Simple Method for Three-Dimensional Representation of 2-DE Spots Using a Spreadsheet Program Martin H. Maurer* Department of Physiology and Pathophysiology, University of Heidelberg, Germany Received February 3, 2004
Abstract: Quantitative protein expression analysis based on two-dimensional gel electrophoresis requires comparison of spot volumes. We describe an algorithm to visualize two-dimensional spot patterns in three dimensions using a spreadsheet program for surface plotting. Keywords: spot volume • 2DE software • 3D algorithm • twodimensional gel electrophoresis • computational biology
Introduction For quantification of protein spots in two-dimensional (2D) electropherograms, most software programs introduce special measures, including area, optical density, percent optical density, volume, and percent volume (for review and mathematical definition, see Appel et al.1 and Seillier-Moiseiwitsch et al.2). Spot quantification based on these data is the prerequisite for any comparison of expression levels between two data sets. Almost all commercially available software programs for twodimensional gel analysis use the spot volume as a means of spot quantification and inter-spot comparison. The term “spot volume” describes the integral of the pixel intensity over the stained spot area.1,2 The comparison of spot volumes between samples allows quantitative analysis of protein expression within the dynamic range of the staining procedure.3 Comparing the available software for 2D analysis, only very expensive large software packages provide implemented functions for three-dimensional (3D) representation of 2D protein spots. In this technical note, we describe an algorithm for how to represent 2D gel spots in three dimensions using a surface plot algorithm from any spreadsheet program providing surface plot options.
Methods Two-Dimensional Gel Electrophoresis. 2DE was performed as described elsewhere in detail.4 Briefly, total protein extracts of neurospheres were separated in the first dimension according to their isoelectric point using immobilized pH gradient gels, and in the second dimension according to their molecular weight using polyacrylamide electrophoresis. The gels were silver-stained, and the images were digitized using a desktop scanner. * To whom correspondence should be addressed. Dr. Martin H. Maurer, Department of Physiology and Pathophysiology, University of Heidelberg, Im Neuenheimer Feld 326, 69120 Heidelberg, Germany. Phone: +49-622154-4075. Fax: +49-6221-54-4561. E-mail:
[email protected] 10.1021/pr049962l CCC: $27.50
2004 American Chemical Society
Figure 1. Part of the data matrix exported from Scion Image for Windows 4.0.2 beta using the ASCII text format export option. Each entry represents the pixel intensity value (28 bit scale, range 0-254) for a given row x and column y. This data matrix can be imported into the spreadsheet program.
Image Conversion. The images were saved in bitmap (BMP) format and converted to ASCII text file format using the export option of Scion Image 4.0.2 beta for Microsoft Windows, available on the Internet for Mac and PC at http:// www.scioncorp.com. This program is based on the NIH Image (http://rsb.info.nih.gov/nih-image), developed for image analysis and quantification. The data file contains a matrix for gray values arranged in rows and columns, representing optical densities for each pixel (Figure 1). Matrixes may be imported into any spreadsheet program (e.g., Microsoft Excel, or Microcal Origin) providing a surface plot function. When using the mentioned programs, only a maximum input of 255 columns is allowed due to internal program limitations, with an unlimited number of rows. The matrix is plotted using the surface plot function (e.g., in Microsoft Excel: Insert > Chart > Chart type: surface; in Microcal Origin: Edit > convert to matrix > Direct, Plot3D > 3D-color map surface). For analytical reasons, we created synthetic images using Micrografx Picture Publisher 7.0 for Microsoft Windows, as these data allow more extensive manipulation than experimental data.
Results and Discussion Three-dimensional spot representation is an important tool to visualize changes in protein expression levels more easily (Figure 2). Currently, only very large and expensive software packages for 2D gel analysis have implemented a 3D spot representation function, which does not allow the common user to create 3D spot representations. Applying our algorithm enables nearly every researcher to create 3D images, as the mentioned software is either freely available in the Internet, or a huge number of users have access to common spreadsheet programs. Journal of Proteome Research 2004, 3, 665-666
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technical notes
3-D Representation of 2-DE Spots
Figure 2. Comparison between 2D and 3D representation. This picture shows the 3D representation of the spot volume, and the easier visualization of changes in the spot volume (arrows). Data were plotted with the surface plot option in the Microcal Origin 5.0 program. MW ) molecular weight, pH ) pH value of the isoelectric point, INT ) pixel intensity, all arbitrary units.
Figure 3. Comparison between 2D and 3D representation in synthetic gel images. Data were plotted as described for Figure 2. Whereas it is easy to outline two distinct spots not overlapping, it is difficult to determine the spot limits in overlapping spots in the 2D representation. The 3D representation allows also easy detection of the spot limits in the overlapping sectors.
Although 3D spot representation is useful for visualization, it should be kept in mind that it does not contribute to the spot quantification process itself, which is based only on pixel intensities in the spot area. On the other hand, 3D algorithms implemented in proteomic analysis software packages use the 3D representations to identify the borders of a protein spot in the digitized gel. The spot limit is hence defined as the relative minimum of the connection of two peak values. In comparison, the 2D gel based algorithm defines spots according to their geometric shape, i.e., the elliptic form.5 The method described in this technical note is extremely helpful in regions of spot overlap (Figure 3), where it may be difficult to determine spot limits precisely enough to outline spots in 2D representations. In this case, the 3D representation helps to determine the border of the spot and the extent of the overlapping region. On the one hand, this is important when manual analysis is involved in spot detection, as any manual spot detection decreases inter-gel reproducibility dramatically.3,6 On the other hand, it is not possible to accept all computer-detected spots, as the present detection algorithms are not without error. Thus time-consuming manual spot editing is necessary. Our algorithm helps the human gel analyzer to decide about the spot cutting line, which are errorprone in the 2D representation.3 The described methods can also be applied for a variety of other purposes, e.g., 1D lanes from agarose gel electrophoresis
or Western blots can be converted to identify peaks and bands more easily. Unfortunately, quantitative analysis is restricted using the 3D spot representation. For this purpose, high-quality imaging software is still required.
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In conclusion, we provide an easy and inexpensive algorithm for 3D representation of 2D spot images.
References (1) Appel, R. D.; Vargas, J. R.; Palagi, P. M.; Walther, D.; Hochstrasser, D. F. Melanie IIsA third-generation software package for analysis of two- dimensional electrophoresis images: II. Algorithms. Electrophoresis 1997, 18, 2735-2748. (2) Seillier-Moiseiwitsch, F.; Trost, D. C.; Moiseiwitsch, J. Statistical methods for proteomics. Methods Mol. Biol. 2002, 184, 51-80. (3) Mahon, P.; Dupree, P. Quantitative and reproducible twodimensional gel analysis using Phoretix 2D Full. Electrophoresis 2001, 22, 2075-2085. (4) Maurer, M. H.; Feldmann, R. E., Jr.; Fu ¨ tterer, C. D.; Kuschinsky, W. The proteome of neural stem cells from adult rat hippocampus. Proteome Sci. 2003, 1, 4. (5) Efrat, A.; Hoffmann, F.; Kriegel, K.; Schultz, C.; Wenk, C. Geometric algorithms for the analysis of 2D-electrophoresis gels. J. Comput. Biol. 2002, 9, 299-315. (6) Rogers, M.; Graham, J.; Tonge, R. P. Using statistical image models for objective evaluation of spot detection in two-dimensional gels. Proteomics 2003, 3, 879-886.
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