title: Aspects of experimental design for plant metabolomics experiments and guidelines for growth of plant material.
abstract: Experiments involve the deliberate variation of one or more factors in order to provoke responses, the identification of which then provides the first step towards functional knowledge. Because environmental, biological, and/or technical noise is unavoidable, biological experiments usually need to be designed. Thus, once the major sources of experimental noise have been identified, individual samples can be grouped, randomised, and/or pooled. Like other 'omics approaches, metabolomics is characterised by the numbers of analytes largely exceeding sample number. While this unprecedented singularity in biology dramatically increases false discovery, experimental error can nevertheless be decreased in plant metabolomics experiments. For this, each step from plant cultivation to data acquisition needs to be evaluated in order to identify the major sources of error and then an appropriate design can be produced, as with any other experimental approach. The choice of technology, the time at which tissues are harvested, and the way metabolism is quenched also need to be taken into consideration, as they decide which metabolites can be studied. A further recommendation is to document data and metadata in a machine readable way. The latter should also describe every aspect of the experiment. This should provide valuable hints for future experimental design and ultimately give metabolomic data a second life. To facilitate the identification of critical steps, a list of items to be considered before embarking on time-consuming and costly metabolomic experiments is proposed.
authors.affiliation: INRA, Centre INRA de Bordeaux, Villenave d'Ornon, France. ygibon@bordeaux.inra.fr
authors.forename: Yves
authors.initials: Y
authors.lastname: Gibon
authors.forename: Dominique
authors.initials: D
authors.lastname: Rolin
mesh_terms: D055432:Metabolomics / Q000379:methods*; D063245:Plant Development; D010944:Plants / Q000378:metabolism*; D012106:Research; D012107:Research Design