Maximising sensitivity for detecting changes in protein expression: Experimental design using minimal CyDyes

DIGE is a powerful tool for measuring changes in protein expression between samples. Here we assess the assumptions of normality and heterogeneity of variance that underlie the univariate statistical tests routinely used to detect proteins with expression changes. Furthermore, the technical variance experienced in a multigel experiment is assessed here and found to be reproducible within‐ and across‐sample types. Utilising the technical variance measured, a power study is completed for several ‘typical’ fold changes in expression commonly used as thresholds by researchers. Based on this study using DeCyder, guidance is given on the number of gel replicates that are needed for the experiment to have sufficient sensitivity to detect expression changes. A two‐dye system based on utilising just Cy3 and Cy5 was found to be more reproducible than the three‐dye system. A power and cost‐benefit analysis performed here suggests that the traditional three‐dye system would use fewer resources in studies where multiple samples are compared. Technical variance was shown to encompass both experimental and analytical noise and thus is dependent on the analytical software utilised. Data is provided as a resource to the community to assess alternative software and upgrades.

[1]  J. Burstin,et al.  Analysis of scaling methods to minimize experimental variations in two‐dimensional electrophoresis quantitative data: Application to the comparison of maize inbred lines , 1993, Electrophoresis.

[2]  Stephen O. David,et al.  A novel experimental design for comparative two‐dimensional gel analysis: Two‐dimensional difference gel electrophoresis incorporating a pooled internal standard , 2003, Proteomics.

[3]  Anders Blomberg,et al.  Interlaboratory reproducibility of yeast protein patterns analyzed by immobilized pH gradient two‐dimensional gel electrophoresis , 1995, Electrophoresis.

[4]  M. Rudemo,et al.  Statistical exploration of variation in quantitative two‐dimensional gel electrophoresis data , 2004, Proteomics.

[5]  Robert Tonge,et al.  Evaluation of saturation labelling two‐dimensional difference gel electrophoresis fluorescent dyes , 2003, Proteomics.

[6]  Matthew Davison,et al.  Validation and development of fluorescence two‐dimensional differential gel electrophoresis proteomics technology , 2001, Proteomics.

[7]  Mark P Molloy,et al.  Overcoming technical variation and biological variation in quantitative proteomics , 2003, Proteomics.

[8]  Gang Wang,et al.  Proteome analysis of Saccharomyces cerevisiae under metal stress by two‐dimensional differential gel electrophoresis , 2003, Electrophoresis.

[9]  N. Karp,et al.  Application of partial least squares discriminant analysis to two‐dimensional difference gel studies in expression proteomics , 2005, Proteomics.

[10]  Hirokazu Yamaguchi,et al.  Differential Protein Analysis of Spasomolytic Polypeptide Expressing Metaplasia Using Laser Capture Microdissection and Two-dimensional Difference Gel Electrophoresis , 2003, Applied immunohistochemistry & molecular morphology : AIMM.

[11]  L. Arckens,et al.  Reversed‐phase high‐performance liquid chromatography prefractionation prior to two‐dimensional difference gel electrophoresis and mass spectrometry identifies new differentially expressed proteins between striate cortex of kitten and adult cat , 2003, Electrophoresis.

[12]  Martin Vingron,et al.  Variance stabilization applied to microarray data calibration and to the quantification of differential expression , 2002, ISMB.

[13]  François Chevalier,et al.  Proteomic capacity of recent fluorescent dyes for protein staining. , 2004, Phytochemistry.

[14]  Alexander Lazarev,et al.  Comparison of fluorescent stains: Relative photostability and differential staining of proteins in two‐dimensional gels , 2004, Electrophoresis.

[15]  Luc Negroni,et al.  Assessing factors for reliable quantitative proteomics based on two‐dimensional gel electrophoresis , 2004, Proteomics.

[16]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[17]  A. Blomberg,et al.  Two‐dimensional electrophoretic separation of yeast proteins using a non‐linear wide range (pH 3–10) immobilized pH gradient in the first dimension; reproducibility and evidence for isoelectric focusing of alkaline (pI >7) proteins , 1997, Yeast.

[18]  R. Wait,et al.  Fluorescence two‐dimensional difference gel electrophoresis and mass spectrometry based proteomic analysis of Escherichia coli , 2002, Proteomics.

[19]  T P Speed,et al.  Experimental design and low-level analysis of microarray data. , 2004, International review of neurobiology.

[20]  M. Ünlü,et al.  Difference gel electrophoresis. A single gel method for detecting changes in protein extracts , 1997, Electrophoresis.

[21]  Russell V. Lenth,et al.  Some Practical Guidelines for Effective Sample Size Determination , 2001 .

[22]  D. Baunsgaard,et al.  Mechanisms of hydrazine toxicity in rat liver investigated by proteomics and multivariate data analysis , 2004, Proteomics.