GOFAST: An Integrated Approach for Efficient and Comprehensive

Oct 2, 2012 - College of Arts and Sciences, Boise State University, Boise, Idaho 83725, United States. Anal. Chem. , 2012, 84 (21), pp 9008–9014...
0 downloads 0 Views 339KB Size
Download PDF
Article pubs.acs.org/ac

GOFAST: An Integrated Approach for Efficient and Comprehensive Membrane Proteome Analysis Yanbao Yu,† Ling Xie,† Harsha P. Gunawardena,† Jainab Khatun,‡ Christopher Maier,† Wendy Spitzer,‡ Maarten Leerkes,† Morgan C. Giddings,†,‡ and Xian Chen*,† †

Department of Biochemistry & Biophysics, University of North Carolina at Chapel Hill, North Carolina 27599, United States College of Arts and Sciences, Boise State University, Boise, Idaho 83725, United States

‡

S Supporting Information *

ABSTRACT: Membrane proteomics, the large-scale analysis of membrane proteins, is often constrained by the difficulties of achieving fully resolvable separation and resistance to proteolysis, both of which could lead to low recovery and low identification rates of membrane proteins. Here, we introduce a novel integrated approach, GELFrEE Optimized FASP Technology (GOFAST) for large-scale and comprehensive membrane proteins analysis. Using an array of sample preparation techniques including gel-eluted liquid fraction entrapment electrophoresis (GELFrEE), filter-aided sample preparation (FASP), and microwave-assisted on-filter enzymatic digestion, we identified 2 090 proteins from the membrane fraction of a leukemia cell line (K562). Of these, 37% are annotated as membrane proteins according to gene ontology analysis, resulting in the largest membrane proteome of leukemia cells reported to date. Our approach combines the advantages of GELFrEE high-loading capacity, gel-free separation, efficient depletion of detergents, and microwave-assisted on-filter digestion, minimizing sample losses and maximizing MS-detectable sequence coverage of individual proteins. In addition, this approach also shows great potential for the identification of alternative splicing products.

T

recovery represent the bottleneck in using SDS-PAGE-based techniques for the analysis of the membrane proteome.13,15 Gel-eluted liquid fraction entrapment electrophoresis (GELFrEE) allows for solution-phase protein fractionation based on their molecular weights.16 In this case, protein mixtures can be first solubilized by detergents (e.g., SDS) and then separated on an SDS tube gel for either bottom-up or top-down analysis.17−19 Better solubility with minimum sample loss can be achieved, yet the relatively high concentration of SDS (0.1%) in GELFrEE protein fractions made this method not readily compatible with concurrent MS analysis.13 In most cases, SDS could be removed by microdialysis or ultrafiltration but with relatively low efficiency and protein recovery.20 Protein precipitation using either CMW (chloroform, methanol, and water)16 or acetone precipitation19 has been applied to recover GELFrEE-purified proteins. However, these methods still suffer from sample loss due to membrane filters or the resolubilization of protein pellets. Recently, Bereman reported a direct in-SDS digestion approach followed by SDS depletion on the peptide level using a homemade spin column.21 However, the lysis buffer (0.1% SDS) used in their study may not be good enough for membrane proteome analysis.4,13,22 In addition, the trypsin may not function optimally in such a case.23

here are a number of technical challenges in the comprehensive analysis of the membrane proteome, primarily due to the low solubility of membrane proteins, their resistance to proteolysis, and poorly resolved protein separation.1,2 These difficulties could lead to inefficient digestion and reduced recovery of peptides, which affects both the precision and confidence of protein identification.1,3,4 To overcome these challenges, strong detergents are often required to first fully solubilize membrane proteins, followed by either conventional gel-based (e.g., 2-D electrophoresis (2DE)) or gel-free approaches (e.g., multidimension liquid chromstography (MDLC)).5,6 However, these approaches suffer from some inherent limitations, such as poor 2DE resolution for proteins with high hydrophobicity7 or choosing appropriate detergents/ solvents to solubilize membrane proteins that will not interfere with subsequent proteolysis and liquid chromatography−mass spectrometry (LC−MS) analysis.8 Sample cleanup, including protein precipitation, is therefore required to remove interfering items but often results in sample loss.2 Shotgun proteomics using both gel-based and gel-free schemes is effective to some extent to address these technical concerns.7,9,10 For instance, the 1D SDSPAGE-RP (reverse phase) LC−MS approach is routinely used for the analysis of protein complexes because SDS-PAGE prefractionation reduces sample complexity with a high tolerance to detergents and impurities.11,12 However, laborious sample processing, limited in-gel digestion efficiency, and peptide © 2012 American Chemical Society

