typical datadependent LC-MS/MS experiments preferentially analyze the more intense peptide ions, this results in a bias toward the more abundant proteins and under-representation of less abundant proteins. Samples are expected to be more comprehensively analyzed by 1D SDS-PAGE separation and subsequent in-gel digestion and LC-MS/MS of the sectioned gel lane (GeLC-MS/MS). With this method, abundant proteins can be visualized and excised separately from the more sparsely populated regions of the gel to circumvent selective sampling of peptides derived from Journal of Proteome Research 2010, 9, 2725–2733 2725 Published on Web 03/10/2010

technical notes abundant proteins. However, although clear and reproducibly resolved bands, or alternatively large intact sections of gel, can be labeled post-digestion with quantitative tags for effective quantitative analysis, post-digestion labeling of multiple excisions from gel lanes entirely and extensively sectioned for comprehensive quantitative analysis is problematic. For example, the quantitative workflow for a six-plex analysis of entire gel lanes each excised into 15 sections would necessitate cutting 90 gel sections, and performing 90 separate in-gel digests and 90 separate labeling reactions, each of which potentially introduces different levels of quantitative variation. A possible method to avoid these problems is to label samples at the protein level as described previously.9 Protein samples can be differentially labeled, combined, and analyzed by GeLC-MS/ MS as a single gel lane. This has the added advantage of an earlier point of labeling compared to the usual post-digestion labeling approach. The caveat to protein-level labeling with isobaric iTRAQ or TMT tags is that only lysine residues are tagged, so only lysine-containing proteolytic peptides can be quantitatively analyzed, and labeling of lysine residues prevents proteolytic action of trypsin, the enzyme of choice for most proteomic studies, which will then only cleave at arginine residues. Protein-level labeling of cysteine residues, in particular the effective use of ICAT10 and deuterated acrylamide11 to quantify proteins in complex samples, can circumvent this issue. However, these methods do not convey the particular advantages of multiplexed analyses using isobaric tags. In this study, we sought to investigate the utility of GeLCMS/MS for quantitative analysis of synaptosomes using isobaric tags. Synaptosomal preparations from mouse hippocampus were analyzed either by labeling at the protein level followed by GeLC-MS/MS analysis, or by labeling post-digestion at the peptide level followed by analysis using a hybrid 2D LC-MS/ MS method recently demonstrated to have a superior sensitivity for analyzing membrane protein samples12 (Figure 1). In addition, a preliminary in silico assessment indicated that digestion of samples with Asp-N, which cleaves N-terminally to aspartic acid residues, could be a suitable alternative to trypsin to avoid pitfalls in labeling lysine at the protein level. To investigate this, samples processed by the two experimental approaches were digested with either endoproteinase Asp-N or trypsin. The experimental results were compared in terms of proteomic coverage, number of peptides identified, number of peptides tagged for quantitation, and ultimately the number of proteins that could be quantified, as well as the quality of the quantitative data generated, to assess the effectiveness of each approach for quantitative proteomic analysis.

2. Materials and Methods 2.1. Isolation and Purification of Synaptosomes. Hippocampal synaptosome fractions were prepared as described.13 Essentially, hippocampi from 6 week-old mice (p25 transgenic mice and wildtype littermates in the C57/BL6 background14) were isolated and homogenized in 0.3 M sucrose, 40 mM Tris, pH 7.4, phosphatase and protease inhibitors (1/100 Phosphatase Inhibitor Cocktail 2 (Sigma), 250 nM okadaic acid, 100 nM fenvalerate, Protease Inhibitor Pill complete EDTA-free (Roche)), followed by two low-speed centrifugations at 1400g for 10 min. The pooled supernatants were spun down at high speed at 13 800g for 10 min and the pellet (P2) was purified on a sucrose gradient (0.8 M/1.0 M/1.2 M in 40 mM Tris, pH 7.4, protease- and phosphatase inhibitors; 2 h at 82 500g in a 2726

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Figure 1. Experimental work-flows for the protein-level labeling and GeLC-MS/MS (scheme 1), and peptide-level labeling and 2D LC-MS/MS (scheme 2) quantitative proteomic strategies. For protein-level labeling, purified synaptosomes from six mice (three wild-type mice, wt, and three p25 mutants, mt) were separately labeled with TMT 6-plex reagents. The samples were pooled and loaded equally onto two gel lanes. After SDS-PAGE, each lane was cut into 15 sections for digestion with Asp-N or trypsin and LC-MS/MS analysis. For peptide-level labeling, individual synaptosomal samples from six mice were loaded equally onto six lanes of two 20% SDS polyacrylamide gels. SDSPAGE was performed until the proteins were observed to collect at the interface of the stacking gel and the resolving gel. Each gel band, comprising the entirety of each sample, was excised for digestion with Asp-N or trypsin. Peptides from each sample were labeled with TMT 6-plex reagents, pooled and separated by strong cation exchange chromatography (SCX) into 15 fractions for LC-MS/MS analysis.

