An Integrated Proteomic Workflow for Two-Dimensional Differential Gel Electrophoresis and Robotic Spot Picking Ryan C. Mahnke,‡ Todd H. Corzett,† Sandra L. McCutchen-Maloney,† and Brett A. Chromy*,† New Technologies Engineering Division (NTED), Defense Biology Division, Biosciences Directorate (BIO), Lawrence Livermore National Laboratory (LLNL), 7000 East Avenue, L-452, Livermore, California 94551 Received December 15, 2005
New technologies have advanced the field of proteomics, and a number of companies have developed innovative platforms to drive this research. However, significant challenges are often encountered when trying to integrate complementary technologies from multiple manufacturers. We have developed a software and hardware solution to integrate the Ettan two-dimensional difference gel electrophoresis (2-D DIGE) system (GE Healthcare) with the Investigator ProPic spot picking robot (Genomic Solutions). We have analyzed protein sample preparations from bacterial and mammalian sources to demonstrate a new workflow with increased throughput for gel-based proteomics. Keywords: 2-D DIGE • ProPic Workstation • Yersinia pestis • high-throughput • proteome • gel-based proteomics • saturation labeling • minimal labeling • CyDye • integration
Introduction New, enabling technologies that use multiplexed protein analysis are becoming increasingly sought after to advance the field of proteomics. The implementation of these technologies is often scientifically challenging and can require specialized equipment. Classical proteomics, bridging two-dimensional electrophoresis (2-DE) with mass spectrometry (MS), provides an effective method for identifying proteins following the extraction, or ‘picking’, of protein spots separated on the 2-D gel, but has limitations. Traditional 2-DE imaging methods are singleplex and require post-electrophoretic staining (poststaining) of gels to visualize proteins of interest. In addition, inherent gel-to-gel variation makes identifying and analyzing subtle protein changes, especially low-abundance proteins, difficult. The introduction of two-dimensional difference gel electrophoresis (2-D DIGE) enables both multiplexing and immediate visualization of protein spots using fluorescent dyes (CyDyes) covalently bound to the protein samples.1 Minimal labeling,1 using 50 µg of protein, and saturation labeling2 using only 5 µg of protein are two methods for 2-D DIGE. By the use of a standard sample, consisting of a ‘pool’ of each of the different sample conditions, the variation associated with gelto-gel comparisons is reduced and the accuracy of relative spot quantitation is increased.3 While 2-D DIGE solves many of the problems associated with classical proteomics, it also introduces some challenges. After * To whom correspondence should be addressed. Brett A. Chromy, 7000 East Ave. L-452, Livermore, CA 94551. Phone: 925-422-2454. Fax: 925-4222282. E-mail:
[email protected]. ‡ New Technologies Engineering Division (NTED), Biosciences Directorate (BIO), Lawrence Livermore National Laboratory (LLNL). † Defense Biology Division, Biosciences Directorate (BIO), Lawrence Livermore National Laboratory (LLNL). 10.1021/pr050465u CCC: $33.50
2006 American Chemical Society
gel electrophoresis, a multicolor fluorescent scanner in conjunction with specialized software must be used to image and analyze 2-D DIGE gels. Covalent binding of the CyDye adds ∼500 Da to the size of the protein, and if all protein is not labeled, as is the case with minimal labeling, a shift will occur between labeled and unlabeled protein during electrophoresis. Post-staining is required to view this shift.1 Due to the additional stresses placed on gels by the use of post-staining and movement between 2-D DIGE equipment, the use of stable glass-backed gels over cheaper but less stable free gels is warranted. Before protein spots can be identified using MS, automated instrumentation is required to accurately and efficiently extract them from the gels. With little integration between companies, many researchers use equipment from a single company, although the need for increased throughput or a more robust workflow may require integration of equipment from more than one vendor. The Investigator ProPic Workstation (Genomic Solutions), a robotic protein spot picker, has a positive reputation for protein picking throughput and accuracy.4-10 However, having been developed for use with post-stained, singleplex, free gels, custom integration is required before the full benefits of 2-D DIGE and the ProPic can be realized. Using a combination of a software and hardware solution, we have overcome these challenges and integrated the ProPic with 2-D DIGE. We have demonstrated that this integration is efficient and allows for high-throughput protein extraction from 2-D DIGE gels. Multiple experiments were conducted to characterize host-pathogen interactions whereby differentially expressed proteins were imaged and analyzed using the Typhoon imager (GE Healthcare) and DeCyder software (GE Healthcare) and picked using the ProPic. Journal of Proteome Research 2006, 5, 2093-2097
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Figure 1. Software and hardware solutions for successful integration of 2-D DIGE system with the ProPic Workstation. (A) A custom fixture allows the glass-backed gel to be securely located on the deck of the ProPic Workstation. Small markers (shown by arrows) are visible in the fluorescent image from the Typhoon and the white light image from the ProPic, providing common reference points. (B) A screen shot from the PDP software shows the newly calculated x, y coordinates for the ProPic pick list derived from the x, y coordinates obtained from the DeCyder software pick list.
