解析影响代谢组学研究的因素以减少生化和化学噪音
前言：
Metabolomics is used within the pharmaceutical industry to investigate biochemical changes resulting from pharmacological responses to potential drug candidates. The ability to identify markers of toxicity/efficacy can significantly accelerate drug discovery and help define the appropriate clinical plan. Data from liquid chromatography-mass spectrometry (LC-MS) metabolomic profiling experiments contains large amounts of chemical background that often confounds biomarker discovery. New mass spectrometer technology and data processing software were utilized here to reduce chemical background in animal experiments investigating the relation of animal age and nutrition to discerning drug-induced changes.
仪器：
Thermo Scientific Q Exactive Orbitrap mass spectrometer
结论：
The Q Exactive™ Orbitrap LC-MS provides a precise and robust platform for untargeted metabolomics studies. To deal with the numerous sources of noise inherent to these studies, intelligent data reduction tools found in SIEVE™ software can be used to significantly reduce the chemical noise. In addition, the use of systematic studies help to characterize biological noise, while metabolomic prescreening can help indentify biological outliers to ensure homogeneity within an entire study.
As demonstrated in this study, fasting is a significant variable in model design, and fasting data can help contextualize drug-induced changes in many metabolites. Fasting in rats was found to have a profound impact on metabolomic profiles. Although most metabolic changes were modest in extent, fasting exacerbated or obscured some drug-induced metabolic effects.