SW41 rotor at 4 °C). The synaptosomal fraction on the interface between 1.0 and 1.2 M sucrose was collected and diluted 3-fold with water. To reduce the lipid content of the sample which can impair the separation of proteins during GeLC-MS/MS, 3 vol of icecold acetone were added and the samples were incubated for 2 h at -20 °C. The precipitate was collected by centrifugation, washed briefly in ice-cold acetone, spun again and solubilized in urea buffer (7.5 M Urea, 2.0 M Thio-Urea, 0.1% SDS, 50 mM triethylammonium bicarbonate (TEAB)). The protein yield was determined with Bradford Assay (Sigma) according to manufacturer instructions. Sixty micrograms of synaptosomal protein were used per mouse, and three mice each of the two genotypes were compared. 2.2. Protein Labeling and 1D SDS-PAGE. Acetone-precipitated samples were resuspended in urea buffer and the volume was adjusted to 50 µL with 50 mM TEAB. Samples were reduced in 2 mM tris(2-carboxyethyl)phosphine hydrochloride (TCEP) for 30 min and cysteine residues were alkylated with 7.5 mM iodoacetamide for 1 h in the dark. TMT reagent stock solutions in acetonitrile (ACN; Proteome Sciences R&D GmbH & Co. KG, Frankfurt am Main, Germany) were added to a final concentration of 25 mM and the solutions were incubated for 1 h. Hydroxylamine (10% w/v) was added to a final concentration of 0.5% for 15 min to reverse potential undesired labeling of serine, threonine and tyrosine residues. Samples were combined, lyophilized and resuspended in 2× loading buffer (100 mM Tris-HCl, pH 6.8, 12% glycerol, 4% SDS, 2% β-mercaptoethanol, bromophenol blue) with sonication, and reconstituted

Quantitative Proteomics of Synaptosomes Using Isobaric Tags samples were centrifuged for 5 min at 3000 rpm, and the supernatant was loaded onto a 10% polyacrylamide-gel. Proteins were separated by 1D SDS-PAGE (60 min at 120 V) and visualized using colloidal coomassie (Sigma). Each lane was cut into 15 bands of approximately equal protein content, dehydrated and digested with trypsin or Asp-N for 2 h at 37 °C, then overnight at room temperature. 2.3. Peptide Labeling and 2D LC. Proteins were isolated for in-gel digestion and quantitative labeling by collecting each sample in its entirety as a single band at the interface between a 4% polyacrylamide stack gel and a 20% resolving gel. This gel-based sample isolation approach was recently demonstrated to improve the sensitivity of proteomic analysis of membrane proteins from hippocampus membrane by 6 times compared to in-solution digestion,12 and was therefore expected to enhance the proteomic analysis of synaptosomes, which comprise numerous membrane and vesicle-bound proteins. Two gels were run with equal loadings per lane for subsequent digestion with Asp-N or trypsin as described above. Peptides were extracted from the gel pieces, lyophilized and reconstituted in 55 µL of 100 mM TEAB. TMT reagent stock solutions in acetonitrile were added (20 µL per sample) and the reactions were incubated for 1 h at ambient temperature. Hydroxylamine was added to a final concentration of 0.25%, the mixture was incubated for 15 min, and the differentially labeled samples were combined and lyophilized. Strong cation exchange chromatography (SCX) was performed as described.12 Briefly, labeled peptides were reconstituted in 500 µL of loading buffer (10 mM potassium phosphate, 25% ACN, pH 3.0). The sample was injected into an ICAT cartridge cation exchange column (Applied Biosystems, Warrington, U.K.) and washed with loading buffer, and retained peptides were slowly eluted in 500 µL fractions using a 15 step gradient of 0, 30, 50, 70, 80, 90, 100, 110, 120, 130, 140, 150, 175, 225, and 350 mM KCl. Samples were lyophilized, reconstituted in 0.1% trifluoroacetic acid and desalted on C18 Ziptips (Millipore), and elutants were lyophilized and then reconstituted in 50 mM TEAB for LC-MS/MS. 2.4. LC-MS/MS and Data Analysis. LC-MS/MS was performed using an Ultimate LC system (Dionex, Camberley, U.K.) linked online to a QToF micro mass spectrometer (Waters, U.K.) as described.12 MS spectra were acquired as 1 s scans over a range of 400-1600 m/z. Data-dependent dual channel MS/MS spectra (two most intense ions selected for sequencing, two minute exclusion window applied after MS/MS acquisition) were acquired as 1.5 s scans over a range of 100-1800 m/z, with up to three individual scans acquired and combined per triggered MS/MS event. All MS data with an error higher than 50 ppm were recalibrated against internal trypsin or AspN peptides. MS/MS peak lists were generated using ProteinLynx Global Server v2.2.5 and searched against the Swiss-Prot database v55.2 (http://www.expasy.org/) Mus musculus taxomony (15 337 entries) as a single merged search for each experimental strategy using Mascot v2.2 (www.matrixscience.com). Parameters for all searches included Precursor ion mass tolerance 1.2 Da, fragment ion mass tolerance 0.6 Da, peptides with up to 2 missed cleavages; variable modifications: carbamidomethylation of cysteine, methionine oxidation, acetylation of protein N-termini, and phosphorylation of serine, threonine and tyrosine. TMT modification of lysine residues and the protein N-terminus were specified for protein labeling experiments, or lysine residues and peptide N-termini for peptide labeling experiments. A wide mass tolerance window was

