Combination of Abundant Protein Depletion and Multi-Lectin Affinity Chromatography (M-LAC) for Plasma Protein Biomarker Discovery Tatiana Plavina,†,‡ Eric Wakshull,†,§ William S. Hancock,‡ and Marina Hincapie*,‡ Biogen Idec, Inc., Boston, Massachusetts 02142, and Barnett Institute and Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts 02115 Received August 15, 2006
We report on the development of a robust and relatively high-throughput method for in-depth proteomic analysis of human plasma suitable for biomarker discovery. The method consists of depletion of albumin and IgG and multi-lectin affinity chromatography (M-LAC), followed by nanoLC-MS/MS analysis of digested proteins and label-free comparative quantitation of proteins. The performance of the method is monitored by multiple quality control points to ensure reproducibility of the analysis. The method identifies proteins that are reported to be present in normal plasma at concentrations of 10-100 ng/ mL and that may be of particular interest when studying a variety of disease conditions. Numerous tissue leakage proteins of potentially even lower concentrations are also identified. When the method was used in a study to identify potential biomarkers of psoriasis, the differential abundance of proteins present at low µg/mL level was quantitated and later verified by ELISA measurements. Keywords: plasma • multi-lectin affinity chromatography (M-LAC) • abundant protein depletion • biomarker discovery • ELISA
Introduction Serum and plasma are generally considered the most useful specimens for biomarker discovery, particularly in clinical settings where other specimens, e.g., cerebrospinal fluid and tissue biopsies, may be difficult or impossible to obtain. In addition, as all cells in an organism interact with blood, serum and plasma contain not only classical “plasma proteins” but also proteins and protein fragments that are either secreted, normally or aberrantly, or leaked into the bloodstream upon physiological tissue remodeling, pathological cell damage or dilution into the blood volume (e.g., cytokines). However, due to the complexity and extremely wide range of concentrations of proteins,1 it is a substantial challenge to perform a comprehensive analysis of serum or plasma that yields useful data. To reduce the complexity of serum or plasma, numerous methods have been proposed and applied in biomarker discovery. Among these methods are removal of the most abundant plasma proteins,2-4 enrichment of proteins with certain post-translational modifications,5-7 prefractionation of proteins by either chromatography,8 gel electrophoresis,9 or free-flow electrophoresis,10 multidimensional separation of peptides,11 and other. However, a tradeoff between throughput and sensitivity often occurs. For example, prefractionation or multidimensional separation into a large number of fractions has been very effective in a comprehensive characterization * Corresponding author: Dr. Marina Hincapie, 341 Mugar Building, Barnett Institute, Northeastern University, Boston, Massachusetts 02115. E-mail:
[email protected]. † Biogen Idec, Inc.. ‡ Northeastern University. § Current address: Genentech, South San Francisco, California 94080.
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of plasma proteome in a limited number of samples;12,13 however, the analysis of tens and hundreds of samples, often required in biomarker discovery is impractically time-consuming. At the same time, not only proteins present in plasma at low concentrations (pg/mL concentrations and lower) but also proteins of intermediate concentration levels (low ng/mL to low µg/mL) are reported to be involved in a variety of disease conditions14,15 and might be of interest as potential biomarkers. Therefore, methods that are capable of assessing relative abundance of proteins present in plasma at intermediate concentrations in a reproducible and relatively high-throughput manner would be valuable. Lectins have been reagents of choice for enrichment and visualization of glycoproteins for decades.16 As approximately 50% of all plasma proteins are glycosylated17 and changes in protein glycosylation have been associated with a variety of disease conditions such as cancer18 and immune disorders,19 lectin-based capture of glycopeptides and glycoproteins has become a desirable method for glycoproteomic analysis. In recent studies, affinity capture with single lectins20 as well as serial21 and multi-lectin affinity chromatography22,23 have been effectively used to study the serum or plasma glycoproteome. In most cases, lectins have been used to capture glycopeptides resulting from the enzymatic digestion of glycoproteins. Captured proteins are then deglycosylated and analyzed by tandem mass spectrometry. However, using lectin affinity capture to isolate glycoproteins rather than glycopeptides may offer several advantages. First, it allows evaluation of glycosylation of intact proteins, preserving the three-dimensional structures of lectin binding sites. Second, multiple glycosylation sites on glycoproteins may cause stronger binding between a lectin and 10.1021/pr060413k CCC: $37.00
2007 American Chemical Society
Plasma Protein Biomarker Discovery
a glycoprotein if compared to the binding between the same lectin and a glycopeptide. This is of particular importance considering the relatively weak binding of lectins to oligosaccharides. In several recent efforts, isolation of serum glycoproteins with lectins prior to mass spectrometric analysis led to the discovery of potential biomarkers.24-26 By utilizing fucosespecific lectins to capture glycoproteins from serum of hepatocellular carcinoma (HCC) patients and healthy controls, several proteins with increased fucosylation were identified.25 Using a similar methodology, a potential biomarker of hepatitis B virus induced HCC (GP73) was identified and further shown to be more sensitive in early detection of HCC, if compared to currently used marker of HCC, alpha-fetoprotein.26 Multi-lectin affinity chromatography (M-LAC), previously developed in our laboratory, has been demonstrated to be an effective method for enriching glycoproteins present in serum or plasma at low levels.22 The M-LAC column consists of a mixture of three agarose-immobilized lectins: concanavalin A (ConA), wheat germ agglutinin (WGA), and jacalin. Each lectin in the M-LAC column targets a different population of glycoproteins: ConA is selective for glycoproteins containing mannose and glucose glycan motifs, jacalin binds glycoproteins containing N-acetylgalactosamine (GalNAc) residues in Olinked glycans, and WGA captures glycoproteins with Nacetylglucosamine and free sialic acid. The advantage of M-LAC over single or serial lectin capture lies in its more complete capture of glycoproteins from complex samples, such as serum or plasma. This is attributed to the combination of specificities of each of the lectins selected to bind major glycosylation classes of plasma proteins. It was also shown that a combination of M-LAC for sample preparation with nanoLC coupled to a linear ion trap Fourier transform mass spectrometer (nanoLC-LTQ/FTMS) for analysis could characterize structural changes in the glycosylation structures of plasma glycoproteins.27 In spite of the effectiveness of the M-LAC to isolate and analyze human plasma glycoproteome, the analysis of the nonbound fraction showed little improvement over whole plasma due to the overwhelming concentration of albumin, a non-glycosylated protein. In addition, the high concentration of immunoglobulin G (IgG) in the glycosylated (bound) fraction after M-LAC fractionation interfered with the identification of proteins of lower abundance. The goal of this work was to devise a robust method that includes depletion of albumin and IgG prior to the M-LAC, and to demonstrate its suitability for plasma protein biomarker discovery. To achieve it, the following objectives were set: (1) to establish a method that combines depletion of albumin and IgG with M-LAC for sample preparation and compare it to using M-LAC alone, (2) to evaluate the performance of the method, (3) to assess the effectiveness of the method in identifying differential abundance of proteins for disease and control samples in a biomarker discovery study, and finally, (4) to verify the potential biomarkers with an ELISA method where possible.
