These authors contributed equally to this work.
Automating proteome analysis: improvements in throughput, quality and accuracy of protein identification by peptide mass fingerprinting†
Article first published online: 27 OCT 2004
Copyright © 2004 John Wiley & Sons, Ltd.
Rapid Communications in Mass Spectrometry
Volume 18, Issue 23, pages 2785–2794, 15 December 2004
How to Cite
Canelle, L., Pionneau, C., Marie, A., Bousquet, J., Bigeard, J., Lutomski, D., Kadri, T., Caron, M. and Joubert-Caron, R. (2004), Automating proteome analysis: improvements in throughput, quality and accuracy of protein identification by peptide mass fingerprinting. Rapid Commun. Mass Spectrom., 18: 2785–2794. doi: 10.1002/rcm.1693
- Issue published online: 27 OCT 2004
- Article first published online: 27 OCT 2004
- Manuscript Revised: 28 SEP 2004
- Manuscript Accepted: 28 SEP 2004
- Manuscript Received: 11 JUN 2004
- Ministère de l'Education Nationale de la Recherche et de la Technologie
- Ministère des Finances et de l'Industrie
- Association de Recherche contre le Cancer. Grant Number: 4526
The use of robots has major effects on maximizing the proteomic workflow required in an increasing number of high-throughput projects and on increasing the quality of the data. In peptide mass finger printing (PMF), automation of steps downstream of two-dimensional gel electrophoresis is essential. To achieve this goal, the workflow must be fluid. We have developed tools using macros written in Microsoft Excel and Word to complete the automation of our platform. Additionally, because sample preparation is crucial for identification of proteins by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry, we optimized a sandwich method usable by any robot for spotting digests on a MALDI target. This procedure enables further efficient automated washing steps directly on the MALDI target. The success rate of PMF identification was evaluated for the automated sandwich method, and for the dried-droplet method implemented on the robot as recommended by the manufacturer. Of the two methods, the sandwich method achieved the highest identification success rate and sequence coverage of proteins. Copyright © 2004 John Wiley & Sons, Ltd.