Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, Florida 32610-0245, United States.
Anal Chem. 2011 Mar 1;83(5):1649-57. doi: 10.1021/ac102724x. Epub 2011 Feb 11.
Nuclear magnetic resonance (NMR) is the most widely used nondestructive technique in analytical chemistry. In recent years, it has been applied to metabolic profiling due to its high reproducibility, capacity for relative and absolute quantification, atomic resolution, and ability to detect a broad range of compounds in an untargeted manner. While one-dimensional (1D) (1)H NMR experiments are popular in metabolic profiling due to their simplicity and fast acquisition times, two-dimensional (2D) NMR spectra offer increased spectral resolution as well as atomic correlations, which aid in the assignment of known small molecules and the structural elucidation of novel compounds. Given the small number of statistical analysis methods for 2D NMR spectra, we developed a new approach for the analysis, information recovery, and display of 2D NMR spectral data. We present a native 2D peak alignment algorithm we term HATS, for hierarchical alignment of two-dimensional spectra, enabling pattern recognition (PR) using full-resolution spectra. Principle component analysis (PCA) and partial least squares (PLS) regression of full resolution total correlation spectroscopy (TOCSY) spectra greatly aid the assignment and interpretation of statistical pattern recognition results by producing back-scaled loading plots that look like traditional TOCSY spectra but incorporate qualitative and quantitative biological information of the resonances. The HATS-PR methodology is demonstrated here using multiple 2D TOCSY spectra of the exudates from two nematode species: Pristionchus pacificus and Panagrellus redivivus. We show the utility of this integrated approach with the rapid, semiautomated assignment of small molecules differentiating the two species and the identification of spectral regions suggesting the presence of species-specific compounds. These results demonstrate that the combination of 2D NMR spectra with full-resolution statistical analysis provides a platform for chemical and biological studies in cellular biochemistry, metabolomics, and chemical ecology.
核磁共振（NMR）是分析化学中应用最广泛的非破坏性技术。近年来，由于其高重复性、相对和绝对定量能力、原子分辨率以及能够以非靶向方式检测广泛化合物的能力，它已被应用于代谢组学。虽然一维（1D）（1）H NMR 实验由于其简单性和快速采集时间而在代谢组学中很受欢迎，但二维（2D）NMR 光谱提供了更高的光谱分辨率以及原子相关性，这有助于分配已知小分子和阐明新化合物的结构。鉴于二维 NMR 光谱的统计分析方法数量有限，我们开发了一种新的方法来分析、恢复和显示 2D NMR 光谱数据。我们提出了一种我们称之为 HATS 的本地 2D 峰对齐算法，用于二维光谱的层次对齐，从而能够使用全分辨率光谱进行模式识别（PR）。全分辨率总相关光谱（TOCSY）图谱的主成分分析（PCA）和偏最小二乘（PLS）回归极大地有助于通过产生类似于传统 TOCSY 图谱的反向标度加载图来分配和解释统计模式识别结果，这些图谱包含共振的定性和定量生物学信息。HATS-PR 方法学在这里使用来自两种线虫物种（Pristionchus pacificus 和 Panagrellus redivivus）分泌物的多个 2D TOCSY 图谱进行了演示。我们展示了这种集成方法的实用性，该方法可快速半自动分配区分两种物种的小分子，并识别表明存在特定于物种的化合物的光谱区域。这些结果表明，2D NMR 光谱与全分辨率统计分析的结合为细胞生物化学、代谢组学和化学生态学中的化学和生物学研究提供了一个平台。