Experimental Methods Reagents. POROS Protein G (0.2 mL) and POROS anti-HSA (2.0 mL) cartridges were purchased from Applied Biosystems, (Foster City, CA). Concanavalin A (Con A), wheat germ agglutinin (WGA), and jacalin (all agarose-bound) were from Vector Laboratories (Burlingame, CA). Trypsin (sequencing grade modified) was obtained from Promega (Madison, WI).
research articles Reagents for ELISA of galectin 3 binding protein (G3BP) were purchased from Bender Medsystems (Vienna, Austria). The BCA protein assay reagent kit and Zeba spin columns (5 mL resinbed capacity) were obtained from Pierce (Rockford, IL). Centricon centrifugal filters, 5 kDa MW cutoff, were from Millipore (Bedford, MA). Discovery BIO Wide Pore C18 cartridges (C18, 2 cm × 4.00 mm, 3 µm particle diameter) were from Supelco (Bellefonte, PA). For all chromatographic steps, HPLC grade reagents were purchased from Fisher Scientific (Pittsburgh, PA). Bovine fetuin and all other chemicals were obtained from Sigma (Saint Louis, MO). Plasma Samples. Twenty plasma samples from healthy donors (control group) and 20 plasma samples from individuals with moderate to severe psoriasis (psoriasis group) were obtained from Bioreclamation, Inc. (East Meadow, NY). Donors from the two groups were carefully selected to represent the same distribution of age, gender, and race to facilitate proper comparative analysis. To minimize the effect of sample collection and storage on results, samples were collected in a uniform manner strictly following established standard operation procedures. Ten milliliters of blood was collected by venipuncture into BD Vacutainer citrate tubes (BD Diagnostics, Franklin Lakes, NJ), centrifuged at 3000 g for 10 min, and separated plasma was frozen within 1 h from the time of collection. Upon receiving from vendor, all samples were aliquoted 200 µL per vial so to avoid unnecessary freeze/thaw cycles. One milliliter aliquots from six control samples were pooled and used for method development. Samples were stored at -70 °C and did not undergo more than two freeze/thaw cycles. Depletion of Albumin and IgG from Human Plasma. Prior to depletion, each plasma sample was spiked with bovine fetuin at 250 µg/mL. Bovine fetuin was used as internal standard for normalization during protein quantitation. The depletion of albumin and IgG from human plasma samples was performed as per manufacturer’s instructions with the exception of using Tris buffered saline instead of phosphate buffered saline in the binding buffer. This was done to keep uniform composition of buffers between depletion of albumin and IgG and the following M-LAC fractionation, eliminating buffer exchange step between the two methods. Briefly, POROS Protein G and POROS anti-HSA cartridges were set up in series on a Vision chromatography workstation (Applied Biosystems, Foster City, CA). Prior to sample loading, the cartridges were equilibrated with 10 mL of binding buffer (20 mM Tris-HCl, 150 mM NaCl, pH 7.2) at 2.4 mL/minute. Spiked plasma samples were diluted 1:10 in a binding buffer and particulates were removed using 0.45 µm spin filter, centrifuging diluted plasma for 30 s at 8000 g. Seven-hundred microliters of the filtrate (corresponding to 70 µL of plasma) was passed through the depletion cartridges at the flow rate of 1.2 mL/min. Cartridges then were washed with 10 mL of binding buffer at 2.4 mL/min, collecting the first 4 mL of the flow-through that contain the depleted plasma proteins. After a washing step, the bound albumin and IgG were eluted with 16 mL of elution buffer (12 mM HCl) at 2.4 mL/ min with the first 8 mL of the eluate being collected. Following elution, cartridges were regenerated with 10 mL of 1 M NaCl and then re-equilibrated with 10 mL of binding buffer at 2.4 mL/min. Small aliquots of samples from each step were retained for total protein concentration assay. All separation steps were carried out in an automated mode using Vision software (Applied Biosystems, Foster City, CA). Multi-Lectin Affinity Chromatography (M-LAC). M-LAC columns were prepared by combining three agarose-bound Journal of Proteome Research • Vol. 6, No. 2, 2007 663
research articles lectins in a 1:1:1 (v/v/v) ratio. For this, 0.6 mL of a 50% slurry of each agarose-bound ConA, agarose-bound WGA, and agarose-bound jacalin were added into empty 5 mL Zeba spin columns. Each M-LAC column was used only once. Both whole plasma and depleted plasma were fractionated using M-LAC. Plasma samples diluted 1:10 in M-LAC binding buffer (25 mM Tris, 0.15 M NaCl, 1 mM MnCl2, 1 mM CaCl2, pH 7.2), or 0.3 mL aliquots of the diluted plasma (corresponds to 30 µL of plasma) were loaded onto a column. For depleted plasma, MnCl2 and CaCl2 were each added to the final concentrations of 1 mM, and 3.5 mL of depleted plasma was loaded onto M-LAC column. After 15 min of incubation (with continuous rocking of depleted plasma), the nonbound fraction was collected, the column washed with an additional 1.5 mL of M-LAC binding buffer, and the wash combined with the nonbound fraction. The column was then washed with an additional 5 mL of M-LAC binding buffer, and the wash was discarded. The glycoprotein-rich bound fraction was eluted with 4.0 mL of M-LAC elution buffer (25 mM Tris, 0.5 M NaCl, 0.2 M methyl-R-mannopyranoside, 0.2 M methyl-D-glucopyrannoside, 0.5 N-acetyl-glucosamine, 0.1 M melibiose, pH 7.2), and the eluate was collected. EDTA was added to both the nonbound and bound glycoprotein-rich fractions to a final concentration of 5 mM to prevent possible clotting of fractionated plasma, which can be triggered by the presence of Mn2+ and Ca2+ ions in the M-LAC binding buffer. Small aliquots of samples from each step were set aside for subsequent total protein concentration assay. Total Protein Concentration Assay. Total protein concentrations were measured using the BCA protein assay as per manufacture’s instructions. Protein recovery for both depletion and M-LAC fractionation steps were determined. Trypsin Digestion. Approximately 100 µg of proteins from both the nonbound and bound fractions were digested with trypsin, using the following procedure. M-LAC fractions were concentrated to 50 µL using 5 kDa centrifugal concentrator, then denaturing buffer (7.2 M guanidine-HCl in 0.1 M ammonium bicarbonate, pH 8.0) was added to a final concentration of guanidine of 5.8 M, and the solution concentrated again in the same filter down to 50 µL. Samples were then removed and filters washed with 50 µL of denaturing buffer, adding the wash to the samples. The samples were reduced with 5 mM DTT for 30 min at 60 °C and alkylated with 15 mM iodoacetamide for 30 min at room temperature in the dark. The reaction was then quenched by addition of another 5 mM DTT. After diluting samples with 50 mM ammonium bicarbonate, pH 8.0 to bring guanidine-HCl concentration down to 1.2 M, trypsin (1:40 w/w) was added to the samples and incubated for 18 h at 37 °C. Another equal amount or trypsin was then added and incubated for an additional 4 h. Digestion was stopped by acidification with 1% formic acid. Peptide Fractionation. Peptides resulting from protein digestion were separated using Discovery BIO C18 cartridges on a Shimadzu HPLC (Shimadzu Scientific Instruments, Columbia MD). The composition of solvent A was 0.1% (v/v) of formic acid in HPLC grade water and that of solvent B 0.1% (v/v) formic acid in acetonitrile. Digested proteins were loaded on C18 cartridges at 2% solvent B and then washed with 2% solvent B for 3 min to remove salts and other reagents from trypsin digestion. The bound peptides were eluted with a stepgradient: 30% solvent B for 3 min, to collect peptides to be analyzed by nanoLC-MS/MS, and then 90% solvent B for 5 min, to elute larger peptides, partially digested and non-digested 664
