低分辨率GC-MS在生物样品代谢谱分析中的应用。方法的更新

A. Ukolov
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引用次数: 2

摘要

介绍。将代谢组学方法引入毒理学研究的实践,以及实验室确定低分子代谢生物标志物作用的方法学能力的扩展,使得更有效地检测和识别新的生物标志物成为可能。材料和方法。血浆和尿液代谢谱分析使用岛津QP2010plus或安捷伦5975C气相色谱仪。利用优化后的内源化合物分析特征数据库和AMDIS系统对结果进行处理;采用NIST/EPA/NIH 2017对检测到的化合物进行鉴定。使用Statistica进行统计处理。结果。采用气相色谱-质谱联用两阶段制备血浆和尿液样品,选择混合内标,确定化合物内源性代谢物清单,并评价其测定的计量学特性。的局限性。适用于GC-MS测定的分析物列表仅限于挥发性和条件挥发性化合物。结论。使用根据标准化程序制备的样品代谢物的优化数据库进行分析,可以过滤掉重复性低的分析物。小型(最多100个)色谱数据库可以提高鉴定的可靠性,消除保留时间漂移的影响,从而在不增加实验动物数量的情况下提高整个实验的统计能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Low-resolution GC-MS in metabolic profiling of biological samples with the mass spectrometry. Updating of the method
Introduction. The introduction of metabolomic approaches into the practice of toxicological studies, as well as the expansion of the methodological capabilities of the laboratory for the determination of low-molecular, metabolic biomarkers of the effect, makes it possible to more effectively detect and identify new biomarkers. Material and methods. For metabolic profiling of blood plasma and urine samples, Shimadzu QP2010plus or Agilent 5975C gas chromatomass spectrometers were used. The results were processed using optimized databases of analytical characteristics of endogenous compounds and the AMDIS system; NIST/EPA/NIH 2017 was used to identify the detected compounds. Statistical processing was performed using Statistica. Results. A two-stage procedure for preparing blood plasma and urine samples for analysis by GC-MS was developed, a mixture of internal standards was selected, a list of compounds - endogenous metabolites was determined, and the metrological characteristics of their determination were evaluated. Limitations. The list of analytes suitable for determination by GC-MS is limited to volatile and conditionally volatile compounds. Conclusion. Using an optimized database of sample metabolites prepared for analysis according to a standardized procedure allows filtering out analytes with low reproducibility. Small (up to 100) chromatospectral databases make it possible to increase the reliability of identification, eliminate the effect of retention time drift, and, as a result, increase the statistical power of the entire experiment without increasing the number of laboratory animals.
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