The Whereabouts of 2D Gels in Quantitative Proteomics

Two-dimensional gel electrophoresis has been instrumental in the development of proteomics. Although it is no longer the exclusive scheme used for proteomics, its unique features make it a still highly valuable tool, especially when multiple quantitative comparisons of samples must be made, and even for large samples series. However, quantitative proteomics using two-dimensional gels is critically dependent on the performances of the protein detection methods used after the electrophoretic separations. This chapter therefore examines critically the various detection methods, (radioactivity, dyes, fluorescence, and silver) as well as the data analysis issues that must be taken into account when quantitative comparative analysis of two-dimensional gels is performed.

Technical variability of 2D gel electrophoresis - Application to soybean allergens

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2-DE is robust and suitable for comparing the GM soybean with its non-GM counterpart; its technical variability is lower than the biological variability.

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Main source of variability is the gels so 3–4 gel replicates should be used.

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Other sources of variability are minor, which gives some experimental flexibility, i.e. study can be run over several days, several operators.

Two-dimensional gel electrophoresis (2-DE) technique is used as a performing technique to assess the variability of protein expression in crops, and especially soybean endogenous food allergens, which are a subset of proteins of interest for assessing whether genetically modified (GM) soybean has a different allergenic profile compared to its non-GM counterpart.

On top of the biological variability of the 2-DE, which has already been studied by several laboratories, technical variability has to be evaluated. In this study, several sources of variability (number of gel replicates, protein extracts, study timings and operators) were assessed qualitatively and quantitatively on all detectable polypeptide spots as well as on food allergen spots. Results showed that the major source of variability was the number of gel replicates. Other sources were minor.

This has a direct practical impact on the laboratory work as this supports the utilization of three or four gel replicates to get robust results. Furthermore, this implies that the study can be run over several days, and be performed by several trained operators, without impacting its reproducibility.

Furthermore, 2-DE could detect a 2-fold change between two samples with an acceptable rate of false positives (below 7%). This level of sensitivity is acceptable in the context of safety assessment of GM soybean as the biological variability of proteins in soybean is higher than the technical variability shown in this study.

Overall, the 2-DE technique is suitable for investigating endogenous food allergen variability between several soybean seeds, including GM and non-GM counterpart.

Proteomic analysis of interstitial aortic valve cells acquiring a pro-calcific profile

Cell-driven processes are now considered of relevance for the pathogenesis of aortic stenosis. In particular, during calcific valve degeneration, interstitial valve cells (VIC) resident in the leaflet can acquire an osteogenic/pro-calcific profile and actively contribute to matrix mineralization. The proteomic study described in this chapter is undertaken to investigate modifications in the proteome of bovine aortic VIC acquiring a calcifying phenotype. This approach can be useful to clarify cellular pathways involved in VIC pro-calcific differentiation and identify innovative therapeutic targets.

Quality assessment for clinical proteomics

Proteomics has emerged from the labs of technologists to enter widespread application in clinical contexts. This transition, however, has been hindered by overstated early claims of accuracy, concerns about reproducibility, and the challenges of handling batch effects properly. New efforts have produced sets of performance metrics and measurements of variability that establish sound expectations for experiments in clinical proteomics. As researchers begin incorporating these metrics in a quality by design paradigm, the variability of individual steps in experimental pipelines will be reduced, regularizing overall outcomes. This review discusses the evolution of quality assessment in 2D gel electrophoresis, mass spectrometry-based proteomic profiling, tandem mass spectrometry-based protein inventories, and proteomic quantitation. Taken together, the advances in each of these technologies are establishing databases that will be increasingly useful for decision-making in clinical experimentation.

Polar electrophoresis: shape of two-dimensional maps is as important as size

The performance of two-dimensional electrophoresis in conventional gels in Cartesian coordinates (2-DE) vs. polar coordinates (2-PE) is here evaluated. Although 2-DE is performed in much longer Immobiline gels in the first dimension (17 cm) vs. barely 7-cm in 2-PE, an equivalent resolving power is found. Moreover, due to the possibility of running up to seven Immobiline strips in the radial gel format, the reproducibility of spot position is seen to be higher, this resulting in a 20% higher matching efficiency. As an extra bonus, strings of “isobaric” spots (i.e. polypeptides of identical mass with different pI values) are more resolved in the radial gel format, especially in the 10 to 30 kDa region, where the gel area fans out leaving extra space for spot resolution. In conclusion, this novel gel format in the second dimension of 2D gels is seen as an important improvement of this technique, still one of the most popular in proteome analysis.