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proteins. The separation was performed at 1.5 mL/min and monitored at both 280 and 214 nm. Peptides eluted with 30% of solvent B were concentrated down to 50 µL using vacuum concentration. NanoLC-MS/MS. All nanoLC-MS/MS experiments were performed using an Ettan MDLC system (GE Healthcare, Piscataway, NJ) interfaced with an LTQ linear ion trap mass spectrometer (Thermo Electron, San Jose, CA). The composition of solvent A was 0.1% (v/v) of formic acid in HPLC grade water and that of solvent B 0.1% (v/v) formic acid in acetonitrile. Concentrated peptide samples, equivalent to approximately 5 µg of total protein after the M-LAC fractionation (see Results and Discussion for potential losses), were injected onto a Peptide Captrap column (Michrom Bioresources, Auburn, CA) using the autosampler of the MDLC system. The trap column was washed with solvent A at a flow rate of 10 µL/min for 10 min, and then the captured peptides were released and separated on an 175 mm × 75 µm i.d. C18 analytical column packed with Magic C18 (particle size 5 µm, pore size 300 Å; Michrom Bioresources, Auburn, CA). The final flow rate was between 200 and 300 nL/min as monitored by flow-meter incorporated within the MDLC. The gradient was from 2% solvent B to 40% solvent B over 120 min, then from 40% to 90% solvent B over 15 min and held isocratically at 90% solvent B for 15 min. The Ettan MDLC was operated from UNICORN control software (GE Healthcare, Piscataway, NJ). The electrospray conditions were: temperature of the ion transfer tube, 245 °C; spray voltage, 2.0 kV; normalized collision energy, 35%. Data dependent MS/MS analysis was carried out using MS acquisition software (Xcalibur 1.4, Thermo Electron, San Jose, CA). Each MS full scan was acquired in a profile mode in the mass range scan between m/z 400 and 2000, followed by 7 MS/ MS scans of the 7 most intense peaks (optimized during the method development). Dynamic exclusion was continued for a duration of 2 min. Database Search and Protein Identification. Peptide sequences and proteins were identified by searching all MS2 spectra against theoretical fragmentation spectra of a human protein database (Swiss-Prot, updated in September, 2005, 12,902 entries). For this, Sequest algorithm incorporated into the Bioworks software, version 3.1, SR1.4 (Thermo Electron, San Jose, CA) was used. Search parameters included carbamidomethylation of cysteines, (1.4 Daltons and (1.0 Dalton tolerance for precursor and product ion masses, respectively. Only peptides resulting from tryptic cleavages with up to one missed cleavage were searched. The Sequest results were filtered by correlation score (Xcorr) values selected to obtain highly confident peptide and protein identifications: Xcorr 1.9, 2.2, 3.75 for singly, doubly and triply charged peptide ions, respectively, and all with dCn g 0.1. Protein identifications were then validated by using ProteinProphet software, accepting identifications made with g95% confidence. Only proteins identified with two or more unique peptides were considered. Assessment of Relative Abundance of Peptides and Proteins. A label-free quantitation method was used for assessing relative abundance of peptides and proteins. An arbitrary sample (usually the first sample analyzed) was selected as a reference. Abundance of a peptide in a sample was determined by the peak area ratio of the extracted ion chromatogram (EIC) relative to the reference sample. The limit of detection for peak area of EIC was 2.5 e5. If a peptide was identified multiple times with different charge states, only one peptide ion charge state with the highest precursor ion intensity was used in compara-
Plasma Protein Biomarker Discovery
tive quantitation. Relative abundance of an individual protein was calculated as the mean peak area ratio for at least two peptides derived from this protein. The relative abundance of a protein was then normalized using bovine fetuin as internal standard. For normalization, four bovine fetuin peptides that satisfied the following requirements were selected: (1) peptides were different from human fetuin peptides, (2) peptides were consistently detected by the mass spectrometer, and (3) retention times of selected peptides covered the range of chromatographic separation. The peak areas of EIC for each of the fetuin peptides were then determined and the ratios of peak areas calculated relative to the reference sample. The mean peak area ratio from four fetuin peptides was then calculated and used as normalization coefficient for determining relative protein abundance. It was required that %CV for peak area ratios from the four fetuin peptides was e40%; peak area ratio for one of the peptides was allowed to be excluded from calculations. Quantitation of Galectin 3 Binding Protein (G3BP) using Enzyme Linked Immunosorbent Assay (ELISA). The concentration of G3BP (Mac-2 BP) in human plasma samples was determined using a G3BP ELISA as per the manufacturer’s instructions with minor modifications. Briefly, plates were coated with a monoclonal anti-G3BP antibody and blocked with 1% casein hydrolysate to minimize nonspecific binding. The plates were then washed, and standards, controls, and samples diluted 1:100 in assay diluent were added to the plates in duplicate. After incubation, the plates were washed and bound G3BP was detected with monoclonal anti-G3BP antibody conjugated to horseradish peroxidase. At the end of the incubation, plates were washed and bound antibody-conjugate was detected by reaction with a chromogenic substrate solution. The colored reaction was stopped and plates were read at 450 nm. A standard curve was constructed from obtained optical densities and the concentration values of G3BP in each sample were interpolated. Statistical Analysis. The Mann-Whitney rank sum test was used to assess the difference in protein abundance (Prism software, version 4, GraphPad Software, San Diego, CA). p values of less than 0.05 were considered significant.
