Unraveling Differential Lipids in Aging Tissues Using a Novel Data Analysis Workflow

Unraveling Differential Lipids in Aging Tissues — Expert Summary

Importance of the topic

Aging drives systemic changes in lipid metabolism that contribute to altered energy homeostasis, inflammation, and metabolic inflexibility. High-resolution, non-targeted lipidomics across multiple tissues (white adipose tissue, brown adipose tissue and liver) enables the detection of subtle, tissue-specific lipid remodeling events that inform mechanisms of age-related metabolic decline and potential intervention points. Integrating robust chromatographic hardware with iterative MS/MS acquisition and streamlined informatics accelerates discovery and raises confidence in biological interpretation.

Study aims and overview

The study aimed to map age-dependent lipid alterations across WAT, BAT and liver using a comprehensive non-targeted LC/Q-TOF lipidomics approach and to demonstrate an end-to-end data analysis workflow that combines MS-DIAL, Lipid Annotator, ChemVista Library Manager and Agilent MassHunter Explorer 2.0. The dataset comprised 72 samples from three age cohorts and leveraged pooled QC iterative MS/MS to build a custom spectral library for high-confidence lipid annotation and downstream chemometrics.

Methodology

• Sample collection and extraction: Age-matched tissue samples (10–15 mg) were processed using a modified methanol/MTBE/water extraction. Samples received an EquiSPLASH internal standard mixture prior to mechanical homogenization with a BeadBug system and were reconstituted in a 9:1 methanol:chloroform solvent for LC–MS/MS analysis.
• Chromatography and acquisition: A 16-minute reversed-phase separation on a ZORBAX Eclipse Plus C18 column was used. Individual samples were acquired in MS1 while pooled QC samples underwent iterative MS/MS acquisition in both positive and negative polarity (n = 6 per polarity) to maximize identification coverage.
• Identification targets: Iterative MS/MS spectra were used to annotate and confirm lipid species, yielding a comprehensive identification set of 743 lipid species across tissues.

Instrumentation used

• LC: Agilent 1290 Infinity III Bio LC (iron-free flow path to reduce metal-induced peak distortion for sensitive analytes).
• MS: Agilent Revident LC/Q-TOF tuned for stability up to m/z 1700 in both polarities, providing high resolution, accurate isotopic fidelity and a wide dynamic range.
• Column: ZORBAX Eclipse Plus C18 with a previously established reversed-phase method.

Data analysis workflow

• Iterative MS/MS spectra were analyzed with Lipid Annotator and MS-DIAL (instrument type set to LC-ESI-QTOF) and manually curated within MS-DIAL.
• Curation export: Curated alignment results were exported from MS-DIAL as SDF files containing retention time, precursor mass, ion type, chemical formula and structural identifiers.
• Library management: SDF libraries were imported into ChemVista Library Manager for refinement and exported as .CDB for use in MassHunter Explorer 2.0.
• Integration in MassHunter Explorer: Positive and negative datasets (MS1 and iterative MS/MS) were imported as separate projects for peak picking, normalization by tissue weight, statistical analysis and identification using the custom CDB plus optional databases (e.g., Plasma Lipid PCDL).

Main results and discussion

• Global separation: PCA on normalized data produced clear tissue-specific clustering, confirming that the analytical workflow preserves biologically meaningful variation between liver, BAT and WAT.
• High-confidence identifications: Combining iterative MS/MS, MS-DIAL curation and MassHunter Explorer matching supported confident assignment of 743 lipid species across classes.
• Feature-level discovery: Heatmap clustering of MS/MS-validated features highlighted tissue- and lipid-class-specific patterns. Volcano plot analysis facilitated rapid detection of significantly altered lipids between age cohorts.
• Example biological finding: A triacylglycerol estolide species (identified as TG 18:1_18:1_18:1;O(FA 16:0)) was enriched in older liver samples, supported by robust MS/MS spectral matching across tools—illustrating the discovery power of the combined workflow.

Benefits and practical applications of the method

• End-to-end integration: The described pipeline streamlines conversion of iterative MS/MS acquisitions into curated libraries and deploys them directly for quantitative/qualitative discovery in MassHunter Explorer.
• Improved confidence: Manual curation in MS-DIAL combined with high-resolution Q-TOF data and pooled QC iterative MS/MS enhances identification credibility for complex lipid species including modified lipids (e.g., estolides).
• Efficient discovery cycle: Built-in statistical tools and publication-quality visualizations in MassHunter Explorer minimize dependence on multiple external tools for downstream analysis and figure generation.
• Transferability: The approach supports addition of external databases (Lipid Annotator outputs, Plasma Lipid PCDL) enabling broader applicability across biological matrices and study designs.

Future trends and opportunities for use

• Automated and AI-guided annotation: Machine-learning approaches to accelerate spectral matching, reduce manual curation workload and prioritize biologically relevant features.
• Expanded spectral repositories: Wider sharing and standardization of high-quality iterative MS/MS libraries will improve coverage for rarer or modified lipid species.
• Multi-omics integration: Combining lipidomics with transcriptomics, proteomics and metabolomics in aging studies will strengthen mechanistic interpretation and biomarker discovery.
• Longitudinal and translational studies: Applying this workflow to time-course experiments and human cohorts will clarify causal links between lipid remodeling and age-related disease.
• Targeted follow-up: High-confidence non-targeted discoveries can be converted into targeted assays for absolute quantitation and validation in larger cohorts.

Conclusion

The combined experimental and informatics strategy—high-quality reversed-phase LC/Q-TOF acquisition, pooled QC iterative MS/MS, and an integrated analysis chain through MS-DIAL, ChemVista and MassHunter Explorer 2.0—delivers a practical, high-confidence workflow for non-targeted lipidomics. The approach enables comprehensive mapping of tissue-specific, age-dependent lipid changes and translates complex datasets into actionable biological hypotheses with publication-ready visualizations and robust statistical support.

References

1. Huynh R., et al. A Comprehensive, Curated, High-Throughput Method for the Detailed Analysis of the Plasma Lipidome. Agilent Application Note 59943447EN, 2021.
2. Takeda H., et al. Nature Communications, 2024.