An End-to-End Untargeted LC/MS Workflow for Metabolomics and Lipidomics

Significance of the Topic

Untargeted metabolomics and lipidomics using LC/MS have become indispensable tools for exploring biological systems at the molecular level. By capturing a broad spectrum of metabolites and lipids without prior target selection, researchers can uncover unexpected biomarkers, elucidate pathway perturbations, and gain comprehensive insights into cellular responses to treatments or environmental stressors. An end-to-end, robust workflow accelerates data generation and enhances confidence in compound identification, enabling laboratories to adopt omics methodologies more rapidly and reliably.

Objectives and Study Overview

This application note describes a comprehensive untargeted LC/MS workflow for simultaneous metabolomics and lipidomics analysis from the same plasma or mammalian cell sample. Key objectives include streamlining sample preparation via automation, achieving reproducible chromatographic separation for polar metabolites and lipids, leveraging high-resolution mass spectrometry for confident detection, and integrating untargeted data analysis solutions with custom library building and unknown compound identification.

Methodology and Instrumentation

Sample Preparation

• Automated fractionation of metabolites and lipids from 20 µL plasma or cell extracts using the Agilent Bravo Metabolomics Sample Prep Platform and Captiva EMR–Lipid plates
• Separation of polar metabolites and lipids into distinct fractions, reducing sample consumption and enabling direct biological comparison
• Solvent reconstitution with appropriate mixtures and internal standards for quality assessment

Chromatography

• Polar metabolite separation on Agilent 1290 Infinity II/III bio LC with Poroshell 120 HILIC-Z column (2.1×150 mm, 2.7 µm), 24 min runtime
• Lipid separation on Agilent ZORBAX RRHD Eclipse Plus C18 column (2.1×100 mm, 1.8 µm), 16 min runtime

Detection

• Agilent Revident quadrupole time-of-flight LC/MS system providing high resolution, mass accuracy, isotopic fidelity, and extended dynamic range
• Iterative data-dependent acquisition to capture MS/MS spectra of low-abundance analytes through automated exclusion lists

Software and Data Analysis

• Agilent MassHunter Explorer for feature finding, normalization, statistical analysis, and MS1-based identification
• MassHunter Qualitative Analysis and MassHunter Lipid Annotator paired with ChemVista for custom library building and curation of over 500 metabolites and 650 lipids with retention times
• SIRIUS with CSI:FingerID for de novo molecular formula and structure proposals for remaining unknown features

Main Results and Discussion

The workflow demonstrated robust performance across 40 mouse plasma samples. The Revident Q-TOF system maintained mass accuracy within ±1 ppm over multiday runs and provided five orders of dynamic range, enabling confident detection of both high- and low-abundance compounds. Automated sample preparation reduced variability by up to 50 %, improving statistical power. Iterative MS/MS analysis and custom library building identified 693 positive and 773 negative polarity metabolites, and 269 positive and 81 negative lipids specific to the sample set. MassHunter Explorer feature finding uncovered over 10 000 features per dataset, with hundreds of statistically significant differences between male and female cohorts revealed by PCA and volcano plot analyses. SIRIUS with CSI:FingerID resolved additional unknowns, enriching the identification coverage.

Benefits and Practical Applications

This end-to-end workflow provides:

• Automated, high-throughput sample preparation for consistent fractionation
• Standardized, reproducible chromatography compatible with Infinity II and III bio LC systems
• High confidence in compound identification through custom libraries and spectral matching
• Scalable data analysis combining feature extraction, statistical assessment, and unknown structure elucidation

Applications include biomarker discovery, pathway mapping, toxicological studies, and comparative profiling in life sciences and industrial analytics.

Future Trends and Opportunities

Advancements are expected in integrating ion mobility separation, expanding spectral libraries with community contributions, and leveraging machine learning for more accurate feature annotation. Cloud-based data sharing and automated workflows will further accelerate multi-omics research. Enhanced software interoperability and real-time quality control can streamline method adoption and continuous workflow refinement.

Conclusion

The presented untargeted LC/MS workflow unifies sample preparation, chromatographic separation, high-resolution detection, and comprehensive data analysis to deliver reliable metabolomics and lipidomics insights. By combining automation with advanced instrumentation and software tools, researchers can rapidly implement discovery studies with high confidence in compound identification and statistical robustness, paving the way for faster biological discoveries.

References

1. Sartain et al. Automated Plasma Metabolite Extraction Workflow
2. Yannell et al. Targeted Metabolomics Workflow
3. Van de Bittner et al. Automated Dual Metabolite and Lipid Preparation
4. Huynh et al. High-Throughput Plasma Lipidome Analysis
5. Duhrkop et al. CSI:FingerID Structure Identification