Uncovering More Biological Insights in Your Samples with Routine LC/Q-TOF Workflows for Metabolites and Lipids

Significance of the Topic

Untargeted metabolomics and lipidomics are essential for discovery research but often lack robust, transferable workflows. Integrating both analyses from the same sample enhances data consistency and biological interpretation.

Objectives and Overview of the Study

This study aims to demonstrate a supportable, end-to-end LC/Q-TOF workflow that extracts lipids and metabolites from mouse plasma; employs high-resolution LC/Q-TOF acquisition in MS1 and iterative MS/MS modes; and utilizes software tools for rapid feature extraction, statistical analysis, and confident identification.

Methodology and Instrumentation

Sample Preparation and Extraction:

• Extraction of lipids and metabolites from 20 male and 20 female mouse plasma replicates using Captiva EMR-Lipid SPE plates on a Metabolomics Bravo automated platform.

Chromatography and Mass Spectrometry:

• Infinity II Bio LC system coupled to Revident LC/Q-TOF.
• Metabolites separated on a 150 mm HILIC-Z column (23 min run). Lipids separated on a 100 mm Zorbax C18 column (16 min run) with back-to-back injections separated by a solvent wash.
• Mass range m/z 60-1000 in positive and negative ion modes for metabolites; positive mode for lipids; instrument tuned in m/z 1700 mode.

Data Acquisition and Software Tools:

• Pooled QC injections for mass accuracy and signal stability evaluation.
• MassHunter Explorer for MS1 feature extraction, normalization, and statistics.
• Iterative MS/MS acquisition using MassHunter Acquisition workflows; library building with Lipid Annotator, MassHunter Qual 12.0, and ChemVista (METLIN) for retention time updates.
• SIRIUS CSI:FingerID for structure elucidation of unknown MS/MS spectra.

Main Results and Discussion

• Pooled QC data demonstrated mass accuracy < 1.3 ppm sustained over seven days with single-point calibration, indicating robust instrument performance.
• Explorer extracted over 10 000 features per dataset in under one hour; statistical analysis revealed significant sex-related metabolic and lipidomic differences.
• Iterative MS/MS workflow identified over 2000 metabolites and several hundred lipids; custom spectral and retention time libraries facilitated Tier 1 identifications.
• Software integration streamlined library curation: retention times exported back to spectral libraries for rapid updates.

Benefits and Practical Applications of the Method

• Dual extraction reduces sample consumption and experimental variability.
• Standardized methods and software support ensure rapid adoption across laboratories.
• High-confidence identifications accelerate biological interpretation in QA/QC, biomarker discovery, and systems biology.

Future Trends and Potential Uses

• Expansion of custom spectral libraries to cover additional chemical classes and species.
• Integration of machine learning for improved feature annotation and quantitation.
• Application of automated workflows to clinical and environmental samples for high-throughput screening.

Conclusion

The described LC/Q-TOF workflow delivers fast, reliable untargeted metabolomics and lipidomics from a single plasma sample. Coupled with advanced software for feature extraction and library management, this approach provides a ready-to-deploy solution for comprehensive small molecule profiling.

References

• Van de Bittner GC et al. Three-in-One Automated Sample Preparation and LC/MS Metabolomics, Lipidomics, and Proteomics Workflow for Plasma. Lorne Proteins, 2024.
• Yannell KE et al. End-to-End Targeted Metabolomics Workflow. Agilent Application Note, 5994-5628EN, 2023.
• Huynh K et al. Comprehensive High-Throughput Method for Plasma Lipidome Analysis. Agilent Application Note, 5994-3747EN, 2021.
• Simmermaker C. Routine Targeted Metabolomic Panel Analysis from Untargeted Acquisition. ASMS 2024 Poster MP540.
• Dührkop K et al. SIRIUS4: Turning Tandem Mass Spectra into Metabolite Structures. Nat Methods, 2019.