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.