Materials and Methods Protein Extraction, 2-DE, and 2-D DIGE. A detailed explanation of the cell growth and lysis for the Yersinia pestis samples have been previously reported.11 Protein was quantified using the ADV01 reagent (Cytoskeleton) and purified using the 2D protein cleanup kit (GE Healthcare). Each protein sample (50 µg) was minimally labeled with Cy3 (200 pmol) or Cy5 (200 pmol). A 50 µg pooled standard consisting of equal amounts of each sample was minimally labeled with Cy2 (200 pmol). Labeling was quenched with lysine (10 nmol), and the two samples along with the pooled standard were combined. Saturation labeling was performed using 5 µg of protein obtained from human white blood cell fractions (Cy5) and 5 µg of a pooled standard (Cy3). For saturation labeling, a semipreparative gel was used, containing 200 µg of labeled protein (Cy5) along with 5 µg of pooled standard (Cy3). Samples were separated by charge on 24 cm pH 3-10 NL IPG (Immobiline) gradient strips. Strips were equilibrated with DTT, followed by IAA for 15 min each. Finally, strips were placed onto 12.5% polyacrylamide gels, and proteins were separated according to the protocol in the Ettan DIGE manual (GE Healthcare). Small reference markers (GE Healthcare), essential for integration with the ProPic, were placed in the top-right and bottom-left corners of the gel’s glass backing after electrophoresis. Gels were scanned using the Typhoon 9410 imager with the glass-backed side down on the platen. Differential analysis was carried out using DeCyder (v 5.01) software. The reference markers and all protein spots showing at least two standard deviations of differential expression were selected, and a pick list was generated containing their locations in the image. ProPic Integration. A custom fixture was designed to securely locate the glass-backed gels to the ProPic deck. The gels were imaged using the white light illuminator and chargecoupled device (CCD) camera on the ProPic where the reference markers were the only spots visible in the resulting image. HT Analyzer, the image analysis software provided with the ProPic, was used to locate and select the reference markers and to generate a ProPic pick list containing the x,y coordinates of these markers. The DeCyder pick list and the ProPic reference marker pick list were fed into a custom software program, Protein Data for Picking (PDP). Using the locations of the reference markers in each image along with the locations of the protein spots in the Typhoon image, PDP calculated 2094
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the locations of the protein spots in the ProPic image and created a new pick list. Using the PDP-generated pick list, the ProPic excised the protein spots and deposited them in a 96well microtiter plate containing 120 µL of a 10% ethanol solution. Following extraction, the gel was rescanned on the Typhoon imager. The gel was post-stained with SYPRO Ruby and scanned once more on the Typhoon imager. Protein Digestion and Mass Spectrometry. Protein spot digestion and MS were carried out by Proteomic Research Services (PRS, Ann Arbor, MI). Picked spots were subjected to robotic in-gel digestion using trypsin (ProGest) following reduction with DTT and alkylation with IAA. A portion of the resulting digest supernatant was used for matrix-assisted laser ionization desorption-mass spectrometry (MALDI-MS) analysis. Spotting was performed robotically (ProMS) with ZipTips; peptides were eluted from the C18 material with matrix (Rcyano 4-hydroxy cinnamic acid) in 60% acetonitrile, 0.2% TFA. MALDI-MS data was acquired on an Applied Biosystems Voyager DE-STR instrument, and the observed m/z values were submitted to ProFound for peptide mass fingerprint searching using the NCBInr database. Those samples that proved inconclusive following MALDI-MS were analyzed by LC/MS/MS on a Micromass Q-Tof2 using a 75 µm C18 column at a flow-rate of 200 nL/min. The MS/MS data were analyzed using MASCOT. Manual de novo sequencing was performed on several samples that obtained a single peptide. Briefly, collision-induced dissociation data (MS/MS) were acquired on the Micromass Q-TOF2 mass spectrometer as above. Accurate masses of the parent ions were determined by deducing the charge states and their monoisotopic masses from the full scan mass spectra. Certain amino acids present in the peptide could be deduced from diagnostic immonium ions present in the lower m/z end of the MS/MS spectrum. The majority of the remaining intense ions were mainly sequence-specific, singly protonated yn-type ions with a few low m/z bn-type ions. As many yn ions as possible were tentatively determined by subtracting the higher product ion masses from the already assigned yn-1 ions. Comparison of the calculated mass for the proposed sequence with the observed parent ion mass would account for the mass of amino acids of missing yn ions, if any. While determining yn ions, ions due to loss of water/ammonia were also assigned as they usually appear at the lower mass end of the respective yn and bn ions. Following the completion of the potential sequence
Integrated Workflow for Gel-Based Proteomics
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Figure 2. Successful picking of CyDye-labeled proteins with the ProPic Robotic Workstation. (A) Image of a minimally labeled 2-D DIGE gel from the Typhoon. Red and blue spots clearly show differentially expressed proteins. (B) Image of the same gel following excision of differential protein spots on the ProPic facilitated by the PDP software. Pick locations are consistent with the CyDye-labeled spots. (C) Image of the same gel following post-staining with SYPRO Ruby dye. Picks resulting in successful protein identification are marked in yellow, and picks resulting in unsuccessful identification are marked in red. (D-E) Zoomed region of the gel showing the accuracy of spot picking, as the CyDye-labeled spots (D) coincide with the SYPRO Ruby-detected spots (E).
