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Molecular & Cellular Proteomics 6:1274-1286, 2007.
© 2007 by The American Society for Biochemistry and Molecular Biology, Inc.

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Technology

Improved Method for Differential Expression Proteomics Using Trypsin-catalyzed ¹⁸O Labeling with a Correction for Labeling Efficiency ^*,S

Antonio Ramos-Fernández, Daniel López-Ferrer and Jesús Vázquez^¶

From the Protein Chemistry and Proteomics Laboratory, Centro de Biología Molecular "Severo Ochoa"-Consejo Superior de Investigaciones Científicas, 28049 Cantoblanco, Madrid, Spain

Quantitative strategies relying on stable isotope labeling and isotope dilution mass spectrometry have proven to be a very robust alternative to the well established gel-based techniques for the study of the dynamic proteome. Postdigestion ¹⁸O labeling is becoming very popular mainly due to the simplicity of the enzyme-catalyzed exchange reaction, the peptide handling and storage procedures, and the flexibility and versatility introduced by decoupling protein digestion from peptide labeling. Despite recent progresses, peptide quantification by postdigestion ¹⁸O labeling still involves several computational problems. In this work we analyzed the behavior of large collections of peptides when they were subjected to postdigestion labeling and concluded that this process can be explained by a universal kinetic model. On the basis of this observation, we developed an advanced quantification algorithm for this kind of labeling. Our method fits the entire isotopic envelope to parameters related with the kinetic exchange model, allowing at the same time an accurate calculation of the relative proportion of peptides in the original samples and of the specific labeling efficiency of each one of the peptides. We demonstrated that the new method eliminates artifacts produced by incomplete oxygen exchange in subsets of peptides that have a relatively low labeling efficiency and that may be considered indicative of false protein ratio deviations. Finally using a rigorous statistical analysis based on the calculation of error rates associated with false expression changes, we showed the validity of the method in the practice by detecting significant expression changes, produced by the activation of a model preparation of T cells, with only 5 µg of protein in three proteins among a pool of more than 100. By allowing a full control over potential artifacts, our method may improve automation of the procedures for relative protein quantification using this labeling strategy.

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