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.

[1]  Kathleen Marchal,et al.  Alternative experimental design with an applied normalization scheme can improve statistical power in 2D-DIGE experiments. , 2010, Journal of proteome research.

[2]  J. L. López,et al.  Two-dimensional electrophoresis in proteome expression analysis. , 2007, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[3]  M. Baker,et al.  High‐abundance protein depletion: Comparison of methods for human plasma biomarker discovery , 2010, Electrophoresis.

[4]  Asa M Wheelock,et al.  Software‐induced variance in two‐dimensional gel electrophoresis image analysis , 2005, Electrophoresis.

[5]  Bo Jørgensen,et al.  Extractin information from two‐dimensional electrophoresis gels by partial least squares regression , 2002 .

[6]  Jim Graham,et al.  Statistical models of shape for the analysis of protein spots in two‐dimensional electrophoresis gel images , 2003, Proteomics.

[7]  R D Appel,et al.  Melanie II – a third‐generation software package for analysis of two‐dimensional electrophoresis images: II. Algorithms , 1997, Electrophoresis.

[8]  Andrea Tura,et al.  Delta2D and Proteomweaver: Performance evaluation of two different approaches for 2‐DE analysis , 2010, Electrophoresis.

[9]  Martina Stessl,et al.  Influence of image‐analysis software on quantitation of two‐dimensional gel electrophoresis data , 2009, Electrophoresis.

[10]  Senthilkumar Damodaran,et al.  Minimizing variability in two-dimensional electrophoresis gel image analysis. , 2007, Omics : a journal of integrative biology.

[11]  François Chevenet,et al.  The pitfalls of proteomics experiments without the correct use of bioinformatics tools , 2006, Proteomics.

[12]  Flemming Jessen,et al.  Extracting information from two-dimensional electrophoresis gels by partial least squares regression. , 2002, Proteomics.

[13]  Benito Cañas,et al.  Trends in sample preparation for classical and second generation proteomics. , 2007, Journal of chromatography. A.

[14]  Kelvin H. Lee,et al.  A comparison of three commercially available isoelectric focusing units for proteome analysis: The Multiphor, the IPGphor and the Protean IEF cell , 2000, Electrophoresis.

[15]  Ulrich Mansmann,et al.  Technical strategies to reduce the amount of “false significant” results in quantitative proteomics , 2008, Proteomics.

[16]  Helmut E Meyer,et al.  Examination of 2‐DE in the Human Proteome Organisation Brain Proteome Project pilot studies with the new RAIN gel matching technique , 2006, Proteomics.

[17]  Brittan N Clark,et al.  The myth of automated, high‐throughput two‐dimensional gel analysis , 2008, Proteomics.

[18]  Martin H Maurer,et al.  Comparison of statistical approaches for the analysis of proteome expression data of differentiating neural stem cells. , 2005, Journal of proteome research.

[19]  Brigitte Picard,et al.  Data analysis methods for detection of differential protein expression in two-dimensional gel electrophoresis. , 2005, Analytical biochemistry.

[20]  Jim Graham,et al.  Using statistical image models for objective evaluation of spot detection in two‐dimensional gels , 2003, Proteomics.

[21]  Morten Beck Rye,et al.  A new method for assigning common spot boundaries for multiple gels in two‐dimensional gel electrophoresis , 2008, Electrophoresis.

[22]  J. Coorssen,et al.  Proteome resolution by two-dimensional gel electrophoresis varies with the commercial source of IPG strips. , 2006, Journal of proteome research.

[23]  Tero Aittokallio,et al.  Comparison of PDQuest and Progenesis software packages in the analysis of two‐dimensional electrophoresis gels , 2003, Proteomics.

[24]  Peak Characterization using Parameter Estimation Methods , 1999 .

[25]  Xianquan Zhan,et al.  Differences in the spatial and quantitative reproducibility between two second‐dimensional gel electrophoresis systems , 2003, Electrophoresis.

[26]  David O. Nelson,et al.  Statistical challenges in the analysis of two-dimensional difference gel electrophoresis experiments using DeCyderTM , 2005, Bioinform..

[27]  Kathryn S. Lilley,et al.  DNA microarray normalization methods can remove bias from differential protein expression analysis of 2D difference gel electrophoresis results , 2004, Bioinform..

[28]  Harald Martens,et al.  Challenges related to analysis of protein spot volumes from two-dimensional gel electrophoresis as revealed by replicate gels. , 2006, Journal of proteome research.

[29]  J R Kettman,et al.  Global analysis of gene expression in cells of the immune system I. Analytical limitations in obtaining sequence information on polypeptides in two‐dimensional gel spots , 2000, Electrophoresis.

[30]  Susanne Becker,et al.  In the eye of the beholder: does the master see the SameSpots as the novice? , 2010, Journal of proteome research.

[31]  J. Bernhardt,et al.  Using standard positions and image fusion to create proteome maps from collections of two‐dimensional gel electrophoresis images , 2003, Proteomics.

[32]  Matthias Berth,et al.  The state of the art in the analysis of two-dimensional gel electrophoresis images , 2007, Applied Microbiology and Biotechnology.

[33]  Andrew J Racher,et al.  Evaluation of individual protein errors in silver-stained two-dimensional gels. , 2003, Biochemical and biophysical research communications.

[34]  Andrew J Racher,et al.  On the statistical analysis of the GS-NS0 cell proteome: imputation, clustering and variability testing. , 2006, Biochimica et biophysica acta.

[35]  Babu Raman,et al.  Quantitative comparison and evaluation of two commercially available, two‐dimensional electrophoresis image analysis software packages, Z3 and Melanie , 2002, Electrophoresis.

[36]  Reinhard Guthke,et al.  Missing values in gel‐based proteomics , 2010, Proteomics.

[37]  Berkenbos-Smit Statistical data processing in clinical proteomics , 2009 .

[38]  Andrea Tura,et al.  The inter‐ and intra‐operator variability in manual spot segmentation and its effect on spot quantitation in two‐dimensional electrophoresis analysis , 2010, Electrophoresis.

[39]  Panagiotis Tsakanikas,et al.  Improving 2‐DE gel image denoising using contourlets , 2009, Proteomics.

[40]  Joachim Klose,et al.  Two‐dimensional electrophoresis of proteins: An updated protocol and implications for a functional analysis of the genome , 1995, Electrophoresis.

[41]  Franco M Montevecchi,et al.  Median‐modified Wiener filter provides efficient denoising, preserving spot edge and morphology in 2‐DE image processing , 2009, Proteomics.

[42]  Reiner Westermeier,et al.  Difference gel electrophoresis based on lys/cys tagging. , 2008, Methods in molecular biology.