Evaluation of non-uniform sampling 2D1H–13C HSQC spectra for semi-quantitative metabolomics

Bo Zhang, Robert Powers, Elizabeth M. O’Day

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Metabolomics is the comprehensive study of metabolism, the biochemical processes that sustain life. By comparing metabolites between healthy and disease states, new insights into disease mechanisms can be uncovered. NMR is a powerful analytical method to detect and quantify metabolites. Standard one-dimensional (1D)1H-NMR metabolite profiling is informative but challenged by significant chemical shift overlap. Multi-dimensional NMR can increase resolution, but the required long acquisition times lead to limited throughput. Non-uniform sampling (NUS) is a well-accepted mode of acquiring multi-dimensional NMR data, enabling either reduced acquisition times or increased sensitivity in equivalent time. Despite these advantages, the technique is not widely applied to metabolomics. In this study, we evaluated the utility of NUS1H–13C heteronuclear single quantum coherence (HSQC) for semi-quantitative metabolomics. We demonstrated that NUS improved sensitivity compared to uniform sampling (US).We verified that the NUS measurement maintains linearity, making it possible to detect metabolite changes across samples and studies. Furthermore, we calculated the lower limit of detection and quantification (LOD/LOQ) of common metabolites. Finally, we demonstrate that the measurements are repeatable on the same system and across different systems. In conclusion, our results detail the analytical capability of NUS and, in doing so, empower the future use of NUS1H–13C HSQC in metabolomic studies.

Original languageEnglish (US)
Article number203
Issue number5
StatePublished - May 2020


  • Metabolomics
  • Reproducibility

ASJC Scopus subject areas

  • Endocrinology, Diabetes and Metabolism
  • Biochemistry
  • Molecular Biology


Dive into the research topics of 'Evaluation of non-uniform sampling 2D1H–13C HSQC spectra for semi-quantitative metabolomics'. Together they form a unique fingerprint.

Cite this