Urine is a popular biofluid for metabolomics studies due to its simple, non-invasive collection and its availability in large quantities, permitting frequent sampling, replicate analyses, and sample banking. The biggest disadvantage with using urine is that it exhibits significant variability in concentration and composition within an individual over relatively short periods of time (arising from various external factors and internal processes regulating the body’s water and solute content). In treating the data from urinary metabolomics studies, one must account for the natural variability of urine concentrations to avoid erroneous data interpretation. Amongst various proposed approaches to account for broadly varying urine sample concentrations, normalization to creatinine has been widely accepted and is most commonly used. MS total useful signal (MSTUS) is another normalization method that has been recently reported for mass spectrometry (MS)-based metabolomics studies. Herein, we explored total useful peak area (TUPA), a modification of MSTUS that is applicable to GC×GC-TOFMS (and data from other separations platforms), for sample normalization in urinary metabolomics studies. Performance of TUPA was compared to the two most common normalization approaches, creatinine adjustment and Total Peak Area (TPA) normalization. Each normalized dataset was evaluated using Principal Component Analysis (PCA). The results showed that TUPA outperformed alternative normalization methods to overcome urine concentration variability. Results also conclusively demonstrate the risks in normalizing data to creatinine.

Comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) is a powerful tool for the analysis of complex mixtures, and it is ideally suited to discovery studies where the entire sample is potentially of interest. Unfortunately, when unit mass resolution mass spectrometers are used, many detected compounds have spectra that do not match well with libraries. This could be due to the compound not being in the library, or the compound having a weak/nonexistent molecular ion cluster. While high-speed, high-resolution mass spectrometers, or ion sources with softer ionization than 70 eV electron impact (EI) may help with some of this, many GC×GC systems presently in use employ low-resolution mass spectrometers and 70 eV EI ionization. Scripting tools that apply filters to GC×GC-TOFMS data based on logical operations applied to spectral and/or retention data have been used previously for environmental and petroleum samples. This approach rapidly filters GC×GC-TOFMS peak tables (or raw data) and is available in software from multiple vendors. In this work, we present a series of scripts that have been developed to rapidly classify major groups of compounds that are of relevance to metabolomics studies including: fatty acid methyl esters, free fatty acids, aldehydes, alcohols, ketones, amino acids, and carbohydrates.

Background: Plants constantly produce primary and secondary metabolites, and a significant fraction of these are volatile organic compounds (VOCs). Factors including the life stage of the plant, temperature, environment, and stress influence the abundance and types of VOCs emitted. The analysis of VOCs released by plants during different stages or with different conditions provides insight into plant metabolism and stress responses. Collecting the VOC profiles of plants in vivo makes it possible to obtain a representative sample of the entire plant volatilome under controlled conditions with minimal invasiveness. In addition, in vivo sampling can also be used to compare the impacts of different environmental conditions or stressors on plants, i.e., the presence/absence of a pest or amount of nitrogen in soil. Methods: In this study, an in vivo plant sampling technique is introduced and validated using active sampling and thermal desorption (TD) tubes with comprehensive two-dimensional gas chromatography coupled to a time-of-flight mass spectrometer (TD-GC×GC-TOFMS). The purpose of this work is to highlight a novel technique to analyze headspace secondary plant metabolites with a minimal invasiveness. Results: It was concluded that in vivo active sampling onto TD tubes provides a wider global coverage of compounds and larger peak areas when compared to extraction by solid-phase microextraction (SPME). Additionally, the Horwitz ratio of active sampling onto TD tubes was 0.893, demonstrating this technique to be a reliable and reproducible method. Lastly, a variety of plants were sampled to assess the versatility of this technique across various plant species with different sizes and volatile profiles. Hundreds of compounds were measured with this analysis, including terpenes, aldehydes, ketones, terpenoids, and alcohols. Conclusions: This novel in vivo active sampling method provides an additional technique for extracting and analyzing volatile secondary plant metabolites.

Stool is a mixture of excrement, microbiota, enzymes, undigested material, and small molecules. Fecal metabolomics has gained interest recently, owing to advances in metabolomics and growing research into both the host’s physiology and the gut microbiome. One challenge with fecal metabolomics is preserving the sample integrity from collection until analysis, as the microbiota and enzymes continue to alter the metabolome following defecation. Currently, flash-freezing or lyophilization are utilized to minimize post-collection metabolome changes; however, this requires complex equipment and immediate processing, precluding the possibility for at-home sampling. Commercial devices containing stabilizing solvents have been developed to facilitate at-home collection, ambient transport, and sample storage. Here, we explore the efficacy of a commercially available stool collection device with a stabilization reagent tailored to fecal metabolomics. Stool samples from six donors were either processed shortly post-collection or stored at room temperature for seven days in the tube, with and without the stabilization reagent. Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS)-based untargeted metabolomics was utilized for analyzing extracted metabolites. Chemometric analysis was used to evaluate the performance of the device. We found that the device with the stabilization reagent minimized changes in the metabolite profile relative to unstabilized stool left at room temperature for one week.