Critical Comparison of Multidimensional Separation Methods for

Mar 26, 2012 - density gradient cell fractionation, strong cation exchange chromatography, reversed-phase chromatography, proteome coverage...
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Critical Comparison of Multidimensional Separation Methods for Increasing Protein Expression Coverage Linn Antberg,† Paolo Cifani,† Marianne Sandin, Fredrik Levander, and Peter James* Protein Technology, Department of Immunotechnology, CREATE Health, Lund University, Sweden S Supporting Information *

ABSTRACT: We present a comparison of two-dimensional separation methods and how they affect the degree of coverage of protein expression in complex mixtures. We investigated the relative merits of various protein and peptide separations prior to acidic reversed-phase chromatography directly coupled to an ion trap mass spectrometer. The first dimensions investigated were density gradient organelle fractionation of cell extracts, 1D SDS-PAGE protein separation followed by digestion by trypsin or GluC proteases, strong cation exchange chromatography, and off-gel isoelectric focusing of tryptic peptides. The number of fractions from each first dimension and the total data accumulation RP-HPLC−MS/MS time was kept constant and the experiments were run in triplicate. We find that the most critical parameters are the data accumulation time, which defines the level of under-sampling and the avoidance of peptides from high expression level proteins eluting over the entire gradient. KEYWORDS: protein and peptide separation, methods comparison, off-gel isoelectric focusing, GluC, trypsin, GelLC, density gradient cell fractionation, strong cation exchange chromatography, reversed-phase chromatography, proteome coverage

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INTRODUCTION Multidimensional separations were the original starting point for the idea that proteins could be analyzed on a global scale. It was the two-dimensional gel electrophoresis that was developed by Patrick O’Farrell that popularized and enabled this train of thought.1 The technique at its most refined allowed the visualization of over 10000 protein spots.2 The bottleneck was the identification of the proteins, which could then only be done by Edman degradation. This was alleviated by the development of MS-based identification based on the masses of peptides obtained by enzymatic digestion, which is an ideal complement to 2D-PAGE. However, the development of automated protein identification using peptide MS/MS fragmentation paved the way for alternative methods to gelbased analysis. The first automated online two-dimensional proteome-wide technology was reported by the lab of John Yates and popularised as multidimensional protein identification technology (MuDPIT).3 An alternative approach to massive sequencing was a “proteome” simplification technology developed in Ruedi Aebersold’s laboratory4 termed “Quantitative analysis of complex protein mixtures using isotope-coded affinity tags”, known simply as ICAT. Both techniques have spawned endless variations and proponents and isotope tagging is enormously popular, both using cell culture incorporation of labeled amino acids5 and by chemical means. The advent of new mass spectrometers with much higher mass accuracy, resolution and speed has led to analyses identifying over 5000 proteins in a single experiment.6 The © 2012 American Chemical Society

question now is how to extend the range of coverage. The limiting factor seems to be the dynamic range of current mass spectrometers masking low abundance proteins. Apart from instrumental improvements, one must understand what degree of orthogonality the various multidimensional separation methods have and where improvements can be made. How much is to be gained by extensive fractionation rather than faster MS and exclusion techniques? There have been several recent reviews on multidimensional separation techniques in proteomics.7,8 Here we attempt to define limiting factors in current multidimensional separations to define what can be done to improve them, if anything. We focus on using a standardized reproducible set up to compare the relative effectiveness of five different two-dimensional separation techniques in terms of proteins identified, the effectiveness of sampling and fractionation as well as the degree of orthogonality.

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MATERIALS AND METHODS

Materials

High Resolution strips (3−10) were purchased from Agilent (Santa Clara, CA). Acrylamide, urea, Tris, magnesium acetate, DTT and iodoacetamide were from Sigma Aldrich (Stockholm, Sweden). Criterion Precast SDS-PAGE gels (12.5%) were from BioRad (Hercules, CA). Sequencing-grade modified trypsin, Received: September 3, 2010 Published: March 26, 2012 2644

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Off-gel IEF Fractionation

GluC protease and Protein Desalting Spin Columns were purchased from Pierce (SDS diagnostics, Falkenberg, Sweden). The Micro-Lowry Protein Assay Kit was from Sigma Diagnostics (Stockholm, Sweden). All HPLC solvents were from Fluka (Sigma Aldrich, Stockholm, Sweden). UltraMicroSpin C18 and UltraMacroSpin C18 columns were from the NestGroup (Southborough, MA).

