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Electron-Activated Dissociation (EAD) is transforming the fields of metabolomics and lipidomics by providing enhanced fragmentation techniques that offer deeper insights into molecular structures. In September, Technology Networks hosted a webinar, “Enhancing Mass-Based Omics Analysis in Model Organisms,” featuring Dr. Valentina Calabrese from the Institute of Analytical Sciences at the University of Lyon. Valentina shared her insights on improving omics-based mass spectrometry analysis for toxicology studies using model organisms, particularly in metabolomics and lipidomics. This blog explores the additional functionalities EAD offers, its benefits in untargeted workflows, its incorporation into GNPS and molecular networking, and the future role it could play in these scientific domains.

What is EAD?

EAD is a mass spectrometry technique that uses electrons to induce fragmentation of molecules. Unlike traditional methods like Collision-Induced Dissociation (CID), which primarily breaks weaker bonds, EAD can cleave stronger bonds, providing more detailed structural information. This capability is particularly valuable for analyzing complex biological molecules such as lipids and metabolites.

One of EAD’s key functionalities is its ability to adjust the electron beam’s kinetic energy across a broad range (0-25 eV), allowing for tailored fragmentation. This flexibility enables researchers to obtain more comprehensive fragmentation patterns, which is crucial for accurate structural elucidation.

Benefits of EAD in untargeted workflows

Valentina highlighted the challenges in studying metabolomics and lipidomics in organisms with unknown genomes, such as Gammarus fossarum. The lack of documented lipids and metabolites necessitates innovative approaches for molecular annotation.

“EAD led to an increase of the fragment compared to CID, especially for compounds and ions at high m/z values, enabling the possibility to obtain additional peaks accounting for, thanks to structure assignment.” Dr Calabrese.

EAD offers several benefits in untargeted omics workflows:

1. Improved structural elucidation: EAD’s capability to break stronger bonds allows for more detailed structural analysis of metabolites and lipids, leading to more accurate and confident molecular annotations.
2. Higher confidence in annotations: The complementary fragmentation pathways offered by EAD result in more accurate and confident molecular annotations. This is particularly important in untargeted workflows where the goal is to identify as many compounds as possible without prior knowledge.
3. Enhanced data quality: EAD improves mass spectrometry data quality, making it easier to interpret and analyze complex biological samples. The ZenoTOF 7600 system, for example, provides high acquisition speeds and sensitivity, enabling high-throughput, untargeted analysis of lipids.

Incorporation into GNPS and molecular networking

Global Natural Products Social Molecular Networking (GNPS) is a platform that facilitates the sharing and analysis of mass spectrometry data. EAD can be incorporated into GNPS workflows to enhance molecular networking, a visualization approach that detects sets of spectra from related molecules. Feature-Based Molecular Networking (FBMN) with GNPS allows researchers to use EAD data to construct more detailed and accurate molecular networks. This approach improves the annotation of metabolites and lipids by leveraging the rich fragmentation data provided by EAD.

Dr. Calabrese described the workflow for constructing molecular networks in her webinar and publication. EAD provided additional structural information for lipids, allowing for the characterization of chain lengths, double bond positions, and oxidation states, improving the annotation of complex lipid structures. She compared the results of CID and EAD, demonstrating that EAD provided more detailed fragmentation spectra, especially for high molecular weight compounds. This enhanced the structural characterization and molecular networking of lipids.

Future role of EAD

Electron-Activated Dissociation (EAD) is a powerful tool for transforming metabolomics and lipidomics. EAD enables more detailed and accurate analysis of complex biological molecules by providing enhanced fragmentation capabilities. Its incorporation into GNPS and molecular networking workflows further enhances its utility, making it an invaluable asset for future research.

The future role of EAD in metabolomics and lipidomics is promising. As research advances, EAD is poised to play a crucial role in unlocking new insights into metabolic and lipidomic processes. The ability to obtain detailed structural information will enhance our understanding of biochemical pathways and disease mechanisms, ultimately contributing to better diagnostics and therapeutics.

Conclusion

Valentina provided valuable insights into the advancements in mass-based omics analysis, highlighting the importance of innovative techniques in studying environmentally sensitive organisms. Integrating molecular networking and advanced mass spectrometry methods promises to improve our understanding of metabolomics and lipidomics in toxicology research.

“So, in summary, EAD led to a great improvement of the structural characterization and the molecular networking construction.”, said Dr Calabrese.

Future research direction could also include integrating data-independent analysis (DIA) workflows, such as SWATH (Sequential Window Acquisition of All Theoretical Mass Spectra), to improve the reproducibility and depth of omics analyses.

“SWATH analysis together with molecular networking construction would lead to a really nice increase of the 150 compounds, compared to common DDA workflows.”

Watch Dr. Valentina Calabrese’s webinar to learn more about the enhanced mass spectrometry-based omics analysis of model organisms. Watch now.

For more detailed information, please refer to Dr. Calabrese’s recent publication in Analytical and Bioanalytical LINK https://link.springer.com/article/10.1007/s00216-024-05232-w

Additional references

EAD whitepaper/webpage

https://www.sciencedirect.com/science/article/pii/S0003267024003349

Molecular Networking – GNPS Documentation – GitHub Pages

FBMN with MS-DIAL – GNPS Documentation – GitHub Pages