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

Synthetic Data Generation for the Development of 2D Gel Electrophoresis Protein Spot Models

Two-dimensional electrophoresis gels (2DE, 2DEG) are the result of the procedure of separating, based on two molecular properties, a protein mixture on gel. Separated similar proteins concentrate in groups, and these groups appear as dark spots in the captured gel image. Gel images are analyzed to detect distinct spots and determine their peak intensity, background, integrated intensity, and other attributes of interest. One of the approaches to parameterizing the protein spots is spot modeling. Spot parameters of interest are obtained after the spot is approximated by a mathematical model. The development of the modeling algorithm requires a rich, diverse, representative dataset. The primary goal of this research is to develop a method for generating a synthetic protein spot dataset that can be used to develop 2DEG image analysis algorithms. The secondary objective is to evaluate the usefulness of the created dataset by developing a neural-network-based protein spot reconstruction algorithm that provides parameterization and denoising functionalities. In this research, a spot modeling algorithm based on autoencoders is developed using only the created synthetic dataset. The algorithm is evaluated on real and synthetic data. Evaluation results show that the created synthetic dataset is effective for the development of protein spot models. The developed algorithm outperformed all baseline algorithms in all experimental cases.

Comparative Evaluation of Software Features and Performances

Analysis of two-dimensional gel images is a crucial step for the determination of changes in the protein expression, but at present, it still represents one of the bottlenecks in 2-DE studies. Over the years, different commercial and academic software packages have been developed for the analysis of 2-DE images. Each of these shows different advantageous characteristics in terms of quality of analysis. In this chapter, the characteristics of the different commercial software packages are compared in order to evaluate their main features and performances.

A Novel Gaussian Extrapolation Approach for 2-D Gel Electrophoresis Saturated Protein Spots

Analysis of images obtained from two-dimensional gel electrophoresis (2-D GE) is a topic of utmost importance in bioinformatics research, since commercial and academic software currently available have proven to be neither completely effective nor fully automatic, often requiring manual revision and refinement of computer generated matches. In this chapter, we present an effective technique for the detection and the reconstruction of over-saturated protein spots. Firstly, the algorithm reveals overexposed areas, where spots may be truncated, and plateau regions caused by smeared and overlapping spots. Next, it reconstructs the correct distribution of pixel values in these overexposed areas and plateau regions, using a two-dimensional least-squares fitting based on a generalized Gaussian distribution. Pixel correction in saturated and smeared spots allows more accurate proteins quantification, providing more reliable image analysis results. The method is validated for processing highly exposed 2-D GE images, comparing reconstructed spots with the corresponding non-saturated image. The results demonstrate that the algorithm enables correct spot quantification.

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.

Identifying differentially expressed proteins in two-dimensional electrophoresis experiments: inputs from transcriptomics statistical tools

BACKGROUND

Two-dimensional electrophoresis is a crucial method in proteomics that allows the characterization of proteins' function and expression. This usually implies the identification of proteins that are differentially expressed between two contrasting conditions, for example, healthy versus diseased in human proteomics biomarker discovery and stressful conditions versus control in animal experimentation. The statistical procedures that lead to such identifications are critical steps in the 2-DE analysis workflow. They include a normalization step and a test and probability correction for multiple testing. Statistical issues caused by the high dimensionality of the data and large-scale multiple testing have been a more active topic in transcriptomics than proteomics, especially in microarray analysis. We thus propose to adapt innovative statistical tools developed for microarray analysis and incorporate them in the 2-DE analysis pipeline.

RESULTS

In this article, we evaluate the performance of different normalization procedures, different statistical tests and false discovery rate calculation methods with both real and simulated datasets. We demonstrate that the use of statistical procedures adapted from microarrays lead to notable increase in power as well as a minimization of false-positive discovery rate. More specifically, we obtained the best results in terms of reliability and sensibility when using the 'moderate t-test' from Smyth in association with classic false discovery rate from Benjamini and Hochberg.

AVAILABILITY

The methods discussed are freely available in the 'prot2D' open source R-package from Bioconductor (http://www.bioconductor.org//) under the terms of the GNU General Public License (version 2 or later).

CONTACT

sebastien.artigaud@univ-brest.fr or sebastien.artigaud@gmx.com.

Green fluorescent protein expression triggers proteome changes in breast cancer cells

Green fluorescent protein (GFP) is the most commonly used reporter of expression in cell biology despite evidence that it affects the cell physiology. The molecular mechanism of GFP-associated modifications has been largely unexplored. In this paper we investigated the proteome modifications following stable expression of GFP in breast cancer cells (MDA-MB-231). A combination of three different proteome analysis methods (2-DE, iTRAQ, label-free) was used to maximise proteome coverage. We found that GFP expression induces changes in expression of proteins that are associated with protein folding, cytoskeletal organisation and cellular immune response. In view of these findings, the use of GFP as a cell reporter should be carefully monitored.

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.

A novel Gaussian extrapolation approach for 2D gel electrophoresis saturated protein spots

Analysis of images obtained from two-dimensional gel electrophoresis (2D-GE) is a topic of utmost importance in bioinformatics research, since commercial and academic software available currently has proven to be neither completely effective nor fully automatic, often requiring manual revision and refinement of computer generated matches. In this work, we present an effective technique for the detection and the reconstruction of over-saturated protein spots. Firstly, the algorithm reveals overexposed areas, where spots may be truncated, and plateau regions caused by smeared and overlapping spots. Next, it reconstructs the correct distribution of pixel values in these overexposed areas and plateau regions, using a two-dimensional least-squares fitting based on a generalized Gaussian distribution. Pixel correction in saturated and smeared spots allows more accurate quantification, providing more reliable image analysis results. The method is validated for processing highly exposed 2D-GE images, comparing reconstructed spots with the corresponding non-saturated image, demonstrating that the algorithm enables correct spot quantification.

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.