Peptides from trypsin digestion were desalted by washing at least five times with water using UltraMacroSpin C18 Columns, eluted with 60% ACN and the sample volume was reduced in a speed vac. Focusing was performed on Agilent 3100 Offgel Fractionator (Agilent, Santa Clara, CA)) according to the manufacturer’s protocol. After 1 h of rehydration with a voltage of 500 V, peptides were focused on 24 cm 3−10 High Resolution strips (Agilent) until 50 kVh was reached. The current, voltage and power were set to a maximum of 100 μA, 8000 V and 300 mW respectively. Fractions were collected by pooling adjacent wells to give a total of 12 fractions. TFA and formic acid were added to the samples to final concentrations of 1% and 0.1% respectively. Fractions were then cleaned with UltraMicroSpin C18 Columns, eluting with 50% ACN, 0.1% formic acid, dried in a speed vac and resuspended in 8 μL of 0.1% formic acid. The 12 fractions were subsequently analyzed by RP-HPLC−MS/MS.

Cell Culture

The human mantle cell lymphoma cell line Granta-519 (DSMZ, Braunschweig, Germany) was cultured in RPMI1640 supplemented with 10% fetal bovine serum (FBS) and 1% L-glutamine (Invitrogen, Carlsbad, CA) at 37 °C in a humidified atmosphere at 5% CO2. The cells were washed twice with a buffer containing 5 mM HEPES and 250 mM sucrose, and pellets were stored at −80 °C. Protein Extraction and Sample Preparation

Cell pellets were thawed on ice and lysed by incubation 20 min with lysis buffer (1% w/v N-octylglucoside, 0.1% w/v DOC, 150 mM NaCl, 1 mM EDTA, 50 mM Tris-HCl, Complete Mini EDTA-free protease inhibitor (Roche Diagnostics GmbH, Mannheim, Germany)) at a ratio of 4 μL per million cells. After 30 min centrifugation at 14000 rcf, the proteins in the supernatant were precipitated by adding DOC to a concentration of 0.08% (w/v) and incubating 1 h on ice, followed by an addition of TCA to a concentration of 2.5% (w/ v) and 30 min incubation on ice. The precipitate was collected by centrifugation at 4000 rcf for 30 min and the pellet was washed with acetone. Proteins were then resuspended by sonication in 100 mM NH4HCO3 and protein concentration was determined using the Micro-Lowry Assay Kit. The protein mixtures were hereafter divided into three different samples for each separation method to be run in triplicates.

1D Gel Followed by Digestion with Trypsin or GluC

A sample of 100 μg of proteins for each fractionation method was reduced in 3 M urea with 10 mM DTT (Fluka, Steinhem, Germany) by incubation for one hour at 56 °C. The sample was alkylated in 55 mM iodoacetamide for 45 min, RT, in the dark. DTT was then added to remove excess iodoacetamide and the sample was digested overnight at 37 °C with Trypsin (Promega, Madison, WI) using a protein/enzyme ratio of 50:1 in 100 mM NH4HCO3, 1 M urea. Digestion was stopped by lowering the pH to 3 with acetic acid.

