Journal des techniques analytiques et bioanalytiques

Abstrait

Optimisation of Sample Preparation for Direct SPME-GC-MS Analysis of Murine and Human Faecal Volatile Organic Compounds for Metabolomic Studies

Reade S, Mayor A, Aggio R, Khalid T, Pritchard DM, Ewer AK and Probert CS

The analysis of volatile organic compounds (VOCs) emitted from biological fluids such as blood, urine, breath and faeces, has been receiving more attention from researchers and clinicians due to their contribution to the metabolome and potential use as diagnostic tools in clinical settings. The faecal metabolome represents the final product of a complex interaction involving the gut microbiota and cell metabolism and an accurate measurement of the faecal volatile organic metabolome enables a better understanding of disease-related metabolic pathways and, eventually, identifying potential biomarkers. However, there is a lack of published evidence evaluating the sample preparation steps for faecal metabolome analysis and no well-defined protocol has been established. Consequently, different research groups employ diverse methodologies, which, ultimately, prevent comparison of results between laboratories.

We evaluated different aspects of sample preparation when processing murine and human faecal samples through a pipeline involving solid phase micro-extraction (SPME) coupled to gas chromatography-mass spectrometry (GC-MS). We identified the sample volume, the SPME fibre coating, the extraction conditions and the vial volume that produce the most accurate and reproducible results. Finally, we propose an optimized method for the direct SPME-GC-MS analysis of VOCs in murine and human faecal samples.

To the best of our knowledge, this is the first work evaluating different aspects of sample preparation for direct SPME-GC-MS analysis of VOCs and the first method proposed for the analysis of murine and human faecal samples. In addition, our proposed method can be coupled to the Automated Mass Spectral Deconvolution and Identification System (AMDIS) and the R software package, Metab, in order to produce results in a reliable and high-throughput manner.