A biological model of the ageing metabolome reveals potential clinically relevant biomarkers

Importance of the Topic

Cellular ageing induces alterations in the metabolome through mechanisms like mitochondrial dysfunction, genomic instability, and senescence-associated secretory changes. Identifying metabolic biomarkers of ageing can improve understanding of age-related diseases and support development of clinical diagnostics and interventions.

Objectives and Study Overview

This study applied an untargeted metabolomics workflow to human foreskin fibroblasts (HFF-1) and their culture media, comparing early-passage (passage 3) and late-passage (passage 20) cells as a model of replicative senescence. Key goals included profiling intracellular and extracellular metabolites, identifying statistically significant changes, and proposing potential biomarkers of ageing.

Methodology

An untargeted HILIC-LC-DIA-MS/MS method was used for broad detection of polar metabolites. Data acquisition employed both data-independent (DIA) and data-dependent (DDA) MS/MS scans. MS-DIAL facilitated feature detection and alignment, while MetaboAnalyst conducted statistical analysis (volcano plots with fold change >1.5, p<0.05). LabSolutions Insight matched significant features against in-house and public MS/MS libraries to confirm metabolite identities.

Used Instrumentation

• Shim-pack Velox HILIC column (2.1×100 mm, 2.7 μm) at 40°C, 0.3 mL/min flow.
• Binary mobile phases: water and acetonitrile with 10 mM ammonium formate and 0.1% formic acid.
• Shimadzu LCMS-9030 Q-TOF mass spectrometer in positive and negative ion modes.
• MS scan range m/z 60–1000, DIA precursor window 35 Da, collision energy 5–55 V, DDA with 20 MS/MS scans per cycle.

Main Results and Discussion

Comparisons between passage 3 and passage 20 cells revealed significant changes in amino acids, nucleotides, vitamins, carnitines, and lysophospholipids. Intracellular senescent cells showed elevated levels of polyamines (e.g., spermidine, N8-acetylspermidine) and certain lipids, alongside reduced levels of key metabolites such as glutathione and essential amino acids. Extracellular profiling identified increased extracellular phosphorylcholine, ribose-5-phosphate, and inosine in aged media, while other nutrients declined. These findings highlight specific metabolic pathways affected by cellular ageing.

Benefits and Practical Applications

• Provides a comprehensive untargeted metabolomics protocol adaptable to various cell models.
• Identifies putative ageing biomarkers with potential clinical relevance for diagnostics.
• Supports quality control in cell culture and pharmaceutical research by monitoring metabolic shifts.

Future Trends and Opportunities

• Integration with transcriptomics and proteomics to build multi-omics ageing profiles.
• Targeted validation of identified biomarkers in clinical samples.
• Application of machine learning for predictive modelling of cellular senescence.
• Development of standardized metabolomics workflows for ageing research in diverse cell types.

Conclusion

The untargeted HILIC-LC-DIA-MS/MS workflow effectively captured senescence-associated metabolic alterations in HFF-1 cells and their media. The study revealed promising biomarkers of replicative ageing, demonstrating the utility of high-resolution metabolomics in ageing research and potential clinical translation.

References

No external literature list provided.