The protein sample was mixed with sample buffer (0.05 M Tris-Cl, 0.05 M SDS, 5% v/v glycerol, 0.1% DTT) and heated at 98 °C for 5 min before separation on a 12.5% Criterion Precast SDS-PAGE gel (BioRad, Hercules, CA) at 25 °C. Onehundred micrograms of protein were loaded per lane and the gel was run with 200 V/gel until the bromophenol blue dye front had runoff the base of the gel. The gel was stained using Gel Code Blue Stain Reagent (Pierce). Each of the lanes was cut into 12 slices that were destained in 50% acetonitrile and 25 mM NH4HCO3. The gel slices were reduced with 10 mM DTT in 100 mM NH4HCO3 at 56 °C for 1 h. Alkylation was performed by adding 55 mM iodoacetic acid in 100 mM NH4HCO3 and incubating for 45 min at RT in the dark. The slices were then washed once with 100 mM NH4HCO3 and several times using ACN. Trypsin (25 μL of 12.5 μg/mL Trypsin in 50 mM NH4HCO3) or Protease V8 (25 μL of 12.5 μg/mL GluC in 50 mM sodium phosphate) was added to the dehydrated gel slices. The samples were left for 30 min at 4 °C prior to incubation at 37 °C overnight (approximately 18 h). The peptides were extracted from the gel by adding 5% TFA in 75% ACN and incubating at RT for 30 min. This was repeated once and the liquid from the two extractions were pooled together. Subsequently, the volume was reduced using a Speed vac and the samples were then dissolved in 20 μL of 0.1% formic acid. The 12 fractions were subsequently analyzed by RP-HPLC− MS/MS.

SCX Fractionation

Organelle Separation by Density Gradient Centrifugation

ICAT Cation Exchange Cartridges (Applied Biosystems, Foster City, CA) were used at a flow rate of 100 μL/minute. The sample of 100 μg of peptides were loaded on the preequilibrated cartridge and then eluted in 200 μL fractions by injecting KCl at increasing concentrations (flow through, 25, 50, 75, 100, 150, 200, 300, 400, 500, 600 mM and 1 M) in 5 mM KH2PO4, 25% ACN. The volume of the fractions was then reduced to less than 100 μL using a speed vac at medium drying rate. The fractions were desalted using UltraMicroSpin C18 Columns (The NestGroup, Southborough, MA) before eluting the peptides in 50 μL 0.1% formic acid, 80% ACN, and then drying in a speed vac. The 12 fractions were resuspended in 8 μL of 0.1% formic acid and subsequently analyzed by RPHPLC−MS/MS.

Granta cells cultured as described above were washed twice with PBS and then resuspended in a hypotonic homogenization buffer (10 mM HEPES pH 7.9, 10 mM KCl, 1.5 mM MgCl2, 0.5 mM DTT, Complete Mini EDTA-free protease inhibitor). The cells were kept on ice for 4 min and then disrupted using 5 strokes with a tight pestle in a 7 mL Dounce homogenizer until 90% of the cells were broken as determined by microscopy. Subsequently, the nonbroken cells and nuclei were pelleted and the supernatant with the cytosolic proteins was removed. The postnuclear supernatant was layered on top of a preformed 5− 45% continuous iodixanol gradient. The gradient was centrifuged at 200000× g in a Beckman SW41 rotor at 4 °C for 2 h. Ten fractions of 1.2 mL were collected from the top of the gradient and each fraction was precipitated with DOC and TCA as described in the protein extraction section. The same

Sample Preparation and Digestion for SCX and Off-gel Separation

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Tandem searches were performed both using standard scoring and with the K-score algorithm. Hits generated were then combined by Proteios to generate Protein Reports with an FDR set to 0.05 at the protein level. The pI and MW values were calculated in Proteios as described in ref 10 and the plots were generated in Matlab 7.9.0.529 (R2009b).

volume of the cytosolic fraction and the nuclei resuspended in PBS were also precipitated in the same way and included in the further analyses as fractions 11 and 12. The pellets were resuspended in 40 μL of 100 mM NH4HCO3 and quantified with the Micro-Lowry Protein Assay. Samples of 20 μg were taken from each fraction and were reduced with in 3 M urea with 10 mM DTT (Fluka, Steinhem, Germany) by incubation for one hour at 56 °C. The samples were alkylated in 55 mM iodoacetamide for 45 min, RT, in the dark. The samples were digested overnight at 37 °C with Trypsin (Promega, Madison, WI) using a protein: enzyme ratio of 50:1 in 100 mM NH4HCO3, 1 M urea. Finally the samples were desalted using UltraMicroSpin C18 Columns before eluting twice in 40 μL of 50% ACN, 0.5% acetic acid. The samples were dried in a speed vac and were finally resuspended in 0.1% formic acid. The 12 fractions were subsequently analyzed by RP-HPLC−MS/MS.

