Targeted Data Mining and Annotation of Untargeted High-Resolution Lipidomics Data

Significance of the Topic

Comprehensive lipid profiling underpins advances in biology, medicine, and quality control by revealing the roles of diverse lipid species in health and disease. High-resolution untargeted lipidomics demands robust workflows to distinguish isomeric and isobaric lipids, ensuring accurate biomarker discovery and mechanistic insights.

Objectives and Study Overview

This application note describes the development of a high-confidence personal compound database library (PCDL) and an untargeted lipidomics workflow for LC/Q-TOF and ion mobility LC/Q-TOF instruments. The goals were:

• To curate a comprehensive lipid database covering 763 lipid standards across major classes
• To transfer retention times, accurate masses, MS/MS spectra, and collision cross section values into a PCDL
• To demonstrate a streamlined untargeted workflow from sample extraction to lipid annotation in plasma extracts

Methodology and Instrumentation

Sample preparation involved extracting 10 µL of plasma with 100 µL of butanol:methanol (1:1) containing 10 mM ammonium formate and internal standards. After vortex mixing and one hour of bath sonication at 21–25 °C, samples were centrifuged, and the supernatant transferred to glass vials for analysis.

Chromatographic separation employed reversed-phase C18 columns (2.1×100 mm, 1.8 µm) at 45 °C with a 16-minute gradient (mobile phases A and B comprising water, acetonitrile, 2-propanol, ammonium formate, and deactivator additive). The flow rate was 0.4 mL/min, and injection volume was 1 µL.

Mass spectrometric acquisition included:

• Auto MS/MS on an LC/Q-TOF with data-independent All Ions fragmentation at 25 eV
• Ion mobility measurements on a 6560 LC/Q-TOF, capturing drift times and CCS values with multiplexed trapping
• Acquisition in positive and negative ion modes over m/z 50–3000

Used Instrumentation

• Agilent 1290 Infinity II LC system with high-speed pump, autosampler, and multicolumn thermostat
• Agilent Revident LC/Q-TOF with Jet Stream ESI source
• Agilent 6560 Ion Mobility LC/Q-TOF with Jet Stream ESI
• Agilent MassHunter Workstation, ID Browser, Mass Profiler, and Mass Profiler Professional software suites

Key Results and Discussion

The curated PCDL enabled confident annotation of 677 lipid species from 44 classes in 10 µL plasma samples. Retention time reproducibility across multiple laboratories yielded RSDs below 0.2% for over 600 lipids. Orthogonal identification criteria—accurate mass, retention time, MS/MS match, and CCS filtering—substantially reduced false positives. Visual tools such as lipid matrices, mass versus retention time plots, and Kendrick mass defect maps improved recognition of homologous series and potential annotation gaps.

Benefits and Practical Applications of the Method

This workflow delivers high throughput (13–16 minutes per sample) and broad lipid coverage, enabling:

• Robust detection of isomeric/isobaric lipids in complex biological matrices
• Standardized data acquisition and processing across laboratories
• Enhanced confidence in biomarker discovery and metabolic studies

Future Trends and Opportunities

Emerging developments include integration of multi-omics LC platforms, expansion of CCS libraries for deeper structural resolution, and application of machine learning for automated lipid annotation. Advances in chromatography and ion mobility will further resolve lipid isomers, supporting personalized health assessments and advanced biochemical research.

Conclusion

The presented workflow combines a rigorously curated PCDL with high-resolution LC/Q-TOF and ion mobility data acquisition to achieve high-confidence, untargeted lipid annotations. By harmonizing orthogonal metrics—accurate mass, retention time, MS/MS spectra, and CCS—this approach offers reproducible, comprehensive lipid profiling suitable for diverse research and QA/QC applications.

References

1. Huynh K et al. A Comprehensive, Curated, High-Throughput Method for the Detailed Analysis of the Plasma Lipidome. Agilent Technologies Application Note; 5994-3747EN, 2021.
2. Mohsin S et al. Creation of a High-Confidence Lipidomics Personal Compound Database Library for Targeted Data Mining and Annotation of Untargeted High-Resolution Lipidomics Data. Agilent Technologies Technical Overview; 5994-7627EN, 2024.
3. Huynh K et al. High-Throughput Plasma Lipidomics: Detailed Mapping of the Associations with Cardiometabolic Risk Factors. Cell Chemical Biology. 2019;26(1):71–84.
4. Sartain M et al. An Interlaboratory Evaluation of a Targeted Lipidomics Method in Plasma. Agilent Technologies Application Note; 5994-6830EN, 2024.
5. Korf A et al. Three-Dimensional Kendrick Mass Plots as a Tool for Graphical Lipid Identification. Rapid Communications in Mass Spectrometry. 2018;32(12):981–991.