technical notes

employed to account for incorrect selection of the precursor ion isotope that occasionally occurs with Masslynx V3.5. Excepting these cases, mass accuracy was typically less than 50 ppm. Scaffold v2.1.2 (Proteome Software, Inc., Portland, OR) was used to validate MS/MS based peptide and protein identifications. Protein identifications were automatically accepted if they contained at least two unique peptide assignments and were established at 100% identification probability by ProteinProphet.15 This typically included at least 2 unique peptides identified with at least 95% probability by PeptideProphet.16 After scoring and grouping of the MS/MS data, sequence assignments to all validated proteins were further filtered by rejecting all sequence assignments with a Mascot score less than 10 for Asp-N experiments or less than 15 for trypsin experiments, corresponding to PeptideProphet probability of ∼5% for both cases. Mass spectra for all protein identifications based on the assignment of a single unique peptide sequence (with PeptideProphet probability of ∼5% or higher) were then manually validated by visual inspection according to accepted procedures.17 Visual inspection resulted in the identification of 174 out of 697 proteins (from 407 spectra inspected) for the protein-level labeling trypsin experiment, 162 out of 516 proteins (from 358 spectra inspected) for the proteinlevel labeling Asp-N experiment, 54 out of 241 proteins (from 133 spectra inspected) for the peptide-level labeling trypsin experiment and 74 out of 150 proteins (from 171 spectra inspected) for the peptide-level labeling Asp-N experiment based on single peptides (Supporting Information). Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony in accordance with the Paris agreement (http://www.mcponline.org/misc/ParisReport_Final.dtl). Protein functional and localization annotations were assigned based on the Uniprot (www.uniprot.org) and NCBI (http:// www.ncbi.nlm.nih.gov/) databases and the transmembrane prediction algorithm Phobius.18 For proteins annotated with multiple cellular localizations, the assignments were made according to the following hierarchy in descending order of priority: Cytoskeletal, Mitochondrial, Vesicle-associated, Membraneous, Cytosolic, Nuclear, Other. For quantitation, reporter ion intensities were extracted from Mascot search results using an in-house PERL script. Prior to analysis, the reporter ion intensities from each of the six reporter ion channels, representing triplicate wild-type and mutant samples, were normalized to the average of all six channels to account for loading differences. This was achieved by calculating the median ratio for all assigned peptides from each reporter ion channel and the six channel average to apply as a correction factor. Proteins were quantified by summing the reporter ion intensities from peptide sequences that were uniquely assigned to each protein and the significance of relative changes between the triplicate wild-type and triplicate mutant samples assessed by two-sample t test using the statistical software SPSS v15.0. 2.5. In Silico Digestion and Peptide Calculations. In silico digestion and peptide calculations were performed using an in-house Perl script. Mouse and human proteomes were extracted from Swiss-Prot database version 56.8 and processed to afford only mature protein sequences (protein regions denoted by ‘chain’ or ‘peptide’ designations in the Swiss-Prot database). The mature protein sequences were digested in silico, to determine the total number of peptides produced, using the Mascot cleavage definitions for trypsin, Asp-N and Journal of Proteome Research • Vol. 9, No. 5, 2010 2727

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Arg-C (the latter to mimic trypsin digestion of proteins with obstructed lysines which occurs for the protein-level labeling approach). The results were filtered to estimate the number of peptides with character suitable for MS (hereafter referred to as peptidesMS) defined as peptides possessing a 2+ or 3+ charge state, and m/z of 400-2000. Charge state was estimated by summing the number of lysine, arginine and/or histidine residues +1, and calculated masses included TMT labeling of lysine residues for the protein-level labeling approach, and TMT labeling of lysine and the peptide N-terminus for the peptidelevel labeling approach. The results were further filtered to calculate the number of peptidesMS that were also tagged for quantitation (hereafter referred to as peptidesMS-Quant). For the peptide-level approach, all peptidesMS were assumed to be labeled at either lysine and/or the N-terminus, and therefore, peptidesMS was equivalent to peptidesMS-Quant. For the proteinlevel approach, peptide N-termini were assumed not to be labeled and only peptidesMS containing lysine qualified as peptidesMS-Quant.