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RESULTS

General Strategy

The approach used in this work was to compare five twodimensional separation methods that have all been claimed to give high protein expression coverage. We chose organelle separation by density gradient centrifugation and 1D SDSPAGE (using two different digestion enzymes) as the proteinbased first dimensions and strong cation exchange and off-gel IEF as the peptide-based first dimensions. The number of fractions collected from each method was fixed at 12. The second dimension was kept constant; a reversed-phase HPLC separation online with a 3D ion trap and the time for each gradient was kept constant as well as the MS/MS collection parameters. The amount of sample used in each separation was also kept constant. In addition, we monitored the system sensitivity everyday to ensure it remained constant and examined the MS traces to ensure that no impurities were affecting the data collection (e.g., ampholytes from the IEF, etc.). All searches were performed using the same parameters and databases at a fixed FDR. All samples were run in triplicate from a single large protein preparation that had been extracted from human Granta cells. The protein extraction was the same for all experiments except for the organelle separation, which required a different initial cell lysis, though after organelle separation a similar harsh protein extraction was used. We chose to use a 3D ion trap for this analysis despite the fact that it is a lower mass accuracy and sensitivity mass spectrometer than QTof or Orbitrap instruments since we are only interested in the relative differences between the various multidimensional separation techniques rather than the absolute limits of detection of the mass spectrometer being used. It has previously been shown that such an instrument, given an adequate number of prefractionation steps, provides proteome coverage at a level similar to a high performance instrument (an LTQ-FT) using a direct analysis approach.11

Reversed-phase HPLC and Mass Spectrometry

The same HPLC and MS conditions and times were used to analyze all samples. Acidified peptide fractions were analyzed using reversed phase liquid chromatography coupled to an LCQ DECA XP Plus (ThermoFisher, San Jose, CA). Liquid chromatography was carried out using a NanoLC 1DPlus (Eksigent technologies, Dublin, CA). Peptide mixtures were loaded at a constant flow of 10 μL/min onto a precolumn (Zorbax 300SB-C18 5 × 0.3 mm, 5 μm particle size, Agilent technologies, Wilmington, DE), washed 10 min and subsequently separated on a RP analytical column (Zorbax 300SBC18 150 × 0.75 mm, 3.5 μm particle size, Agilent technologies) at a flow rate of 400 nL/min. Peptides were eluted from the analytical column using a 60 min 5 to 50% ACN in 0.1% formic acid gradient, followed by 10 min column washing with 80% ACN and 10 min re-equilibration at 5%. Peptides were analyzed using a “triple-play” data-dependent acquisition. The instrument was set to scan 400−2000 in 2 s. After each full mass range scan, zoom scans (mass window 6 Da) and MS/MS spectra (minimum fill 5 × 103 counts, normalized collision energy 35, activation Q 0.25) were collected sequentially for three most intense peptide ions in a single cycle. A dynamic exclusion list of 50 m/z values was used for 3 min. A daily sensitivity check was performed by injecting 100 fmol of trypsin digested ADH: the spectra obtained from ADH were submitted to Mascot (MatrixScience, London, U.K.) and searched against UniProtKB/Swiss-Prot database release 2010−8, filtered for S. cerevisae only, with 1 Da error for precursor and 0.8 Da for fragment ions. A minimum of 7 peptides identified (or 30% protein coverage) was required to consider the sensitivity as acceptable. Three replicates from each fractionation method were independently analyzed.

Peptide Separation as a First Dimension

The first and still the most popular approach is the SCX-RP combination proposed by John Yates and commonly termed MuDPIT. Tryptic digestion yields three main classes of peptides, 1+, 2+ and multiply charged which are separated well by SCX chromatography. In our hands, the approach yielded a respectable number of identified peptides (651) and corresponding unique proteins (323), especially given that we were using an older 3D type ion trap (Table 1). We wanted to get an overview of the protein expression coverage that we were obtaining and so we plotted the molecular masses of the identified proteins against their pIs (Figure 1C). The distribution was very much like expected from a theoretical plot of the human proteome. Most proteins have a molecular mass between 20 and 80 kDa and isoelectric points ranging from 3.5 to 12 with more proteins concentrated at around pH 4.5−6 and 7.5−10.5. High mass and low mass as well as both extremes of pI were covered and the expected gap at the pI of the cell was present. We did not use any detergents in the

