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  • Title: [Development of a widely-targeted metabolomics method based on gas chromatography-mass spectrometry].
    Author: Wang YT, Yang Y, Sun XL, Ji J.
    Journal: Se Pu; 2023 Jun 08; 41(6):520-526. PubMed ID: 37259877.
    Abstract:
    Gas chromatography-mass spectrometry (GC-MS) detectors are widely used detection instruments owing to their distinct advantages over other analytical techniques, including lower sample consumption, higher sensitivity, faster analysis speed, and simultaneous separation and analysis. Metabolomics is an important component of system physiology that concerns systematic studies of the metabolite spectrum in one or more biological systems, such as cells, tissues, organs, body fluids, and organisms. Unfortunately, conventional GC-MS detectors also feature low scan rates, high ion loss rates, and a narrow concentration detection range, which limit their applications in the field of metabolomics. Therefore, establishing a GC-MS-based metabolomic analysis method with wide coverage is of great importance. In this research, a widely-targeted metabolomics method based on GC-MS is proposed. This method combines the universality of untargeted metabolomics with the accuracy of targeted metabolomics to realize the qualitative and semi-quantitative detection of numerous metabolites. It does not require a self-built database and exhibits high sensitivity, good repeatability, and strong support for a wide range of metabolic substances. The proposed method was used to establish the relationship between the retention time of straight-chain fatty acid methyl esters (FAMEs) and their retention index (RI) in the FiehnLib database based on the metabolite information stored in this database. We obtained a linear relationship that could be described by the equation y=40878x-47530, r2=0.9999. We then calculated the retention times of metabolites in the FiehnLib database under the experimental conditions based on their RI. In this way, the effects of significant variations in peak retention times owing to differences in the chromatographic column, temperature, carrier gas flow rate, and so on can be avoided. The retention time of a substance fluctuates within a certain threshold because of variations in instrument performance, matrix interference, and other factors. As such, the retention time threshold of the substance must be determined. In this paper, the retention time threshold was set to 0.15 min to avoid instrument fluctuations. The optimal scan interval was optimized to 0.20 s (possible values=0.10, 0.15, 0.20, 0.25, and 0.30 s) because longer sampling periods can lead to spectral data loss and reductions in the resolution of adjacent chromatographic peaks, whereas shorter sampling periods can result in deterioration of the signal-to-noise ratio of the collected signals. The metabolite quantification ions were optimized to avoid the interference of quantification ion peak accumulation in the case of similar peak times, and a selected ion monitoring (SIM) method table was constructed for 611 metabolites, covering 65% of the metabolic pathways in the KEGG (Kyoto Encyclopedia of Genes and Genomes). The developed method covered 39 pathways, including glycolysis, the tricarboxylic acid cycle, purine metabolism, pyrimidine metabolism, amino acid metabolism, and biosynthesis. Compared with the full-scan untargeted GC-MS method, the widely-targeted GC-MS method demonstrated a 20%-30% increase in the number of metabolites detected, as well as a 15%-20% increase in signal-to-noise ratio. The results of stability tests showed that 84% of the intraday relative standard deviations (RSDs) of metabolite retention times were less than 2% and 91% of that were less than 3%; moreover, 54% of the interday RSDs of metabolite retention times were less than 2% and 76% of that were less than 3%. The detection and analysis results of common biological samples confirmed that the proposed method greatly improved the quantity and signal-to-noise ratio of the detected metabolites and is applicable to substances that are thermally stable, volatile, or volatile after derivation and have relative molecular masses lower than 600. Thus, the widely-targeted GC-MS method can expand the application scope of GC-MS in metabolomics. 气相色谱-质谱法(GC-MS)的四极杆检测器具有扫描速率低、离子流失率高、浓度检测范围窄的特点,这些缺陷限制了该技术在代谢组学领域的广泛应用,因此亟需建立一种基于GC-MS的高覆盖率代谢组学分析方法。本文提出了一种基于GC-MS的广泛靶向代谢组学方法,广泛靶向代谢组学结合了靶向和非靶向的优点,可以实现对代谢物质的定性和半定量检测,该方法以The Fiehn library(FiehnLib)数据库中的代谢物质信息为基础,建立直链脂肪酸甲酯(FAMEs)的保留时间与FiehnLib数据库中的保留指数(RI)的关系,根据FiehnLib数据库中的保留指数计算数据库中代谢物质在具体实验条件下的保留时间;对比分析并确定保留时间的阈值为0.15 min,优化最佳扫描间隔为0.20 s;优化代谢物质的定量离子以避免出峰时间相近离子的干扰;最终构建了含有611种代谢物质的选择离子监测(SIM)方法表,这611种代谢物质覆盖了KEGG(Kyoto Encyclopedia of Genes and Genomes)中65%的代谢通路。与全扫描非靶向GC-MS方法相比,该广泛靶向GC-MS方法所检测的代谢物质数量增加20%~30%,信噪比提高15%~20%;稳定性试验结果表明,使用该方法分析样品时,84%的代谢物质保留时间的日内相对标准偏差(RSD)均小于2%,91%的代谢物质保留时间的日内RSD均小于3%;54%的代谢物质保留时间的日间RSD均小于2%,76%的代谢物质保留时间的日间RSD均小于3%;通过对常见生物样本的检测分析,证明该方法大大提升了被检测到的代谢物质的数量和信噪比,可扩展GC-MS在代谢组学中的应用范围。
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