GIPMA：基于 2D 1H–13C HSQC 的代谢组学的全局强度引导峰匹配和对齐,Analytical Chemistry

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GIPMA：基于 2D 1H–13C HSQC 的代谢组学的全局强度引导峰匹配和对齐
Analytical Chemistry ( IF 6.7 ) Pub Date : 2023-02-02 , DOI: 10.1021/acs.analchem.2c03323
Huan Du _{1,

2} , Xiu Gu _{1,

2} , Jialuo Chen _{1,

2} , Caihong Bai _{1,

2} , Xiaohui Duan _{3,

4} , Kaifeng Hu _{1,

3}

Affiliation

二维 (2D) ¹ H– ¹³ C 异核单量子相干 (HSQC) 越来越多地应用于代谢组学研究，因为与一维 (1D) ¹ H NMR 相比，它可以大大提高分辨能力。然而，用于基于 2D NMR 的代谢组学的峰匹配和对齐工具等预处理方法落后于 1D ^{1的类似方法}基于 H NMR 的代谢组学。跨多个样本正确匹配和对齐 2D NMR 光谱特征对于后续的多变量数据分析尤为重要。考虑到各种代谢物的不同强度动态范围和多个样品光谱中的化学位移变化，我们在这里开发了一种高效的 2D ¹ H– ^{13峰匹配和对齐算法}C 基于 HSQC 的代谢组学，称为全局强度引导峰匹配和对齐 (GIPMA)。在 GIPMA 中，所有光谱中识别的峰汇集在一起并按强度排序。较强峰的化学位移被认为比较弱峰的化学位移更准确和可靠。如果最强的未指定峰不在任何现有峰簇 (PC) 的化学位移公差范围内，则选择它作为新簇的参考，或者它与现有 PC 匹配并且 PC 的对齐化学位移是更新为簇中所有峰的化学位移的强度加权平均值。设置最佳化学位移公差 (Δδ _o) 对于跨多个样本的峰匹配和对齐至关重要。GIPMA动态搜索并智能选择Δδ _o用于峰匹配以最大化有效峰簇（vPC）的数量，即多个样本之间的光谱特征。通过GIPMA，全自动峰向匹配和对齐不需要任何光谱作为初始参考，而每个PC的化学位移更新为同一PC中所有峰化学位移的强度加权平均值，保证是统计上更准确。每个代表性光谱特征的准确化学位移将有助于后续的峰分配，并且对于正确的代谢物鉴定和结果解释至关重要。所提出的方法在由七种典型代谢物组成的六种模型混合物的光谱上成功证明，产生了所有已知光谱特征的正确匹配。GIPMA 的性能也在 2D 上得到了展示五种石斛的 29 个样品的 87 种实际提取物的¹ H– ¹³ C HSQC 光谱。GIPMA 对 87 个匹配和对齐的光谱进行层次聚类分析 (HCA) 和主成分分析 (PCA)，将 29 个样本正确分类为五组。总之，所提出的 GIPMA 算法提供了一种实用的峰匹配和对齐方法，以促进基于 2D NMR 的代谢组学研究。

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GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D 1H–13C HSQC-Based Metabolomics

Two-dimensional (2D) ¹H–¹³C heteronuclear single quantum coherence (HSQC) has been increasingly applied to metabolomics studies because it can greatly improve the resolving capability compared with one-dimensional (1D) ¹H NMR. However, preprocessing methods such as peak matching and alignment tools for 2D NMR-based metabolomics have lagged behind similar methods for 1D ¹H NMR-based metabolomics. Correct matching and alignment of 2D NMR spectral features across multiple samples are particularly important for subsequent multivariate data analysis. Considering different intensity dynamic ranges of a variety of metabolites and the chemical shift variation across the spectra of multiple samples, here, we developed an efficient peak matching and alignment algorithm for 2D ¹H–¹³C HSQC-based metabolomics, called global intensity-guided peak matching and alignment (GIPMA). In GIPMA, peaks identified in all spectra are pooled together and sorted by intensity. Chemical shift of a stronger peak is regarded to be more accurate and reliable than that of a weaker peak. The strongest undesignated peak is chosen as the reference of a new cluster if it is not located within the chemical shift tolerance of any existing peak cluster (PC), or otherwise it is matched to an existing PC and the aligned chemical shift of the PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the cluster. Setting an optimum chemical shift tolerance (Δδ_o) is critical for the peak matching and alignment across multiple samples. GIPMA dynamically searches for and intelligently selects the Δδ_o for peak matching to maximize the number of valid peak clusters (vPC), that is, spectral features, among multiple samples. By GIPMA, fully automatic peakwise matching and alignment do not require any spectrum as initial reference, while the chemical shift of each PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the same PC, which is warranted to be statistically more accurate. Accurate chemical shifts for each representative spectral feature will facilitate subsequent peak assignment and are essential for correct metabolite identification and result interpretation. The proposed method was demonstrated successfully on the spectra of six model mixtures consisting of seven typical metabolites, yielding correct matching of all known spectral features. The performance of GIPMA was also demonstrated on 2D ¹H–¹³C HSQC spectra of 87 real extracts of 29 samples of five Dendrobium species. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of the 87 matched and aligned spectra by GIPMA generates correct classification of the 29 samples into five groups. In summary, the proposed algorithm of GIPMA provided a practical peak matching and alignment method to facilitate 2D NMR-based metabolomics studies.

更新日期：2023-02-02

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