Review Arabidopsis thaliana as a model organism for plant proteome research Stefanie Wienkoop a, ⁎ , Sacha Baginsky b , Wolfram Weckwerth a a Department of Molecular Systems Biology, University of Vienna, Faculty of Life Sciences, Althanstrasse 14, A-1090, Vienna, Austria b Department of Biochemistry, Martin-Luther-University Halle-Wittenberg, Faculty of Plant Biochemistry, Weinbergweg 22, D-06120, Halle, Germany ARTICLE INFO ABSTRACT Genome sequencing and systems biology revolutionized life sciences. Proteomics emerged as a fundamental technique of this novel research area. This review aims to summarize the contribution of Arabidopsis thaliana as a model organism for plants and the increasing impact of proteome research. Techniques for proteomics based on 2-DE and especially gel-free shotgun LC-MS/MS platforms have improved significantly during the last decades. Proteomics has proven to be complementary to other -omics techniques such as transcriptomics and metabolomics. Arabidopsis thaliana as one of the first model organisms worldwide, served in several of the most comprehensive studies for enhance genome annotation, profiling of organelles, tissues, cells or sub-cellular proteomes, as well as developmental processes and responses to biotic and abiotic stresses using differential relative and absolute quantitative strategies. Consequently, insights into plant proteome dynamics and cell functions are rapidly increasing. A proteomics-toolbox developed for systems biology research on Arabidopsis will be introduced. © 2010 Published by Elsevier B.V. Keywords: Arabidopsis thaliana Model organism Plant proteomics Contents 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2240 2. Proteomic strategies in the post-genomic era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2240 2.1. Organelle proteomics — focus on chloroplasts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2240 2.1.1. Organelle isolation and protein profiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2240 2.1.2. Computational target-predictions versus experimental protein localization . . . . . . . . . . . . . . . . . . . 2241 2.1.3. Benefits of sub-cellular databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2242 2.2. Quantitative proteomics and systems biology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2243 2.2.1. MAPA (Mass Accuracy Precursor Alignment) and ProtMax for relative quantification . . . . . . . . . . . . . . . 2243 2.2.2. Mass western and ProMEX for absolute quantification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2243 2.2.3. Proteomics and sample pattern recognition in systems biology . . . . . . . . . . . . . . . . . . . . . . . . . . 2244 2.3. Genome annotation: shotgun proteomics complements shotgun genomics . . . . . . . . . . . . . . . . . . . . . . . 2244 2.4. Proteomic data integration with metabolomics technologies for the significant improvement of novel biological interpretations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2245 3. Concluding remarks and perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2246 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2246 JOURNAL OF PROTEOMICS 73 (2010) 2239 – 2248 ⁎ Corresponding author. Tel.: +43 1 4277 577 03; fax: +43 1 4277 9 577. E-mail address: Stefanie.wienkoop@univie.ac.at (S. Wienkoop). 1874-3919/$ – see front matter © 2010 Published by Elsevier B.V. doi:10.1016/j.jprot.2010.07.012 available at www.sciencedirect.com www.elsevier.com/locate/jprot