Data Analysis

Raw spectra were converted into mzData (XML) format using Bioworks Browser (Thermo) and submitted to Proteios version 2.17.0 for further analysis9 (2009). Within the Proteios workspace, each XML file was independently searched against the human part of Swiss-Prot 2011−08−17 with separate isoform entries and concatenated with a reverse sequence database of equal size, totalling 71324 entries, using both Mascot version 2.3.01 and X!Tandem (version TORNADO (2008.12.01.1)). The mass tolerance was set to 1.2 Da for parent ions and 0.6 Da for fragment ions and one missed protease cleavage was allowed. Cys carbamidomethylation was set as fixed modification, and Met oxidation as variable. X! 2646

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ampholyte carry over was present. The overall distribution of proteins identified (Figure 2C) appears to have the same overall contours of the SCX separation in both the mass and pI axes. The under-sampling problem is also present using this technique, as can be seen from the lack of overlap between the replicates (Figure 2A and B). Surprisingly we found that there was more overlap between fractions using IEF when compared to SCX (Supporting Information). The peptide identifications showed that hydrolysis was taking place, for example, of phosphopeptides and deamidation of glutamine and asparagine and that this appeared to happen more often at the alkaline end of the gradient.

Table 1. Summary of Peptide and Protein Coverage by the Five Separation Methodsa peptides replicate A replicate B SCX Offgel IEF Iodixanol Gel trypsin Gel Glu-C Gel trypsin long gradient

687 287 321 1994 725 4936

675 416 194 2003 693 3405

proteins replicate C

total

748 215 350 1975 707 4265

327 185 202 688 274 1602

a

Number of unique peptides identified in each run are given together with the total number of unique proteins identified using the combined data from the three runs. The effect of increasing MS analysis time from 1 h to 4 is shown at the bottom of the table for the GelLC−trypsin approach.

Protein Separation as a First Dimension

Cell Organelle Separation. It would seem logical that a good orthogonal method for use a first dimension would be the use of density gradient centrifugation to separate cell organelles. We adopted the iodixanol gradient method for separation and although a clear separation of organelles was obtained between the fractions, the overall number of proteins identified was low (189, Table 1). The distribution of proteins identified showed no selectivity toward mass or pI and the overlap between replicates was low (Figure 3A−C). This was perhaps to be expected since organelles do not in general separate as distinct entities but generally give a wider distribution of sizes and shapes, for example, as with the continuum between the nucleus, ER, Golgi and several vesicle types. Although there was extensive overlap between fractions, some showed distinctive profiles. Closer inspection of the search results showed that the identifications were dominated by high expression cytosolic proteins that appear to have smeared across the entire gradient (Supporting Information). Possibly extension washing of the pellets would have helped but at a price of large drop in yield. In-gel Trypsin-RP. The most effective method of all, leading to a much higher number of protein identifications (688, Table 1) than any other was 1D-SDS-PAGE, gel slicing and in-gel digestion with trypsin. This doubled the number of peptides identified in each run and gave an excellent overall coverage (Figure 4C). Like all methods, the under-sampling was clear and there was a distinct lack of overlap between the replicates (Figure 4A). Inspection of the individual fractions showed there was virtually no overlapping although often, it could be clearly seen that proteins had been proteolyzed and

extraction procedures so membrane proteins were poorly covered as expected. When we examined the individual replicates, it became obvious that although the number of peptides being identified was fairly constant, there was very little overlap between the runs (Figure 1A). Clearly the density of peptides per scan was too high, leading to drastic under-sampling. We tried elongating the HPLC separation time and up to 4 h this alleviated the problem to some extent but increasing the time beyond this did not produce any increase in the number of peptides being identified due a degradation in the chromatographic separation leading to band broadening (data not shown). The actual efficiency of the peptide separation in the SCX dimension was high as can be seen from the lack of peptide overlap between fractions (Supporting Information). Off-gel IEF. A popular alternative to SCX as a first dimension for peptide separation is off-gel isoelectric focusing. We used the Agilent Offgel Fractionator to separate a tryptic digestion of the Granta cells. The overall number of proteins identified was much lower than with the SCX approach and the run-to-run variability was much higher (Table 1). The low number of identifications could possibly be explained by the low amount (100 μg) loaded.12−14 Preliminary experiments had indicated that sample clean up after focusing to remove the ampholytes was critical so we optimized this. Inspection of the MS data files showed that no

