Volatile Organic Compound and Proteomics Data from the Same Exhaled Breath Condensate Sample.

IF 3.4 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS
Shannon E Schrader, Joshua R Hansen, Isabelle O'Bryon, Laura E Ruebush, Nicolaas E Deutz, Jon H Wahl, Brooke Kaiser
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Abstract

Obtaining multiple sample types from the same exhaled breath condensate (EBC) sample can reduce the number of samples needed for diagnostics purposes, allowing for sampling to be completed quicker and making it even easier to collect breath from patients. In this study, we performed analysis for volatile organic compounds (VOCs) and proteins from the same EBC sample. Pooled EBC samples were split into two groups: three samples that utilized immersion thin film-solid phase microextraction (TF-SPME) sampling for VOCs analysis and three samples that did not undergo TF-SPME sampling (non-TF-SPME). All six EBC samples were analyzed using liquid chromatography with tandem mass spectrometry (LC-MS/MS) for proteomics analysis. VOCs were analyzed via two-dimensional gas chromatography-mass spectrometry (GC x GC-MS). One hundred and eighty-four VOCs were found to be more abundant in EBC samples compared to blank or controls. There was no significant difference in the number of proteins detected in the TF-SPME samples compared to the non-TF-SPME samples and 144 of the 206 total unique proteins detected were found in both sample groups. These results indicate that TF-SPME sampling does not negatively affect the number of proteins that can be detected in EBC. This work is a step towards linking VOC and protein data together to obtain multi-omics breath data from a single breath sample. EBC samples were collected as part of a vaccination clinical trial (NCT05346302).

挥发性有机化合物和蛋白质组学数据来自相同的呼出冷凝水样本。
从相同的呼气冷凝水(EBC)样本中获取多种样本类型可以减少诊断所需的样本数量,从而更快地完成采样,并使收集患者的呼吸变得更加容易。在本研究中,我们对来自同一EBC样品的挥发性有机化合物(VOCs)和蛋白质进行了分析。将收集到的EBC样品分为两组:三组采用浸没式薄膜-固相微萃取(TF-SPME)取样进行VOCs分析,三组不采用TF-SPME取样(非TF-SPME)。采用液相色谱串联质谱法(LC-MS/MS)对6份EBC样品进行蛋白质组学分析。通过二维气相色谱-质谱(GC x GC- ms)分析挥发性有机化合物。与空白或对照相比,在EBC样本中发现了184种更丰富的挥发性有机化合物。与非TF-SPME样品相比,TF-SPME样品中检测到的蛋白质数量没有显著差异,两组样品中检测到的206种独特蛋白质中有144种。这些结果表明,TF-SPME取样不会对EBC中可检测到的蛋白质数量产生负面影响。这项工作是将VOC和蛋白质数据连接在一起以从单个呼吸样本中获得多组学呼吸数据的一步。EBC样本是作为疫苗接种临床试验(NCT05346302)的一部分收集的。
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来源期刊
Journal of breath research
Journal of breath research BIOCHEMICAL RESEARCH METHODS-RESPIRATORY SYSTEM
CiteScore
7.60
自引率
21.10%
发文量
49
审稿时长
>12 weeks
期刊介绍: Journal of Breath Research is dedicated to all aspects of scientific breath research. The traditional focus is on analysis of volatile compounds and aerosols in exhaled breath for the investigation of exogenous exposures, metabolism, toxicology, health status and the diagnosis of disease and breath odours. The journal also welcomes other breath-related topics. Typical areas of interest include: Big laboratory instrumentation: describing new state-of-the-art analytical instrumentation capable of performing high-resolution discovery and targeted breath research; exploiting complex technologies drawn from other areas of biochemistry and genetics for breath research. Engineering solutions: developing new breath sampling technologies for condensate and aerosols, for chemical and optical sensors, for extraction and sample preparation methods, for automation and standardization, and for multiplex analyses to preserve the breath matrix and facilitating analytical throughput. Measure exhaled constituents (e.g. CO2, acetone, isoprene) as markers of human presence or mitigate such contaminants in enclosed environments. Human and animal in vivo studies: decoding the ''breath exposome'', implementing exposure and intervention studies, performing cross-sectional and case-control research, assaying immune and inflammatory response, and testing mammalian host response to infections and exogenous exposures to develop information directly applicable to systems biology. Studying inhalation toxicology; inhaled breath as a source of internal dose; resultant blood, breath and urinary biomarkers linked to inhalation pathway. Cellular and molecular level in vitro studies. Clinical, pharmacological and forensic applications. Mathematical, statistical and graphical data interpretation.
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