In spite of the revolution that has occurred in the quality of 2-DGE-based separation techniques since its introduction 32 years ago, gel-to-gel variability persists in 2D gel analysis. As such, fast and robust image processing is a crucial step in quantitative gel-based proteomics, and the fast development in 2-DE analysis software in recent years has removed some of the major stumbling blocks in the field. Future efforts are likely to focus on the improvement and evaluation of previously neglected areas such as background subtraction algorithms and statistical analysis as well as decreasing overall analysis time. Furthermore, standardized sets of 2D gels as well as performance benchmarks for the evaluation of 2-DE analysis software need to be established. Attempts have been made to produce sophisticated sets of synthetic gel images that reflect the true characteristics of authentic 2D gels [8, 9]. Rogers et al.  created a novel model for the creation of synthetic protein spots based on a training set of authentic 2-DGE spots . Unfortunately, no background was introduced to these images; and as evidenced by the results in Figure 8.5, the background subtraction algorithm can be one of the main sources of variance introduced by the 2-DE analysis software . Until an ideal set of synthetic gel images reflecting all aspects of the authentic 2D gel has been constructed, diverse sets of authentic 2D gels representative of common distortions (high background, speckles or irregularly shaped spots ) ought to be utilized. As user awareness of the effects of varying algorithms upon software performance grows, manufacturers may finally be forced to provide detailed information on the performance of their products. These types of data would greatly assist in software acquisition, enabling potential buyers to evaluate which software is most appropriate for their intended applications. More flexibility and user-defined functions will be on demand as alternative normalization methods are developed. Recent reports that the state-of-the-art fluorescent protein stain, SYPRO Ruby, may have polynomial rather than linear correlations to protein quantity may increase the demand for user-defined quantitation curves in commercial 2-DE software . Furthermore, the limitations of algorithms for background subtraction may be resolved through the introduction of time-resolved fluorescence. The delayed measurement of a fluor's emission excludes autofluorescence from plates, reagents, or cell debris, which generally have very short life spans (low nanosecond range) and thus eliminate a major source of background noise. In contrast, fluorescent protein stains have very long life spans (high nanosecond to microsecond); and delayed, cumulative detection over time may improve sensitivity [32, 33]. The use of both covalently labeled fluors (CyDyes and Alexa-dyes)  and ruthenium chelates [32, 33] have been utilized in time-resolved fluorescence applications in related fields. However, the lack of this feature in modern 2D gel image acquisition equipment is currently prohibiting the use and development of time-resolved fluorescence in 2-DGE.
The growth in data acquisition combined with increased quantification capability will continue to expand. These large bodies of data will enable researchers to further understand complex cellular processes and move the field another step closer to comprehension at the organism level. Proteomics data will enable researchers to model and understand interactions in a cell and to predict the effects of fluctuations upon other intracellular processes. These effects will be seen through, for example, the application of proteomics to web-based models of cellular metabolism such as an electronic cell . Proteomics research will assist in the constant march toward true systems biology that will revolutionize personal medicine and fundamentally shift our understanding of biological processes.
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