2D electrophoresis image segmentation within a pixel-based framework

Abstract Two-dimensional electrophoresis (2DE) is a traditional proteomics tool still used extensively to study differences in complex protein expression profiles between related biological samples. The methods can resolve thousands of intact proteins on a gel. However, the resulting image pattern is complex. Traditionally, each individual 2DE image, representing one replicate of one biological sample, is first segmented into its many different spots and the volume is quantified for each spot in each gel image; thereafter lists of protein spot volumes from different samples are collected for statistical analysis. This segmentation-before-analysis approach is known to cause considerable segmentation problems due to weak or overlapping protein spots, which in turn causes, for instance, missing values and other misrepresentations in the resulting spot volume table and hence problems in the statistical analysis. The pixel-based approach was introduced to solve some of the challenges inherited in the analysis of 2DE images, in particular, caused by early spot detection. The current paper follows a complete pixel-based approach from aligning to a resulting spot volume table, with several novel steps along the workflow. The workflow encompasses image pre-processing, pixel-based analyses, segmentation where the experimental design is utilised, and final data table reporting. Our approach employs optical flow estimation for alignment, and technical variation is removed prior to the statistical validation. The statistical data analyses are based on ANOVA adjusted by rotation testing and on resampled PLS regression at pixel level. A novel approach for segmentation is performed based on the statistical output performed on pixel level (regression coefficients and their p-values or t-values), and refolded to image representation (meta image). A double threshold is used to distinguish random false positive pixels from structured information of protein spots. Finally, the end user is presented a list of protein spots of interest at spot volume level rather than pixel level where the spot detection procedure has taken into account the relevance in light of the experimental design.