Results and Discussion In this work, we introduce a robust and rapid proteomic method that combines depletion of most abundant plasma proteins and multi-lectin affinity chromatography (M-LAC) and demonstrate its suitability for plasma protein biomarker discovery. First, we describe the method and compare it to using M-LAC fractionation alone, where whole plasma is directly fractionated by M-LAC. Second, we demonstrate the performance of the method using data from three independent analytical runs of a pooled control plasma. Next, we discuss experimental details that are essential for making the method applicable to biomarker discovery, including quality control check-points, throughput, and data normalization using an internal standard. We also depict advantages of the method and directions for its further improvement. Finally, we present the data from a biomarker discovery study where the described method is used, and demonstrate the effectiveness of the method in detecting differential abundance of proteins present in plasma at low µg/mL concentrations. Method Workflow. A schematic flowchart of the method is presented in Figure 1. The method consists of the following steps: (1) spiking plasma samples with an internal standard, (2) removal of albumin and IgG from plasma, (3) fractionation
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Figure 1. Flowchart of plasma analysis. To maintain the quality of the analysis, standard operating procedures were devised for each step of the method. The following quality control checkpoints were monitored: total protein recovery during the abundant protein depletion and M-LAC fractionation, as calculated from measured total protein concentrations; peak areas of chromatographic traces at 280 nm during peptide fractionation step, to monitor reproducibility of trypsin digestion; retention times and peak areas of selected peptides, as measures of reproducibility of nanoLC separation and comparative quantitation, respectively. To ensure accurate comparative quantitation of proteins in each sample, an internal standard (bovine fetuin) was spiked into plasma samples prior to analysis and used for data normalization.
of depleted plasma using M-LAC into nonbound and bound glycosylated fractions, (4) trypsin digestion of the two fractions, (5) fractionation of peptides resulting from protein digestion into two fractions, (6) nanoLC-MS/MS analysis of peptides in a data-dependent mode using a linear ion trap mass spectrometer (LTQ), and (7) label-free comparative quantitation of identified peptides and proteins. Plasma was chosen over serum for proteomic analysis due to its shorter processing time between venipuncture and freezing and thus potentially greater integrity.28 In addition, it has more complete composition, and lacks changes introduced by the activation of proteolytic cascade, if compared to serum. Special attention was paid to sample collection and storage protocols, which are known to greatly influence the results of proteomic analysis.29 Samples were collected in a uniform manner strictly following established standard operation procedures (see Experimental Methods). Aliquots from six control samples were pooled and used for method development. To maintain the quality of the analysis, standard operation procedures were devised for each step and followed during both method development and biomarker discovery study. The following quality control check-points were monitored at different stages of the method: total protein recovery during the abundant protein depletion and M-LAC fractionation, as Journal of Proteome Research • Vol. 6, No. 2, 2007 665
research articles calculated from measured total protein concentrations; peak areas of chromatographic traces at 280 nm during peptide fractionation on Discovery BIO C18 cartridges, to monitor reproducibility of trypsin digestion; retention times and peak areas of selected peptides during nanoLC-MS/MS analysis, to assess the reproducibility of nanoLC separation and comparative quantitation, respectively. To ensure accurate comparative quantitation of proteins in each sample, an internal standard (bovine fetuin) was spiked into plasma samples prior to analysis and used for data normalization. To improve robustness of the method, we introduced several changes to the previously developed procedures. First, we modified the composition of the binding buffer used during the abundant protein depletion from phosphate buffered saline (suggested by manufacturer) to Tris-HCl. This change was instituted to maintain uniform composition of buffers between depletion of albumin and IgG and the following M-LAC fractionation, eliminating a buffer exchange step and allowing for direct loading of depleted plasma onto the M-LAC column. Second, we made an adjustment to the formerly established M-LAC fractionation procedure22 to minimize the effect of Ca2+ and Mn2+ ions on plasma coagulation. During preliminary experiments it was noted that both the nonbound and bound fractions had noticeable clotting soon after the M-LAC fractionation. This was attributed to the presence of Ca2+ and Mn 2+ ions in the M-LAC binding buffer. The ions are included in the binding buffer in order to facilitate binding of lectins to glycoproteins but at the same time they may trigger the coagulation cascade in whole, depleted or fractionated plasma. To circumvent this problem, 5 mM EDTA was added to both the nonbound and bound fractions immediately following the M-LAC fractionation for chelation of ions. Addition of EDTA is not necessary when one fractionates serum, as serum lacks fibrinogen that is being removed by clot formation. In future work, to minimize the possibility of clotting, we are planning to remove fibrinogen along with albumin and IgG from plasma prior to the M-LAC fractionation. Following the depletion of abundant proteins and M-LAC, both the nonbound and bound fractions from each sample were digested with trypsin, fractionated on Discovery BIO C18 column, and analyzed by nanoLC-MS/MS using a linear ion trap mass spectrometer. Fractionation of peptides on the C18 column was aimed at separating peptides to be analyzed by nanoLC-MS/MS from larger peptides and partially and nondigested proteins. Although this step causes additional losses of peptides, it leads to more reproducible nanoLC separation and a longer life time of nanoLC columns. It is also worth pointing out that we chose not to deglycosylate peptides from the bound glycosylated fraction prior to nanoLC-MS/MS, in contrast to commonly used procedures in glycoproteomic analysis. Deglycosylation is usually instituted to facilitate the analysis of glycosylated peptides and determine glycosylation sites, as glycosylated peptides are often too hydrophilic to be retained on reversed-phase columns and also have lower ionization efficiency relative to non-glycosylated peptides. The detection of glycoproteins in this work was based on observation of only non-glycosylated peptides resulting from trypsin digestion. In order to improve the limit of protein detection, we injected relatively high amount of peptides onto the nanoLC column. The amount of injected peptides corresponded to approximately 5 µg of total protein in a sample after the M-LAC fractionation. Losses during downstream sample processing, 666