3. Results and Discussion 3.1. In Silico Assessment of Sample Digestion for the Proteomic Strategies. The specific objective of this study was to determine if a shotgun approach to quantitative proteomics of synaptosomes employing peptide-level labeling and 2D LC-MS/MS could be improved upon using a protein-level labeling and GeLC-MS/MS approach. Although GeLC-MS/MS should potentially increase both protein and proteomic coverage, in silico digestion of the mature mouse proteome predicted that labeling of proteins at lysine residues for quantitation would profoundly reduce the total number of peptides produced by trypsin digestion (Figure 2A). This included the number of peptides with a suitable character for MS (peptidesMS) and therefore also the number of peptides suited to MS that are also labeled for quantitation (peptidesMS-Quant; see Materials and Methods for criteria for peptide definitions). Similar results were also obtained for analysis of the mature human proteome (Figure 2B). In all, for tryptic digestion of the mouse proteome, the potential number of total peptides was reduced from 820 970 to 449 385, the number of peptidesMS from 530 131 to 139 811, and most critically the number of peptidesMS-Quant, representing the peptide subset containing quantitative information, from 530 131 to 47 912. This reduction was a direct result of protein-level labeling of lysine that obstructs action of trypsin, which can then only cleave at arginine residues. This in turn results in the generation of larger peptides, less of which are suited to MS/MS sequencing (less peptidesMS). Moreover, since only lysine is tagged for quantitation in a protein-level labeling approach, only lysine-containing peptides from the peptidesMS subset are useful for quantitation (peptidesMS-Quant). This contrasts with the peptide-level labeling approach in which trypsin cleaves at both lysine and arginine to generate a larger set of peptidesMS, almost all of which can be potentially labeled for quantitation either at the peptide N-terminus and/or lysine residue. The interference of lysine labeling on protein digestion can be circumvented by employing alternative proteolytic enzymes to trypsin. Asp-N is a commonly used enzyme in proteomics that cleaves N-terminally to aspartic acid, and its activity is not expected to be obstructed by lysine labeling. In silico digestion of the mouse proteome by Asp-N generated fewer total peptides, but of slightly higher mass, compared to tryptic 2728

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Figure 2. In silico prediction of the total number of peptides (filled), peptides suitable for MS (peptidesMS, empty bars) and peptides suitable for MS and quantitation (peptidesMS-Quant, crosshatched) expected from digestion of (A) the mature mouse proteome, or (B) the mature human proteome, with trypsin or Asp-N endoproteinases for the protein-level and peptide-level labeling strategies. Note that for protein-level labeling Asp-N digestion yields more peptidesMS-Quant than trypsin digestion.

digestion (Supporting Information), reflecting the lower frequency of aspartic acid compared to lysine and arginine combined in the mature proteome. Although fewer peptidesMS were also generated by Asp-N digestion compared to trypsin for the protein-level labeling approach (116 373 vs 139 811), almost 40% more peptidesMS-Quant were generated (67 188 vs 47 912) indicating Asp-N could be a suitable alternative to trypsin to improve the retention of quantitative information. To investigate this, sample digestion for both the protein-level and peptide-level labeling strategies were experimentally undertaken using both trypsin and Asp-N endoproteinases. 3.2. Assessment of the Proteomic Coverage Achieved by the Protein-Level and Peptide-Level Experimental Strategies. The effectiveness of the individual strategies was gauged by determining the number of peptides and proteins confidently identified in each experimental workflow. This enabled a comparison of both the protein-level labeling GeLC-MS/MS and the peptide-level labeling 2D LC-MS/MS methods we used, in which samples were fractionated into 15 gel sections or 15 SCX fractions, respectively. In the trypsin experiments, 2.5 times more unique peptide sequences (3131 vs 1250) were assigned after protein-level labeling and GeLC-MS/MS analysis compared to peptide-level labeling and 2D LC-MS/MS analysis (Figure 3A), corresponding to almost 3 times more protein

