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ARS Home » Research » Publications at this Location » Publication #210171

Title: Shotgun Proteomic Analysis of Arabidopsis thaliana Leaves

item Lee, Joohyun
item Garrett, Wesley
item Cooper, Bret

Submitted to: Journal of Separation Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/13/2007
Publication Date: 7/20/2007
Citation: Lee, J., Garrett, W.M., Cooper, B. 2007. Shotgun Proteomic Analysis of Arabidopsis thaliana Leaves. Journal of Separation Science. 30:2225-2230.

Interpretive Summary: It is important to separate a mixture of peptides into individual peptides to be able to identify them by mass spectrometry. Efficient separation leads to the analysis of thousands of proteins within a sample, cell or tissue and contributes to the eventual understanding of a cellular proteome. By using two independent separation methods coupled with mass spectrometry, and various protein extraction methods utilizing different detergents for the extraction of proteins, the deepest survey of a plant leaf proteome was performed. Thus, it is shown that by combining different sample processing and separation strategies, researchers can improve the quality of their proteomic reference maps. These data are most likely to influence scientists at universities, government agencies and companies who are interested in knowing which proteins are in a plant leaf.

Technical Abstract: Two shotgun tandem mass spectrometry proteomics approaches, Multidimensional Protein Identification Technology (MudPIT) and 1D-Gel-LC-MS/MS, were used to identify Arabidopsis thaliana leaf proteins. These methods utilize different protein/peptide separation strategies. Detergents not compatible with MudPIT were used with 1D-Gel-LC-MS/MS to help enrich for the detection of membrane-spanning and hydrophobic proteins. By combining the data from all MudPIT and 1D-Gel-LC-MS/MS experiments, nearly 3,000 non-redundant proteins spanning a broad range of molecular weights and pI values were detected. With the exception of unknown proteins, the distribution of Gene Ontology (GO) classifications for the detected proteins was similar to that encoded by the genome, which shows that these extraction and separation procedures are useful for a broad proteomic survey of plant cells. Unknown proteins will likely have to be targeted by using additional methods, some of which should be compatible with separation strategies taken here.