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including losses from centrifugal concentration, peptide fractionation and vacuum concentration, were estimated to be as high as 60% (based on the chromatographic peak areas of samples from different steps monitored at 280 nm during separation on Discovery BIO C18 cartridges). The total ion current intensity of between 2 × 108 and 5 × 108 was consistent for all the runs performed. Comparison of M-LAC With and Without Prior Depletion of Albumin and IgG. Previously, M-LAC has been shown to be an effective method to isolate glycoproteins from serum and plasma, thus improving detection and identification of glycoproteins of lower abundance.22,23 However, the analysis of the nonbound M-LAC fraction showed little improvement over whole plasma due to the overwhelming concentration of albumin, a non-glycosylated protein. In addition, the high concentration of IgG in the glycosylated (bound) fraction after M-LAC fractionation interfered with the identification of proteins of lower abundance. In this study, we investigated whether depleting albumin and IgG prior to M-LAC improves the depth of analysis of both bound (glycosylated) and nonbound fractions when compared to M-LAC alone. By the depth of analysis, we mean the number of identified proteins, the limit of detection, and the confidence of protein identification. To this end, pooled control plasma samples were analyzed using two different sample preparation protocols: M-LAC fractionation with and without prior albumin and IgG depletion. Seventy microliters and 30 µL of plasma spiked with bovine fetuin was analyzed using M-LAC fractionation with and without depletion, respectively. The results indicated that with depletion of albumin and IgG prior to M-LAC, the number of identified proteins (at least 2 unique peptides, >95% confidence) increased from 120 to 191 (total number of proteins identified in bound and nonbound fraction; average of three runs). Many of newly identified proteins were of lower abundance. For the list of the identified proteins in one of the selected runs see the Supporting Information. In addition, the limit of detection for both the nonbound and bound (glycosylated) fractions was improved as more proteins of lower abundance were identified when compared to the protocol using M-LAC alone (Table 1). Among the identified lower abundance proteins were protein kinases, ADAM(T)s proteins, zinc finger proteins, adhesion molecules, and cytoskeleton-associated proteins. It is not quite clear how these proteins are released into the blood circulatory system and their concentration in plasma is difficult to estimate as release of the tissue-derived proteins may differ for normal and diseased state as well as depend on the type of tissue of origin. In addition, concentrations of cellular proteins released into the blood in the absence of major tissue damage, such as myocardial infarction, are not thoroughly studied. We found, unfortunately, that for the most part, the concentration values for a number of identified proteins in normal serum or plasma were not available in scientific literature. Also, in some instances, the reported concentrations varied more then 30 fold, as shown below for L-selectin. The normal serum or plasma concentrations for several identified proteins presented in Table 1 have been found, for example, calgranulin A (8.4 ng/m14), neutrophil defensin 3 (42 ng/mL14), and cartilage glycoprotein-39 (28 ng/mL14). This result indicates that the method allows confident (by the presence of at least two unique peptides) identification of some proteins present in plasma at the low ng/mL concentrations. It is important to note that the selected proteins listed in Table 1 were either identified only when albumin and IgG were depleted prior to M-LAC, or
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Table 1. List of Selected Medium and Lower Level Proteins Identified in Plasma when Using Different Enrichment Methods mean # of unique peptides, n ) 2a
SwissProt accession no.
M-LAC fraction
protein name
bound (glycosylated)
Ankyrin 3 ADAM 9 ADAMTS 18 Breast cancer type 2 susceptibility protein T-cadherin CD5 antigen-like CD44 Cholinesterase Cartilage glycoprotein-39 Colon carcinoma kinase-4 Inositol 1, 4, 5-triphosphate receptor type 2 Laminin B2 L-selectin Mitogen-activated protein kinase 11 Monocyte differentiation antigen CD14 Phosphatidylinositol-glycan-specific phospholipase D1 Serine/threonine protein kinase Nek-3 T-lymphocyte surface antigen Ly-9 Von Willebrandt factor
Q5VXD5 Q13443 Q8TE60 P51587 P55290 O43866 P16070 P06276 P36222 Q13308 Q14571 P11047 P14151 Q15759 P08571 P80108 P51956 Q9HBG7 P04275
Cadherin-related tumor suppressor homolog Calgranulin A Carbonic anhydrase 1 Ciliary dynein heavy chain 5 C-reactive protein Cystatin C Integrin alpha 5 Low-density lipoprotein receptor-related protein 1B Mast/stem cell growth factor receptor Microtubule-actin crosslinking factor 1, isoform 4 Midasin Nesprin 2 Neutrophil defensin 3 Platelet basic protein Profilin 1 Protocadherin gamma A9 Proto-oncogene tyrosine-protein kinase Fes/Fps Proto-oncogene tyrosine-protein kinase FGR Ryanodyne receptor 1 Ryanodyne receptor 2 Zinc finger protein 40 (MHC binding protein 1)
Q14517 P05109 P00915 Q8TE73 P08185 P01034 P06756 Q9NZR2 P10721 Q96PK2 Q9NU22 Q8NF91 P59666 P02775 P07737 Q9Y5G9 P07332 P09769 P21817 Q92736 P15822
nonbound
reported norma plasma or serum concentration, ng/mL, (reference)
M-LAC
depletion + M-LAC
0.0 0.0 0.0 0.0 0.0 2.0 0.0 3.5 0.0 0.0 0.5 0.0 2.0 0.0 0.0 0.0 0.0 0.0 3.5
2.0 1.5 1.5 2.5 2.0 7.5 2.0 10.5 1.5 2.0 2.0 2.0 4.5 2.0 2.5 3.0 1.0 2.5 9.5
0.0 0.0 1.5 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2.0 2.0 4.5 2.0 6.5 4.0 2.0 2.5 2.5 3.0 3.5 2.5 2.0 3.5 3.0 2.0 1.5 1.5 2.5 3.5 2.0
223 (14) 28 (14)
601 (14), 17 (27) b
112 (14) 8.4 (14) 2020 (14) 320 (14) 333 (14)
42 (14)
a Mean results based on duplicate analysis. If the protein was identified by g two peptides in one of the duplicate runs, even the single peptide identification from the second run was considered and included in the calculations of the mean number of identified unique peptides. All identifications were done with the following filtering criteria: for peptides, Xcorr 1.9, 2.2, 3.75 for +1, +2, and +3 charged peptide ions, respectively; dCn g 0.1; for proteins, g95% confidence of identification using ProteinProphet software. b Reported concentrations from two different sources varied more than 30 fold.