Received: January 13, 2012 Accepted: October 2, 2012 Published: October 2, 2012 9008

dx.doi.org/10.1021/ac300134e | Anal. Chem. 2012, 84, 9008−9014

Analytical Chemistry

Article

Figure 1. (A) A diagram showing the GOFAST workflow. The membrane fraction was first isolated from K562 cells and then was subjected to GELFrEE separation. Each GELFrEE protein fraction was processed by FASP. The proteins mixed with the enzyme on the filter units were then transferred to an in-house built microwave oven and digested for less than 1 min. The peptides on the filter were collected and then desalted followed by LC−MS/MS analysis. (B) Coomassie blue stained gel image of fractions collected following GELFrEE separation. The discrete mass range of each fraction can be seen on the gel indicating the protein complex was well fractionated using the GELFrEE approach.

Filter-aided sample preparation (FASP)13 was recently developed as a convenient method for depleting detergent containments while still producing a relatively high yield of LC− MS-friendly protein samples. FASP uses an on-filter scheme for both in situ cleanup and concurrent proteolysis in a single experimental run, so on-filter digests can be recovered for LC− MS analysis with minimal sample losses.13,14 Recently, FASP was combined with anion exchange StageTip fractionation to analyze the proteome of the mouse hippocampus.22 Given that proteolysis using trypsin requires hours of incubation, microwave assistance is an emerging approach for efficient in-solution or in-gel digestion which can be completed in minutes.24 However, the performance of on-filter digestion with microwave assistance has not been proved by proteomics studies yet. Here we integrated the strengths of GELFrEE separation, FASP cleanup, and microwave-assisted on-filter digestion to process the fully detergent-solubilized membrane proteins extracted from the K562 leukemia cell line for immediate liquid chromatography−tandem mass spectrometry (LC−MS/MS) analysis. Membrane proteins are directly involved and play diverse functional roles in cell signaling, ion transport, and cell−cell interactions;6,10 therefore, many pharmaceutical strategies have been designed to target novel membrane proteins with characterized functions.6,25 Analyses of the membrane proteome of K562 leukemia cells have been the focus for revealing potential drug targets.26−28 Here, by using our newly integrated GELFrEE

Optimized FASP Technology (GOFAST), we were able to generate a comprehensive map of the membrane proteome of K562 with maximized sequence coverage of individual proteins. In addition, this approach also shows great potential for the identification of alternative splicing products.

■

EXPERIMENTAL PROCEDURES Cell Culture and Membrane Protein Isolation. Human leukemia K562 cells were cultured in the RPMI 1640 medium supplemented with 10% FBS, penicillin (100 units/mL), and streptomycin (100 μg/mL). K562 cells were maintained in a humidified incubator with 5% CO2 at 37 °C. The cell density was kept at 2 × 105 to 1 × 106 cells/mL. Cells were collected by centrifugation at 600 g for 10 min. Cell pellets were washed once by PBS and resuspended in ice-cold lysis buffer (0.1 M Tris, pH 7.4, 0.1 M EGTA and 1 M sucrose). The cells were subjected to subcellular fractionation using differential centrifugation following the published protocol with minimal modifications (Supporting Information, method).29 The membrane samples were stored at −80 °C until further processing. GELFrEE and SDS-PAGE Separation. Proteins were separated on a GELFrEE 8100 Fractionation System (Protein Discovery, Knoxville, TN) according to the manufacturer’s protocol. Briefly, about 250 μg of proteins in approximately 150 μL of sample buffer was loaded onto the commercial gel cartridge. With different time intervals, protein elution was collected for 100 min starting after the elution of the dye front. 9009

dx.doi.org/10.1021/ac300134e | Anal. Chem. 2012, 84, 9008−9014

Analytical Chemistry

Article

Figure 2. (A) Cellular compartment analysis according to the gene ontology annotation via the DAVID Bioinformatics Resources. The FDR values are indicated next to each column. The percentages do not total 100 as proteins may map with multiple GO terms. Here, the membrane includes the plasma membrane and other organelle membranes. (B) Distribution of transmembrane helices of the K562 membrane proteins. The number of TM proteins in each category is indicated on top of the column.

