The human respiratory system is a highly complex matrix that exhales many volatile organic compounds (VOCs). Breath‐exhaled VOCs are often “unknowns” and possess low concentrations, which make their analysis, peak digging and data processing challenging. We report a new methodology, applied in a proof‐of‐concept experiment, for the detection of VOCs in breath. For this purpose, we developed and compared four complementary analysis methods based on solid‐phase microextraction and thermal desorption (TD) tubes with two GC–mass spectrometer (MS) methods. Using eight model compounds, we obtained an LOD range of 0.02–20 ng/ml. We found that in breath analysis, sampling the exhausted air from Tedlar bags is better when TD tubes are used, not only because of the preconcentration but also due to the stability of analytes in the TD tubes. Data processing (peak picking) was based on two data retrieval approaches with an in‐house script written for comparison and differentiation between two populations: sick and healthy. We found it best to use “raw” AMDIS deconvolution data (.ELU) rather than its NIST (.FIN) identification data for comparison between samples. A successful demonstration of this method was conducted in a pilot study (n = 21) that took place in a closed hospital ward (Covid‐19 ward) with the discovery of four potential markers. These preliminary findings, at the molecular level, demonstrate the capabilities of our method and can be applied in larger and more comprehensive experiments in the omics world.
In this paper we describe a rapid method for microscale microwave assisted acid hydrolysis followed by quantitative amino acid analysis, using liquid chromatography mass spectrometry UPLC-ESI-MS (QTOF) without derivatization.
Volatile metabolites in exhaled air have promising potential as diagnostic biomarkers. However, the combination of low mass, similar chemical composition, and low concentrations introduces the challenge of sorting the data to identify markers of value. In this paper, we report the development of pyAIR, a software tool for searching for volatile organic compounds (VOCs) markers in multi-group datasets, tailored for Thermal-Desorption Gas-Chromatography High Resolution Mass-Spectrometry (TD-GC-HRMS) output. pyAIR aligns the compounds between samples by spectral similarity coupled with retention times (RT), and statistically compares the groups for compounds that differ by intensity. This workflow was successfully tested and evaluated on gaseous samples spiked with 27 model VOCs at six concentrations, divided into three groups, down to 0.3 nL/L. All analytes were correctly detected and aligned. More than 80% were found to be significant markers with a p-value < 0.05; several were classified as possibly significant markers (p-value < 0.1), while a few were removed due to background level. In all group comparisons, low rates of false markers were found. These results showed the potential of pyAIR in the field of trace-level breathomics, with the capability to differentially examine several groups, such as stages of illness.
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