Quantitative Proteomics of Synaptosomes Using Isobaric Tags

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results for our 2D LC-MS/MS strategy. In particular, for the GeLC-MS/MS approach, samples were dried after labeling, after which the proteins were reconstituted in strongly solubilizing Laemmli buffer and run on a gel. Samples were also dried immediately after in-gel digestion and reconstituted in TEAB prior to LC-MS/MS. In contrast, 2D LC-MS/MS samples were dried after peptide labeling, SCX fractionation and final reversephase desalting before LC-MS/MS. Furthermore, the proteinlevel approach only required GeLC-MS/MS analysis of two pooled samples, whereas the peptide-level approach as employed here necessitated the isolation, digestion and labeling of 12 separate protein samples before pooling into two samples for 2D LC-MS/MS analysis. In the latter case, the additional sample handling steps of smaller sample amounts may also have contributed to the poorer overall sensitivity observed for our peptide-level labeling 2D LC-MS/MS approach. In addition, 2D LC-MS/MS should be improved by employing SCX using a continuous salt gradient, instead of the off-line step gradient method we used here.

Figure 3. Assessment of the proteomic coverage of mouse hippocampal synaptosomes achieved by the protein-level and peptide-level experimental strategies. (A) The number of total MS/MS events (filled bars), MS/MS events assigned as peptides (empty bars) and MS/MS events assigned as unique peptide sequences (grey) observed in the protein-level and peptide-level experiments using either trypsin or Asp-N for sample digestion. (B) The number of proteins identified with at least one (filled bars), two (empty bars) or three (grey) unique peptide assignments from the same experiments.

identifications (697 vs 241) (Figure 3B). This was expected since GeLC-MS/MS should enable deeper proteomic mining and afford a more comprehensive analysis of moderately complex samples such as synaptosomes. However, the total number of MS/MS events in the GeLC-MS/MS experiments was also twice that of the 2D LC-MS/MS experiments. This indicates that in general a far greater analytical sensitivity was achieved by GeLC-MS/MS, which would also account for the more comprehensive peptide and protein identifications. Similarly, for the Asp-N experiments, GeLC-MS/MS out-performed 2D LCMS/MS analysis: 4.7 times more unique peptide sequences (1677 vs 359) were assigned after GeLC-MS/MS (Figure 3A), corresponding to 3.4 times more protein identifications (516 vs 150) (Figure 3B), although the number of peptides assigned was noticeably lower than for the trypsin digestion results. The GeLC-MS/MS approach was expected to outperform the 2D LC-MS/MS method in terms of proteomic coverage by reducing the analytical bias for abundant proteins. However, it was surprising to observe a 2-fold reduction in the total number of MS/MS events for the 2D LC-MS/MS method we used compared to the GeLC-MS/MS approach. This probably stems from differences in sample handling. Multiple drying and reconstituting of samples in aqueous or aqueous/organic solvents may have led to more sample losses and hence poorer

The experimental data also enabled a comparison of the results produced from Asp-N and trypsin sample digestion. Within both the GeLC-MS/MS and 2D LC-MS/MS experiments, far fewer proteins were identified after digestion with Asp-N than with Trypsin (Figure 3B). This was in agreement with the preliminary in silico analysis that suggested digestion with Asp-N should generate fewer peptidesMS. Interestingly, a lower proportion of the MS/MS events recorded were successfully assigned to peptides or unique sequences for Asp-N compared to trypsin in either the GeLC-MS/MS (30% and 44% assigned as peptides, and 14% and 20% assigned as unique sequences, for Asp-N and trypsin, respectively) or the 2D LC-MS/MS experiments (22% and 50% assigned as peptides, and 10% and 17% assigned as unique sequences, for Asp-N and trypsin, respectively). It is unlikely that this was a result of inefficient digestion with Asp-N since both Asp-N and trypsin generated a high proportion of fully digested peptides. Instead, it is probable that more tryptic peptides were assigned to peptide sequences because they are inherently suited to MS/MS analysis by virtue of containing a C-terminal lysine or arginine basic residue that can promote both peptide ionization and the detection of y-ion fragmentation series. In contrast, a high proportion of Asp-N peptides was not expected to contain basic residues and exhibit this character. In fact, 98% of Asp-N MS/MS events that were assigned as bona fide peptides contained histidine (26%), lysine (74%) and/or arginine (44%), despite in silico calculations suggesting frequencies of 23%, 31%, and 27% for histidine, lysine, and arginine, respectively. This indicated that MS/MS sequencing for peptide and protein identification was clearly more effective for the subset of peptides containing these basic residues. Collectively, these data indicated that a more comprehensive protein analysis of synaptosomes was achieved by using the GeLC-MS/MS method in contrast with the 2D LC/MS method we used here, and by using trypsin compared to Asp-N. However, the GeLC-MS/MS analysis using Asp-N identified more unique peptides and proteins than did the 2D LC-MS/ MS analysis using trypsin, indicating that using the GeLC-MS/ Journal of Proteome Research • Vol. 9, No. 5, 2010 2729

