Department of Biomedical Engineering, The University of Memphis, Memphis, TN, USA.
College of Health Sciences, The University of Memphis, Memphis, TN, USA.
Magn Reson Chem. 2021 Feb;59(2):138-146. doi: 10.1002/mrc.5092. Epub 2020 Sep 22.
Lipid profiling by H-NMR has gained increasing utility in many fields because of its intrinsically quantitative, nondestructive nature and the ability to differentiate small molecules based on their spectral location. Most nuclear magnetic resonance (NMR) techniques for metabolite quantification use frequency domain analysis that involves many user-dependent steps such as phase and baseline correction and quantification by either manual integration or peak fitting. Recently, Bayesian analysis of time-domain NMR data has been shown to reduce operator bias and increase automation in NMR spectroscopy. In this study, we demonstrate the use of CRAFT (complete reduction to amplitude-frequency table), a Bayesian-based approach to automate processing in NMR-based lipidomics using lipid standards and tissue samples of healthy and tumor-bearing mice supplemented with leucine. Complex mixtures of lipid standards were prepared and examined using CRAFT to validate it against conventional Fourier transform (FT)-NMR and derive a fingerprint to be used for analyzing lipid profiles of serum and liver samples. CRAFT and FT-NMR were comparable in accuracy, with CRAFT achieving higher correlation in quantifying several lipid species. Analysis of the serum lipidome of tumor-bearing mice revealed hyperlipidemia and no signs of hepatic triglyceride accumulation compared with that of the healthy group demonstrating that the tumor-bearing mice were in a state of precachexia. Leucine-supplementation was associated with minimal changes in the lipid profile in both tissues. In conclusion, our study demonstrates that the CRAFT method can accurately identify and quantify lipids in complex lipid mixtures and murine tissue samples and, hence, will increase automation and reproducibility in NMR-based lipidomics.
基于质子核磁共振（1H-NMR）的脂质组学分析因其固有的定量、非破坏性本质以及基于其谱峰位置区分小分子的能力，在许多领域得到了越来越广泛的应用。大多数用于代谢物定量的核磁共振（NMR）技术都采用频域分析，涉及许多依赖于用户的步骤，如相位和基线校正，以及通过手动积分或峰拟合进行定量。最近，基于贝叶斯的时域 NMR 数据分析方法已被证明可以减少操作员的偏差并增加 NMR 光谱分析的自动化程度。在这项研究中，我们展示了 CRAFT（完全转化为振幅-频率表）的应用，这是一种基于贝叶斯的方法，用于使用脂质标准品和补充亮氨酸的健康和荷瘤小鼠的组织样本，对基于 NMR 的脂质组学进行自动化处理。我们制备了复杂的脂质标准混合物，并使用 CRAFT 对其进行了验证，与传统的傅里叶变换（FT）-NMR 进行了比较，并得出了一个指纹，用于分析血清和肝样品的脂质图谱。CRAFT 和 FT-NMR 在准确性上相当，CRAFT 在定量几种脂质方面具有更高的相关性。对荷瘤小鼠血清脂质组学的分析显示，与健康组相比，荷瘤小鼠存在高脂血症且无肝甘油三酯蓄积的迹象，表明荷瘤小鼠处于预恶病质状态。亮氨酸补充与两种组织的脂质图谱变化最小有关。总之，我们的研究表明，CRAFT 方法可以准确识别和定量复杂脂质混合物和鼠组织样本中的脂质，从而提高基于 NMR 的脂质组学的自动化和重现性。
