Environmental Molecular Sciences Division , Pacific Northwest National Laboratory , Richland , Washington 99354 , United States.
Geochemical and Environmental Research Group , Texas A&M University , College Station , Texas 77845 , United States.
Anal Chem. 2018 May 15;90(10):6152-6160. doi: 10.1021/acs.analchem.8b00529. Epub 2018 Apr 27.
van Krevelen diagrams (O/C vs H/C ratios of elemental formulas) have been widely used in studies to obtain an estimation of the main compound categories present in environmental samples. However, the limits defining a specific compound category based solely on O/C and H/C ratios of elemental formulas have never been accurately listed or proposed to classify metabolites in biological samples. Furthermore, while O/C vs H/C ratios of elemental formulas can provide an overview of the compound categories, such classification is inefficient because of the large overlap among different compound categories along both axes. We propose a more accurate compound classification for biological samples analyzed by high-resolution mass spectrometry based on an assessment of the C/H/O/N/P stoichiometric ratios of over 130 000 elemental formulas of compounds classified in 6 main categories: lipids, peptides, amino sugars, carbohydrates, nucleotides, and phytochemical compounds (oxy-aromatic compounds). Our multidimensional stoichiometric compound classification (MSCC) constraints showed a highly accurate categorization of elemental formulas to the main compound categories in biological samples with over 98% of accuracy representing a substantial improvement over any classification based on the classic van Krevelen diagram. This method represents a signficant step forward in environmental research, especially ecological stoichiometry and eco-metabolomics studies, by providing a novel and robust tool to improve our understanding of the ecosystem structure and function through the chemical characterization of biological samples.
范克里夫图谱(元素公式的 O/C 与 H/C 比值)已广泛应用于研究中,以估算环境样本中存在的主要化合物类别。然而,基于元素公式的 O/C 和 H/C 比值来定义特定化合物类别的界限从未被准确列出或提出,也无法用于对生物样本中的代谢物进行分类。此外,虽然元素公式的 O/C 与 H/C 比值可以提供化合物类别的概述,但由于不同化合物类别在两个轴上的重叠较大,这种分类效率低下。我们提出了一种更准确的基于高分辨质谱分析的生物样本化合物分类方法,该方法基于对 6 个主要类别中 130000 多种化合物的 C/H/O/N/P 化学计量比的评估:脂质、肽、氨基糖、碳水化合物、核苷酸和植物化学化合物(含氧芳香族化合物)。我们的多维化学计量化合物分类(MSCC)约束条件显示,对生物样本中主要化合物类别的元素公式进行分类的准确率非常高,达到 98%以上,这比任何基于经典范克里夫图谱的分类方法都有了实质性的提高。该方法通过提供一种新颖而强大的工具,通过对生物样本进行化学特征描述来提高我们对生态系统结构和功能的理解,为环境研究,特别是生态化学计量学和生态代谢组学研究,迈出了重要的一步。