Quantification of diacylglycerol and triacylglycerol species in human fecal samples by flow injection Fourier transform mass spectrometry

Abstract

The intestinal microbiome plays an important role in human health and disease and fecal materials reflect the microbial activity. Thus, analysis of fecal metabolites provides insight in metabolic interactions between gut microbiota and host organism. In this work, we applied flow injection analysis coupled to Fourier transform mass spectrometry (FIA-FTMS) to identify and quantify lipid species in human fecal samples. Fecal homogenates were subjected to lipid extraction and analyzed by FIA-FTMS. The analysis of different subjects revealed a vast heterogeneity of lipid species abundance. The majority of samples displayed prominent signals of triacylglycerol (TG) and diacylglycerol (DG) species that could be verified by MS2 spectra. Therefore, we focused on the quantification of TG and DG. Method validation included limit of quantification, linearity, evaluation of matrix effects, recovery, and reproducibility. The validation experiments demonstrated the suitability of the method, with exception for approximately 10% of samples, where we observed coefficients of variation higher than 15%. Impaired reproducibility was related to sample inhomogeneity and could not be improved by additional sample preparation steps. Additionally, these experiments demonstrated that compared with aqueous samples, samples containing isopropanol showed higher amounts of DG, presumably due to lysis of bacteria and increased TG lipolysis. These effects were sample-specific and substantiate the high heterogeneity of fecal materials as well as the need for further evaluation of pre-analytic conditions. In summary, FIA-FTMS offers a fast and accurate tool to quantify DG and TG species and is suitable to provide insight into the fecal lipidome and its role in health and disease.

Overview of Lipidomic Analysis of Triglyceride Molecular Species in Biological Lipid Extracts

“…However, because this method represents a direct-infusion-based approach, some scientists also call it shotgun lipidomics. Analysis of TG species after flow injection has also been performed. , In fact, after flow injection, either MDMS-SL-type or DDA-driven analysis as described above could be conducted to a certain degree depending upon the coupled mass spectrometer. Moreover, similar to LC–MS MRM methodology, setting up different MRM transitions for detection of neutral losses of different fatty acids during the flow could also be conducted, although resolving isomeric TG species is difficult and inclusion of false-positive identification of TG species is very likely in this case.…”

Exploration of the Lipid Profile of Edible Oleaginous Microgreens by Mass Spectrometry-Based Lipidomics

“…As an alternative to RPLC, promising analytical methods based on flow injection analysis (FIA) coupled to ESI-HRMS have been recently developed for the characterization of TGs and SEs in human biological samples. , The use of FIA-ESI-HRMS methods is also highly suitable for the reliable quantification of lipids, due to the existence of the same ionization conditions in the ESI source, , although a more extensive ion suppression effect might be observed with respect to approaches relying on liquid chromatography. In the present study, a previously developed FIA-ESI-HRMS workflow was thus implemented for the quantitative characterization of TGs and SEs in chia, flax, soybean, sunflower, and rapeseed microgreens.…”

Fecal and Urinary Adipokines as Disease Biomarkers

“…The necessity to standardize sample collection is apparent when considering urine for biomarker detection, as urine collected in the morning will differ in protein composition compared to urine collected at later times during the day (10–24 h), and their analysis could easily produce discordant results [ 23 ]. Other factors contributing to the currently limited utilization of urine and fecal biomarkers include the need for data normalization [ 23 , 41 , 42 ], adequate test sensitivity and specificity that can discriminate between patients and controls, and defined and established positive and negative predictive values [ 43 ].…”