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Engmann et al. studies have quantified proteins based on measurements from two uniquely assigned peptides for which our data indicate the protein-level GeLC-MS/MS strategy performs best for the analysis of synaptosomes. However, if the confidence in quantitation was to be increased by basing protein quantitation on at least three unique peptides, then the peptide-level and protein-level strategies both performed similarly when trypsin was used for digestion. In contrast, in the Asp-N experiments, more labeled peptides (1150 vs 353) and more proteins with these peptides assigned were identified (448 vs 146) in the protein-level GeLC-MS/MS experiments than in the peptidelevel 2D LC-MS/MS experiments (Figure 4). This was consistent with the generally poorer results from the 2D LC-MS/MS analysis of Asp-N digested samples, which was confirmed in a repeat experiment (data not shown). Encouragingly, Asp-N digestion performed similarly to trypsin digestion for the protein-level labeling experiments in terms of quantitative coverage, and enabled the identification of a similar number of peptides labeled for quantitation and corresponding proteins. This demonstrated that Asp-N digestion was as effective as trypsin digestion for quantifying proteins using the proteinlevel labeling approach, despite Asp-N digestion on the whole yielding fewer overall MS/MS events and fewer total number of assigned peptide sequences.

Figure 4. Assessment of quantitative coverage of mouse hippocampal synaptosomes achieved by the protein-level and peptidelevel experimental strategies. (A) The total number of peptides labeled for quantitation (open bars) and number of labeled peptides with unique sequences (grey) identified in the proteinlevel and peptide-level experiments, using either trypsin or Asp-N for sample digestion. (B) The number of proteins identified with at least one (filled bars), two (empty bars) or three (cross-hatched) unique peptide assignments labeled for quantitation in the same experiments.

MS method instead of our 2D LC-MS/MS method yielded a greater analytical improvement than using trypsin instead of Asp-N. 3.3. Assessment of Quantitative Coverage Achieved by the Protein-Level and Peptide-Level Experimental Strategies. To investigate the direct impact of the two strategies on quantitation of synaptosomes using isobaric tags, the data were further analyzed in terms of the number of peptides identified that were also labeled with isobaric tags and the number of proteins that could be in effect quantified. In the trypsin experiments, slightly more labeled peptides with unique sequences were identified by the peptide-level 2D LC-MS/MS strategy than by the protein-level GeLC-MS/MS strategy (1256 vs 1181) (Figure 4A). In total, these peptides were assigned to half as many proteins (232 vs 480) (Figure 4B), but when multiple assignments of labeled peptides unique to the proteins were considered, the results converged, that is, at least two labeled unique peptides were assigned to 180 and 264 proteins, and at least three labeled unique peptides were assigned to 135 and 132 proteins for the peptide-level and protein-level labeling strategies, respectively. Although the protein-level GeLC-MS/MS strategy clearly identified more proteins to be potentially quantified, the reliability of peptide-centric quantitation should be improved by deriving quantitative measurements from higher numbers of peptides per protein. Numerous 2730

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3.4. Assessment of Quantitative Data Quality from the Protein-Level and Peptide-Level Experimental Strategies. An advantage of six-plex multiplexing is that triplicate sets of each sample type can be run within each proteomic analysis. This approach has a key advantage in that within each six-plex experiment, quantitative data are generated from an identical set of peptides for each of the six samples, which enables the direct comparison of reporter ion intensities within triplicate sample sets as well as between two sets of triplicate samples. In our experiments, the measurement for each protein was determined by summing the reporter ion intensities from all MS/MS spectra confidently assigned to the protein. Quantitative changes were then assessed by comparing the triplicate summed intensity values from wild-type samples against the triplicate summed intensity values from the mutant samples. In this experimental design, the analysis can be performed using statistically robust independent samples tests, for example, two-sample t test or ANOVA, which account for the internal variance within each triplicate set to determine the significance of differences between two triplicate sets, for example, wild-type versus mutant. This circumvents issues with more commonly employed methods of reporter ion quantitation derived from the pairing of independent samples to calculate ratios of protein change, which does not easily account for the variance within replicates of a sample. To investigate the quality of the quantitative data generated by the different experimental strategies, scatter plots of the peptide and protein reporter ion intensities for wild-type versus mutant p25 samples were constructed (Figure 5 and Supporting Information). A good linear relationship was observed for all experiments, indicating that, although the different experimental approaches affected the quantity of quantitative data, the overall quality of the quantitative data was similar. Furthermore, scatter plots of the coefficient of variation (CV) of the triplicate measurements of each peptide assignment within either the wild-type or mutant sample sets were also similar (10-20% for wild-type samples, 20-30% for mutant samples). The CV of triplicate measurements of each protein assignment in the wild-type and mutant samples also fell in the 10-20%

