利用化学位移变化，从重叠谱峰中精准恢复代谢物信号
Exploiting chemical shift variability enables recovery of overlapping metabolites from 1H nuclear magnetic resonance spectra
- 提出贝叶斯不变位移非负矩阵分解模型，建模化学位移变异
- 在模拟、人工数据和2439人尿液数据中均显著提升代谢物识别率
- 为复杂样本中代谢物分离提供新思路，适合代谢组学研究者
重叠峰与样品依赖的化学位移变异阻碍了复杂生物谱图中代谢物的可靠恢复。这一问题在快速获取且信息丰富的质子一维核磁共振（1D 1H NMR）中尤为关键，已成为代谢组学与食品组学的标准方法。本研究展示如何通过提出的贝叶斯位移不变非负矩阵分解（BSI-NMF）方法，将化学位移作为优势因素加以利用。结果显示，该方法在模拟数据、实验室构建数据集及来自欧洲2439人的大型尿液数据集中，均能准确恢复现有分析方法遗漏的原始化学信号。研究揭示，以往被视为干扰的化学位移变化，经适当建模后可成为唯一识别代谢物的关键工具，为实验诱导化学位移变化以促进谱图解卷积提供了新可能。
原文摘要 · Abstract (English)
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.
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