Lights, Camera, Action! Systematic variation in 2‐D difference gel electrophoresis images

Abstract

Lights, Camera, Action! Systematic variation in 2-D difference gel electrophoresis images2-D Difference gel electrophoresis (DIGE) circumvents many of the problems associated with gel comparison via the traditional 2-DE approach. DIGE's accuracy and precision, however, is compromised by the existence of other significant sources of systematic variation, including that caused by the apparatus used for imaging proteins (location of the camera and lighting units, background material, imperfections within that material, etc.). Through a series of experiments, we estimate some of these factors, and account for their effect on the DIGE experimental data, thus providing improved estimates of the true relative protein intensities. The model presented here includes 2-DE images as a special case. Keywords IntroductionÜnlü et al.[1] developed a novel modified 2-DE technique called 2-D difference gel electrophoresis (DIGE), which circumvents many of the problems associated with comparison of separate gels. In 2-D DIGE, two different protein samples are pre-labeled with a pair of matched cyanine dyes which fluoresce at different wavelengths of light but have identical charge and almost identical mass, thus allowing two different samples to be run within one gel mixture in both dimensions. Because the two samples have been subjected to essentially the same environment during separation, the "between-gel" variation is significantly reduced; however, the "within-gel" variation that results from the imaging process is essentially unaffected [2,3]. This paper focuses on the systematic variation which occurs due to the DIGE data imaging process and the system used to image the proteins; for example, see Figs. 1 and 2.The purpose is to aid in the automatic detection of differential protein expression and modification. This problem's difficulty lies in developing a model that accurately represents the complexity of the experimental conditions. Only through extensive experimentation and deliberation have we successfully identified and modelled several nuisance effects influencing the overall experimental data. Section 2 describes the imaging apparatus and the sequence of steps performed in creating a protein image, thus helping to establish a statistical model that describes the effect caused by the system components. The general approach is widely applicable, while the specific model is appropriate for this particular type of imaging system. Materials and methods System descriptionThe fluorescence gel imager contains a cabinet and platform, and a scientific-grade CCD camera is situated and focused approximately five inches above the platform with a lamp approximately three inches away from the camera on each side. There are several machines of this specific type, while other commercial fluorescent gel imagers exist (e.g. a laser scanner).The camera image area is approximately 35 mm635 mm, while a gel is approximately 140 mm6175 mm. Thus, to Abbreviations: DIGE, difference gel electrophoresis; MS, mean square; MSE, mean squared erro...

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“…Transfer from IEF to the second dimension was carried out using in-gel equilibration protocol (Van et al, 2014). The gel was imaged and quantified using an in-house-built fluorescent gel imager (Sellers et al, 2007). Gel images were analyzed in ImageJ.…”

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“…Data denoising and normalization serve to remove these effects in order to give accurate signal measurements from the 2-DE data. Sellers et al (2007) addressed this issue, focusing on factors caused by the apparatus used to image 2D-DIGE data. Through a series of experiments, they estimate the associated factors and establish a model to remove these estimates from the raw gel images to obtain respective images for the true relative protein intensities.…”

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“…A general approach for background correction is to simply subtract the blank sample response, i.e. the response which is obtained before any sample is placed within the sensor (see Jurs et al (2000), or Sellers et al (2007) for an analogous approach). Other general approaches subtract either the global minimum from the data, or perform some local Winsorization at a low-percentile value.…”

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