To assess the quality of GELFrEE separation, about 1/10 (15 μL) of each GELFrEE fraction was separated using a 10% polyacrylamide gel slab (Tris-glycine) and stained with Coomassie Blue. The same membrane protein sample (80 μg) was separated using conventional SDS-PAGE. The entire lane was cut into 24 slices which were then subjected to in-gel digestion following the standard protocol.30 FASP Cleanup, Microwave, and Overnight Digestion. FASP cleanup was carried out using 30k Microcon filtration devices (Millipore, Billerica, MA) following the procedures described previously.22 Briefly, the GELFrEE fractions were mixed with 0.2 mL of 8 M urea in 0.1 M Tris/HCl, pH 8.5 (UA solution), loaded into the membrane filter, and centrifuged at 14 000 g for 15 min. The concentrates were diluted in the devices with 0.2 mL of UA solution and centrifuged again. After centrifugation, the concentrates were diluted with 120 μL of 25 mM NH4HCO3 containing 2 μg of trypsin and placed into an inhouse built microwave oven with 600 W power and a water bath. After a 40-s irradiation, the digestion was quenched by adding 100 μL of H2O with 0.1% formic acid (FA) followed by 15 min of centrifugation at 14 000 g. The overnight on-filter digestion was carried out at 37 °C. Desalting and LC−MS/MS. After digestion, the peptides on the filter were collected by centrifugation and then desalted using a C18 ziptip (Millipore, Billerica, MA). The elution was lyophilized and resuspended into buffer A (2% acetonitrile, 98% water, and 0.1% formic acid) prior to LC separation. MS analyses were performed using an LTQ Orbitrap (Thermo Scientific, Bremen, Germany) coupled with a nanoLC-Ultra system (Eksigent, Dublin, CA). About 5 μL samples were loaded onto an IntegraFrit column (C18, 75 μm × 15 cm, 300 Å, 5 μm, New Objective, MA). A linear gradient was run from 100% buffer A to 40% buffer B (98% acetonitrile, 2% water, and 0.1% formic acid) in 150 min and then to 80% B in another 30 min. Eluting peptides were acquired in a data-dependent mode using XCalibur software (version 1.7, Thermo Scientific). Survey scans were performed in the Orbitrap analyzer at a resolution of 60 000 over a mass range between m/z 300 and 2 000. For each cycle, the top five most intense ions were subjected to CID fragmentation in the LTQ with normalized collision energy at 35% and activation Q 0.25. Dynamic exclusion was enabled.

Selected ions were repeated once and then excluded from further analysis for 45 s. Unassigned ions or those with a charge of 1+ were rejected. Maximum ion accumulation times were 200 ms for each full MS scan and 100 ms for MS/MS scans. One microscan was acquired for each MS and MS/MS scan. Database Searches and Bioinformatics Analyses. All raw files acquired from the LTQ Orbitrap were processed using the Proteome Discoverer platform (version 1.1, Thermo Scientific), which supports both the Sequest and Mascot search engines and allows users to combine their outputs to maximize and cross-validate results. Using a customized workflow in the Proteome Discoverer software, the LC−MS/MS raw files were processed and searched against the human International Protein Index (IPI version 3.63, 67 665 sequences), the UniProtKB database (release 2010_9, 20 286 human sequences), or the UniProtKB Splice Variants database (release 2010_8, 14 747 human sequences). Trypsin and two miscleavage sites were chosen. The oxidation (M) and N-terminal acetylation were set as variable modifications. Only peptides identified as having a peptide rank of 1 and at least 7 amino acids were considered. Peptide and MS/MS tolerances were set to 200 ppm and 0.8 Da, respectively. The false discovery rate (FDR) was estimated on the peptide level by Proteome Discoverer software with an embedded decoy search function. Only peptides at less than 1% FDR were accepted. Gene ontology annotation analysis was performed with DAVID Bioinformatics Resources.31 Only terms with an FDR of less than 1 × 10−4 were considered as enriched.

■

RESULTS AND DISCUSSION Integrated GOFAST Workflow. As illustrated in Figure 1A, the GOFAST workflow includes four major techniques. First, the protein mixture is fractionated based on molecular weight using the GELFrEE method; then, each GELFrEE fraction is cleaned by FASP, followed by on-filter tryptic digestion with microwave assistance. The peptide digests directly eluted from the filter are then subjected to LC−MS/MS and concurrent bioinformatics analysis. Figure 1B shows the discrete mass range for each GELFrEE fraction, indicating that the complex membrane proteome was well fractionated. Generally, as the proteins were constantly eluted from the gel, the number of GELFrEE fractions and the time intervals in

