2020
DOI: 10.1002/mrc.5106
|View full text |Cite
|
Sign up to set email alerts
|

Rapid nuclear magnetic resonance data acquisition with improved resolution and sensitivity for high‐throughput metabolomic analysis

Abstract: Nuclear magnetic resonance (NMR)-based metabolomics has witnessed rapid advancements in recent years with the continuous development of new methods to enhance the sensitivity, resolution, and speed of data acquisition. Some of the approaches were earlier used for peptide and protein resonance assignments and have now been adapted to metabolomics. At the same time, new NMR methods involving novel data acquisition techniques, suited particularly for high-throughput analysis in metabolomics, have been developed. … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
11
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
5
1

Relationship

0
6

Authors

Journals

citations
Cited by 10 publications
(11 citation statements)
references
References 108 publications
(211 reference statements)
0
11
0
Order By: Relevance
“…This is important also for 2D NMR of small molecules as demonstrated by the NUS applications in the context of pharmaceutical research [ 21 ] and metabolomics. [ 22 ]…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…This is important also for 2D NMR of small molecules as demonstrated by the NUS applications in the context of pharmaceutical research [ 21 ] and metabolomics. [ 22 ]…”
Section: Resultsmentioning
confidence: 99%
“…This is important also for 2D NMR of small molecules as demonstrated by the NUS applications in the context of pharmaceutical research [21] and metabolomics. [22] NUS requires different data processing based on non-Fourier methods to reconstruct the frequency domain. A large variety of reconstruction algorithms and protocols have been proposed over the last years [23] ; here we have applied the sparse multidimensional iterative lineshapeenhanced (SMILE) reconstruction method, included in the nmrPipe package.…”
Section: Nonuniform Samplingmentioning
confidence: 99%
“…For example, there have been significant advancements to achieve higher throughput NMR-based metabolomics, which are intrinsically quantitative with metabolite concentrations proportional to the number of nuclei. 7 , 8 However, compared to NMR, the sensitivity and selectivity of mass spectrometry (MS) based metabolomics techniques is imperative for using smaller sample volumes and acquiring a wider metabolite coverage. Thus, MS has evolved into the most universally applied instrumentation for modern metabolomics.…”
mentioning
confidence: 99%
“…Profiling an organism’s dynamic metabolome can lead to objective disease diagnosis, , personalized medicine, , and realization of fundamental molecular mechanisms involved in complex biological processes. , Important applications such as these have catalyzed rapid technology development. For example, there have been significant advancements to achieve higher throughput NMR-based metabolomics, which are intrinsically quantitative with metabolite concentrations proportional to the number of nuclei. , However, compared to NMR, the sensitivity and selectivity of mass spectrometry (MS) based metabolomics techniques is imperative for using smaller sample volumes and acquiring a wider metabolite coverage. Thus, MS has evolved into the most universally applied instrumentation for modern metabolomics.…”
mentioning
confidence: 99%
“…Further, the applications of alternative sampling in the solid-state, where NMR experiments are still usually performed in a classical manner, are described by Porat et al [3] and Paluch et al [4] The former describes the acceleration of high-dimensional (4D) acquisition, while the latter the application of the Hadamard approach. Also, medical applications are described, including Hoyt et al [5] and Szigetvári et al [6] discussing the applications of NUS in the pharmaceutical industry, while Jeeves et al [7] and Joseph et al [8] show how NUS can be useful in metabolomics.…”
mentioning
confidence: 99%