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What is "Image Warping"?

Image warping is the core technolgy in Delta2D to align the images, i.e. to eliminate the dirtortions of the gels. After warping the images look like as the gels would have run perfectly - spots of the same protein have the same position.

Background: Why warping is important for 2D gel image analysis

If you are used to conventional 2D gel analysis packages, you might know those long and tedious editing sessions for spot matches and spot boundaries that were found "automatically" by the software. Most of the difficulties you face with those packages stem from the fact that proteins will migrate to slightly different positions on different gels. Delta2D's image warping eliminates these running differences between 2D gel images. DECODON pioneered the use of image warping in 2D gel analysis with the release of Delta2D version 1 in 2000.

Here you see a combined image that is made from two gels. One is colored in orange, the other one in blue. In the dual channel image without warping it is hard to find corresponding spots and do comparisons of expression patterns.

Dual View Unwarped Dual View Warped
Two gel images, overlaid into a dual channel image, not warped. Two gel images, overlaid into a dual channel image, after warping. Differences in expression levels are clearly visible. Warping allows Delta2D to assign coresponding image positions across a whole set of images.
Grid Unwarped Grid Warped
Gel image with overlaid grid. The same gel image after warping.

After the warping, the dual channel image gives valuable insight for comparing the spot patterns qualitatively. Black means that spots have roughly the same intensity on both images. Blue means a spot is much stronger on gel A, orange means it is much stronger on gel B.

But the advantages of warping go far beyond the making of dual channel images. The effect of applying image warping is as if you had made perfect 2D gels: those would have all proteins migrating exactly to the same position. And because Delta2D knows about pixel-by-pixel correspondences between images, other core technologies are enabled:

  • 100% Spot Matching delivers higher statistical confidence for the analysis of expression profiles
  • Image Fusion lets you combine multiple images to produce, for example average images
  • Proteome Maps for protein identifications use union fusion images
  • Analysis of Expression profiles with spot color coding combines image fusion and 100% spot matching

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