Department of Food Science, University of Copenhagen, Rolighedsvej 26, Frederiksberg, 1958, Denmark.
Department of Food Science, University of Copenhagen, Rolighedsvej 26, Frederiksberg, 1958, Denmark; Department of Chemistry, Faculty of Science, Shiraz University, Shiraz, 7194684795, Iran.
Anal Chim Acta. 2020 Apr 29;1108:142-151. doi: 10.1016/j.aca.2020.02.025. Epub 2020 Feb 13.
Proton Nuclear Magnetic Resonance (NMR) spectroscopic analysis of urine generates rich but complex spectra. Retrieving metabolite information from such spectra is challenging due to signal overlapping, chemical shift changes, and large concentration variations of complex urine metabolome. This study demonstrates a new method, Signature Mapping (SigMa), for the rapid and efficient conversion of raw urine NMR spectra into an informative metabolite table. The principle behind SigMa relies on a division of the urine NMR spectra into Signature Signals (SS), Signals of Unknown spin Systems (SUS) and bins of complex unresolved regions (BINS). The method allows simultaneous detection of urinary metabolites in large NMR metabolomics studies using a SigMa chemical shift library and a new automatic peak picking algorithm. For quantification of SS and SUS SigMa uses multivariate curve resolution, while the unresolved inter-SS spectral regions are binned (BINS). SigMa is tested on three human urine H-NMR datasets including spiking experiments, and has proven to be extraordinarily efficient, quantitatively reliable and robust.
质子磁共振波谱（NMR）分析尿液可生成丰富但复杂的谱图。由于信号重叠、化学位移变化以及复杂尿液代谢组浓度变化较大，从这些谱图中提取代谢物信息具有挑战性。本研究展示了一种新方法，即特征图谱（SigMa），用于快速有效地将原始尿液 NMR 谱图转换为信息丰富的代谢物表。SigMa 的原理依赖于将尿液 NMR 谱图分为特征信号（SS）、未知自旋系统信号（SUS）和复杂未解析区域的谱图（BINS）。该方法允许使用 SigMa 化学位移库和新的自动峰提取算法，在大型 NMR 代谢组学研究中同时检测尿液代谢物。对于 SS 和 SUS 的定量，SigMa 使用多元曲线分辨，而未解析的 SS 之间的光谱区域被分为谱图（BINS）。SigMa 在包括加标实验的三个人类尿液 H-NMR 数据集上进行了测试，结果证明它非常高效、定量可靠且稳健。
