SUMMARYThe unbiased and comprehensive analysis of metabolites in any organism presents a major challenge if proper peak annotation and unambiguous assignment of the biological origin of the peaks are required. Here we provide a comprehensive multi-isotope labelling-based strategy using fully labelled 13 C, 15 N and 34 S plant tissues, in combination with a fractionated metabolite extraction protocol. The extraction procedure allows for the simultaneous extraction of polar, semi-polar and hydrophobic metabolites, as well as for the extraction of proteins and starch. After labelling and extraction, the metabolites and lipids were analysed using a highresolution mass spectrometer providing accurate MS and all-ion fragmentation data, providing an unambiguous readout for every detectable isotope-labelled peak. The isotope labelling assisted peak annotation process employed can be applied in either an automated database-dependent or a database-independent analysis of the plant polar metabolome and lipidome. As a proof of concept, the developed methods and technologies were applied and validated using Arabidopsis thaliana leaf and root extracts. Along with a large repository of assigned elemental compositions, which is provided, we show, using selected examples, the accuracy and reliability of the developed workflow.

The PhosPhAt database provides a resource consolidating our current knowledge of mass spectrometry-based identified phosphorylation sites in Arabidopsis and combines it with phosphorylation site prediction specifically trained on experimentally identified Arabidopsis phosphorylation motifs. The database currently contains 1187 unique tryptic peptide sequences encompassing 1053 Arabidopsis proteins. Among the characterized phosphorylation sites, there are over 1000 with unambiguous site assignments, and nearly 500 for which the precise phosphorylation site could not be determined. The database is searchable by protein accession number, physical peptide characteristics, as well as by experimental conditions (tissue sampled, phosphopeptide enrichment method). For each protein, a phosphorylation site overview is presented in tabular form with detailed information on each identified phosphopeptide. We have utilized a set of 802 experimentally validated serine phosphorylation sites to develop a method for prediction of serine phosphorylation (pSer) in Arabidopsis. An analysis of the current annotated Arabidopsis proteome yielded in 27 782 predicted phosphoserine sites distributed across 17 035 proteins. These prediction results are summarized graphically in the database together with the experimental phosphorylation sites in a whole sequence context. The Arabidopsis Protein Phosphorylation Site Database (PhosPhAt) provides a valuable resource to the plant science community and can be accessed through the following link http://phosphat.mpimp-golm.mpg.de

Holistic analysis of lipids is becoming increasingly popular in the life sciences. Recently, several interesting, mass spectrometry-based studies have been conducted, especially in plant biology. However, while great advancements have been made we are still far from detecting all the lipids species in an organism. In this study we developed an ultra performance liquid chromatography-based method using a high resolution, accurate mass, mass spectrometer for the comprehensive profiling of more than 260 polar and non-polar Arabidopsis thaliana leaf lipids. The method is fully compatible to the commonly used lipid extraction protocols and provides a viable alternative to the commonly used direct infusion-based shotgun lipidomics approaches. The whole process is described in detail and compared to alternative lipidomic approaches. Next to the developed method we also introduce an in-house developed database search software (GoBioSpace), which allows one to perform targeted or un-targeted lipidomic and metabolomic analysis on mass spectrometric data of every kind.

Gas chromatography coupled to mass spectrometry (GC-MS) is one of the most widespread routine technologies applied to the large scale screening and discovery of novel metabolic biomarkers. However, currently the majority of mass spectral tags (MSTs) remains unidentified due to the lack of authenticated pure reference substances required for compound identification by GC-MS. Here, we accessed the information on reference compounds stored in the Golm Metabolome Database (GMD) to apply supervised machine learning approaches to the classification and identification of unidentified MSTs without relying on library searches. Non-annotated MSTs with mass spectral and retention index (RI) information together with data of already identified metabolites and reference substances have been archived in the GMD. Structural feature extraction was applied to sub-divide the metabolite space contained in the GMD and to define the prediction target classes. Decision tree (DT)-based prediction of the most frequent substructures based on mass spectral features and RI information is demonstrated to result in highly sensitive and specific detections of sub-structures contained in the compounds. The underlying set of DTs can be inspected by the user and are made available for batch processing via SOAP (Simple Object Access Protocol)-based web services. The GMD mass spectral library with the integrated DTs is freely accessible for non-commercial use at http://gmd.mpimp-golm.mpg.de/. All matching and structure search functionalities are available as SOAP-based web services. A XML + HTTP interface, which follows Representational State Transfer (REST) principles, facilitates read-only access to data base entities.

Metabolomics is rapidly becoming an integral part of many life science studies ranging from disease diagnostics to systems biology. However, a number of problems such as the discrimination of biological from non-biological signals, efficient compound annotation, and reliable quantification are still not satisfactorily solved in untargeted LC-MS-based metabolomics research. Extending our previous work on direct infusion-based metabolomics, we here describe a (13)C isotope labeling strategy in combination with an Ultra Performance Liquid Chromatography Fourier Transform Ion Cyclotron Resonance Mass Spectrometry-based approach (UPLC-FTICR MS) which provides a technological platform offering solutions to a number of the above-mentioned problems. We further demonstrate that the use of a fully labeled metabolome is not only beneficial for high end mass spectrometers, such as that used in this study but also provides a considerable improvement to every other mass spectrometry-based metabolomic platform.