A simple mathematical model to aid quantification of electrophoresis gels by image analysis

The Human Proteome Organisation (HUPO) Brain Proteome Project (BPP) pilot studies have generated over 200 2-D gels from eight participating laboratories. This data includes 67 single-channel and 60 DIGE gels comparing 30 whole frozen C57/BL6 female mouse brains, ten each at embryonic day 16, postnatal day 7 (juvenile) and postnatal day 54-56 (adult); and ten single-channel and three DIGE gels comparing human epilepsy surgery of the temporal front lobe with a corresponding post-mortem specimen. The samples were generated centrally and distributed to the participating laboratories, but otherwise no restrictions were placed on sample preparation, running and staining protocols, nor on the 2-D gel analysis packages used. Spots were characterised by MS and the annotated gel images published on a ProteinScape web server. In order to examine the resultant differential expression and protein identifications, we have reprocessed a large subset of the gels using the newly developed RAIN (Robust Automated Image Normalisation) 2-D gel matching algorithm. Traditional approaches use symbolic representation of spots at the very early stages of the analysis, which introduces persistent errors due to inaccuracies in spot modelling and matching. With RAIN, image intensity distributions, rather than selected features, are used, where smooth geometric deformation and expression bias are modelled using multi-resolution image registration and bias-field correction. The method includes a new approach of volume-invariant warping which ensures the volume of protein expression under transformation is preserved. An image-based statistical expression analysis phase is then proposed, where small insignificant expression changes over one gel pair can be revealed when reinforced by the same consistent changes in others. Results of the proposed method as applied to the HUPO BPP data show significant intra-laboratory improvements in matching accuracy over a previous state-of-the-art technique, Multi-resolution Image Registration (MIR), and the commercial Progenesis PG240 package.

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“…(v) Contrast varies from gel to gel due to stain exposure [5], sample loading errors and protein losses at different stages of gel running.…”

Section: -D Gel Matchingmentioning

confidence: 99%

Examination of 2‐DE in the Human Proteome Organisation Brain Proteome Project pilot studies with the new RAIN gel matching technique

et al. 2006

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“…Silver staining density and protein concentration is also dependent on the type of protein -the amino acid concentration and post-translational modifications. Contrast varies from gel to gel due to stain exposure [17], sample loading errors and protein losses at different stages of gel running, which is a challenge in quantifying differential expression. In Fig.…”

mentioning

confidence: 99%

The role of bioinformatics in two‐dimensional gel electrophoresis

2003

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Over the last two decades, two-dimensional electrophoresis (2-DE) gel has established itself as the de facto approach to separating proteins from cell and tissue samples. Due to the sheer volume of data and its experimental geometric and expression uncertainties, quantitative analysis of these data with image processing and modelling has become an actively pursued research topic. The results of these analyses include accurate protein quantification, isoelectric point and relative molecular mass estimation, and the detection of differential expression between samples run on different gels. Systematic errors such as current leakage and regional expression inhomogeneities are corrected for, followed by each protein spot in the gel being segmented and modelled for quantification. To assess differential expression of protein spots in different samples run on a series of two-dimensional gels, a number of image registration techniques for correcting geometric distortion have been proposed. This paper provides a comprehensive review of the computation techniques used in the analysis of 2-DE gels, together with a discussion of current and future trends in large scale analysis. We examine the pitfalls of existing techniques and highlight some of the key areas that need to be developed in the coming years, especially those related to statistical approaches based on multiple gel runs and image mining techniques through the use of parallel processing based on cluster computing and the grid technology.

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2-DE Gel Analysis: The Spot Detection

Martinotti

Ranzato

2016

Methods in Molecular Biology

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The abundance of different proteins on a 2-DE gel is reflected by the shape, size, and intensity of the corresponding spots. Protein quantitation requires the conversion of an analog gel image into digital data, resulting into a catalog of individual spots listed as x, y positions, shape parameters, and quantitative values. So, it is possible to carry out objective comparisons of equivalent spots on different gels, determining whether a particular protein is more or less abundant in one sample compared with another. Unfortunately, spots on protein gels are not uniform in shape, size, or density, and detection, quantitation, and comparison can be challenging without intervention. Once a processed image is available, a number of different algorithms can be applied to detect and quantitate individual spots.

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A simple mathematical model to aid quantification of electrophoresis gels by image analysis

Cited by 4 publications

References 12 publications

Examination of 2‐DE in the Human Proteome Organisation Brain Proteome Project pilot studies with the new RAIN gel matching technique

Examination of 2‐DE in the Human Proteome Organisation Brain Proteome Project pilot studies with the new RAIN gel matching technique

The role of bioinformatics in two‐dimensional gel electrophoresis

2-DE Gel Analysis: The Spot Detection

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