9010

dx.doi.org/10.1021/ac300134e | Anal. Chem. 2012, 84, 9008−9014

Analytical Chemistry

Article

Figure 3. (A) Comparison of the K562 membrane proteome from the current study with other published results. Here, to avoid issues of proteins that overlapped or were missed during the conversion between different accessions, the IPI proteins (2 121) from our result were used for this comparison. (B) Venn plot showing the comparison analysis of the proteins identified by GOFAST (microwave digestion) and classical FASP (overnight digestion). Numbers in parentheses indicate the corresponding membrane proteins in each section of the plot. (C) Protein sequence coverage comparison of the 36 overlapping membrane proteins (indicated by UniProt accession along with the number of predicted helix predicted by TMHMM).

mass spectrometry instrumentation, the depth of a particular proteome can be advanced drastically.11,36 Here, we compared our data with previously published results solely on the basis of the total number of identified proteins to obtain a comparative view of the proteomic techniques used for the K562 membrane proteome study and then a general idea of the current depth of K562 membrane proteome. For instance, Ghosh and co-workers identified 594 proteins from K562 microsomes using the SDSPAGE-RPLC−MS/MS approach26 of which 176 were predicted by TMHMM to be integral membrane proteins. Figure 3A shows that 141 (including 104 TM proteins) of a total of 346 proteins were also identified by our study. Ruth et al. used a combined membrane protein extraction protocol and SCX-RPLC−MS/ MS approach to study the K562 membrane proteome, which resulted in 1 307 total protein identifications.27 When compared to our study, 466 of them were commonly identified. In addition, further comparison based on the proteins matched with ≥2 unique peptides was also performed (data not shown). Because not all the peptide information is available in the published results,26 this comparison might be biased, but it indeed implied that our study deepened the K562 membrane proteome coverage. Wang and co-workers identified 319 membrane proteins from a whole cell lysate of the K562 cell line using a FFE (free-flow electrophoresis)−LC-MS/MS approach.28 Here, no comparison was made because of the unavailability of their protein list. As we mentioned above, the differences in protein identifications may come from the differences in membrane sample preparation as well as variations in protein digestion and LC−MS instrumentation. Notably, comparison on the peptide level indicated that the GOFAST peptides tend to have more missed cleavage sites than peptides from hours-long digestion,27 which otherwise would be undetectable by common mass spectrometry (Supporting Information, Table S-3). In summary, the present study identified approximately 700 membrane proteins according to the GO analysis and TMHMM prediction -by far the largest number of membrane proteins identified in the K562 leukemia cell line, thus dramatically deepening the membrane proteome coverage. The more advanced mass spectrometry instrumentation used in this study in comparison with previous studies26,27 certainly aided protein identifications. Meanwhile, the optimized sample

sample collection could vary. While too many fractions could decrease the total number of proteins in each fraction and the likelihood of identifying low-abundant proteins, conversely, too few fractions could sacrifice the resolution and efficiency of protein separation. Optimized fractionations demonstrated by previous studies include 23 GELFrEE fractions in a top-down proteomics analysis by Vellaichamy et al.,17 while Lee and coworkers focused their study on 5−8 fractions containing proteins of less than 25 kDa.32 In our analysis of the K562 membrane proteome, 12 GELFrEE fractions were first collected across a wide molecular weight range, following the manufacturer’s protocol. Following RPLC with a 180 min gradient for each fraction and MS/MS analysis, 2 090 unique proteins (listed in the Supporting Information, Table S-1) were identified at a 1% FDR using UniProt databases. Among them, 1 207 proteins or 57% of the total proteins were confidently identified by at least two unique peptides, compared to 47% of proteins identified using the same criteria as previously reported.27 Gene ontology (GO) analysis indicated that, of all proteins identified, more than 37% were annotated as “membrane” proteins (Figure 2A). To further verify the membrane content of our proteomic data, the transmembranehidden Markov model (TMHMM) algorithm33 was used to predict theoretical transmembrane (TM) helices in all proteins identified. As shown in Figure 2B, in agreement with previous reports,26,34 314 proteins contained at least one TM helix, representing the largest number of TM proteins found so far in the K562 membrane proteome. In addition, since different informatics tools for TM prediction produced different results regarding the number and/or location of TM regions,35 to provide a more comprehensive view of a membrane proteome, multiple methods should be taken into consideration when studying predicted TM proteins. Herein, we used only the TMHMM algorithm, but it is almost a certainty that there are still a number of nonpredicted membrane proteins on our list. Studies of K562 Membrane Proteome. Several proteomic studies have been reported regarding the membrane proteome of K562 leukemia cells.26−28 Differences in sample preparation, data acquisition, as well as LC−MS instrumentation make the comparable evaluation of these methods difficult (Supporting Information, Table S-2). In particular, when considering the increased sensitivity and scan speed of newly developed hybrid 9011

