Challenges and perspectives in quantitative NMR

Giraudeau, Patrick

doi:10.1002/mrc.4475

Cited by 97 publications

(58 citation statements)

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Even under idealized conditions, error rates in overlapped 1D NMR spectra exceed 16% (111); real-world error reaches 20% (112) and is likely higher for low-abundance compounds. This quantitative performance is disappointing given that NMR can achieve error rates under 0.1% (113).…”

Section: Nuclear Magnetic Resonancementioning

confidence: 99%

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow

Klein

et al. 2017

Annu. Rev. Biochem.

359

341

View full text Add to dashboard Cite

Metabolites are the small biological molecules involved in energy conversion and biosynthesis. Studying metabolism is inherently challenging due to metabolites’ reactivity, structural diversity, and broad concentration range. Herein, we review the common pitfalls encountered in metabolomics and provide concrete guidelines for obtaining accurate metabolite measurements, focusing on water-soluble primary metabolites. We show how seemingly straightforward sample preparation methods can introduce systematic errors (e.g., owing to interconversion among metabolites) and how proper selection of quenching solvent (e.g., acidic acetonitrile:methanol:water) can mitigate such problems. We discuss the specific strengths, pitfalls, and best practices for each common analytical platform: liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance (NMR), and enzyme assays. Together this information provides a pragmatic knowledge base for carrying out biologically informative metabolite measurements.

show abstract

Section: Nuclear Magnetic Resonancementioning

confidence: 99%

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow

Klein

et al. 2017

Annu. Rev. Biochem.

359

341

View full text Add to dashboard Cite

show abstract

“…Noticeable strategies have been developed to reduce the impact of molecular diffusion on the sensitivity of UF experiments, by pulse sequence improvements [17] or by physically reducing the translational diffusion of the analytes. [18] The sensitivity may also be improved by coupling UF 2D NMR with Hadamard spectroscopy [19] or by processing procedures aiming at improving the strategies to obtain better line shapes.…”

Section: Principle and Limitations Of Single-scan Nd Nmr Methodsmentioning

confidence: 99%

“…[28] However, both strategies require the implementation of additional selective pulses associated with a number of constraints (necessity for precise calibration, sensitivity to pulse imperfections) and whose implementation is not straightforward. Giraudeau et al proposed a gradient-controlled folding, [40] which does not require any selective pulse. It is based on the use of suitably chosen gradients placed on each side of the mixing period.…”

Section: Spectral Width Improvementsmentioning

confidence: 99%

Fast hybrid multi‐dimensional NMR methods based on ultrafast 2D NMR

Akoka

Giraudeau

2015

Magnetic Reson in Chemistry

Self Cite

View full text Add to dashboard Cite

Conventional multi-dimensional (nD) NMR experiments are characterized by inherent long acquisition durations, while ultrafast (UF) NMR makes it possible to reduce to a few hundreds of milliseconds the overall acquisition duration of a complete nD NMR dataset. Although extremely promising for a number of specific applications, the UF strategy suffers from significant limitations compared with its conventional counterpart. The main limitations concern the sensitivity, the resolution, and the accessible spectral width. However, when the targeted applications are compatible with an acquisition duration between a few seconds and a few minutes, hybrid UF techniques can be used to improve the performance of UF nD NMR while remaining faster than conventional acquisitions. Much better results in terms of signal-to-noise ratio can be achieved with the multi-scan single-shot approach or with interleaved acquisitions. Even more, for the same experimental duration, and in the case of homonuclear 2D NMR, the multi-scan single-shot approach has a much higher precision than conventional 2D NMR. Interleaved 2D NMR overcomes the drawbacks of single-scan UF NMR in terms of spectral width and provides spectra for which the quality is not significantly different from that obtained with conventional 2D NMR. Finally, high spectral qualities have been demonstrated from hybrid conventional/UF 3D approaches capable of recording a whole 3D spectrum in the time needed to record a conventional 2D spectrum. This mini-review aims at describing the principles, the recent advances and the latest applications of these hybrid techniques. Copyright © 2015 John Wiley & Sons, Ltd.

show abstract

“…Current use of 2D for quantitative purposes can be hindered by the lower proportionality between signal volumes and metabolite concentrations. 27 This lower proportionality is mediated by the much higher complexity of the pulses used during spectrum acquisition and by the requirement of long experiment times which may lead to greater noise in the acquired data. 8 Prediction of the signal properties may help increase this proportionality and expand its quantitative potential.…”

Section: Future Directionsmentioning

confidence: 99%

Maximizing the quality of NMR automatic metabolite profiling by a machine learning based prediction of signal parameters

Cañueto

Navarro²,

Bulló³

et al. 2018

Preprint

View full text Add to dashboard Cite

The quality of automatic metabolite profiling in NMR datasets in complex matrices can be compromised by the multiple sources of variability in the samples. These sources cause uncertainty in the metabolite signal parameters and the presence of multiple low-intensity signals. Lineshape fitting approaches might produce suboptimal resolutions or distort the fitted signals to adapt them to the complex spectrum lineshape. As a result, tools tend to restrict their use to specific matrices and strict protocols to reduce this uncertainty.However, the analysis and modelling of the signal parameters collected during a first profiling iteration can further reduce the uncertainty by the generation of narrow and accurate predictions of the expected signal parameters. In this study, we show that, thanks to the predictions generated, better profiling quality indicators can be outputted and the performance of automatic profiling can be maximized. Thanks to the ability of our workflow to learn and model the sample properties, restrictions in the matrix or protocol and limitations of lineshape fitting approaches can be overcome.

show abstract

Challenges and perspectives in quantitative NMR

Cited by 97 publications

References 85 publications

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow

Fast hybrid multi‐dimensional NMR methods based on ultrafast 2D NMR

Maximizing the quality of NMR automatic metabolite profiling by a machine learning based prediction of signal parameters

Contact Info

Product

Resources

About