of the peptide, other ions present in the MS/MS spectrum not taken into consideration in deriving the sequence were accounted for with the proposed sequence as internal fragments. In the case of poor MS/MS data, partial sequence was derived starting from y1 ion as long as residue-mass-matching yn ions can be determined. Cutoff scores were different for each successful protein identification, but for each successful protein identified by MALDI-MS or LC/MS/MS, multiple peptides matched the protein and the bulk of the mass peaks matched to one protein. For de novo sequencing, single peptides were unique to the database search and were at least six amino acids long. Also, the molecular weight and pI of the identified protein was consistent with the spot migration on the gel.
Results and Discussion For successful integration of the ProPic workstation with the 2-D DIGE system, both software and hardware solutions were required. The accuracy of the ProPic is, in part, due to the ability to image, analyze, and pick a 2-DE gel directly on the robot deck, but the fixture included with the instrument is
designed for use with free gels. To adapt the ProPic for use with 2-D DIGE, we developed a custom fixture that allows for secure positioning of glass-backed gels on the deck (Figure 1A). The critical physical dimensions of the original fixture were retained preventing collision with the spot picker probe, and an integral reservoir traps hydration fluid protecting the sensitive components of the light box located below the deck. The PDP software program is the key component for the success of the integration of the 2-D DIGE system with the ProPic, a screenshot of which is shown in Figure 1B. The ProPic, lacking multicolor imaging, is unable to discriminate between the multiple fluorophores used in a 2-D DIGE gel. Without the benefit of PDP, gels would require post-staining to allow for secondary imaging on the ProPic. The spots selected for extraction in the differential analysis would have to be manually identified and selected using the HT Analyzer software. Not only is this process time-consuming, but is also prone to errors, as protein spots in the ProPic image can be easily misidentified. Previously, an alternative software solution was described, that translated X and Y coordinates from a DeCyder picklist for use Journal of Proteome Research • Vol. 5, No. 9, 2006 2095
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Figure 3. Schematic showing the old workflow and the new PDP-integrated workflow. The old workflow had 10 definable steps (left), while the new workflow has 8 (right). Four of the time-consuming steps were eliminated (red) and replaced with two time-saving steps (blue).
on the ProPic.12 Although that software enabled the ProPic to excise protein spots determined by DeCyder, we describe a complete solution including a hardware platform and a workflow for automated higher-throughput gel-based proteomics using 2-D DIGE and the ProPic spot picker. The new PDP software automates the manual comparison through the use of two reference markers placed on the gel prior to 2-D DIGE imaging. The reference markers autofluoresce under the excitation wavelengths used by the Typhoon imager and are visible in the white light CCD camera image taken on the ProPic, providing common reference points in the two systems. PDP inputs the locations of the reference markers and the protein spots of interest from the DeCyder pick list and the location of the reference markers from the HT Analyzer pick list. The PDP conversion algorithm analyzes the trigonometric relationship between the reference markers in each image. By applying this relationship to each protein spot from the DeCyder pick list, the algorithm calculates the corresponding location of the spot in the ProPic image. Using the new converted locations, PDP generates a pick list that is used by the ProPic to locate the spots for extraction.