dx.doi.org/10.1021/ac300134e | Anal. Chem. 2012, 84, 9008−9014

Analytical Chemistry

Article

Second, we compared the FASP and GOFAST approaches at the peptide level, which showed that about 45% of the assigned peptides had the same sequences (Supporting Information, Figure S-1B). This percentage is far less than the percentage of the overlapping proteins (>61%, Figure 3B and Figure S-1A in the Supporting Information). A possible reason for this variation is that LC−MS runs were not fully reproducible, i.e., different LC−MS runs of the same sample might identify different sets of peptides, whereas these peptides could be derived from the same proteins. Thus variations at the peptide level tend be higher than at the protein level (Supporting Information, Figure S-2). Furthermore, we also compared the digestion efficiency by examining the rate of missed cleavages. For fraction 4, 30% of the total GOFAST peptides had at least one missed cleavage site, whereas only 19% of FASP peptides were found with one or more missed cleavages. For fraction 11, there were 18% and 14% peptides having at least one missed cleavage found using the GOFAST and FASP methods, respectively. Our observations here are consistent with a previous result.24 Reddy et al. reported more than half of microwave-digested peptides had missed cleavage sites, which was twice more than in overnight digests.37 In our study, for instance, XRCC5 (X-ray repair crosscomplementing protein 5, 82.7 kDa) had more microwavedigested peptides containing miscleaved sites than in overnight digests, and these incompletely digested peptides contributed to the variation of sequence coverage (Supporting Information, Table S-4). The amino acid composition analysis indicated that the percentages of lysine and arginine residues in the current K562 membrane proteome (6.8%/5.9%) are slightly higher than in the whole human proteome (5.8%/5.7%), which is consistent with our experimental results. On the other hand, only 1.8% (fraction 11) and 3.7% (fraction 4) of peptides had two or more missed cleavages, indicating that microwave treatment could cleave intact proteins into generally large (but not too large) peptides, suitable for concurrent MS analysis. Our results also implied that basic proteins that contain more K or R amino acids (e.g., histone proteins) may benefit more from microwaveassisted tryptic digestion. In addition, we compared GOFAST with the conventional SDS-PAGE to evaluate the throughput of this integrated approach. The GOFAST identified more proteins with relatively more peptide/spectrum matches (PSMs) and higher sequence coverage than SDS-PAGE (Supporting Information, Figure S-3). Here, the high loading capacity of GOFAST may have facilitated the protein identifications; nevertheless, it gives GOFAST a distinct advantage over SDS-PAGE that will ultimately benefit the identification of low abundance proteins. Identification of Alternative Splicing Variants Using Integrated GOFAST Approach. Alternative splicing (AS) of pre-mRNAs, which allows a single gene to produce multiple protein isomers/variants with different functions, is a major source of proteome diversity in humans and is believed to be highly relevant to various diseases including carcinogenesis.38 MS-based proteomic approaches represent an alternative to conventional genomic technologies that can globally characterize the functional AS variants at the proteome level.39,40 However, because of limited MS-detectable peptides generated by currently available bottom-up-based sample preparation protocols, unique peptides carrying the sequence variations can be easily missed, which makes different protein isomers/variants indistinguishable.41 Therefore, to overcome the bottleneck associated with high sequence similarity, advanced sample preparation methods that can improve sequence coverage for

preparation approach outlined in this study may have also facilitated the identifications. For instance, Tran et al. reported around 60% recovery by using CMW precipitation to clean GELFrEE fractions.16 Most recently, Botelho et al. pointed out that the resolubilization step inevitably led to sample loss in the GELFrEE-MS workflow,19 thus reducing the number of protein identifications.16 The GOFAST approach avoids precipitation cleanup and then potential sample loss during the resolubilization step. Furthermore, the FASP cleanup can efficiently be completed in a couple of hours, while the acetone precipitation method used in previous reports may take overnight.19 Therefore, the integration of the GELFrEE-FASP approach in proteomic sample preparation is a significant improvement in terms of both efficiency and throughput. Comparison of the Digestion Performance of Conventional FASP versus GOFAST. To test the efficiency of microwave-assisted on-filter digestion, equal aliquots of two FASP-cleaned protein fractions (fractions 4 and 11) were subjected to conventional FASP (overnight digestion) and GOFAST (microwave digestion) processing, respectively. First, we performed a comparison at the protein level. For fraction 4, 287 and 290 proteins were identified from the overnight and microwave digestions, respectively, 179 of which (more than 61%) were common to both digestion approaches (Supporting Information, Figure S-1A). For fraction 11, comparable numbers of proteins were identified as well (470 via FASP vs 451 via GOFAST), including 318 (67%) overlapping proteins (Figure 3B). Further analysis of the membrane proteins (Figure 3B,C) indicated that similar numbers of membrane proteins can be obtained by the two approaches. Interestingly, GOFAST provided relatively high protein sequence coverage. Regarding individual protein sequence coverage, both methods provided comparable results as well. As depicted in Figure 4, most of the overlapping proteins from fraction 11 had

