Mice fecal metabolomics by LC-MS/MS: Comparison between young mice and old mice

Significance of the Topic

The gut microbiome influences host health and disease through metabolite production. Profiling these low-molecular-weight compounds in feces provides insights into microbial activity and age-related physiological changes.

Study Objectives and Overview

This work compares fecal metabolite profiles of young (10-week-old) and old (70-week-old) mice using complementary LC-MS/MS methods, aiming to identify age-dependent markers.

Methods and Sample Preparation

Fecal samples were suspended in phosphate-buffered saline, vortexed, cooled, centrifuged and filtered. Two LC-MS/MS approaches were applied:

• Ion-pairing LC-MS/MS: Mastro C18 column, 15 mM acetate and 10 mM tributylamine mobile phase, negative-ion MRM.
• Non-ion-pairing LC-MS/MS: HS F5 column, 0.1% formic acid in water/acetonitrile, positive/negative MRM.

Used Instrumentation

The analyses employed a Shimadzu Nexera X2 UHPLC system coupled to LCMS-8040 and LCMS-8050 mass spectrometers. Chromatographic separation used Mastro C18 and Discovery HS F5 columns under controlled temperature and flow conditions.

Key Results and Discussion

The ion-pairing method detected 17 metabolites (mainly amino acids), while the non-ion-pairing method quantified 75 compounds including amino acids, nucleotides and organic acids (RSD <20%). PCA distinctly separated young and old mouse fecal profiles.
Age-related differences included:

• Decreased in old mice: bile acids (cholic acid), amino acids (glutamic acid, glycine), nucleosides (adenine, adenosine).
• Increased in old mice: arginine, adenosine monophosphate, GABA, histamine.

The decline in cholic acid suggests reduced bile secretion with age, while altered arginine and polyamine metabolism reflect changes in cellular proliferation dynamics.

Benefits and Practical Applications

This metabolomic platform enables biomarker discovery for aging and intestinal health, with applications in nutritional studies, disease modeling and gut microbiome research.

Future Trends and Applications

Integration with metagenomic and transcriptomic data will enhance mechanistic understanding. Expanding compound coverage and automating sample throughput can facilitate clinical translation and longitudinal studies.

Conclusion

LC-MS/MS metabolomic profiling of mouse feces using ion-pairing and non-ion-pairing methods provides a robust approach to detect age-dependent metabolic shifts and supports broader applications in microbiome and aging research.