Protein lysates of Y. pestis were examined using the new integrated workflow for the Ettan 2-D DIGE system and the ProPic Workstation. Figure 2A shows an overlay 2-D DIGE fluorescent image from the Typhoon imager containing minimally labeled Y. pestis proteins. By the use of DeCyder software, the image was analyzed to determine the relative expression levels of the proteins under different growth conditions. Out of a total of over 2800 protein spots detected, 58 protein spots that showed at least 2 standard deviations of differential expression were selected for extraction using the new workflow described herein. Figure 2B shows the image of the gel scanned on the Typhoon after the protein spots were extracted using the ProPic workstation. The pick locations, clearly visible in the image, show the accuracy of our PDP software at calculating the location of the CyDye spots. Figure 2C shows the SYPRO Ruby post-stained gel with pick locations color-coded to illustrate whether protein identification by mass spectrometry was successful (yellow) or not (red). Figure 2D-E show a zoomed-in region of picked protein spots demonstrating the accuracy of spot picking. The significance of the identified proteins has been described previously.11
On the basis of the ability of PDP to calculate the proper spot locations, a 2-D DIGE gel can be loaded onto the ProPic in any orientation with successful picking, providing the reference markers are properly placed. Locating the reference markers in opposite corners of the gel (Figure 1A) is optimum, as the accuracy of the PDP calculations improves with greater distance between their x and y coordinates in the image. It is also critical that the glass backing on the gel is placed face down on the Typhoon imager during fluorescent imaging, or extensive manual corrections in DeCyder are necessary before generating the pick list.
The number of successful protein identifications by MS following protein extraction facilitated by the PDP software highlights the effectiveness of this new workflow. In three separate experiments, the protein identification success rate by mass spectrometry was 85.2% (n ) 92 of 108). Of these three experiments, a gel image from one of these experiments is shown in Figure 2, containing 58 of the selected protein spots. The differential success between high and low molecular weight proteins is notable, shown in Figure 2C. When proteins of approximately 25 kDa and greater were considered, the success rate increased to 89% compared to 67% for proteins below this
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molecular weight. The reduction in successful identifications of smaller sized proteins is an inherent problem of peptide mass fingerprinting,13 as smaller molecular weight proteins will likely have fewer tryptic digestion sites due to fewer total amino acids. Fewer sites for cleavage will lower the overall number of peptides and reduce the likelihood that enough peptides will be available to successfully identify the protein. Moreover, the electrophoretic shift between labeled and unlabeled protein due to minimal labeling can result in exclusive extraction of the labeled portion, which is likely an insufficient total quantity for identification.14 Although we noted no migration shift in proteins above 25 kDa, protein spots below this molecular weight showed some separation. Future versions of the PDP software (in development by Genomic Solutions) will attempt to calculate the dye-related molecular weight shift (∼500 Da) and make the necessary adjustments to the spot location calculations. When saturation labeling is used, the PDP software is the most efficient and reliable method for picking with the ProPic. Because CyDye is covalently bound to all protein in a sample, this technique does not suffer from the shift problems observed when using minimal labeling.2 Preliminary work using saturation labeling has resulted in a success rate of 93.8% (15 of 16). This success rate is substantially better than that reported previously, which demonstrated a 40% success rate without post-staining and 72% with the use of a post-stain.2 Protein spot pickers without multicolor fluorescent detection capabilities, such as the ProPic and the Ettan Spot Picker (GE Healthcare),2 require post-staining to visualize protein spots. However, PDP enables the ProPic to directly pick the spots, and use of a post-stain with this workflow is a redundant process resulting in increased labor effort and additional stress on critical gels of an experiment. Figure 3 is a schematic showing a side-by-side comparison of the workflow prior to and after integration of 2-D DIGE with the ProPic. The old workflow (left) contains a total of 10 steps. Four labor-intensive steps, shown in red, have been replaced by two time-saving steps, shown in blue in the new workflow (right). Although post-staining of minimally labeled gels is timeconsuming, it does yield a small and potentially beneficial increase in successful identification for some proteins of interest, especially lower molecular weight proteins. Even if post-staining is desired, the PDP software eliminates the requirement for manual spot correlation and identification on the ProPic. Over the course of an experiment, the use of the PDP software, and the new workflow, can save 2 or more days of time from start of electrophoresis to start of MS.
Conclusion The new workflow shown here has led to improved biomarker discovery. Facilitated by the PDP software, this workflow saves time and improves protein identification while integrating multiple proteomic platforms. The integration of 2-D DIGE, using both minimal and saturation labeling techniques, and the ProPic Workstation represents a significant advance in proteomics, providing for reliable, high-throughput identification of differentially expressed proteins.
Acknowledgment. The authors thank Dr. Anne Clatworthy for critical evaluation of the paper. This work was
performed under the auspices of the U.S. Department of Energy by University of California Lawrence Livermore National Laboratory under contract No. W-7405-ENG-48 with support from the Department of Homeland Security (Biological Warning and Incident Characterization Program). UCRL-JRNL217341.
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