Integrating LC-MS and Network-Based Analyses for Untargeted Fecal Metabolomics in Microbiome Research

The gut microbiome plays a critical role in host health, and faecal microbiota transplantation (FMT) has emerged as a promising strategy to restore microbial and metabolic balance after antibiotic-induced disruption. To investigate the metabolic mechanisms underlying this process, we conducted untargeted LC-MS-based metabolomics on faecal samples from antibiotic-treated mice.

Faecal metabolite profiling was performed using a Thermo Fisher Scientific UltiMate 3000 HPLC system coupled to a Q Exactive Plus mass spectrometer with electrospray ionization operated in both positive and negative modes. Separation was achieved on a Thermo Hypersil Gold column (1.9 μm, 100 mm × 2.1 mm) with a mobile phase of water (0.1% formic acid) and acetonitrile (0.1% formic acid) at 300 μL/min. The gradient increased from 2% to 100% B over 11 minutes, was held for 4 minutes, and then re-equilibrated for 5 minutes.

Raw spectral data were processed through a computational workflow integrating preprocessing, feature extraction, and statistical analysis. Data interpretation employed MZmine, MetaboAnalyst, and network-based tools such as GNPS and Cytoscape for clustering and visualization, with compound annotation guided by spectral library matching and in silico prediction (MS2Query). Preliminary results revealed approximately 1,000 metabolite features, with clear separation between antibiotic-treated and untreated groups in PCA analysis. Around 60 discriminant features have been prioritised for subsequent putative annotation.

This study establishes a reproducible workflow for untargeted faecal metabolomics, combining high-resolution LC-MS acquisition with open-source computational platforms. Ongoing work will extend the analysis to longitudinal sampling and integrate parallel microbiome sequencing to assess the durability of FMT effects and provide a systems-level view of host–microbiome interactions.