An automated in-gel digestion/iTRAQ-labeling workflow for robust quantification of gel-separated proteins

Authors

  • Carla Schmidt,

    1. Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
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    • Current address: Dr. Carla Schmidt, Department of Chemistry, University of Oxford, Oxford, UK

  • Dörte Hesse,

    1. Proteomics Group, Max Planck Institute of Experimental Medicine, Göttingen, Germany
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  • Monika Raabe,

    1. Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
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  • Henning Urlaub,

    1. Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
    2. Bioanalytics, Department of Clinical Chemistry, University Medical Center Göttingen, Göttingen, Germany
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  • Olaf Jahn

    Corresponding author
    1. Proteomics Group, Max Planck Institute of Experimental Medicine, Göttingen, Germany
    2. Cluster of Excellence Nanoscale Microscopy and Molecular Physiology of the Brain, Göttingen, Germany
    • Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
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  • Colour Online: See the article online to view Fig. 1 in colour.

Correspondence: Dr. Olaf Jahn, Proteomics Group, Max Planck Institute of Experimental Medicine, Hermann-Rein-Str. 3, 37075 Göttingen, Germany

E-mail: jahn@em.mpg.de

Fax: +49-551-3899323

Abstract

Simple protein separation by 1DE is a widely used method to reduce sample complexity and to prepare proteins for mass spectrometric identification via in-gel digestion. While several automated solutions are available for in-gel digestion particularly of small cylindric gel plugs derived from 2D gels, the processing of larger 1D gel-derived gel bands with liquid handling work stations is less well established in the field. Here, we introduce a digestion device tailored to this purpose and validate its performance in comparison to manual in-gel digestion. For relative quantification purposes, we extend the in-gel digestion procedure by iTRAQ labeling of the tryptic peptides and show that automation of the entire workflow results in robust quantification of proteins from samples of different complexity and dynamic range. We conclude that automation improves accuracy and reproducibility of our iTRAQ workflow as it minimizes the variability in both, digestion and labeling efficiency, the two major causes of irreproducible results in chemical labeling approaches.

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