76
NMR suffers from limited spectral bandwidth when analyzing complex metabolomics samples, however, which can make untargeted complex mixtures difficult to
analyze. 2D NMR spectra allows for more information at overlapping resonances,
helping with further separation of peaks. 2D NMR involves the plotting of two frequency axes against each other, allowing for visualization of correlation be-tween
different peaks using either homonuclear or heteronuclear correlations [92]. While
2D NMR applied to metabolomics can be cumbersome, the Gi-raudeau group has
recently described a fast quantitative 2D NMR workflow for metabolomics and lipidomics [93]. This approach specifically mentions UF COSY (ultrafast correlation
spectroscopy),
1
H13
C HSQC (heteronuclear single-quantum correlation spectroscopy), and ZF-TOCSY (Z-filter total correlation spectroscopy) as their approaches,
but their workflow can be applied to any 2D NMR approach. Previously 2D NMR
experiments required long acquisition, up to several hours per spectrum, as well as
difficulties in quantitation. This new fast 2D NMR workflow reduces acquisition
time and allows for quantitation for both targeted and untargeted approaches. For
targeted approaches, standard additions or calibration are incorporated into the sample design, which untargeted approaches utilize involved data processing and
statistics.
4.7 Conclusion
Metabolomics is a growing field with a variety of analytical and computational tools
for analyzing a broad, dynamic, and diverse chemical and biological spaces.
Strategies exist for analyzing the metabolome for both hypothesis generation and
hypothesis testing. Specifically, mass spectrometry enables the interrogation of this
chemical space to answer a biological question. However, the experimental design
including design (targeted/untargeted), sample preparation, separations, data acquisition, and data analysis tailored towards the ultimate question is integral to a successful experiment. As the endpoint of biochemical processes, the metabolome is
uniquely suited to provide a broad, yet specific view biologically processes that
closely relate to phenotype, especially for biological and medicinal applications.
References
1. Ryan D, Robards K (2006) Metabolomics: the greatest omics of them all?. https://doi.
org/10.1021/AC0614341
2. Ren J-L, Zhang A-H, Kong L, Wang X-J (2018) Advances in mass spectrometry-based
metabolomics for investigation of metabolites. RSC Adv 8(40):22335–22350. https://doi.
org/10.1039/C8RA01574K
3. Guo S, Tian J, Zhu B, Yang S, Yu K, Zhao Z (2018) Trends in metabolomics research: A
Scientometric analysis (1992–2017). Curr Sci 114(11)
4. Markley JL, Brüschweiler R, Edison AS, Eghbalnia HR, Powers R, Raftery D, Wishart DS
(2017) The future of NMR-based metabolomics. Curr Opin Biotechnol 43:34–40. https://doi.
org/10.1016/J.COPBIO.2016.08.001
E. S. Rivera et al.
NMR suffers from limited spectral bandwidth when analyzing complex metabolomics samples, however, which can make untargeted complex mixtures difficult to
analyze. 2D NMR spectra allows for more information at overlapping resonances,
helping with further separation of peaks. 2D NMR involves the plotting of two frequency axes against each other, allowing for visualization of correlation be-tween
different peaks using either homonuclear or heteronuclear correlations [92]. While
2D NMR applied to metabolomics can be cumbersome, the Gi-raudeau group has
recently described a fast quantitative 2D NMR workflow for metabolomics and lipidomics [93]. This approach specifically mentions UF COSY (ultrafast correlation
spectroscopy),
1
H13
C HSQC (heteronuclear single-quantum correlation spectroscopy), and ZF-TOCSY (Z-filter total correlation spectroscopy) as their approaches,
but their workflow can be applied to any 2D NMR approach. Previously 2D NMR
experiments required long acquisition, up to several hours per spectrum, as well as
difficulties in quantitation. This new fast 2D NMR workflow reduces acquisition
time and allows for quantitation for both targeted and untargeted approaches. For
targeted approaches, standard additions or calibration are incorporated into the sample design, which untargeted approaches utilize involved data processing and
statistics.
4.7 Conclusion
Metabolomics is a growing field with a variety of analytical and computational tools
for analyzing a broad, dynamic, and diverse chemical and biological spaces.
Strategies exist for analyzing the metabolome for both hypothesis generation and
hypothesis testing. Specifically, mass spectrometry enables the interrogation of this
chemical space to answer a biological question. However, the experimental design
including design (targeted/untargeted), sample preparation, separations, data acquisition, and data analysis tailored towards the ultimate question is integral to a successful experiment. As the endpoint of biochemical processes, the metabolome is
uniquely suited to provide a broad, yet specific view biologically processes that
closely relate to phenotype, especially for biological and medicinal applications.
References
1. Ryan D, Robards K (2006) Metabolomics: the greatest omics of them all?. https://doi.
org/10.1021/AC0614341
2. Ren J-L, Zhang A-H, Kong L, Wang X-J (2018) Advances in mass spectrometry-based
metabolomics for investigation of metabolites. RSC Adv 8(40):22335–22350. https://doi.
org/10.1039/C8RA01574K
3. Guo S, Tian J, Zhu B, Yang S, Yu K, Zhao Z (2018) Trends in metabolomics research: A
Scientometric analysis (1992–2017). Curr Sci 114(11)
4. Markley JL, Brüschweiler R, Edison AS, Eghbalnia HR, Powers R, Raftery D, Wishart DS
(2017) The future of NMR-based metabolomics. Curr Opin Biotechnol 43:34–40. https://doi.
org/10.1016/J.COPBIO.2016.08.001
E. S. Rivera et al.