Confident annotation of GPNAE and HexGPNAE in Caenorhabditis elegans aided by homologous series extension in LC-MS/MS data

Significance of Topic

Lipidomics relies on accurate identification of lipid species to understand biological processes and metabolic pathways. The systematic nature of lipids, which form series of related molecules differing by defined structural increments, can be exploited to enhance annotation confidence. Extending homologous series in multidimensional LC-MS/MS data strengthens the validation of lipid assignments and reveals previously undetected species in complex samples such as Caenorhabditis elegans.

Objectives and Study Overview

This study presents a workflow to detect and extrapolate homologous series of glycerophospho-N-acylethanolamides GPNAE and their hexosylated counterparts HexGPNAE in Caenorhabditis elegans. Key objectives included

• Annotating known GPNAE and HexGPNAE features through targeted MassQL queries
• Extending these series by extrapolating retention time and collision cross section trends to predict additional members
• Validating newly identified lipids by their alignment within homologous series

Methodology

Mixed stage C elegans samples were extracted with a water methanol mixture. Lipid-like molecules were separated on a reversed phase C18 column using a linear gradient from aqueous formic acid to acetonitrile formic acid. Data acquisition employed a trapped ion mobility spectrometry time of flight MS instrument in both positive and negative ion modes. Feature detection and 4D processing were performed in MetaboScape 2025b with T ReX 4D. Annotated features were selected using class specific MassQL queries and manual curation to generate an initial target list. Subsequent interactive analysis automatically detected and extrapolated homologous series across m z, retention time and ion mobility to propose new lipid candidates.

Used Instrumentation

• Bruker timsMetabo Tims TOF MS with trapped ion mobility
• Kinetex C18 column 100 mm x 2 1 mm, 1 7 micrometer particle size
• MetaboScape 2025b software with T ReX 4D and MassQL modules

Main Results and Discussion

The workflow successfully annotated known GPNAE and HexGPNAE species and revealed additional, previously unannotated lipids by examining homologous series trends. For example, features corresponding to GPNAE 15 0 and 16 1 and HexGPNAE 17 2 and 18 2 were confirmed by their positions within series defined by consistent retention time and mobility increments. This approach demonstrated that even features lacking characteristic MSMS fragments can be confidently classified when they align with a homologous series, supporting level 2c and 3c annotation standards.

Benefits and Practical Applications

By leveraging multidimensional data to detect series level relationships, this method reduces false positives and expands lipid coverage in complex biological samples. It offers

• Enhanced annotation confidence for lipidomics studies
• Automated workflows for rapid discovery of novel lipids
• Potential integration into QA QC and metabolomics pipelines

Future Trends and Opportunities

Future developments may include incorporation of additional chemical classes into homologous series workflows, improved automation with machine learning for series detection, and integration with other omics platforms. Expanded databases of homologous lipid series and standardized annotation confidence frameworks will further strengthen lipidomics research.

Conclusion

Homologous series extension in 4D LC MS MS data provides a robust framework for lipid annotation, enabling discovery of novel GPNAE and HexGPNAE species in C elegans. The approach enhances confidence in identifications and can be broadly applied to other lipid classes and biological systems.

References

• Damiani et al Nature Methods 2025 May 12
• Charbonnet et al Environmental Science Technology Letters 2022 Jun 14 9 6 473 481