Electron-activated dissociation (EAD) for the complementary annotation of metabolites and lipids through data-dependent acquisition analysis and feature-based molecular networking, applied to the sentinel amphipod Gammarus fossarum.

The past decades have marked the rise of metabolomics and lipidomics as the -omics sciences which reflect the most phenotypes in living systems. Mass spectrometry-based approaches are acknowledged for both quantification and identification of molecular signatures, the latter relying primarily on fragmentation spectra interpretation. However, the high structural diversity of biological small molecules poses a considerable challenge in compound annotation. Feature-based molecular networking (FBMN) combined with database searches currently sets the gold standard for annotation of large datasets. Nevertheless, FBMN is usually based on collision-induced dissociation (CID) data, which may lead to unsatisfying information. The use of alternative fragmentation methods, such as electron-activated dissociation (EAD), is undergoing a re-evaluation for the annotation of small molecules, as it gives access to additional fragmentation routes. In this study, we apply the performances of data-dependent acquisition mass spectrometry (DDA-MS) under CID and EAD fragmentation along with FBMN construction, to perform extensive compound annotation in the crude extracts of the freshwater sentinel organism Gammarus fossarum. We discuss the analytical aspects of the use of the two fragmentation modes, perform a general comparison of the information delivered, and compare the CID and EAD fragmentation pathways for specific classes of compounds, including previously unstudied species. In addition, we discuss the potential use of FBMN constructed with EAD fragmentation spectra to improve lipid annotation, compared to the classic CID-based networks. Our approach has enabled higher confidence annotations and finer structure characterization of 823 features, including both metabolites and lipids detected in G. fossarum extracts.

Comprehensive bile acid pool analysis during ex-vivo liver perfusion in a porcine model of ischemia-reperfusion injury.

Bile acids (BA) are key for liver regeneration and injury. This study aims at analyzing the changes in the BA pool induced by ischemia-reperfusion (IRI) and investigates the impact of hypothermic oxygenated perfusion (HOPE) on the BA pool compared to static cold storage (SCS). In a porcine model of IRI, liver grafts underwent 30 min of asystolic warm ischemia followed by 6 h of SCS (n = 6) ± 2 h of HOPE (n = 6) and 2 h of ex-situ warm reperfusion. The BA pool in bile samples was analyzed with liquid chromatography coupled with tandem mass spectrometry. We identified 16 BA and observed significant changes in response to ischemia-reperfusion, which were associated with both protective and injury mechanisms. Second, HOPE-treated liver grafts exhibited a more protective BA phenotype, characterized by a more hydrophilic BA pool compared to SCS. Key BA, such as GlycoCholic Acid, were identified and were associated with a decreased transaminase release and improved lactate clearance during reperfusion. Partial Least Square-Discriminant Analysis revealed a distinct injury profile for the HOPE group. In conclusion, the BA pool changes with liver graft IRI, and preservation with HOPE results in a protective BA phenotype compared to SCS.

Concomitant investigation of crustacean amphipods lipidome and metabolome during the molting cycle by Zeno SWATH data-independent acquisition coupled with electron activated dissociation and machine learning.

