Division of Electronics, Ruđer Bošković Institute, Bijenička cesta 54, HR-10000, Zagreb, Croatia.
Division of Organic Chemistry and Biochemistry, Ruđer Bošković Institute, Bijenička cesta 54, HR-10000, Zagreb, Croatia.
Anal Chim Acta. 2019 Nov 8;1080:55-65. doi: 10.1016/j.aca.2019.07.004. Epub 2019 Jul 3.
Due to its capability for high-throughput screening H nuclear magnetic resonance (NMR) spectroscopy is commonly used for metabolite research. The key problem in H NMR spectroscopy of multicomponent mixtures is overlapping of component signals and that is increasing with the number of components, their complexity and structural similarity. It makes metabolic profiling, that is carried out through matching acquired spectra with metabolites from the library, a hard problem. Here, we propose a method for nonlinear blind separation of highly correlated components spectra from a single H NMR mixture spectra. The method transforms a single nonlinear mixture into multiple high-dimensional reproducible kernel Hilbert Spaces (mRKHSs). Therein, highly correlated components are separated by sparseness constrained nonnegative matrix factorization in each induced RKHS. Afterwards, metabolites are identified through comparison of separated components with the library comprised of 160 pure components. Thereby, a significant number of them are expected to be related with diabetes type 2. Conceptually similar methodology for nonlinear blind separation of correlated components from two or more mixtures is presented in the Supplementary material. Single-mixture blind source separation is exemplified on: (i) annotation of five components spectra separated from one H NMR model mixture spectra; (ii) annotation of fifty five metabolites separated from one H NMR mixture spectra of urine of subjects with and without diabetes type 2. Arguably, it is for the first time a method for blind separation of a large number of components from a single nonlinear mixture has been proposed. Moreover, the proposed method pinpoints urinary creatine, glutamic acid and 5-hydroxyindoleacetic acid as the most prominent metabolites in samples from subjects with diabetes type 2, when compared to healthy controls.
由于其高通量筛选的能力，氢核磁共振（NMR）光谱通常用于代谢物研究。多分量混合物的 H NMR 光谱的关键问题是组分信号的重叠，随着组分数量的增加、其复杂性和结构相似性的增加，这种重叠情况也在增加。这使得通过将获得的光谱与库中的代谢物进行匹配来进行代谢物分析成为一个难题。在这里，我们提出了一种从单 NMR 混合物光谱中非线性盲分离高度相关成分光谱的方法。该方法将单非线性混合物转化为多个高维可重复核希尔伯特空间（mRKHS）。在每个诱导的 RKHS 中，通过稀疏约束非负矩阵分解来分离高度相关的成分。然后，通过将分离的成分与由 160 种纯成分组成的库进行比较来识别代谢物。因此，预计其中许多与 2 型糖尿病有关。在补充材料中提出了一种从两个或更多混合物中非线性盲分离相关成分的类似方法。单混合物盲源分离的示例包括：（i）从一个 NMR 模型混合物光谱中分离出的五个成分光谱的注释；（ii）从一个 NMR 混合物光谱中分离出的五十五个尿液代谢物的注释患有和不患有 2 型糖尿病的受试者。可以说，这是首次提出从单个非线性混合物中分离大量成分的方法。此外，与健康对照组相比，该方法指出尿液肌酸、谷氨酸和 5-羟吲哚乙酸是 2 型糖尿病患者样本中最显著的代谢物。