Figure 1. Strong cation exchange of a trypsin digest of a cell protein extract in combination with reversed-phase peptide separation. (A) Venn diagram indicating the distribution of unique peptides between the three replicate runs. (B) pI/MW distribution of the all proteins identified from the 3 runs combined. (C) Protein mass and pI distribution of the proteins identified. 2647

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Figure 2. Off-gel isoelectric focusing of a trypsin digest of cell protein extract in combination with reversed-phase peptide separation. (A) Venn diagram indicating the distribution of unique peptides between the three replicate runs. (B) pI/MW distribution of the all proteins identified from the 3 runs combined. (C) Protein mass and pI distribution of the proteins identified.

Figure 3. Density gradient organelle separation followed by trypsin digestion in combination with reversed-phase peptide separation. (A) Venn diagram indicating the distribution of unique peptides between the three replicate runs. (B) Distribution of proteins between the runs. (C) pI/MW distribution of the all proteins identified from the 3 runs combined.

Figure 4. 1D SDS-PAGE protein separation followed by trypsin digestion in combination with reversed-phase peptide separation over 1 h. (A) Venn diagram indicating the distribution of unique peptides between the three replicate runs. (B) Distribution of proteins between the runs. (C) pI/MW distribution of the all proteins identified from the 3 runs combined.

In-gel Glu-C-RP. The low number of identifications with the Glu-C digestion (266, Table 1) was somewhat unexpected, and to preclude variables such as bad quality of an enzyme batch, several different batches and manufacturers were tested, all with the same results. The run-to-run reproducibility was of the same order as the other separations and the overall pI/MW distribution remained the same (Figure 5). We closely inspected the chromatograms and there was neither a source

lower molecular weight products were being identified (Supporting Information). The main reason for the large increase in peptide coverage appears to be that the high abundance proteins are relatively isolated within specific bands. This prevents high expression level peptides from smearing over the entire separation as occurs in SCX-RP separations and allows time for other less abundant peptides to be sampled. 2648

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Figure 5. 1D SDS-PAGE protein separation followed by GluC digestion in combination with reversed-phase peptide separation. (A) Venn diagram indicating the distribution of unique peptides between the three replicate runs. (B) Distribution of proteins between the runs. (C) pI/MW distribution of the all proteins identified from the 3 runs combined.

of contamination (e.g., PEG which is sometimes used as an enzyme stabilizer, but which dominates the HPLC−MS) nor could a digestion problem be found. The number of MS/MS spectra accumulated was almost the same as for the trypsindigested gel slices. Manual inspection showed them to appear as normal peptide fragmentation patterns. However, the number of proteins identified was low. Test runs on an Orbitrap mass spectrometer however gave much better results. The only explanation that we can offer is that the Glu-C peptides appear to give a large number of spectra dominated by internal ions and by incomplete ion series. Obtaining high mass accuracy for the parent ion using an instrument (as opposed to the limited accuracy of the 3D ion trap) appears to compensate for the odd fragmentation by greatly limiting the number of candidate sequence matches. High pI proteins contain a large number of lysine and arginine residues and hence generate very small peptides that are not analyzed by the mass spectrometer. GluC on the other hand, due to the paucity of glutamate residues, should provide reasonably sized peptides suitable for MS/MS analysis. The hoped-for increase in identification of high pI proteins did not appear to be happening judging from the limited data obtained since these would be predicted to be more abundant than in tryptic digests since larger peptides should be obtained that are MS detectable (Figure 6). We tried to find other explanations for the proteome coverage given by the various methods. The two main factors that are used in most of the first dimension separations is either size or charge. The second dimension is usually fixed as hydrophobicity (though hydrophilic separations are also effective but less easy to implement in an online mode). Thus we investigated the distribution of peptide charge with reverse-phase retention time. Most of the methods gave the same pI distribution against retention time (Figure 7) except for the in-gel trypsin digestion method that showed a much large number of peptides at longer retention times. This was due to a higher recovery of hydrophobic peptides and also due to an increase in the overall size of the peptides, which results in longer retention times on average. This probably arises from the solubilizing effect of SDS that allows more hydrophobic proteins to be separated without extensive losses. We also investigated the relative complementarity of the different methods. Figure 8 shows Venn diagrams of the peptide overlap between the various methods. Clearly the in-gel