Table 2. Reproducibility of Total Protein Recovery for Albumin and IgG Depletion and M-LAC Fractionation step
run 1a
run 2
run 3
mean
SD
%CV
albumin and IgG depletion
loaded total protein amount, µg flow-through (depleted) fraction total protein amount, µg bound (albumin + IgG) fraction total protein amount, µg total protein recovery, %
4060 1650 2320 98
4060 1630 2170 94
4060 1600 2300 96
96
2
2
M-LAC
loaded total protein amount, µg nonbound fraction total protein amount, µg glycosylated (bound) fraction total protein amount, µg total protein recovery, %
1490 610 490 74
1470 620 520 79
1440 590 460 73
75
3
4
a Run 1, run 2, and run 3 are three independent analytical replicates of a control plasma sample performed according to the method workflow as depicted in figure 1.
identified with higher confidence if compared to using M-LAC alone. Proteins of intermediate abundance, present in plasma at mid ng/mL to low µg/mL concentrations, such as L-selectin (reported 600 ng/mL14 and 17 ng/mL30 in two different studies) and C-reactive protein (2 µg/mL14) were consistently detected
with 4-8 unique peptides, whereas the same proteins were detected at most with 2-3 unique peptides or not at all when only M-LAC was used during the sample preparation step. Another important improvement of the method was the potential to analyze in depth the nonbound fraction of M-LAC, Journal of Proteome Research • Vol. 6, No. 2, 2007 667
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of a pooled control plasma sample were performed according to the established workflow (Figure 1). Upon examination, the total protein recovery for both depletion of abundant proteins and the M-LAC fractionation was consistent, within 4% (Table 2). The mean total protein recovery for albumin and IgG depletion was 96%, which indicates minimal losses during this step. The total protein recovery of the M-LAC fractionation was reasonable (mean of 75%). The losses during this step likely occur for several reasons, including the nonspecific binding of proteins to the agarose-based M-LAC support and incomplete elution of glycoproteins with sugar solutions. Further improvements to the M-LAC fractionation are currently being made in our laboratory. Preliminary experiments of using M-LAC with lectins bound to a solid support (e.g., silica beads) as well as using buffers with higher ionic strength indicate greatly improved total protein recovery and throughput (data to be published). Additionally, removing fibrinogen from plasma along with albumin and IgG was shown to eliminate any noticeable clotting that may lead to protein losses. In spite of the relatively low total protein recovery, M-LAC fractionation was consistent between runs, as demonstrated by the total protein recovery (Table 2) and relative quantitation of the internal standard (Table 4), with CVs of comparative quantity in three analytical replicates within 17%.
Table 3. Reproducibility of Peptide Quantitation Based on Peak Areas of EIC peak area peptide intensity
run 1a
run 2
run 3
mean
SD
%CV
high intensity
1.61 e9 3.53 e9
1.64 e9 3.59 e9
1.60 e9 3.61 e9
1.62 e9 3.57 e9
2.1 e7 3.58 e7
1.3 1.1
medium intensity
8.92 e6 2.84 e7
6.52 e6 2.40 e7
7.31 e6 2. 19 e7
7.59 e6 2.47 e7
1.21 e6 3.3 e6
15.9 13.4
low intensityb
6.35 e5 4.50 e5
7.20 e5 2.62 e5
4.46 e5 3.86 e5
6.0 e5 3.66 e5
1.4 e5 9.5 e4
23.3 26.1
a Run 1, run 2, and run 3 are three independent analytical replicates of a control plasma sample performed according to the method workflow as depicted in Figure 1. b Limit of quantitation for peak area of EIC is 2.5 e5.
which has previously been hindered by the presence of albumin. Our earlier publications discussed M-LAC primarily as a glycoprotein enrichment technique;22,23 however, considering the improved analysis of the nonbound fraction, M-LAC is clearly a valuable protein fractionation tool if combined with depletion of abundant proteins. To summarize, integration of albumin and IgG depletion with M-LAC fractionation improved the limit of detection for both nonbound and glycosylated fraction down in some cases to low ng/mL, as well as increased the confidence of identification of proteins detected when using M-LAC alone. Evaluation of the Performance of the Method. The reproducibility of the sample preparation steps is crucial for comparing different samples, as any variation in this step propagates through nanoLC-MS/MS analysis to the results of comparative quantitation. To evaluate the performance of the proposed method, three independent complete analytical runs
Another parameter that was evaluated is variability of peak areas of chromatographic traces during peptide fractionation on the C18 column. The chromatographic profiles for three independent analytical runs were very similar, with CVs for peak areas of peptides eluting at 30% solvent B within 9% between runs, indicating a consistent trypsin digestion. Also, the separation of peptides during nanoLC-MS/MS analysis was reproducible, with CVs for the absolute retention times of selected peptides below 1%. This result allowed proper align-
Table 4. Reproducibility of Bovine Fetuin Quantitation in a Pooled Control Plasma Sample ratio of peak areas
1 2 3 4
+
b
a
peptide sequence
[M+H] , Da
charge state
run 1 (reference)
K.HTLNQIDSVKVWPR.R K.QDGQFSVLFTK.C R.AQFVPLPVSVSVEFAVAATPCIAK.E K.EVVDPTKCNLLAEK.Q
1693.946 1270.427 2520.946 1616.870
+3 +2 +3 +2
mean SD %CV
run 2
run 3
mean
SD
%CV
1.00 1.00 1.00 1.00
1.29 1.57 1.16 1.50
1.21 1.05 0.86 1.33
1.17 1.21 1.01 1.28
0.15 0.31 0.15 0.25
12.84 26.08 15.09 19.91
1.00 0.00 0.00
1.38 0.19 13.64
1.11 0.21 18.45
1.16
0.20
17.0
a Run 1, run 2, and run 3 are three independent analytical replicates of a pooled control plasma sample performed according to the method workflow as depicted in Figure 1. b Theoretical unmodified average isotope with a hydrogen mass [M+H]+, including the molecular weight of carbamidomethylation of cysteine residues.
Table 5. Reproducibility of Bovine Fetuin Quantitation in Different Individual Plasma Samples: Example from the Biomarker Discovery Study ratio of peak areas
1 2 3 4
peptide sequence
[M+H]+, Daa
charge state
control 1 (refer.)
control 2
control 3
control 4
psor. 1
psor. 2
psor. 3
K.HTLNQIDSVKVWPR.R K.QDGQFSVLFTK.C R.AQFVPLPVSVSVEFAVAATPCIAK.E K.EVVDPTKCNLLAEK.Q
1693.946 1270.427 2520.946 1616.870
+3 +2 +3 +2
1.00 1.00 1.00 1.00
1.35 1.56 0.77 1.45
1.15 1.33 0.73 1.35
0.67 0.84 0.30 0.76
0.44 0.61 0.48 0.40
0.54 0.86 0.62 0.65
0.94 0.81 0.66 1.31
1.00 0.00 0.00
1.28 0.35 27.57
1.14 0.29 25.28
0.64 0.24 36.89
0.48 0.09 19.11
0.67 0.14 20.27
0.93 0.28 30.01
mean SD %CV a
668
Theoretical unmodified average isotope with a hydrogen mass [M+H]+, including the molecular weight of carbamidomethylation of cysteine residues.