BACKGROUND: DIA (Data-Independent Acquisition) is a powerful technique in Liquid Chromatography coupled with high-resolution tandem Mass Spectrometry (LC-MS/MS) initially developed for proteomics studies and recently emerging in metabolomics and lipidomics. It provides a comprehensive and unbiased coverage of molecules with improved reproducibility and quantitative accuracy compared to Data-Dependent Acquisition (DDA). Combined with the Zeno trap and Electron-Activated Dissociation (EAD), DIA enhances data quality and structural elucidation compared to conventional fragmentation under CID. These tools were applied to study the lipidome and metabolome of the freshwater amphipod Gammarus fossarum, successfully discriminating stages and highlighting significant biological features. Despite being underused, DIA, along with the Zeno trap and EAD, holds great potential for advancing research in the omics field. RESULTS: DIA combined with the Zeno trap enhances detection reproducibility compared to conventional DDA, improving fragmentation spectra quality and putative identifications. LC coupled with Zeno-SWATH-DIA methods were used to characterize molecular changes in reproductive cycle of female gammarids. Multivariate data analysis including Principal Component Analysis and Partial Least Square Discriminant Analysis successfully identified significant features. EAD fragmentation helped to identify unknown features and to confirm their molecular structure using fragmentation spectra database annotation or machine learning. EAD database matching accurately annotated five glycerophospholipids, including the position of double bonds on fatty acid chain moieties. SIRIUS database predicted structures of unknown features based on experimental fragmentation spectra to compensate for database incompleteness. SIGNIFICANCE: Reproducible detection of features and confident identification of putative compounds are pivotal stages within analytical pipelines. The DIA approach combined with Zeno pulsing enhances detection sensitivity and targeted fragmentation with EAD in positive polarity provides orthogonal fragmentation information. In our study, Zeno-DIA and EAD thereby facilitated a comprehensive and insightful exploration of pertinent biological molecules associated with the reproductive cycle of gammarids. The developed methodology holds great promises for identifying informative biomarkers on the health status of an environmental sentinel species.

Scout triggered multiple reaction monitoring mass spectrometry for the rapid transfer of large multiplexed targeted methods in metabolomics.

The field of metabolomics based on mass spectrometry has grown considerably in recent years due to the need to detect and, above all, quantify a very large number of metabolites, simultaneously. Up to now, targeted multiplexed analysis on complex samples by Liquid Chromatography coupled with tandem Mass Spectrometry (LC-MS/MS) has relied almost exclusively on compound detection based on absolute retention times, as in the Scheduled-MRM (sMRM) approach. Those methods turn out to be poorly transferable from one instrument to another and result in a time-consuming and tedious method development involving a significant number of critical parameters that need specific re-optimisation. To address this challenge, we introduce a novel acquisition mode called scout-triggered MRM (stMRM). In stMRM, a marker transition is used to trigger MS analysis for a group of dependent target analytes. These marker transitions are strategically distributed throughout the chromatographic run, and the dependent analytes are associated based on their retention times. The result is a targeted assay that remains robust even in the presence of retention time shifts. A 3 to 5-fold increase in the number of detected transitions associated to plasma metabolites was obtained when transferring from a direct application of a published sMRM to a stMRM method. This significant improvement highlights the universal applicability of the stMRM method, as it can be implemented on any LC system without the need for extensive method development. We subsequently illustrate the robustness of stMRM in modified chromatographic elution conditions. Despite a large change in metabolite's selectivity, the multiplexed assay successfully recovered 70% of the monitored transitions when consequently modifying the gradient method. These findings demonstrate the versatility and adaptability of stMRM, opening new avenues for the development of highly multiplexed LC-MS/MS methods in metabolomics. These methods are characterized by their analytical transparency and straightforward implementation using existing literature data.

Development of a multi-omics extraction method for ecotoxicology: investigation of the reproductive cycle of Gammarus fossarum.

Omics study exemplified by proteomics, lipidomics or metabolomics, provides the opportunity to get insight of the molecular modifications occurring in living organisms in response to contaminants or in different physiological conditions. However, individual omics discloses only a single layer of information leading to a partial image of the biological complexity. Multiplication of samples preparation and processing can generate analytical variations resulting from several extractions and instrumental runs. To get all the -omics information at the proteins, metabolites and lipids level coming from a unique sample, a specific sample preparation must be optimized. In this study, we streamlined a biphasic extraction procedure based on a MTBE/Methanol mixture to provide the simultaneous extraction of polar (proteins, metabolites) and apolar compounds (lipids) for multi-omics analyses from a unique biological sample by a liquid chromatography (LC)/mass spectrometry (MS)/MS-based targeted approach. We applied the methodology for the study of female amphipod Gammarus fossarum during the reproductive cycle. Multivariate data analyses including Partial Least Squares Discriminant Analysis and multiple factor analysis were applied for the integration of the multi-omics data sets and highlighted molecular signatures, specific to the different stages.