Figure 4. Comparison of sequence coverage of the proteins identified by the two methods, classical FASP and GOFAST. More than one-third (35.9%) of the proteins showed similar sequence coverage (y = x). The proteins with extremely high or low coverage in one method were indicated in the plot.

similar sequence coverage, indicating the effectiveness of the minute-scale microwave-assisted digestion. Meanwhile, there are some exceptions. For instance, one more peptide of MARCKSL1 protein (MARCKS-like protein 1) was identified in its overnight digests than in microwave digests which contributed to the increased sequence coverage (because of the relatively low MW of 19.5 kDa). 9012

dx.doi.org/10.1021/ac300134e | Anal. Chem. 2012, 84, 9008−9014

Analytical Chemistry

Article

available two-dimensional separations, e.g., GELFrEE and RPLC. Other approaches (e.g., hydrophilic interaction chromatography (HILIC), strong anion exchange (SAX), and weak cation exchange (WCX))5,9 can also be integrated into this platform, which could increase the dynamic range and throughput of GOFAST.

each individual AS product are highly needed. Since our GOFAST approach provides more peptide identifications for each individual protein, we analyzed our results to determine if it might be able to facilitate the identification of AS variants (detailed procedure can be found in the Supporting Information, Figure S-4). Briefly, from our GOFAST experiment, 28 AS isoform-specific peptides derived from 23 different splicing variants were successfully identified (listed in the Supporting Information, Table S-5). In addition, the SDS-PAGE approach identified three AS variants, including one overlapping product by GOFAST (Supporting Information, Figure S-4). As one of the major differences of the two approaches used in this study is the loading amount, the GOFAST may have facilitated the identification of some of the low abundance alternative splicing products. These AS products represent a group of proteins that may have isoform-specific functions. For instance, a membrane protein TPM3 (isoform 2 of tropomyosin α-3 chain), which was recently shown to be associated with breast cancer,42 was confidently identified by three distinct peptides, all containing AS isoformspecific sequence elements, using GOFAST (Supporting Information, Figure S-6). In contrast, a recent study failed to identify TPM3 proteins in K562 leukemia cells using a 2DEbased approach,43 illustrating the potential power of GOFAST for global profiling of AS products.

■

ASSOCIATED CONTENT

S Supporting Information *

Additional information as noted in text. This material is available free of charge via the Internet at http://pubs.acs.org.

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Fax: (919) 966-2852. Phone: (919) 843-5310. Notes

The authors declare no competing financial interest.

