Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes but is hindered by a lack of automated annotation and standardized methods for structure elucidation of candidate disease biomarkers. Here we describe a system for identifying molecular species derived from nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping studies, with detailed information on sample preparation, data acquisition and data modeling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as Statistical Total Correlation Spectroscopy (STOCSY), Subset Optimization by Reference Matching (STORM) and Resolution-Enhanced (RED)-STORM to identify other signals in the NMR spectra relating to the same molecule. It also uses two-dimensional NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multi-dimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take 2 or 3 days. This approach to biomarker discovery is efficient and cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies. It requires a basic understanding of MATLAB to use the statistical spectroscopic tools and analytical skills to perform solid phase extraction (SPE), liquid chromatography (LC) fraction collection, LC-NMR-mass spectroscopy and one-dimensional and two-dimensional NMR experiments.

生物样本的代谢分析提供了对多种生理和病理过程的重要见解，但由于缺乏用于候选疾病生物标志物结构解析的自动注释和标准化方法而受到阻碍。在这里，我们描述了一个系统，用于识别基于核磁共振 (NMR) 光谱的代谢表型研究的分子种类，并提供有关样品制备、数据采集和数据建模的详细信息。我们提供了八种不同的模块化工作流程，可根据其难度级别按推荐的顺序执行。这个多平台系统涉及统计光谱工具的使用，例如统计全相关光谱（STOCSY），通过参考匹配 (STORM) 和分辨率增强 (RED)-STORM 进行子集优化，以识别 NMR 光谱中与同一分子相关的其他信号。它还使用二维 NMR 光谱分析、分离和预浓缩技术、多个联用分析平台以及从现有数据库中提取数据。使用所有八个工作流程的完整系统最多需要一个月的时间，因为它包括需要延长实验时间的多维核磁共振实验。但是，使用更少步骤更容易识别的案例将需要 2 或 3 天。这种发现生物标志物的方法高效且具有成本效益，并增加了代谢组的化学空间覆盖率，从而可以更快、更准确地分配由代谢表型研究产生的 NMR 生成的生物标志物。