Elastic image registration of 2‐D gels for differential and repeatability studies

Abstract

One of the main applications of electrophoretic 2-D gels is the analysis of differential responses between different conditions. For this reason, specific spots are present in one of the images, but not in the other. In some other occasions, the same experiment is repeated between 2 and 12 times in order to increase statistical significance. In both situations, one of the major difficulties of these analysis is that 2-D gels are affected by spatial distortions due to run-time differences and dye-front deformations, resulting in images that are significantly dissimilar not only because of their content, but also because of their geometry. In this technical brief, we show how to use free, state-of-the-art image registration and fusion algorithms developed by us for solving the problem of comparing differential expression profiles, or computing an "average" image from a series of virtually identical gels.

Image Analysis Workflow for 2-D Electrophoresis Gels Based on ImageJ

“…First, all images were warped by bUnwarpJ, 14 an algorithm for elastic and consistent image registration developed as an ImageJ plug-in. It performs a simultaneous registration of two images, allowing us to solve the problem of spatial distortions due to run-time differences and dye-front deformations.…”

“…Several research groups have developed freeware systems to handle certain key aspects of gel analysis, including archiving (SwissProt 2D), 11 comparison (Flicker), 12 interactive exploration (WebGel), 13 registration (bUnwarpJ and Sili2DGel), 14,15 spot detection, 16 spot quantification precision and differential expression (Pinnacle). 17 However nobody has developed a complete package freely available and platform independent able to perform all the steps of a 2D-GE gel analysis experiment.…”

Image analysis tools and emerging algorithms for expression proteomics

“…By the central limit theorem, averaging n gels (pixel‐by‐pixel) will result in a mean (‘master’) gel with noise reduced by a factor of √ n . While other image fusion methods have been proposed that maximise the number of spots in the master gel 62, 63, statistically weak spots are artificially amplified, hence, there is a risk of an increased false positives rate. Morris et al 58 show that simple background correction and peak detection after wavelet denoising on the mean gel gives results with greater validity and more reliable quantifications than commercial packages, including Progenesis Same Spots 64.…”

“…Despite the good performance, it was noted MIR suffered some robustness issues in areas with local spatial bias and regarding the irregularity of the transformation. To solve this, Sorzano et al 67, 62 replaced the transformation with a more realistically smooth hierarchical piecewise cubic B‐spline model, adding regularisation to constrain local expansion and rotation of the warp. For difficult gels, they also added the option of specifying landmarks to aid the registration.…”

Mass Spectrometry-Coupled Techniques for Viral-Related Disease Biomarker Identification

“…Although various algorithms for alignment to compare samples across gels, including landmark based [12,13], intensity based [14,15] and a combination of the two [13,16], can significantly improve gel-to-gel reproducibility, they cannot completely eliminate differential protein expression due to nonbiological factors. In addition, selection of spots using manual methods is subjective.…”