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April 01, 2008 (Vol. 28, No. 7)

Combined Correctness Can Enrich Proteomics

New Metrics Improve Potential in 2-D Gels

  • Proteomics plays a central role in drug discovery, molecular diagnostics, and the practice of medicine in the postgenomic era. Among all the proteomics technologies, 2-D gel electrophoresis is by far the most commonly used technique for protein separation. In most cases where 2-D gel electrophoresis is employed, the purpose is to find statistically significant differences between predefined conditions. These differences should be related to distinct protein spots that have been correctly detected, matched, and quantified.

    To achieve these goals, sophisticated image-analysis algorithms are applied to extract relevant data from complex spot patterns. The acknowledged problems of reproducibility and resolution inherent in the technology present a challenge for any software. As a result, in nearly all cases, the extracted data is partly incorrect and incomplete.

    As a consequence, researchers have to deal with two serious problems in image analysis—false positives and false negatives. Both are costly, not just in terms of the resources spent on downstream analysis of false hits but perhaps more importantly, by impeding a true understanding of the underlying biological system.

    False Positives

    In nearly all 2-D gel-based proteomic studies, the interesting data is from the protein spots whose intensities have been calculated to be significantly different between the predefined conditions. One could say that those are the desired hits. Upon visual inspection, however, some of these hits turn out to be image-analysis errors such as incorrectly detected or mismatched spots.

  • Click Image To Enlarge +
    Figure 1

    An example of a false positive in terms of image analysis is shown in Figure 1A and B. A zoomed-in area of a gel is shown where the spots have been detected and matched using two different image-analysis software packages. The red border in Figure 1A outlines a spot that, according to the plotted data, shows a clear change in signal intensity. The applied statistical test on this data results in a significantly low p-value.

  • In actuality, this significance is a result of unsuccessful detection and matching of the spots during image analysis. The matched spot border has been placed next to the spot in question resulting in flawed data and a false positive. In Figure 1B, the image analysis on the same area has been performed correctly. The resulting data shows no significant difference between the two groups. It is a true negative.

    It is not unusual that 2-D gel-image analysis can result in 40–50% false positives when working with conventional 2-D gel software—even after significant manual adjustments. In some cases, the false-positive ratio can be much higher.

    False Negatives

    False negatives in this context are true spot changes that are masked or simply not detected due to errors in image analysis.

  • Click Image To Enlarge +
    Figure 2

    In Figure 2A, a clear difference in protein spot intensity that has been correctly identified by the image-analysis software (true positive) is shown. The same intensity change has been missed in Figure 2B because of errors in spot detection. The red border encompasses several spots thereby effectively masking the difference. The result is a false negative and an undetected biological difference.

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