■

REFERENCES

(1) Masuda, T.; Saito, N.; Tomita, M.; Ishihama, Y. Mol. Cell. Proteomics 2009, 8, 2770−2777. (2) Tan, S.; Tan, H. T.; Chung, M. C. M. Proteomics 2008, 8, 3924− 3932. (3) Fischer, F.; Wolters, D.; Rögner, M.; Poetsch, A. Mol. Cell. Proteomics 2006, 5, 444−453. (4) Che, F.-Y.; Madrid-Aliste, C.; Burd, B.; Zhang, H.; Nieves, E.; Kim, K.; Fiser, A.; Angeletti, R. H.; Weiss, L. M. Mol. Cell. Proteomics 2011, DOI: 10.1074/mcp.M110.000745. (5) Motoyama, A.; Yates, J. R. Anal. Chem. 2008, 80, 7187−7193. (6) Wu, C. C.; Yates, J. R. Nat. Biotechnol. 2003, 21, 262−267. (7) Wolff, S.; Otto, A.; Albrecht, D.; Zeng, J. S.; Buttner, K.; Gluckmann, M.; Hecker, M.; Becher, D. Mol. Cell. Proteomics 2006, 5, 1183−1192. (8) Zhang, H.; Lin, Q.; Ponnusamy, S.; Kothandaraman, N.; Lim, T. K.; Zhao, C.; Kit, H. S.; Arijit, B.; Rauff, M.; Hew, C.-L.; Chung, M. C. M.; Joshi, S. B.; Choolani, M. Proteomics 2007, 7, 1654−1663. (9) Gilmore, J. M.; Washburn, M. P. J. Proteomics 2010, 73, 2078− 2091. (10) Leth-Larsen, R.; Lund, R. R.; Ditzel, H. J. Mol. Cell. Proteomics 2010, 9, 1369−1382. (11) Pasini, E. M.; Kirkegaard, M.; Mortensen, P.; Lutz, H. U.; Thomas, A. W.; Mann, M. Blood 2006, 108, 791−801. (12) Yu, Y. B.; Leng, T. H.; Yun, D.; Liu, N.; Yao, J.; Dai, Y.; Yang, P.; Chen, X. Proteomics 2010, 10, 2444−2457. (13) Wisniewski, J. R.; Zougman, A.; Nagaraj, N.; Mann, M. Nat. Methods 2009, 6, 359−362. (14) Manza, L. L.; Stamer, S. L.; Ham, A. J.; Codreanu, S. G.; Liebler, D. C. Proteomics 2005, 5, 1742−1745. (15) Lu, B.; McClatchy, D. B.; Kim, J. Y.; Yates, J. R. Proteomics 2008, 8, 3947−3955. (16) Tran, J. C.; Doucette, A. A. Anal. Chem. 2009, 81, 6201−6209. (17) Vellaichamy, A.; Tran, J. C.; Catherman, A. D.; Lee, J. E.; Kellie, J. F.; Sweet, S. M. M.; Zamdborg, L.; Thomas, P. M.; Ahlf, D. R.; Durbin, K. R.; Valaskovic, G. A.; Kelleher, N. L. Anal. Chem. 2010, 82, 1234− 1244. (18) Collier, T. S.; Sarkar, P.; Rao, B.; Muddiman, D. C. J. Am. Soc. Mass Spectrom. 2010, 21, 879−889. (19) Botelho, D. M.; Wall, M. J.; Vieira, D. B.; Fitzsimmons, S.; Liu, F.; Doucette, A. A. J. Proteome Res. 2010, 9, 2863−2870. (20) Cox, H. D.; Chao, C.-K.; Patel, S. A.; Thompson, C. M. J. Proteome Res. 2008, 7, 570−578. (21) Bereman, M. S.; Egertson, J. D.; MacCoss, M. J. Proteomics 2011, 11, 2931−2935. (22) Wisniewski, J. R.; Zougman, A.; Mann, M. J. Proteome Res. 2009, 8, 5674−5678.

■

CONCLUSIONS Because of the enormous diversity and complexity of a proteome and the large, dynamic range of protein expression, in addition to state-of-the-art mass spectrometers, efforts have focused on improving the quality and efficiency of sample preparation.44 Here, our GOFAST method integrates effective separation with maximum resolution, efficient digestion, and high protein/ peptide recovery, particularly useful for meeting challenges associated with the analysis of a hydrophobic, low-solubility, and low-abundance membrane proteome.2 Compared to other conventional proteomic approaches, our setup also simplifies the complicated combinations of SDS-PAGE-LC−MS/MS, TLSGE-MudPIT, and BDAP techniques which, for example, were used to analyze the membrane proteins in Toxoplasma gondii.4 The improved robustness of this approach includes effective prefractionation of complex proteins with high sample loading, efficient depletion of detergents, and ultrafast and efficient on-filter digestion by microwave irradiation. The gelmimic GELFrEE separation is tolerant of detergents and salts, and the gel-free fraction collection step avoids the possible sample losses during in-gel digestion and peptide extraction associated with any gel-based approach. Meanwhile, FASP cleanup provides digestion-compatible protein samples. In addition, microwave-assisted on-filter tryptic digestion significantly reduces processing time to less than 1 min, while yielding sufficient amounts of proteolytic peptides with rich information that seems to facilitate the identification of the AS variants. Although microwave digestion has been widely used in the proteomics field,24 to our knowledge, this is the first study that shows the power of microwave digestion for AS analysis. Microwave-assisted on-filter tryptic digestion here also provides an ultrafast and cost-effective approach for histone modification analysis regarding the varied digestion time (1−20 h) and multiple enzymes used in a previous study.45 The integrated workflow presented in this study is readily applicable to various biological samples extracted from cells, tissues, or immunoprecipitates and is not limited to currently 9013

