Department of Analytical Chemistry, Israel Institute for Biological Research1, P.O. Box 19, Ness Ziona 7410001, Israel.
Scent Medical Technologies, Rehovot 7670107, Israel.
Molecules. 2022 Mar 23;27(7):2063. doi: 10.3390/molecules27072063.
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.
呼出气挥发性代谢物有望成为有诊断价值的生物标志物。然而，由于质量低、化学组成相似、浓度低，因此在数据处理中需要解决如何区分有价值的标志物的问题。本文介绍了一种名为 pyAIR 的软件工具，用于搜索多组数据集的挥发性有机化合物（VOC）标志物，专门针对热解吸气相色谱-高分辨率质谱（TD-GC-HRMS）输出设计。pyAIR 通过光谱相似性和保留时间（RT）对样品中的化合物进行对齐，并对具有强度差异的化合物进行统计比较。该工作流程已成功在六个浓度的 27 种模型 VOC 气态样品上进行了测试和评估，分为三组，浓度低至 0.3 nL/L。所有分析物均被正确检测和对齐。超过 80%的分析物被发现是具有统计学意义的标志物（p 值<0.05）；其中一些被归类为可能有意义的标志物（p 值<0.1），而一些由于背景水平被去除。在所有组间比较中，假标志物的比例都很低。这些结果表明了 pyAIR 在痕量呼吸组学领域的潜力，具有区分多个组（如疾病阶段）的能力。