Figure 6. SDS-PAGE protein separation followed by trypsin digestion in combination with reversed-phase peptide separation over 4 h. (A) Venn diagram indicating the distribution of unique peptides between the three replicate runs. (B) Distribution of proteins between the runs. (C) pI/MW distribution of the all proteins identified from the 3 runs combined.

trypsin method dominates the coverage but SCX appears to be the best option if one is to combine the results from two multidimensional methods. SCX gives 2-fold more peptide coverage than off-gel IEF and clearly more of these peptides are unique to the method than with the off-gel IEF.

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DISCUSSION The initial implementation of a fully automated proteome analysis system was based on a combination of a multidimensional peptide separation system together with soft- and hardware for automatic MS/MS analysis and protein identification.3 This approach, with or without isotopic labeling, is the mainstay of modern proteomics analysis. Other approaches like the simplification of proteomes by affinity chromatography is in use for specific analyses like phospho- or glycoprotein analyses. The current directions now appear to be how to increase proteome coverage. There is a large body of work describing the development of multidimensional separation systems for increasing coverage. New 2D Approaches

A simple approach that is easily automated is the use of a high pH reversed-phase separation with a standard low pH reversed2649

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Figure 8. Venn diagrams of the overlap of peptide identification between the various multidimensional methods. (A) Venn diagram indicating the distribution of unique peptides between the Offgel IEFRP and SCX-RP approaches. (B) Venn diagram indicating the distribution of unique peptides between the 1D-PAGE-RP and SCXRP approaches. (C) Venn diagram indicating the distribution of unique peptides between the 1D-PAGE-RP and Offgel IEF-RP approaches. Figure 7. Plots of peptide retention time vs pI. (A) pI/RT distribution of the peptides obtained from the 1D-PAGE-RP approach. (B) pI/RT distribution of the peptides obtained from the Offgel IEF-RP approach. (C) pI/RT distribution of the peptides obtained from the SCX-RP approach.

gives comparable coverage to the 1D PAGE in gel digestion approach.6 This system has in turn been shown to allow a doubling of protein identifications in comparison to SCX-RP approaches.22 The increase in peptide identification with 1DPAGE-RP over SCX-RP is is in line with the results that we present here (Table 1).

phase second dimension. Although it shows a lower degree of orthogonality compared to the standard SCX-RP system, this was counterbalanced by higher separation efficiency and a more homogeneous distribution of peptide elution.15 A novel approach to an old idea is the recent use of pH gradient elution in a standard SCX-RP system rather than using salt16 and results in fewer peptide overlaps between fractions and a higher number of basic peptides. Approaches employing mixed mode matrices such as anion exchange/reversed-phase17 have been demonstrated to give higher coverage than SCX/RP combinations and completely automated systems using weak anion/weak cation exchange protein separation followed by online trypsin digestion and reversed-phase peptide separation have been developed.18 Others have focused on the ultrahigh resolving power of CITP/CZE that shows virtually no peptide overlap between fractions.19 One advantage of CIEF systems is that they can be combined with SDS in the extraction procedure that allows for membrane protein coverage.20 The use of CIEF for peptide separation has also been found to be highly effective21 and it

