Profiling volatile organic compounds from human plasma using GC × GC-ToFMS
Ning Sun1, Preethi Krishnan2, Christiaan A Rees3
1School of Biomedical Engineering, University of British Columbia, Vancouver, Canada.
Abstract:
Volatile organic compounds (VOCs) originating from human metabolic activities can be detected in, for example, breath, urine, feces, and blood. Thus, attention has been given to identifying VOCs from the above matrices. Studies identifying and measuring human blood VOCs are limited to those focusing on monitoring specific pollutants, or blood storage and/or decomposition. However, a comprehensive characterization of VOCs in human blood collected for routine diagnostic testing is lacking. In this pilot study, 72 blood-derived plasma samples were obtained from apparently healthy adult participants. VOCs were extracted from plasma using solid-phase microextraction and analyzed using comprehensive two-dimensional gas chromatography tandem time-of-flight mass spectrometry. Chromatographic data were aligned, and putative compound identities were assigned via spectral library comparison. All statistical analysis, including contaminant removal, data normalization, and transformation were performed usingR. We identified 401 features which we called the pan volatilome of human plasma. Of the 401 features, 34 were present in all the samples with less than 15% variance (core molecules), 210 were present in ⩾10% but <100% of the samples (accessory molecules), and 157 were present in less than 10% of the samples (rare molecules). The core molecules, consisting of aliphatic, aromatic, and carbonyl compounds were validated using 25 additional samples. The validation accuracy was 99.9%. Of the 34 core molecules, 2 molecules (octan-2-one and 4-methyl heptane) have been identified from the plasma samples for the first time. Overall, our pilot study establishes the methodology of profiling VOCs in human plasma and will serve as a resource for blood-derived VOCs that can complement future biomarker studies using different matrices with more heterogeneous cohorts.
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