Journal of Proteome Research • Vol. 6, No. 2, 2007
Plasma Protein Biomarker Discovery
ment of chromatograms during peptide quantitation. Both the total ion current and base peak chromatograms for the three replicate runs had similar chromatographic peak patterns, including peak intensity and retention times (data not shown). The ability of the method to assess accurately the comparative quantity of a protein was demonstrated by the example of the internal standard as well as by selected peptides of different intensities. The CVs of peak areas of EIC used for peptide quantitation ranged from 1% for the high-intensity peptide ions to 23% for the low-intensity peptide ions (Table 3). High variability for peptide quantitation for low-intensity peptides was likely due to the low signal-to-noise ratio as well as the interference of higher abundance peptides. The observed variability is in good agreement with the results of a comprehensive study of label-free quantitation applied to plasma and cell extracts in which the reproducibility of relative quantitation for more than 90% of peptides was within 20%.31 In this work, Wang G. et al.31 also established that an ion trap mass spectrometer can be successfully used for label-free relative quantitation of proteins as long as more than one peptide is used in quantitation. Accordingly, we used four peptides for comparative quantitation of the internal standard and at least two peptides for relative quantitation of each evaluated protein in the biomarker discovery study described below. For the comparative quantitation of the internal standard, four bovine fetuin peptides were selected and evaluated as described in Experimental Methods. The charge state of the peptide ion with the highest precursor ion intensity was used for comparative quantitation of each peptide. Chromatographic peak areas from EIC of each fetuin peptide were determined and the ratios of peak areas calculated relative to the reference sample. The mean peak area ratio for the fetuin peptides was then calculated and used as normalization coefficient for determining relative protein abundance. The variability of peak area ratios from the four different peptides was within 19% for each of the three runs evaluated and the inter-run variability of the relative peptide and fetuin concentration (three analytical runs) was within 26% and 19%, respectively (Table 4). The matrix effect, expressed as variability of comparative quantitation of internal standard in different individual plasma samples was derived from the data of the biomarker discovery study described below. Based on these data, the intra-run variation of peak area ratios from the four different peptides in both control and diseased plasma samples was within 30% for 90% of samples. As an example, a comparative quantitation for four control and three disease samples are presented in Table 5. To summarize, the proposed method demonstrated performance reproducibility and suitability for comparative quantitation of intermediate abundance plasma proteins. In the biomarker discovery study we decided to consider comparative quantitation data for both internal standard (bovine fetuin) and any protein as valid, provided the CV of peptide area ratios for all the assessed peptides specific to a protein (minimum of 2) was e40%. Application of the Method for Biomarker Discovery. The described method was used in a biomarker discovery study. 20 plasma samples from healthy individuals (control group) and 20 plasma samples from individuals with moderate to severe psoriasis (psoriasis group) were analyzed following the established method workflow (Figure 1). Samples from the two groups were carefully matched to represent the same distribution of age, gender and race to facilitate proper comparative analysis. In this paper, we present only a limited portion of
research articles
Figure 2. Levels of galectin 3 binding protein (G3BP) assessed by peak area ratios of EIC (b - psoriasis samples, [ - control samples). G3BP levels were significantly elevated (p < 0.0001) in psoriasis plasma samples, compared to control plasma samples. Horizontal lines show the medians.
Figure 3. Example of comparative quantitation for galectin 3 binding protein (G3BP). Extracted ion chromatograms (EIC) of a tryptic peptide (R.GQWGTVCDNLWDLTDASVVCR.A) derived from G3BP: P1 - psoriasis sample 1, P2 - psoriasis sample 2, C1 control sample 1, C2 - control plasma sample 2. PA represents peak area of extracted ion chromatograms. The limit of detection for peak area of EIC is 2.5 × 105.
the data from the study with the purpose of demonstrating the ability of the method to detect the difference in concentrations for proteins of intermediate abundance in two sets of samples and its suitability for biomarker discovery. Comprehensive results of the study will be published elsewhere. By analyzing 40 samples, more than 600 proteins were identified with high confidence (see criteria described in Experimental Methods). Among these, 11 proteins were found to have significantly different concentrations between control and psoriasis plasma samples as determined from the mean peak area ratios of the EIC and later verified by ELISA measurements (see below). Galectin-3 binding protein (G3BP, SwissProt association no. Q08380) is one of the proteins that was found to be significantly elevated in psoriasis plasma samples when compared to control samples (ratio of median values for peak area ratios 2.4 and p < 0.0001, Figure 2). Two peptides derived from G3BP were used for comparative quantitation (see Table 6 for the list of all peptides identified for G3BP). An example of comparative Journal of Proteome Research • Vol. 6, No. 2, 2007 669
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Table 6. Peptides Identified for Galectin 3 Binding Protein (G3BP) peptide sequencea
residue no
[M+H]+, Dab
charge state
R.GQWGTVCDNLWDLTDASVVCR.A R.AAFGQGSGPIMLDEVQCTGTEASLADCK.S R.ELSEALGQIFDSQR.G R.SDLAVPSELALLK.A K.TLQALEFHTVPFQLLAR.Y R.IYTSPTWSAFVTDSSWSAR.K K.YSSDYFQAPSDYR.Y R.YYPYQSFQTPQHPSFLFQDK.R
43-63 77-104 138-151 311-323 377-393 408-426 442-454 455-474
2453.637 2857.319 1593.720 1356.590 1985.101 2163.343 1599.645 2522.754
+2 +3 +2 +2 +3 +2 +2 +3
a In bold, peptides used for comparative quantitation. b Theoretical unmodified average isotope with a hydrogen mass [M+H]+, including the molecular weight of carbamidomethylation of cysteine residues.
