All-in-one Data-Processing and Interactive Visualizations of Lipid LC-HRMS/MS Data using LipidMatch 4.0

Importance of Lipidomics Data Processing and Visualization

Modern lipidomics is critical for identifying biomarkers and understanding disease mechanisms because lipid metabolic pathways are altered in virtually every pathological state. High-confidence, high-coverage annotation workflows reduce manual intervention and accelerate discovery of novel lipid species.

Objectives and Study Overview

The primary goal of the work was to develop and benchmark LipidMatch 4.0, an integrated software platform for automated annotation and interactive visualization of LC-HRMS and LC-HRMS/MS lipidomics data. Key objectives included:

• Building a user-friendly interface for automated peak picking, feature annotation, and library matching.
• Expanding coverage by incorporating >300,000 lipid species with fragmentation data.
• Enabling discovery of unknown lipids via homologous series, isotopic patterns, and fragment screening.
• Comparing performance against existing tools (Lipid Annotator, MS-DIAL, GREAZY).

Methods and Used Instrumentation

Cell culture model: K562 acute myeloid leukemia cells treated with various drug combinations to induce lipid alterations.
Chromatography: Agilent 1290 Infinity II uHPLC with Poroshell 120 EC-C18 column (3.0×100 mm, 2.7 µm); mobile phases water/methanol (9:1) and acetonitrile/methanol/isopropanol (2:3:5) with 10 mM ammonium acetate.
Mass spectrometry: Agilent 6546 LC/Q-TOF operated in data-dependent acquisition (DDA), iterative exclusion DDA, or targeted MS/MS modes.
Software workflow: Initial peak picking (e.g., Agilent Mass Profiler, MZMine), blank filtration, grouping by homologue series, library matching, isotopic pattern scoring, substructure assignment, and interactive visualizer for manual validation.

Main Results and Discussion

LipidMatch 4.0 achieved rapid, high-coverage annotation with <5 % false-positive rate. Key findings:

• Automated homologous series assignment revealed chain-length and unsaturation patterns not captured by MS/MS alone.
• Isotopic pattern plots facilitated validation of complex lipids containing elements like sulfur and chlorine.
• Fragment-screened m/z vs. retention time plots pinpointed unknown species sharing characteristic head-group fragments (e.g., m/z 184.073 for phosphocholine).
• Interactive EIC displays allowed simultaneous inspection of peak shape, isomers, and relative abundances.

The platform uncovered numerous previously unidentified polar lipids and oxidized derivatives overlapping with common PC/SM classes, highlighting gaps in traditional workflows.

Benefits and Practical Applications

The LipidMatch ecosystem provides:

• End-to-end automation from raw data to annotated lipid lists.
• Comprehensive coverage via extensive fragmentation libraries.
• Interactive tools for discovery of novel lipids and manual curation.
• Modular scripts for integrating into in-house pipelines or using the LipidMatch Flow turnkey solution.

These features support biomarker discovery, drug mechanism studies, QA/QC in lipid analysis, and large-scale clinical or environmental lipidomics.

Future Trends and Opportunities

Emerging directions include:

• Integration of ion mobility separation to resolve isomeric lipids.
• Incorporation of machine learning models for automated substructure assignment.
• Expansion of community-curated fragmentation libraries with novel lipid classes.
• Cloud-based platforms for collaborative annotation and sharing of lipidomics datasets.

Such developments will further reduce manual workload and enhance discovery of low-abundance or atypical lipid species.

Conclusion

LipidMatch 4.0 delivers a robust, high-throughput solution for comprehensive lipid annotation and visualization in LC-HRMS/MS datasets. By combining automated workflows with interactive exploration tools, it streamlines both confident identification and novel lipid discovery.

Reference

Koelmel J., Stelben P., Brooks B., Suh J., Sartain M., Garrett T. J., Bowden J. A., Rennie E. E., Godri Pollitt K. J. All-in-one Data-Processing and Interactive Visualizations of Lipid LC-HRMS/MS Data using LipidMatch 4.0. ASMS 2023.