Zhu Chenglin, Vitali Beatrice, Donders Gilbert, Parolin Carola, Li Yan, Laghi Luca
Department of Agri-Food Science and Technology, University of Bologna, 40126 Bologna, Italy.
Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy.
Metabolites. 2019 Jan 15;9(1):15. doi: 10.3390/metabo9010015.
In Proton Nuclear Magnetic Resonance (¹H-NMR) spectroscopy, the signals assignment procedure is normally conducted by visual inspection of the spectra, by taking advantage of the innate predisposition of human eye for pattern recognition. In the case of untargeted metabolomics investigations on food and body fluids, the complexity of the spectra may lead the user to overlook signals, independently from their biological relevance. Here, we describe a four steps procedure that is designed to guide signals assignment task by visual inspection. The procedure can be employed whenever an experimental plan allows for the application of a univariate statistical analysis on a point-by-point basis, which is commonly the case. By comparing, as a proof of concept, ¹H-NMR spectra of vaginal fluids of healthy and bacterial vaginosis (BV) affected women, we show that the procedure is also readily usable by non-experts in three particularly challenging cases: overlapping multiplets, poorly aligned signals, and signals with very poor signal-to-noise ratio. The paper is accompanied by the necessary codes and examples written in R computational language to allow the interested user gaining a hands-on impression of the procedure's strengths and weaknesses.
在质子核磁共振（¹H-NMR）光谱分析中，信号归属过程通常是通过目视检查光谱来进行的，利用人眼对模式识别的固有倾向。在对食物和体液进行非靶向代谢组学研究时，光谱的复杂性可能会导致用户忽略信号，而不管其生物学相关性如何。在此，我们描述了一个四步程序，旨在通过目视检查来指导信号归属任务。只要实验方案允许逐点应用单变量统计分析（通常情况如此），该程序就可以使用。作为概念验证，通过比较健康女性和患有细菌性阴道病（BV）女性的阴道液¹H-NMR光谱，我们表明，在三种特别具有挑战性的情况下，非专业人员也可以轻松使用该程序：多重峰重叠、信号对齐不佳以及信噪比非常低的信号。本文还附带了用R计算语言编写的必要代码和示例，以使感兴趣的用户能够亲身体验该程序的优缺点。
