Liquid chromatography–mass spectrometry (LC–MS) is a commonly used analytical platform for non-targeted metabolite profiling experiments. Although data acquisition, processing and statistical analyses are almost routine in such experiments, further annotation and subsequent identification of chemical compounds are not. For identification, tandem mass spectra provide valuable information towards the structure of chemical compounds. These are typically acquired online, in data-dependent mode, or offline, using handcrafted acquisition methods and manually extracted from raw data. Here, we present several methods to fast-track and improve both the acquisition and processing of LC–MS/MS data. Our nearly online (nearline) data-dependent tandem MS strategy creates a minimal set of LC–MS/MS acquisition methods for relevant features revealed by a preceding non-targeted profiling experiment. Using different filtering criteria, such as intensity or ion type, the acquisition of irrelevant spectra is minimized. Afterwards, LC–MS/MS raw data are processed with feature detection and grouping algorithms. The extracted tandem mass spectra can be used for both library search and de-novo identification methods. The algorithms are implemented in the R package MetShot and support the export to Bruker, Agilent or Waters QTOF instruments and the vendor-independent TraML standard. We evaluate the performance of our workflow on a Bruker micrOTOF-Q by comparison of automatically acquired and extracted tandem mass spectra obtained from a mixture of natural product standards against manually extracted reference spectra. Using Arabidopsis thaliana wild-type and biosynthetic gene knockout plants, we characterize the metabolic products of a biosynthetic pathway and demonstrate the integration of our approach into a typical non-targeted metabolite profiling workflow.

As a result of the phenylpropanoid pathway, many Brassicaceae produce considerable amounts of soluble hydroxycinnamate conjugates, mainly sinapate esters. From oilseed rape (Brassica napus), we cloned two orthologs of the Arabidopsis (Arabidopsis thaliana) gene REDUCED EPIDERMAL FLUORESCENCE1 (REF1) encoding a coniferaldehyde/sinapaldehyde dehydrogenase. The enzyme is involved in the formation of ferulate and sinapate from the corresponding aldehydes, thereby linking lignin and hydroxycinnamate biosynthesis as a potential branch-point enzyme. We used RNA interference to silence REF1 genes in seeds of oilseed rape. Nontargeted metabolite profiling showed that BnREF1-suppressing seeds produced a novel chemotype characterized by reduced levels of sinapate esters, the appearance of conjugated monolignols, dilignols, and trilignols, altered accumulation patterns of kaempferol glycosides, and changes in minor conjugates of caffeate, ferulate, and 5-hydroxyferulate. BnREF1 suppression affected the level of minor sinapate conjugates more severely than that of the major component sinapine. Mapping of the changed metabolites onto the phenylpropanoid metabolic network revealed partial redirection of metabolic sequences as a major impact of BnREF1 suppression.

The seed residues left after pressing of rapeseed oil are rich in proteins and could be used for human nutrition and animal feeding. These press cakes contain, however, antinutritives, with fiber being the most abundant one. The analysis of fiber phenolic component (localized to seed coat cell walls) is, therefore, important in breeding and food quality control. However, correct structure and content assignments of cell wall-bound phenolics are challenging due to their low stability during sample preparation. Here, a novel LC-MS/MS-based method for the simultaneous identification and quantitation of 66 cell wall-bound phenolics and their derivatives is described. The method was internally standardized, corrected for degradation effects during sample preparation, and cross-validated with a well-established UV-based procedure. This approach was successfully applied to the analysis of cell wall phenolic patterns in different B. napus cultivars and proved to be suitable for marker compound search as well as assay development.

Liquid chromatography–mass spectrometry (LC–MS) is a commonly used analytical platform for non-targeted metabolite profiling experiments. Although data acquisition, processing and statistical analyses are almost routine in such experiments, further annotation and subsequent identification of chemical compounds are not. For identification, tandem mass spectra provide valuable information towards the structure of chemical compounds. These are typically acquired online, in data-dependent mode, or offline, using handcrafted acquisition methods and manually extracted from raw data. Here, we present several methods to fast-track and improve both the acquisition and processing of LC–MS/MS data. Our nearly online (nearline) data-dependent tandem MS strategy creates a minimal set of LC–MS/MS acquisition methods for relevant features revealed by a preceding non-targeted profiling experiment. Using different filtering criteria, such as intensity or ion type, the acquisition of irrelevant spectra is minimized. Afterwards, LC–MS/MS raw data are processed with feature detection and grouping algorithms. The extracted tandem mass spectra can be used for both library search and de-novo identification methods. The algorithms are implemented in the R package MetShot and support the export to Bruker, Agilent or Waters QTOF instruments and the vendor-independent TraML standard. We evaluate the performance of our workflow on a Bruker micrOTOF-Q by comparison of automatically acquired and extracted tandem mass spectra obtained from a mixture of natural product standards against manually extracted reference spectra. Using Arabidopsis thaliana wild-type and biosynthetic gene knockout plants, we characterize the metabolic products of a biosynthetic pathway and demonstrate the integration of our approach into a typical non-targeted metabolite profiling workflow.

As a result of the phenylpropanoid pathway, many Brassicaceae produce considerable amounts of soluble hydroxycinnamate conjugates, mainly sinapate esters. From oilseed rape (Brassica napus), we cloned two orthologs of the Arabidopsis (Arabidopsis thaliana) gene REDUCED EPIDERMAL FLUORESCENCE1 (REF1) encoding a coniferaldehyde/sinapaldehyde dehydrogenase. The enzyme is involved in the formation of ferulate and sinapate from the corresponding aldehydes, thereby linking lignin and hydroxycinnamate biosynthesis as a potential branch-point enzyme. We used RNA interference to silence REF1 genes in seeds of oilseed rape. Nontargeted metabolite profiling showed that BnREF1-suppressing seeds produced a novel chemotype characterized by reduced levels of sinapate esters, the appearance of conjugated monolignols, dilignols, and trilignols, altered accumulation patterns of kaempferol glycosides, and changes in minor conjugates of caffeate, ferulate, and 5-hydroxyferulate. BnREF1 suppression affected the level of minor sinapate conjugates more severely than that of the major component sinapine. Mapping of the changed metabolites onto the phenylpropanoid metabolic network revealed partial redirection of metabolic sequences as a major impact of BnREF1 suppression.

The seed residues left after pressing of rapeseed oil are rich in proteins and could be used for human nutrition and animal feeding. These press cakes contain, however, antinutritives, with fiber being the most abundant one. The analysis of fiber phenolic component (localized to seed coat cell walls) is, therefore, important in breeding and food quality control. However, correct structure and content assignments of cell wall-bound phenolics are challenging due to their low stability during sample preparation. Here, a novel LC-MS/MS-based method for the simultaneous identification and quantitation of 66 cell wall-bound phenolics and their derivatives is described. The method was internally standardized, corrected for degradation effects during sample preparation, and cross-validated with a well-established UV-based procedure. This approach was successfully applied to the analysis of cell wall phenolic patterns in different B. napus cultivars and proved to be suitable for marker compound search as well as assay development.