Advanced Analytical Workflow for Fecal Volatilome Profiling in Non-Celiac Gluten Sensitivity Using Comprehensive Two-Dimensional Gas Chromatography-Time of Flight Mass Spectrometry (GC×GC-TOF MS)

The fecal volatilome refers to the totality of volatile organic compounds (VOCs) present in fecal samples, which are produced through metabolic processes involving the host and gut microbiota. These compounds, resulting from fermentation, microbial degradation and metabolic activities in the gastrointestinal tract, serve as molecular signatures of digestive health, diet and microbiota composition. Despite its potential as a non-invasive marker of gut function, the study of the fecal volatilome has been limited by the challenges associated with analyzing its complex chemical profile. To overcome these limitations, we employed comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOF MS), a powerful analytical technique offering high resolution, sensitivity and the ability to resolve co-eluting compounds in highly complex matrices [1]. This study focused on a cohort of individuals (n=50) diagnosed with non-celiac gluten sensitivity (NCGS), subjected to a controlled six-week intervention comprising a gluten-free diet combined with probiotic supplementation [2]. Fecal samples were collected after four weeks (T1) of gluten-free diet and after six weeks (T2), following continued supplementation and reintroduction of gluten. Sample preparation involved headspace solid-phase microextraction (HS-SPME), optimized to capture a broader range of volatiles. Data processing followed a hybrid untargeted/targeted fingerprinting (UT fingerprinting) strategy [3], enabling the detection and alignment of over 960 VOC features across the whole sample set. Approximately 200 compounds were putatively identified based on mass spectral matching and retention index criteria, including short-chain fatty acid esters, primary alcohols, aldehydes and terpenoids. These features were evaluated using multivariate statistical tools including Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA). Classification models yielded 89% and 90% cross-validation accuracy at T1 and T2, respectively, based on both normalized and absolute feature responses. The analysis revealed significant shifts in the composition of the fecal volatilome profiles of glutensensitive individuals, with characteristic enrichment patterns of 1-hexanol, acetone, benzaldehyde, phenol and propanoic acid. These features contributed to differentiate the groups under different experimental conditions. The use of GC×GC-TOF MS in combination with untargeted-targeted fingerprinting enabled high-resolution, reproducible detection of semi-quantitative VOC patterns overcoming limitations associated with co-elution and signal complexity. Despite the limited sample size, the analytical workflow allowed for detailed mapping of the fecal volatilome and supported the detection of intervention-related chemical signatures. This approach lays the foundation for future integration of multi-omics and provides a robust tool to investigate complex biochemical responses to dietary modulations.