dx.doi.org/10.1021/ac300134e | Anal. Chem. 2012, 84, 9008−9014

Analytical Chemistry

Article

(23) Zhou, J.-Y.; Dann, G. P.; Shi, T.; Wang, L.; Gao, X.; Su, D.; Nicora, C. D.; Shukla, A. K.; Moore, R. J.; Liu, T.; Camp, Ii, D. G.; Smith, R. D.; Qian, W.-J. Anal. Chem. 2012, 84, 2862−2867. (24) Lill, J. R.; Ingle, E. S.; Liu, P. S.; Pham, V.; Sandoval, W. N. Mass Spectrom. Rev. 2007, 26, 657−671. (25) Roesli, C.; Borgia, B.; Schliemann, C.; Gunthert, M.; WunderliAllenspach, H.; Giavazzi, R.; Neri, D. Cancer Res. 2009, 69, 5406−5414. (26) Ghosh, D.; Beavis, R. C.; Wilkins, J. A. J. Proteome Res. 2008, 7, 1572−1583. (27) Ruth, M. C.; Old, W. M.; Emrick, M. A.; Meyer-Arendt, K.; Aveline-Wolf, L. D.; Pierce, K. G.; Mendoza, A. M.; Sevinsky, J. R.; Hamady, M.; Knight, R. D.; Resing, K. A.; Ahn, N. G. J. Proteome Res. 2006, 5, 709−719. (28) Wang, Y.; Hancock, W. S.; Weber, G.; Eckerskorn, C.; PalmerToy, D. J. Chromatogr., A 2004, 1053, 269−278. (29) Cox, B.; Emili, A. Nat. Protoc. 2006, 1, 1872−1878. (30) Shevchenko, A.; Tomas, H.; Havlis, J.; Olsen, J. V.; Mann, M. Nat. Protoc. 2007, 1, 2856−2860. (31) Huang, D. W.; Sherman, B. T.; Lempicki, R. A. Nat. Protoc. 2008, 4, 44−57. (32) Lee, J. E.; Kellie, J. F.; Tran, J. C.; Tipton, J. D.; Catherman, A. D.; Thomas, H. M.; Ahlf, D. R.; Durbin, K. R.; Vellaichamy, A.; Ntai, I.; Marshall, A. G.; Kelleher, N. L. J. Am. Soc. Mass Spectrom. 2009, 20, 2183−2191. (33) Krogh, A.; Larsson, B.; von Heijne, G.; Sonnhammer, E. L. L. J. Mol. Biol. 2001, 305, 567−580. (34) Gu, S.; Chen, J.; Dobos, K. M.; Bradbury, E. M.; Belisle, J. T.; Chen, X. Mol. Cell. Proteomics 2003, 2, 1284−1296. (35) Fagerberg, L.; Jonasson, K.; Heijne, G. v.; Uhlen, M.; Berglund, L. Proteomics 2010, 10, 1141−1149. (36) Nagaraj, N.; Wisniewski, J. R.; Geiger, T.; Cox, J.; Kircher, M.; Kelso, J.; Paabo, S.; Mann, M. Mol. Syst. Biol. 2011, 7, 548. (37) Reddy, P. M.; Hsu, W.-Y.; Hu, J.-F.; Ho, Y.-P. J. Am. Soc. Mass Spectrom. 2010, 21, 421−424. (38) Graveley, B. R. Trends Genet. 2001, 17, 100−107. (39) Chang, K.-Y.; Georgianna, D. R.; Heber, S.; Payne, G. A.; Muddiman, D. C. J. Proteome Res. 2010, 9, 1209−1217. (40) Tress, M.; Bodenmiller, B.; Aebersold, R.; Valencia, A. Genome Biol. 2008, 9, R162. (41) Liu, H.; Sadygov, R. G.; Yates, J. R. Anal. Chem. 2004, 76, 4193− 4201. (42) Zhang, D. H.; Tai, L. K.; Wong, L. L.; Sethi, S. K.; Koay, E. S. C. Proteomics 2005, 5, 1797−1805. (43) Fontana, S.; Alessandro, R.; Barranca, M.; Giordano, M.; Corrado, C.; Zanella-Cleon, I.; Becchi, M.; Kohn, E. C.; De Leo, G. J. Proteome Res. 2007, 6, 4330−4342. (44) Nilsson, T.; Mann, M.; Aebersold, R.; Yates, J. R.; Bairoch, A.; Bergeron, J. J. M. Nat. Methods 2010, 7, 681−685. (45) Jufvas, Å.; Strålfors, P.; Vener, A. V. PLoS One 2011, 6, e15960.

9014

dx.doi.org/10.1021/ac300134e | Anal. Chem. 2012, 84, 9008−9014