The whereabouts of 2D gels in quantitative proteomics

Two-dimensional gel electrophoresis has been instrumental in the development of proteomics. Although it is no longer the exclusive scheme used for proteomics, its unique features make it a still highly valuable tool, especially when multiple quantitative comparisons of samples must be made, and even for large samples series. However, quantitative proteomics using 2D gels is critically dependent on the performances of the protein detection methods used after the electrophoretic separations. This chapter therefore examines critically the various detection methods (radioactivity, dyes, fluorescence, and silver) as well as the data analysis issues that must be taken into account when quantitative comparative analysis of 2D gels is performed.

Operator- and software-related post-experimental variability and source of error in 2-DE analysis

In the field of proteomics, several approaches have been developed for separating proteins and analyzing their differential relative abundance. One of the oldest, yet still widely used, is 2-DE. Despite the continuous advance of new methods, which are less demanding from a technical standpoint, 2-DE is still compelling and has a lot of potential for improvement. The overall variability which affects 2-DE includes biological, experimental, and post-experimental (software-related) variance. It is important to highlight how much of the total variability of this technique is due to post-experimental variability, which, so far, has been largely neglected. In this short review, we have focused on this topic and explained that post-experimental variability and source of error can be further divided into those which are software-dependent and those which are operator-dependent. We discuss these issues in detail, offering suggestions for reducing errors that may affect the quality of results, summarizing the advantages and drawbacks of each approach.

Effects of thawing, refreezing and storage conditions of tissue samples and protein extracts on 2-DE spot intensity

We report that reliable quantitative proteome analyses can be performed with tissue samples stored at -80 degrees C for up to 10 years. However, storing protein extracts at 4 degrees C for 24 h and freezing protein extracts at -80 degrees C and thawing them significantly altered 41.6 and 17.5% of all spot intensities on 2-DE gels, respectively. Fortunately, these storing effects did not impair the reliability of quantifying 2-DE experiments. Nonetheless, the results show that freezing and storage conditions should be carefully controlled in proteomic experiments.

Glioma pathophysiology: insights emerging from proteomics

Proteomics is increasingly employed in both neurological and oncological research to provide insight into the molecular basis of disease but rarely has a coherent, novel pathophysiological insight emerged. Gliomas account for >50% of adult primary intracranial tumors, with malignant gliomas (anaplastic astrocytomas and glioblastoma multiforme) being the most common. In glioma, the application of proteomic technology has identified altered protein expression but without consistency of these alterations or their biological significance being established. A systematic review of multiple independent proteomic analyses of glioma has demonstrated alterations of 99 different proteins. Importantly 10 of the 99 proteins found differentially expressed in glioma [PHB, Hsp20, serum albumin, epidermal growth factor receptor (EGFR), EA-15, RhoGDI, APOA1, GFAP, HSP70, PDIA3] were identified in multiple publications. An assessment of protein-protein interactions between these proteins compiled using novel web-based technology, revealed a robust and cohesive network for glioblastoma. The protein network discovered (containing TP53 and RB1 at its core) compliments recent findings in genomic studies of malignant glioma. The novel perspective provided by network analysis indicates that the potential of this technology to explore crucial aspects of glioma pathophysiology can now be realized but only if the conceptual and technical limitations highlighted in this review are addressed.

The utility of functional interaction and cluster analysis in CNS proteomics

Proteomic studies offer enormous potential for gaining insight into cellular dynamics and disease processes. An immediate challenge for enhancing the utility of proteomics in translational research lies in methods of handling and interpreting the large datasets generated. Publications rarely extend beyond lists of proteins, putatively altered derived from basic statistics. Here we describe two additional distinct approaches (with particular strengths and limitations) that will enhance the analysis of proteomic datasets. Arithmetic and functional cluster analyses have been performed on proteins found differentially regulated in human glioma. These two approaches highlight (i) subgroups of proteins that may be co-regulated and play a role in glioma pathophysiology, and (ii) functional protein interactions that may improve comprehension of the biological mechanisms involved. A coherent proteomic strategy which involves both arithmetic and functional clustering, (together with careful consideration of conceptual limitations), is imperative for quantitative proteomics to deliver and advance the biological understanding of disease of the CNS. A strategy which combines arithmetic analysis and bioinformatics of protein-protein interactions is both generally applicable and will facilitate the interpretation of proteomic data.