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COLMAR1d2d:一维与二维核磁共振的协同组合,用于增强复杂混合物中代谢物的高通量鉴定和定量分析。

COLMAR1d2d: Synergistic Combination of 1D with 2D NMR for Enhanced High-Throughput Identification and Quantification of Metabolites in Complex Mixtures.

作者信息

Cabrera Allpas R, Li D-W, Choo M, Lee K, Bruschweiler-Li L, Brüschweiler R

机构信息

Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States.

Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, Ohio 43210, United States.

出版信息

Anal Chem. 2025 May 13;97(18):10019-10026. doi: 10.1021/acs.analchem.5c00957. Epub 2025 May 1.

Abstract

A major challenge in 1D H NMR-based metabolomics studies is the occurrence of slight shifts of the resonances of mixture compounds compared to the reference spectra in the metabolomics spectral databases due to variations in buffer conditions, temperature, and matrix effects. This hampers both the automated spectral deconvolution and metabolite quantification of crowded regions in 1D H NMR spectra of complex mixtures whose analysis is particularly susceptible to such effects. 2D NMR-based metabolomics, on the other hand, is substantially more robust but also much more demanding in terms of NMR spectrometer time. Here we introduce an approach, termed COLMAR1d2d, which uses selected 2D H-C HSQC and H-H TOCSY NMR spectra of a subset of samples along with 1D H NMR spectra of all samples to overcome this bottleneck. It relies on our 2D NMR-based platform COLMARm using 2D H-C HSQC and 2D H-H TOCSY spectra measured for a representative subset of samples to unambiguously and comprehensively determine the metabolite composition and the exact peak positions of the identified compounds under the sample conditions present. This information is then used to update the spectral database for the automated analysis of a potentially large cohort of 1D H NMR spectra using the COLMAR1d platform. It is demonstrated how this synergistic combination of 1D with selected 2D NMR spectra allows the analysis of a significantly larger number of metabolites than would be possible with 1D NMR alone. Moreover, COLMAR1d2d also improves quantitation, as is demonstrated for samples from mouse urine and biofilm.

摘要

基于一维氢核磁共振(¹H NMR)的代谢组学研究面临的一个主要挑战是,由于缓冲条件、温度和基质效应的变化,混合物化合物的共振峰与代谢组学光谱数据库中的参考光谱相比会出现轻微位移。这阻碍了复杂混合物一维¹H NMR光谱中拥挤区域的自动光谱解卷积和代谢物定量分析,因为这类混合物的分析对上述效应尤为敏感。另一方面,基于二维核磁共振(2D NMR)的代谢组学则更为稳健,但对核磁共振仪的时间要求也更高。在此,我们介绍一种名为COLMAR1d2d的方法,该方法利用部分样品的选定二维¹H-¹³C异核单量子相干(HSQC)和¹H-¹H全相关谱(TOCSY)核磁共振光谱以及所有样品的一维¹H NMR光谱来克服这一瓶颈。它依赖于我们基于二维核磁共振的平台COLMARm,该平台使用为代表性样品子集测量的二维¹H-¹³C HSQC和二维¹H-¹H TOCSY光谱,在当前样品条件下明确且全面地确定代谢物组成以及已鉴定化合物的确切峰位置。然后,这些信息被用于更新光谱数据库,以便使用COLMAR1d平台对可能数量庞大的一维¹H NMR光谱进行自动分析。结果表明,一维光谱与选定二维核磁共振光谱的这种协同组合能够分析比单独使用一维核磁共振更多数量的代谢物。此外,正如对小鼠尿液和生物膜样品所证明的那样,COLMAR1d2d还提高了定量分析的准确性。

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