Department of Human Nutrition, Faculty of Life Sciences, University of Copenhagen, Frederiksberg, Denmark.
Anal Chim Acta. 2012 Mar 9;718:47-57. doi: 10.1016/j.aca.2011.12.065. Epub 2012 Jan 10.
Metabolomics and metabolic fingerprinting are being extensively employed for improved understanding of biological changes induced by endogenous or exogenous factors. Blood serum or plasma samples are often employed for metabolomics studies. Plasma protein precipitation (PPP) is currently performed in most laboratories before LC-MS analysis. However, the impact of fat content in plasma samples on metabolite coverage has not previously been investigated. Here, we have studied whether PPP procedures influence coverage of plasma metabolites from high-fat plasma samples. An optimized UPLC-QTOF/MS metabolic fingerprinting approach and multivariate modeling (PCA and OPLS-DA) were utilized for finding characteristic metabolite changes induced by two PPP procedures; centrifugation and filtration. We used 12-h fasting samples and postprandial samples collected at 2h after a standardized high-fat protein-rich meal in obese non-diabetic subjects recruited in a dietary intervention. The two PPP procedures as well as external and internal standards (ISs) were used to track errors in response normalization and quantification. Remarkably and sometimes uniquely, the fPPP, but not the cPPP approach, recovered not only high molecular weight (HMW) lipophilic metabolites, but also small molecular weight (SMW) relatively polar metabolites. Characteristic SMW markers of postprandial samples were aromatic and branched-chain amino acids that were elevated (p<0.001) as a consequence of the protein challenge. In contrast, some HMW lipophilic species, e.g. acylcarnitines, were moderately lower (p<0.001) in postprandial samples. LysoPCs were largely unaffected. In conclusion, the fPPP procedure is recommended for processing high-fat plasma samples in metabolomics studies. While method improvements presented here were clear, use of several ISs revealed substantial challenges to untargeted metabolomics due to large and variable matrix effects.
代谢组学和代谢指纹图谱广泛应用于深入了解内源性或外源性因素引起的生物学变化。通常使用血清或血浆样本进行代谢组学研究。在 LC-MS 分析之前，大多数实验室都进行血浆蛋白沉淀（PPP）。然而，以前尚未研究过血浆样本中的脂肪含量对代谢物覆盖率的影响。在这里，我们研究了 PPP 程序是否会影响高脂肪血浆样品中血浆代谢物的覆盖范围。我们使用优化的 UPLC-QTOF/MS 代谢指纹图谱方法和多元建模（PCA 和 OPLS-DA）来寻找两种 PPP 程序（离心和过滤）诱导的特征代谢物变化。我们使用 12 小时禁食样本和肥胖非糖尿病受试者在进行高蛋白高脂肪餐后 2 小时采集的餐后样本进行研究。我们使用两种 PPP 程序以及外部和内部标准（IS）来跟踪响应归一化和定量中的误差。值得注意的是，有时是唯一的，fPPP 而不是 cPPP 方法不仅恢复了高分子量（HMW）疏水性代谢物，而且还恢复了小分子质量（SMW）相对极性代谢物。餐后样本的特征 SMW 标志物是芳香族和支链氨基酸，由于蛋白质挑战而升高（p<0.001）。相反，一些 HMW 亲脂性物种，例如酰基辅酶 A，在餐后样本中适度降低（p<0.001）。溶血磷脂酰胆碱（LysoPCs）受影响不大。总之，建议在代谢组学研究中使用 fPPP 程序处理高脂肪血浆样本。虽然这里提出的方法改进是明确的，但使用多个 IS 显示出由于基质效应大且可变，对非靶向代谢组学存在重大挑战。
