School of Pharmaceutical Sciences, University of Geneva, 1211 Geneva, Switzerland.
Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1211 Geneva, Switzerland.
J Agric Food Chem. 2023 Nov 22;71(46):18010-18023. doi: 10.1021/acs.jafc.3c03099. Epub 2023 Nov 10.
Recent developments in mass spectrometry-based metabolite profiling allow unprecedented qualitative coverage of complex biological extract composition. However, the electrospray ionization used in metabolite profiling generates multiple artifactual signals for a single analyte. This leads to thousands of signals per analysis without satisfactory means of filtering those corresponding to abundant constituents. Generic approaches are therefore needed for the qualitative and quantitative annotation of a broad range of relevant constituents. For this, we used an analytical platform combining liquid chromatography-mass spectrometry (LC-MS) with Charged Aerosol Detection (CAD). We established a generic metabolite profiling for the concomitant recording of qualitative MS data and semiquantitative CAD profiles. The MS features (recorded in high-resolution tandem MS) are grouped and annotated using state-of-the-art tools. To efficiently attribute features to their corresponding extracted and integrated CAD peaks, a custom signal pretreatment and peak-shape comparison workflow is built. This strategy allows us to automatically contextualize features at both major and minor metabolome levels, together with a detailed reporting of their annotation including relevant orthogonal information (taxonomy, retention time). Signals not attributed to CAD peaks are considered minor metabolites. Results are illustrated on an ethanolic extract of (Roxb.) H. Karst., a bitter plant of industrial interest, exhibiting the typical complexity of plant extracts as a proof of concept. This generic qualitative and quantitative approach paves the way to automatically assess the composition of single natural extracts of interest or broader collections, thus facilitating new ingredient registrations or natural-extracts-based drug discovery campaigns.
基于质谱的代谢物分析的最新进展允许对复杂的生物提取物成分进行前所未有的定性覆盖。然而，代谢物分析中使用的电喷雾电离会为单个分析物产生多个人为信号。这导致每次分析产生数千个信号，而没有令人满意的方法来过滤那些对应于丰富成分的信号。因此，需要通用方法来对广泛相关成分进行定性和定量注释。为此，我们使用了一种结合液相色谱-质谱（LC-MS）和带电气溶胶检测（CAD）的分析平台。我们建立了一种通用的代谢物分析方法，用于同时记录定性 MS 数据和半定量 CAD 图谱。使用最先进的工具对 MS 特征（在高分辨率串联 MS 中记录）进行分组和注释。为了有效地将特征分配给它们对应的提取和集成的 CAD 峰，构建了自定义信号预处理和峰形比较工作流程。该策略允许我们在主要和次要代谢组水平上自动上下文化特征，并详细报告其注释，包括相关的正交信息（分类学、保留时间）。未归因于 CAD 峰的信号被视为次要代谢物。结果以工业感兴趣的苦味植物（Roxb.）H. Karst.的乙醇提取物为例进行说明，展示了植物提取物的典型复杂性，作为概念验证。这种通用的定性和定量方法为自动评估单个感兴趣的天然提取物或更广泛的天然提取物的组成铺平了道路，从而促进了新成分的注册或基于天然提取物的药物发现活动。
