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The state of the art in the analysis of two-dimensional gel electrophoresis images.

Berth M, Moser FM, Kolbe M, Bernhardt J - Appl. Microbiol. Biotechnol. (2007)

Bottom Line: Recent significant advances in image processing methods combined with powerful computing hardware have enabled the routine analysis of large experiments.Challenges for analysis software as well as good practices are highlighted.We close with an overview of visualization and presentation methods (proteome maps) and current challenges in the field.

View Article: PubMed Central - PubMed

Affiliation: DECODON GmbH, Rathenau-Strasse 49a, 17489 Greifswald, Germany.

ABSTRACT
Software-based image analysis is a crucial step in the biological interpretation of two-dimensional gel electrophoresis experiments. Recent significant advances in image processing methods combined with powerful computing hardware have enabled the routine analysis of large experiments. We cover the process starting with the imaging of 2-D gels, quantitation of spots, creation of expression profiles to statistical expression analysis followed by the presentation of results. Challenges for analysis software as well as good practices are highlighted. We emphasize image warping and related methods that are able to overcome the difficulties that are due to varying migration positions of spots between gels. Spot detection, quantitation, normalization, and the creation of expression profiles are described in detail. The recent development of consensus spot patterns and complete expression profiles enables one to take full advantage of statistical methods for expression analysis that are well established for the analysis of DNA microarray experiments. We close with an overview of visualization and presentation methods (proteome maps) and current challenges in the field.

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Related in: MedlinePlus

Flamingo-stained protein amount (green), Diamond ProQ Phosphoprotein staining (red), and 33P in vivo phosphoprotein labeling (blue) in an exponentially growing B. subtilis 168 sample. While the green and blue subimages seem to be almost complementary, the red subimage highlights spots from the protein level pattern as well as from the phosphate autoradiograph, so it can be used to find correspondences
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Fig5: Flamingo-stained protein amount (green), Diamond ProQ Phosphoprotein staining (red), and 33P in vivo phosphoprotein labeling (blue) in an exponentially growing B. subtilis 168 sample. While the green and blue subimages seem to be almost complementary, the red subimage highlights spots from the protein level pattern as well as from the phosphate autoradiograph, so it can be used to find correspondences

Mentions: Radiophosphate 32/33P labeling can be used for in vivo detection of short-time effects in protein phosphorylation in the cell. Eymann et al. 2007 suggested to support the analysis with a Diamond ProQ (Invitrogen) stained pattern. The Diamond ProQ stain binds highly specific to phosphorylated proteins but also to a lower extent to nonphosphorylated ones. This allows for the determination of landmarks between stained and radiophosphate-labeled protein patterns that can be used to superimpose 33P-labeled and stained protein patterns (Fig. 5).Fig. 5


The state of the art in the analysis of two-dimensional gel electrophoresis images.

Berth M, Moser FM, Kolbe M, Bernhardt J - Appl. Microbiol. Biotechnol. (2007)

Flamingo-stained protein amount (green), Diamond ProQ Phosphoprotein staining (red), and 33P in vivo phosphoprotein labeling (blue) in an exponentially growing B. subtilis 168 sample. While the green and blue subimages seem to be almost complementary, the red subimage highlights spots from the protein level pattern as well as from the phosphate autoradiograph, so it can be used to find correspondences
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2279157&req=5

Fig5: Flamingo-stained protein amount (green), Diamond ProQ Phosphoprotein staining (red), and 33P in vivo phosphoprotein labeling (blue) in an exponentially growing B. subtilis 168 sample. While the green and blue subimages seem to be almost complementary, the red subimage highlights spots from the protein level pattern as well as from the phosphate autoradiograph, so it can be used to find correspondences
Mentions: Radiophosphate 32/33P labeling can be used for in vivo detection of short-time effects in protein phosphorylation in the cell. Eymann et al. 2007 suggested to support the analysis with a Diamond ProQ (Invitrogen) stained pattern. The Diamond ProQ stain binds highly specific to phosphorylated proteins but also to a lower extent to nonphosphorylated ones. This allows for the determination of landmarks between stained and radiophosphate-labeled protein patterns that can be used to superimpose 33P-labeled and stained protein patterns (Fig. 5).Fig. 5

Bottom Line: Recent significant advances in image processing methods combined with powerful computing hardware have enabled the routine analysis of large experiments.Challenges for analysis software as well as good practices are highlighted.We close with an overview of visualization and presentation methods (proteome maps) and current challenges in the field.

View Article: PubMed Central - PubMed

Affiliation: DECODON GmbH, Rathenau-Strasse 49a, 17489 Greifswald, Germany.

ABSTRACT
Software-based image analysis is a crucial step in the biological interpretation of two-dimensional gel electrophoresis experiments. Recent significant advances in image processing methods combined with powerful computing hardware have enabled the routine analysis of large experiments. We cover the process starting with the imaging of 2-D gels, quantitation of spots, creation of expression profiles to statistical expression analysis followed by the presentation of results. Challenges for analysis software as well as good practices are highlighted. We emphasize image warping and related methods that are able to overcome the difficulties that are due to varying migration positions of spots between gels. Spot detection, quantitation, normalization, and the creation of expression profiles are described in detail. The recent development of consensus spot patterns and complete expression profiles enables one to take full advantage of statistical methods for expression analysis that are well established for the analysis of DNA microarray experiments. We close with an overview of visualization and presentation methods (proteome maps) and current challenges in the field.

Show MeSH
Related in: MedlinePlus