利用化学位移变异性可从1H核磁共振谱中恢复重叠代谢物
Exploiting chemical shift variability enables recovery of overlapping metabolites from 1H nuclear magnetic resonance spectra
- Technical University of Denmark（丹麦技术大学）
- University of Copenhagen（哥本哈根大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出BSI-NMF方法，利用化学位移变异性解决1D 1H NMR谱中代谢物重叠峰问题，可准确恢复现有方法遗漏的代谢物信号，为代谢物分析提供新途径。
AI中文摘要：
重叠峰与样本依赖的化学位移变异性阻碍了从复杂生物谱中可靠地恢复代谢物，该问题在一维质子（1D 1H）核磁共振（NMR）中尤为关键，1D 1H NMR已成为代谢组学与食品组学中实现快速采集和获取信息丰富谱图的标准方法。本研究展示了如何将化学位移作为1D 1H NMR的优势，通过提出的贝叶斯位移不变非负矩阵分解（BSI-NMF）程序进行适当建模。在模拟、实验室构建的数据集以及来自欧洲2439人的大型尿液数据集上，我们发现BSI-NMF能准确恢复现有分析方法遗漏的1D 1H NMR谱中的潜在化学信号。本研究强调，此前被视为干扰项的化学特征位移，经适当建模后实际上可有助于代谢物的独特恢复，这为实验诱导化学位移变化以促进谱图的独特恢复创造了机会。
英文摘要：
Overlapping peaks and sample-dependent chemical shift variability prevent reliable metabolite recovery from complex biological spectra. This problem is critical in one-dimensional proton (1D 1H) NMR which has become the standard method providing fast acquisition and information-rich spectra in metabolomics and foodomics. This study demonstrates how chemical shifts can be utilised as a strength in 1D 1H NMR, when suitably modeled through the proposed Bayesian Shift-Invariant Non-negative Matrix Factorization (BSI-NMF) procedure. We find that BSI-NMF accurately recovers the underlying chemical signals in 1D 1H NMR spectra missed by existing analyses approaches across simulations, laboratory created datasets, and a large urine dataset obtained from 2439 people across Europe. Our study highlights how shifts in the chemical signatures - until now perceived as a nuisance - can in fact when suitably modelled be instrumental for unique recovery of metabolites. This creates an opportunity to experimentally induce chemical shifts changes to facilitate unique recovery of spectra.