Higher Order Separations 3D or Combinations of 2D Methods

More extensive separation schemes have been developed. A 3D protein separation using hydrophobic interaction chromatography, isoelectric focusing, and SDS gel electrophoresis has been demonstrated.23 A useful approach, if material is not limiting, is to use size exclusion chromatography of proteins that allows a large loading and is a useful first step24 and should lead to coverage of lower abundance proteins if the separation is effective enough. Comparisons of 2 and 3D LC have been made.25 However these extensive prefractionation and multiple separations yield large number of peptides but not more proteins and dramatically increase the analysis time.26 What appears to be more useful is to use multiple 2D techniques and then to combine the results.27 Phosphocellulose P11-SDSPAGE led to the highest number of identifications (1687 proteins). SCX-RP, SDS-PAGE-RP and peptide IEF-RP added a total of 202 proteins to this analysis of the nucleus. 2650

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Effect of Under-sampling and Dynamic Range

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CONCLUSIONS Our results show that the main limiting factor common to all multidimensional separations in terms of superior proteome coverage is the speed of MS/MS analysis. All methods showed very little overlap between replicates indicating a large undersampling effect. The second problem is the dynamic range of detection. This can be alleviated by carrying out a protein level separation to try and keep high expression level proteins in a small number of fractions preventing high expression level peptides from dominating whole fractions as in the case of SCX-RP. 1D SDS-PAGE partially does this and hence gives the best coverage in the system that we used here. Ultimately, if RP chromatography and MS can be made fast enough allowing a large number of fractions to be analyzed, the best method maybe a return to protein separation by mass and pI followed by digestion and RP-LC using accurate mass and RT exclusion lists and sequencing of only new peptidesa return to the ideas of O’Farrell.

Due to the limited dynamic range and speed of current MS instruments, under-sampling is usually quite extensive. Without extensive exclusion or inclusion lists, a 90% coverage may only be reached after 20 samplings. Recently a theoretical analysis of this effect has been carried out with infinite Markov models. This approaches allows the prediction of the number of LC runs that are needed to obtain maximum coverage using data from a pilot experiment.28 An earlier study evaluated the state of proteome analysis by mass spectrometry using SILAClabeled yeast as a model system and determining the coverage of known copy number proteins (from a genome wide GFP expression approach). The conclusion was that the analysis was not limited by sensitivity but by a combination of dynamic range (high abundance peptides preventing sequencing of low abundance ones) and by effective sequencing speed.29 Some attempts have been made to alleviate the dynamic range problem by data-dependent selective ejection of highly abundant species followed by prolonged accumulation of low abundance species. 30 This allowed the assignment of approximately 80% more peptide pairs in a single analysis. An alternative approach is to use chromatographic peak parking, slowing down nanoscale flows to allow more accumulation of low abundance species, though this can lead to excessively long chromatography run times.31

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ASSOCIATED CONTENT

S Supporting Information *

Supplementary Figure 1. Graphs showing the peptide overlap between all fractions in each of the multidimensional separations. The total number of peptide positively identified in the fraction being considered is given and then the number of peptides matching these are given for all the other fractions. This material is available free of charge via the Internet at http://pubs.acs.org.

Future Directions

Several strategies have been proposed to increase the speed and depth of proteome coverage. Most experiments involve the resequencing of peptides that have been previously identified in other experiments. One possibility would be to exclude all these from future analysis using accurate mass (which at subppm levels reduces most masses to 2−3 possible peptides in a translated database if one ignores PTMs) and possibly reversedphase retention time if that could be made reproducible enough. This approach, called accurate mass tags, has been shown to be capable of characterizing peptide mixtures with significantly more than 10(5) components in a single RP-LC separation using mass accuracies of 2800 peptides and >760 proteins from >13000 different putative peptides from a Shewanellaoneidensis tryptic digest within a three minute gradient.33 This would be an ideal approach to combine with multidimensional separations, allowing large numbers of fractions to spread out peptides yet keeping the analysis time down to a reasonable level.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Author Contributions †

These authors contributed equally to this study.

Notes Competing Interests Statement

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by grants from the Knut and Alice Wallenberg Foundation (PJ), the Swedish Research Council, Vetenskapsrådet (PJ) and from the Swedish Strategic Research Council to CREATE Health (PJ).

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ABBREVIATIONS DOC, deoxycholate; FDR, false discovery rate; GelLC, 1D SDS-PAGE followed by RP-HPLC analysis of proteolytic digests of the sliced gel pieces; RT, room temperature; SCX, strong cation exchange chromatography.

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