Comparing Targeted vs. Untargeted MS2 Data-Dependent Acquisition for Peak Annotation in LC–MS Metabolomics

One of the most widely used strategies for metabolite annotation in untargeted LCMS is based on the analysis of MSn spectra acquired using data-dependent acquisition (DDA), where precursor ions are sequentially selected from MS scans based on user-selected criteria. However, the number of MSn spectra that can be acquired during a chromatogram is limited and a trade-off between analytical speed, sensitivity and coverage must be ensured. In this research, we compare four different strategies for automated MS2 DDA, which can be easily implemented in the frame of standard QA/QC workflows for untargeted LC–MS. These strategies consist of (i) DDA in the MS working range; (ii) iterated DDA split into several m/z intervals; (iii) dynamic iterated DDA of (pre)selected potentially informative features; and (iv) dynamic iterated DDA of (pre)annotated metabolic features using a reference database. Their performance was assessed using the analysis of human milk samples as model example by comparing the percentage of LC–MS features selected as the precursor ion for MS2, the number, and class of annotated features, the speed and confidence of feature annotation, and the number of LC runs required.

rMSIcleanup: An open-source tool for matrix-related peak annotation in mass spectrometry imaging and its application to silver-assisted laser desorption/ionization

Mass spectrometry imaging (MSI) has become a mature, widespread analytical technique to perform non-targeted spatial metabolomics. However, the compounds used to promote desorption and ionization of the analyte during acquisition cause spectral interferences in the low mass range that hinder downstream data processing in metabolomics applications. Thus, it is advisable to annotate and remove matrix-related peaks to reduce the number of redundant and non-biologically-relevant variables in the dataset. We have developed rMSIcleanup, an open-source R package to annotate and remove matrix-related signals based on its chemical formula and the spatial distribution of its ions. To validate the annotation method, rMSIcleanup was challenged with several images acquired using silver-assisted laser desorption ionization MSI (AgLDI MSI). The algorithm was able to correctly classify m/z signals related to silver clusters. Visual exploration of the data using Principal Component Analysis (PCA) demonstrated that annotation and removal of matrix-related signals improved spectral data post-processing. The results highlight the need for including matrix-related peak annotation tools such as rMSIcleanup in MSI workflows.Resources availabilityThe R package presented in this publication is freely available under the terms of the GNU General Public License v3.0 at https://github.com/gbaquer/rMSIcleanup. The datasets used in the experiments can be accessed upon request to the corresponding author.

Data-Dependent Acquisition and Database-Driven Efficient Peak Annotation for the Comprehensive Profiling and Characterization of the Multicomponents from Compound Xueshuantong Capsule by UHPLC/IM-QTOF-MS

The state of the art ion mobility quadrupole time of flight (IM-QTOF) mass spectrometer coupled with ultra-high performance liquid chromatography (UHPLC) can offer four-dimensional information supporting the comprehensive multicomponent characterization of traditional Chinese medicine (TCM). Compound Xueshuantong Capsule (CXC) is a four-component Chinese patent medicine prescribed to treat ophthalmic disease and angina. However, research systematically elucidating its chemical composition is not available. An approach was established by integrating reversed-phase UHPLC separation, IM-QTOF-MS operating in both the negative and positive electrospray ionization modes, and a “Component Knockout” strategy. An in-house ginsenoside library and the incorporated TCM library of UNIFITM drove automated peak annotation. With the aid of 85 reference compounds, we could separate and characterize 230 components from CXC, including 155 ginsenosides, six astragalosides, 16 phenolic acids, 16 tanshinones, 13 flavonoids, six iridoids, ten phenylpropanoid, and eight others. Major components of CXC were from the monarch drug, Notoginseng Radix et Rhizoma. This study first clarifies the chemical complexity of CXC and the results obtained can assist to unveil the bioactive components and improve its quality control.

Integration of Data-Dependent Acquisition (DDA) and Data-Independent High-Definition MSE (HDMSE) for the Comprehensive Profiling and Characterization of Multicomponents from Panax japonicus by UHPLC/IM-QTOF-MS

The complexity of herbal matrix necessitates the development of powerful analytical strategies to enable comprehensive multicomponent characterization. In this work, targeting the multicomponents from Panax japonicus C.A. Meyer, both data dependent acquisition (DDA) and data-independent high-definition MSE (HDMSE) in the negative electrospray ionization mode were used to extend the coverage of untargeted metabolites characterization by ultra-high-performance liquid chromatography (UHPLC) coupled to a VionTM IM-QTOF (ion-mobility/quadrupole time-of-flight) high-resolution mass spectrometer. Efficient chromatographic separation was achieved by using a BEH Shield RP18 column. Optimized mass-dependent ramp collision energy of DDA enabled more balanced MS/MS fragmentation for mono- to penta-glycosidic ginsenosides. An in-house ginsenoside database containing 504 known ginsenosides and 60 reference compounds was established and incorporated into UNIFITM, by which efficient and automated peak annotation was accomplished. By streamlined data processing workflows, we could identify or tentatively characterize 178 saponins from P. japonicus, of which 75 may have not been isolated from the Panax genus. Amongst them, 168 ginsenosides were characterized based on the DDA data, while 10 ones were newly identified from the HDMSE data, which indicated their complementary role. Conclusively, the in-depth deconvolution and characterization of multicomponents from P. japonicus were achieved, and the approaches we developed can be an example for comprehensive chemical basis elucidation of traditional Chinese medicine (TCM).

MetaboAnalystR 2.0: From Raw Spectra to Biological Insights

Global metabolomics based on high-resolution liquid chromatography mass spectrometry (LC-MS) has been increasingly employed in recent large-scale multi-omics studies. Processing and interpretation of these complex metabolomics datasets have become a key challenge in current computational metabolomics. Here, we introduce MetaboAnalystR 2.0 for comprehensive LC-MS data processing, statistical analysis, and functional interpretation. Compared to the previous version, this new release seamlessly integrates XCMS and CAMERA to support raw spectral processing and peak annotation, and also features high-performance implementations of mummichog and GSEA approaches for predictions of pathway activities. The application and utility of the MetaboAnalystR 2.0 workflow were demonstrated using a synthetic benchmark dataset and a clinical dataset. In summary, MetaboAnalystR 2.0 offers a unified and flexible workflow that enables end-to-end analysis of LC-MS metabolomics data within the open-source R environment.