Investigating the increased lifespan in C. elegans daf-2 mutants by 4D-Lipidomics

Importance of the topic

The comprehensive profiling of lipid species in biological systems is essential for understanding metabolic regulation, cellular signaling and physiological adaptations. In the model organism Caenorhabditis elegans, alterations in lipid composition are closely linked to aging, longevity and stress responses. The 4D-Lipidomics approach combines ultra-high resolution liquid chromatography, trapped ion mobility separation, high-speed MS/MS acquisition (PASEF) and machine learning–based CCS prediction to achieve deep coverage, confident identification and robust quantification of complex lipidomes.

Objectives and overview

This study aimed to apply a fully integrated 4D-Lipidomics workflow to compare wild type C. elegans with long-lived daf-2 mutants. Key goals included:

• Extraction and high-resolution analysis of total lipidomes
• Automatic assignment of lipid species in positive and negative ion modes
• Statistical discrimination of genotype-specific lipid changes
• Validation of lipid identities via MS/MS fragments and CCS matching

Methods and used instrumentation

Cultivation and extraction

• Strains N2 (wild type) and daf-2(e1370) synchronized, harvested at first adult day (5,000 worms per replicate)
• Modified MTBE extraction with methanol homogenization, phase separation and reconstitution in acetonitrile/isopropanol/water

LC-PASEF MS on timsTOF Pro

• LC column: C18 core-shell (100 × 2.1 mm, 1.9 µm), 55 °C, gradient elution (40 → 99 % B over 18 min)
• ESI(+) 4,500 V, ESI(–) 4,200 V, m/z 100–1,500
• PASEF acquisition: parallel accumulation serial fragmentation for rapid, high-quality MS/MS
• Trapped ion mobility for collisional cross section (CCS) determination

Data processing with MetaboScape 5.0

• T-ReX 4D: automated retention time alignment, feature extraction (mass, isotopes, MS/MS, CCS)
• Lipid identification via LipidBlast library, custom C. elegans analyte list and CCSPredict CCS prediction
• Annotation Quality scoring combining mass accuracy, RT fit, isotopic pattern, MS/MS and CCS criteria

Main results and discussion

Deep lipidome coverage

• 824 features annotated in positive mode, 660 in negative mode, 1,358 after merging modes
• MS/MS spectra and isotopic patterns enabled high-confidence assignment of glycerophospholipids, sphingolipids, glycerolipids

Statistical discrimination

• PCA clearly separated wild type and daf-2 extracts; QC samples clustered centrally
• Feature PC 40:10 (PC 20:5_20:5) drove separation, showing higher abundance in wild type

Verification of lipid IDs

• Positive and negative MS/MS fragments confirmed phosphocholine headgroup (m/z 184) and dual C20:5 side chains
• CCS measurements showed excellent reproducibility (0.23 % RSD) across 21 runs
• Measured CCS matched CCSPredict values with minimal deviation and agreed with DT-IMS repository (<1 % difference)

Benefits and practical applications

The presented 4D-Lipidomics workflow delivers:

• Comprehensive lipid coverage in a single integrated platform
• Automated, high-confidence assignments leveraging multi-dimensional data
• Rapid statistical and visual tools for pinpointing phenotype-specific lipid markers
• Reproducible CCS values as orthogonal identifiers to enhance annotation fidelity

This approach facilitates aging and longevity research, metabolic phenotyping and biomarker discovery in C. elegans and other organisms.

Future trends and possibilities

Continued advancements are expected in:

• Machine learning–based prediction of CCS and fragmentation patterns for broader lipid classes
• Expansion of public CCS and MS/MS repositories to support cross-platform standardization
• Integration with multi-omics and spatial lipidomics for tissue-specific mapping
• High-throughput screening of genetic or environmental perturbations in model organisms

Conclusion

The 4D-Lipidomics workflow on timsTOF Pro combined with MetaboScape offers a powerful, streamlined solution for in-depth lipid profiling. It successfully characterized over 1,300 lipid species, revealed genotype-dependent changes in daf-2 mutants and validated key markers through multi-dimensional criteria. The high reproducibility and CCS‐based confidence scoring further strengthen its applicability in diverse analytical and biological contexts.

References

• [1] Castro C et al. Mol BioSyst 9:1632–1642 (2013)
• [2] Witting M, Schmitt-Kopplin P. Arch Biochem Biophys 589:27–37 (2016)
• [3] Witting M et al. J Chromatogr A 1359:91–99 (2014)
• [4] Hänel V et al. Chem Phys Lipids 222:15–22 (2019)
• [5] Meier F et al. J Proteome Res 14:5378–5387 (2015)
• [6] Bruker App Note LCMS-158 (2019)
• [7] Matyash V et al. J Lipid Res 49:1137–1146 (2008)
• [8] Kind T et al. Nature Methods 10:755–758 (2013)
• [9] Kind T et al. Anal Chem 86:11024–11027 (2014)
• [10] Ma Y et al. J Cheminformatics 7:53 (2015)
• [11] Tsugawa H et al. Nature Methods 12:523–526 (2015)
• [12] Zhou Z et al. Anal Chem 89:9559–9566 (2017)
• [13] Picache JA et al. Chem Sci 10:983–993 (2019)