Figure 4. Concentration of galectin 3 binding protein (G3BP) measured by ELISA (b - psoriasis samples, [ - control samples). G3BP levels were significantly elevated (p < 0.0001) in psoriasis plasma samples, compared to control plasma samples. Horizontal lines show the medians.
quantitation for one of the peptides (R.GQWGTVCDNLWDLTDASVVCR.A) is presented in Figure 3. The example demonstrates comparative quantitation of a peptide with peak areas close to the established limit of detection of 2.5 × 105. G3BP is an extracellular matrix and secreted protein that promotes integrin-mediated cell adhesion.32 Its normal concentration in human serum is in the low µg/mL;33 however, it is found to be elevated in the serum of patients with cancer34 and immunodeficiency virus35 as well as in the synovial fluid of rheumatoid arthritis (RA) patients.36 In addition, G3BP has been shown to induce interleukin 2 production and implicated in the immune response, being a potent stimulator of host immune defense systems,37 roles that would be etiologically relevant if G3BP is validated as biomarker of psoriasis. To verify the finding that G3BP is present at different concentrations in plasma of control and psoriasis donors, the concentration of G3BP was measured by sandwich ELISA using the same set of samples. As shown in Figure 4, the concentration of G3BP was found to be significantly higher in samples of psoriasis patients than in those of control donors (the ratio of median values for concentrations 2.0, p < 0.0001). The median concentration of G3BP in normal plasma was 6.7 µg/ mL which is in relative agreement with a previously reported value of 9 µg/mL of G3BP in serum.33 The mass spectrometrybased measurements and ELISA concentration values for G3BP were found to be in good correlation, as illustrated in Figure 5. This indicates that the established method allows accurate quantitation of differences in protein abundance at the low µg/ mL level. It is also worthwhile to point out that due to a relative simplicity of the proposed method, the analysis of 40 samples 670
Journal of Proteome Research • Vol. 6, No. 2, 2007
Figure 5. Correlation between mass spectrometry-based relative quantitation (peak areas ratios) and ELISA measurements of galectin 3 binding protein (G3BP).
from the biomarker discovery study was completed within a four-week period. Sample preparation steps yield only two fractions from each plasma sample, which are analyzed by onedimensional nanoLC separation. However, the method possesses sufficient sensitivity to allow detection of numerous tissue leakage proteins and comparative quantitation of proteins present in plasma at the low µg/mL level. This contrasts favorably with many methods commonly used for in-depth analysis of plasma samples that separate plasma into numerous fractions and use more then one dimension of chromatographic separations. Therefore, this method offers a good balance between throughput and sensitivity that is of importance for biomarker discovery. Also, using a label-free method for comparative quantitation greatly facilitated analysis of 40 samples; by choosing one sample as a reference, statistical methods were readily applied and provided meaningful results.
Conclusions The proposed method that combines depletion of albumin and IgG with M-LAC fractionation for sample preparation, followed by nano LC-MS/MS analysis of digested proteins and label-free comparative quantitation of peptides and proteins, is shown to be suitable for biomarker discovery. The method permits in-depth analysis of both nonbound and bound glycosylated fractions from M-LAC, enabling identification of numerous tissue leakage proteins present in plasma at low ng/ mL concentrations. Furthermore, using label-free quantitation, we measured significant differences in abundance of proteins present in plasma at low µg/mL concentrations as subsequently verified by ELISA results. Thus, this method allows both a
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Plasma Protein Biomarker Discovery
comprehensive survey of plasma proteome and more targeted approach to interrogate plasma glycoproteome. In addition, the method offers a good balance between throughput and sensitivity that is of crucial importance for biomarker discovery. As plasma and serum contain large numbers of intermediate abundance proteins that can potentially be developed into biomarkers, we conclude that the proposed method may be successfully employed for search of potential biomarkers in human plasma.
Acknowledgment. We thank Professor Barry L. Karger for scientific discussions and input as well as for valuable suggestions for the manuscript. We acknowledge Drs. Kautz and Hochman for critical reading of the manuscript. We also thank Dr. Rejtar and Dr. Wo for technical expertise and Dr. Wang for assistance in sample preparation. We thank Biogen Idec, Inc. for the financial support of this research. We also thank Thermo Electron and GE Healthcare for support with instrumentation and software. W.S.H. and M.H. disclose that they have a financial interest in current efforts by Northeastern University and PeptiFarma to license the M-LAC technology for biomarker discovery. The previous paper in this series is listed as ref 38. Contribution Number 891 from the Barnett Institute. Supporting Information Available: Table A. List of Proteins Identified in Control Plasma During Single Analytical Run. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Anderson, N. L.; Anderson, N. G. Mol. Cel. Proteomics 2002, 1, 845-867. (2) Steel, L. F.; Trotter, M. G.; Nakajma, P. B.; Mattu, T. S. et al. Mol. Cell Proteomics 2003, 2, 262-270. (3) Huang, L.; Harvie, G.; Feitelson, J. S.; Gramatikoff, K. et al. Proteomics 2005, 5, 3314-3328. (4) Bjo¨rhall, K.; Miliotis, T.; Davidsson, P. Proteomics 2005, 5, 307317. (5) Larsen, M. R.; Thingholm, T. E.; Jensen, O. N.; Roepstorff, P.; Jørgensen T. J. D. Mol. Cell. Proteomics 2005, 4, 873-886. (6) Zhang, H. Yi. E.; Li, X.-J.; Mallick, P.; Kelly-Spratt, K. S.; Masselon, C. D.; Camp, D. G., II; Smith, R. D.; Kemp, C. J.; Aebersold, R. Mol. Cell. Proteomics 2005, 4, 144-155. (7) Ward, D. G.; Suggett, N.; Cheng, Y.; Wei, W.; Johnson, H.; Billingham, L. J.; Ismail, T.; Wakelam, M. J.; Johnson, P. J.; Martin, A. Br. J. Cancer 2006, 19, 1898-1905. (8) Sheng, S.; Chen, D.; Van Eyk, J. E. Mol. Cell. Proteomics 2005, 5, 26-34. (9) Piepper, R.; Gatlin, C. L.; Makusky, A. J.; Russo, P. S.; Schatz, C. R.; Miller, S. S.; Su, Q.; McGrath, A. M.; Estock, M. A.; Parmar, P. P.; Zhao, M.; Huang, S. T.; Zhou, J.; Wang, F.; Esquesr-Blasco, R.; Anderson, N. L.; Taylor, J.; Steiner, S. Proteomics 2003, 3, 13451364. (10) Moritz, R. L.; Clippingdale, A. B.; Kapp, E. A.; Eddes, J. S.; Ji, H.; Gilbert, S.; Connolly, L. M.; Simpson, R. J. Proteomics 2005, 5, 3402-3413. (FFE) (11) Washburn, M. P.; Wolters, D.; Yates, J. R. Nat. Biotechnol. 2001, 19, 242-247. (12) Shen, Y.; Kim, J.; Strittmatter, E. F.; Jacobs, J. M.; Camp, D. G., II; Fang, R.; Tolie´, N.; Moore, R. J.; Smith, R. D. Proteomics 2005, 5, 1-12.
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