Mass spectrometry (MS) has become the analytical method of choice in plant metabolomics. Nevertheless, metabolite annotation remains a major challenge and implies the integration of structural searches in compound libraries with biological knowledge inferred from metabolite regulation studies. Here we propose a novel integrative approach to process and exploit the rich structural information contained in in-source fragmentation patterns of high-resolution LC–MS profiles. In this approach, a correlation matrix is first calculated from individual mass features extracted by xcms processing. Mass feature co-regulation patterns corresponding to metabolite in-source fragmentation are then detected and assembled into compound spectra using the R package CAMERA and processed for in silico fragment-based structure elucidation using MetFrag. We validate the performance of this approach for the rapid annotation of the twelve largest compound spectra, including four O-acyl sugars and six 17-hydroxygeranyllinalool diterpene glycosides in metabolic profiles of insect-attacked Nicotiana attenuata leaves. Additionally, we demonstrate the power of refining MetFrag metabolite annotations based on co-regulation patterns between known and unknown compounds in the correlation matrix and proposed structural annotations on two previously un-characterized O-acyl sugars. In summary, this novel approach facilitates compound annotation from in-source fragmentation patterns using correlation between intensities of mass features of one or several metabolites. Additionally, this analysis provides further support that insect herbivory activates major metabolic reconfigurations in N. attenuata leaves.

Metabolomics has advanced significantly in the past 10 years with important developments related to hardware, software and methodologies and an increasing complexity of applications. In discovery-based investigations, applying untargeted analytical methods, thousands of metabolites can be detected with no or limited prior knowledge of the metabolite composition of samples. In these cases, metabolite identification is required following data acquisition and processing. Currently, the process of metabolite identification in untargeted metabolomic studies is a significant bottleneck in deriving biological knowledge from metabolomic studies. In this review we highlight the different traditional and emerging tools and strategies applied to identify subsets of metabolites detected in untargeted metabolomic studies applying various mass spectrometry platforms. We indicate the workflows which are routinely applied and highlight the current limitations which need to be overcome to provide efficient, accurate and robust identification of metabolites in untargeted metabolomic studies. These workflows apply to the identification of metabolites, for which the structure can be assigned based on entries in databases, and for those which are not yet stored in databases and which require a de novo structure elucidation.

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.