Quantitative Proteomics of Synaptosomes Using Isobaric Tags

technical notes

Figure 5. Scatter plots of the log10 transformed average reporter ion intensity for each peptide from the triplicate wildtype samples versus the p25 samples for each of the following experiments: (A) trypsin digestion, peptide-level labeling; (B) trypsin digestion, proteinlevel labeling; (C) Asp-N digestion, peptide-level labeling; and (D) Asp-N digestion, protein-level labeling.

and 20-30% range, respectively. These values were generally in agreement with the variations previously reported for isobaric tagging experiments,19 and indicated that variation in individual quantitative measurements for each method employed were not substantially different. Scatter plots of the ratios of the same proteins observed in the different methods were also constructed. The data generally scattered between -log 0.1 and log 0.1 for both axis in all comparisons representing 20-30% noise level (data not shown), as would be expected for complex sample sets in which only a few proteins differ significantly in abundance. If a significant group of proteins or subproteome in the sample was increased or decreased in one sample relative to the other, then this would be evident, but this was not the case for our experiments. However, the noise observed in the scatter plots did corroborate the CV measurements, and also showed that sample normalization was effective, since the scatter centered around zero. 3.5. Proteomic Profiling of Synaptosomal Preparations. The number of proteins identified from synaptosomal preparations has widely differed in previous investigations, which likely stems from the variety of techniques, instrumentation used and quantity of starting material available. In gel experiments, Sato et al.20 analyzed 500 µg of synaptosomal preparation by 2D gel electrophoresis and visualized 1009 spots, although only 24 were selected for identification by MS. Li et al.21 selected 250 spots for MS from 2D gel separated samples and identified approximately 100 different proteins. Peng et al.22 performed GeLC-MS/MS analysis (14 gel sections) of 80 µg of synaptosomal preparation and identified 374 proteins. More recently, 2D LC-MS/MS experiments have been performed: Yoshimura et al.23 identified 492 proteins in three combined analyses, Schrimpf et al.24 employed extensive SCX fractionation (37 and

41 fractions) of a total of 1.34 mg of sample to identify 1131 proteins from two analyses; and McClatchy et al.25 reported by far the most comprehensive synaptosomal proteomic profile, identifying 4001 proteins from three analyses using an optimized 12-step gradient for online 2D chromatography. In our experiments, the protein-level labeling GeLC-MS/MS experiments afforded the most protein identifications: 697 and 516 for trypsin and Asp-N digestions, respectively (Figure 6). In combination, this afforded 861 identifications, 95% of the total from the four experiments, with our 2D LC-MS/MS experiments accounting for the additional 5% of identifications. Interestingly, the 2D LC-MS/MS strategy contributed to only 5-6% of protein identifications if either trypsin or Asp-N was used for digestion (Figure 5C,D). This low contribution of additional protein identifications by 2D LC-MS/MS was confirmed in a second experiment (data not shown), indicating that GeLC-MS/MS was able to more comprehensively identify proteins in the samples than the 2D LC-MS/MS approach we used here. In these experiments, we employed consistent sample fractionation (15 sections for both methods) followed by LC-MS/MS on a QToF micro instrument using the same operational methods to enable a valid direct comparison of the results. In either case, the depth of proteomic analysis is expected to be further improved by increasing the extent of fractionation, for example, analyzing 30 gel sections by GeLCMS/MS or 30 SCX fractions by 2D LC-MS/MS, and by combining the results of replicate analyses. Use of newer mass spectrometers capable of more sensitive analysis and faster duty cycles would also increase the number of peptides identified and assigned. Applying these changes to either method we employed here should lead to both quantitation Journal of Proteome Research • Vol. 9, No. 5, 2010 2731

technical notes

Engmann et al. keeping proteins). Importantly, a significant proportion of the profile also comprised membrane proteins indicating effective analysis of generally hydrophobic proteins by these methods.

