Pinnacle: a fast, automatic and accurate method for detecting and quantifying protein spots in 2-dimensional gel electrophoresis data

MOTIVATION One of the key limitations for proteomic studies using 2-dimensional gel electrophoresis (2DE) is the lack of rapid, robust and reproducible methods for detecting, matching and quantifying protein spots. The most commonly used approaches involve first detecting spots and drawing spot boundaries on individual gels, then matching spots across gels and finally quantifying each spot by calculating normalized spot volumes. This approach is time consuming, error-prone and frequently requires extensive manual editing, which can unintentionally introduce bias into the results. RESULTS We introduce a new method for spot detection and quantification called Pinnacle that is automatic, quick, sensitive and specific and yields spot quantifications that are reliable and precise. This method incorporates a spot definition that is based on simple, straightforward criteria rather than complex arbitrary definitions, and results in no missing data. Using dilution series for validation, we demonstrate Pinnacle outperformed two well-established 2DE analysis packages, proving to be more accurate and yielding smaller coefficiant of variations (CVs). More accurate quantifications may lead to increased power for detecting differentially expressed spots, an idea supported by the results of our group comparison experiment. Our fast, automatic analysis method makes it feasible to conduct very large 2DE-based proteomic studies that are adequately powered to find important protein expression differences. AVAILABILITY Matlab code to implement Pinnacle is available from the authors upon request for non-commercial use.

[1]  J. Nishihara,et al.  Quantitative evaluation of proteins in one‐ and two‐dimensional polyacrylamide gels using a fluorescent stain , 2002, Electrophoresis.

[2]  J. Nishihara,et al.  Similarity of the Escherichia coli proteome upon completion of different biopharmaceutical fermentation processes , 2001, Proteomics.

[3]  A. C. Cuello,et al.  Brain Microdissection Techniques , 1983 .

[4]  J. Klose.  Protein mapping by combined isoelectric focusing and electrophoresis of mouse tissues , 1975, Humangenetik.

[5]  S. Gygi,et al.  Correlation between Protein and mRNA Abundance in Yeast , 1999, Molecular and Cellular Biology.

[6]  Jeffrey S. Morris,et al.  Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum , 2005, Bioinform..

[7]  Romesh Stanislaus,et al.  Normalization and analysis of residual variation in two‐dimensional gel electrophoresis for quantitative differential proteomics , 2005, Proteomics.

[8]  John D. Storey.  The positive false discovery rate: a Bayesian interpretation and the q-value , 2003 .

[9]  P. O’Farrell.  High resolution two-dimensional electrophoresis of proteins. , 1975, The Journal of biological chemistry.

[10]  P Dupree,et al.  Quantitative and reproducible two‐dimensional gel analysis using Phoretix 2D Full , 2001, Electrophoresis.

[11]  Jeffrey S. Morris,et al.  Improved peak detection and quantification of mass spectrometry data acquired from surface‐enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform , 2005, Proteomics.

[12]  J. Seilhamer,et al.  A comparison of selected mRNA and protein abundances in human liver , 1997, Electrophoresis.

[13]  References , 1971 .

[14]  D. L. Donoho,et al.  Ideal spacial adaptation via wavelet shrinkage , 1994 .

[15]  Paul Cutler,et al.  A novel approach to spot detection for two‐dimensional gel electrophoresis images using pixel value collection , 2003, Proteomics.

[16]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[17]  Howard B Gutstein,et al.  Intermittent Lumbar Puncture in Rats: A Novel Method for the Experimental Study of Opioid Tolerance , 2006, Anesthesia and analgesia.