Two-dimensional Gel Electrophoresis Image Analysis Research Articles

Software-induced variance in two-dimensional gel electrophoresis image analysis

Experimental variability in 2-DE is well documented, but little attention has been paid to variability arising from postexperimental quantitative analyses using various 2-DE software packages. The performance of two 2-DE analysis software programs, Phoretix 2D Expression v2004 (Expression) and PDQuest 7.2 (PDQuest), was evaluated in this study. All available background subtraction and smoothing algorithms were tested using both data generated from one single 2-DE gel image, thus excluding experimental variance, and with authentic sets of replicate gels (n = 5). A slight shift of the image boundaries (the "cropping area") caused both programs to induce variance in protein spot quantification of otherwise identical gel images. The resulting variance for PDQuest (CV(mean) = 8%) was approximately twice that for Expression (CV(mean) = 4%). In authentic sets of replicate 2-DE gels (n = 5), the experimental variance confounded the software-induced variance to some extent. However, Expression still outperformed PDQuest, which exhibited software-induced variance as high as 25% of the total observed variance. Surprisingly, the complete omission of background subtraction algorithms resulted in the least amount of software-based variance. These data indicate that 2-DE gel analysis software constitutes a significant source of the variance observed in quantitative proteomics, and that the use of background subtraction algorithms can further increase the variance.

Comparative evaluation of two two-dimensional gel electrophoresis image analysis software applications using synovial fluids from patients with joint disease

The proteomic composition of synovial fluid (SF) may hold clues to understanding the molecular basis of arthritis. However, the highly viscous nature and proteomic complexity of SF present a challenge when analyzing results obtained by two-dimensional gel electrophoresis (2D-GE). Several software applications are available for analyzing 2D- GE images. Despite inherent strengths and weaknesses, no comparison between these applications has been reported using SF or any human fluid specimens. We evaluated two common software packages — PD Quest and Progenesis Workstation — for spot detection, matching, and quantitation of 2D-GE images of SF from four patients with arthritic disease. Initially, whole 2D-gel images were analyzed for spot detection, which suggested that PDQuest is more consistent than Progenesis; however, PDQuest appeared to require more user intervention than Progenesis. Subsequently, two small areas (spots well resolved and spots not well resolved) were selected from each gel image, which were analyzed by the software for spot detection, matching, volume, and resolution. These analyses suggest that both tools can quantify well-resolved spots relatively consistently when compared with manual spot detection (the “gold standard”). The “3D viewer” option offered by both tools enables correct spot identification and matching. The strengths and weaknesses of these computer tools can provide guidance in the choice of a particular workstation for identifying biomarkers of arthritis.

A fast spot segmentation algorithm for two-dimensional gel electrophoresis analysis.

An important issue in the automation of two-dimensional gel electrophoresis image analysis is the detection and quantification of protein spots. A spot segmentation algorithm must detect, define the extent of, and measure the integrated density of spots under a wide variety of actual gel image conditions. Besides these functions, the algorithm must be memory efficient to be able to process very large gel images and do this in a reasonable amount of computation time on low-cost computers, such as workstations and personal computers. We have developed a fast spot segmentation algorithm, extending the GELLAB-II segmenter, which extracts spots in a single raster scanning pass through the gel image. The performance analysis of the algorithm will be given in the paper as well as a discussion of the algorithm.