4. Conclusions

Figure 6. The overlap of proteins identified using the different strategies. Distribution of proteins identified in (A) protein-level labeling GeLC-MS/MS experiments using Asp-N and trypsin digestion, (B) peptide-level labeling 2D LC-MS/MS experiments using Asp-N and trypsin digestion, (C) protein-level labeling GeLC-MS/MS and peptide-level labeling 2D LC-MS/MS experiments using trypsin digestion, and (D) protein-level labeling GeLC-MS/MS and peptide-level labeling 2D LC-MS/MS experiments using Asp-N digestion.

of more proteins, and more robust quantitation of proteins that have additional peptides assigned to them. In total, 909 proteins were identified in the four experiments (Supplementary Table 1). Proteins in the synaptosome are expected to derive from several cellular components. These include the presynaptic machinery of synaptic vesicles; the postsynaptic density (PSD); membrane-bound and cytosolic machineries for synaptic and/or cell-cell signaling (receptors, second messengers, kinases and the like); mitochondria that supply the high energy demands in the synapse; cytoskeletal proteins that regulate the shape of spines and maintain contact with soma for transport of proteins and organelles; and housekeeping proteins for cellular metabolism and local protein synthesis.24,26 Additionally, previously published preparations also contain small quantities of contaminants such as myelin, and nuclear and endoplasmatic reticulum proteins.13,24 Alternatively, more studies might reveal that these proteins might not be specific for the nucleus, myelin and endoplasmatic reticulum as currently believed. The present experiments identified proteins from all the expected categories as well as small amounts of contaminations from myelin and nuclear fractions (Supplementary Figure 1). Previously identified proteins were detected, including syntaxin, synaptophysin, vacuolar ATP synthase and NSF (presynaptic machinery of synaptic vesicles); MAP2, tau, Camk2, PP2A and 2B (PSD); 14-3-3 proteins, NCAM and NMDAR (membrane signaling); rab isoforms, rab- and rho GDIA, cdc42 (GTP signaling); ATP synthase, creatine kinase and glutamate dehydrogenase (mitochondrial); actin, tubulin, cofilin, sthathmin and WAVE1 (cytoskeletal); fructose-bisphosphate aldolase alpha enolase, peroxiredoxins and acetyl CoA-acetyltransferase (house 2732

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Analysis of synaptosomal preparations from mouse hippocampus by a protein-level labeling GeLC-MS/MS strategy for quantitative proteomics and a peptide-level labeling 2D LCMS/MS strategy revealed considerable differences in the proteomic coverage obtained. Although these results should only be interpreted in the context of synaptosomes or other moderately complex proteomes with a similar dynamic range in protein abundances, in our experiments the protein-level strategy clearly enabled the identification of more peptides and proteins than the peptide-level strategy we used, despite digestion with trypsin being partially blocked by labeling of lysine residues prior to digestion. Importantly for quantitative analysis, the ability to more comprehensively probe the synaptosome proteome by GeLC-MS/MS offset the anticipated disadvantage of partially obstructing trypsin digestion caused by labeling of lysine residues at the protein level. Use of Asp-N to circumvent this effect was partially successful: a higher proportion of labeled peptides were identified, but this was offset by fewer peptides in total being assigned. In effect, digestion with Asp-N afforded approximately the same quantitative proteomic coverage as trypsin for the protein-level strategy. Furthermore, the Asp-N experiments highlighted a dependence of effective peptide sequencing by ESI-MS/MS on peptides containing basic residues histidine, lysine and/or arginine. Trypsin inherently generates basic peptides, which contributes to the effectiveness of this enzyme for peptide-level proteomic studies. Alternative endoproteases to trypsin, such as Asp-N, remain useful to inform on regions of proteins that trypsin cannot cleave. Currently, this results in spectral sets unsuitable for matching against community spectral libraries based on tryptic digests cleave. However, this disadvantage can be resolved by the addition of peptide data sets generated using other endoproteases, to improve the analytical coverage of proteins represented in these libraries and assist assay development for protein regions unsuited to tryptic digestion.

Acknowledgment. The authors thank Karsten Kuhn for advice on TMT labeling protocols. This research was supported by the Medical Research Council, U.K.. Supporting Information Available: Table S1, proteomic profiles of synaptosomal preparations; Table S2, predicted distribution of peptide masses resulting from the digestion of mature human and mouse proteomes with trypsin or Asp-N for each of the experimental strategies; Figure S1, distribution of the identified synaptosomal proteins according to localization; MS/MS spectra used to identify proteins based on a single unique peptide assignment; Figure S2, scatter plots of peptide reporter ion intensities; Figure S3, scatter plots of protein reporter ion intensities; Figure S4, scatter plots of log transformed protein reporter ion intensities; Figure S5, scatter plots of the peptide CV versus reporter ion intensities from wild-type samples; Figure S6, scatter plots of peptide CV versus reporter ion intensities from mutant p25 samples; Figure S7, scatter plots of protein CV versus reporter ion intensities from wildtype samples; Figure S8, scatter plots of protein CV versus reporter ion intensities from mutant p25 samples. This material is available free of charge via the Internet at http://pubs.acs.org.

technical notes

Quantitative Proteomics of Synaptosomes Using Isobaric Tags

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