LETTER TO THE EDITOR Recommendations for Reporting Metabolite DataW

The last decade has witnessed dramatic advances in our ability to determine the levels of an ever broadening range of metabolites. That said, while transcriptomic approaches provide almost complete coverage (González-Ballester et al., 2010) and proteomics approaches are now capable of detecting upwards of 45% of the cellular protein complement (Baerenfaller et al., 2008), metabolomics is currently capable of determining only a small fraction of the metabolites found in any one cell (Saito and Matsuda, 2010). It has been estimated that at least 200,000 different metabolites occur across the plant kingdom, and between 7000 and 15,000 within an individual species (D’Auria and Gershenzon, 2005; Fernie, 2007). However, to date, even the most comprehensive methods detect only between 1000 and 2000 molecular features, which are putatively indicative of bona fide chemical entities synthesized in plant tissues (Aharoni et al., 2002; Giavalisco et al., 2009; Iijima et al., 2008). Metabolite measurements are further complicated by the chemical diversity of metabolites and their broad dynamic range in cellular abundance. These features currently prohibit the possibility of extracting and measuring all metabolites using single extraction and analytical procedures. Consequently, many different extraction techniques and combinations of analytical methods are employed in attempts to achieve adequate metabolite coverage (Lisec et al., 2006; De Vos et al., 2007; Kruger et al., 2008; Tohge and Fernie, 2010). This, in turn, renders the establishment of good working practices more difficult than those, for example, associated with quantitative RT-PCR (Udvardi et al., 2008). This is exacerbated by the breadth of aims of metabolite analyses that encompass targeted metabolite analysis, metabolite profiling, metabolomic-scale analyses, and metabolite fingerprinting techniques (Fiehn, 2002). As a result of the diversity in aims and methodologies of metabolite analyses, it is particularly important to define guidelines for obtaining and reporting metabolite data, since there are so many potential sources of error or misinterpretation. Our aim is to highlight potential sources of error and provide recommendations to ensure the robustness of the metabolite data obtained and reported. Our recommendations includemethods for sampling, extraction and storage, metabolite identification, processing of large sample numbers, and recommendations for reporting the methods of metabolite identification and the levels of certainty in metabolite quantification. Good suggestions for standards in reporting chemical ontology and supporting metadata have already been made by Sumner et al. (2007) and Bais et al. (2010).