Understanding the variability of compound quantification from targeted profiling metabolomics of 1D-1H-NMR spectra in synthetic mixtures and urine with additional insights on choice of pulse sequences and robotic sampling

Sokolenko, Stanislav; McKay, Roy T.; Blondeel, Eric J. M.; Lewis, Michael; Chang, David D.; George, Ben; Aucoin, Marc G.

doi:10.1007/s11306-013-0503-3

Cited by 32 publications

(28 citation statements)

References 40 publications

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“…This new quality indicator, called the anomaly score, may provide higher-quality information to improve the detection of suboptimal quantifications and enable the detection of wrong annotations, two current bottlenecks in metabolomic studies which contribute to the introduction of false positives and negatives into the metabolomics literature. 12,15,16 In addition, our machinelearning based pipeline (contained in the 'signparpred' function in the 'rDolphin' R package) can be exported to any other profiling tool in any other programming language.…”

Section: Discussionmentioning

confidence: 99%

“…These matrix-and protocol-based restrictions hinder the high-throughput potential of NMR or might mean the incorporation of false positives and negatives into the metabolomics literature when these restrictions are not strictly followed. 15,16 To maximize the quality of lineshape fitting during NMR automatic profiling, the ranges of possible parameter values selected during fitting must be as narrow as possible. Likewise, the estimation of these narrow ranges must be robust to the variable and complex properties of metabolomics study datasets in complex matrices.…”

Section: Introductionmentioning

confidence: 99%

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

Cañueto

Navarro²,

Bulló³

et al. 2018

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

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

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“…Among various qNMR techniques, 1D 1 H qNMR is extensively used for quantitative metabolomics in complex biological samples (see Table 1), which is mainly classified as: (a) biofluids, such as plasma [71], serum [72][73][74][75][76][77], urine [71][72][73]76,[78][79][80], and cerebral spinal fluid [72]; (b) cell [83,86] and culture media [86,87]; (c) tissue extracts [30,73,88]; (d) plants [89,90]; and (e) microorganisms [73,91]. In spite of a good number of strengths for quantitatively detecting multiple components in complex mixture by 1 H 1D qNMR, its critical drawback is severe spectral overlap.…”

Section: Metabolomicsmentioning

confidence: 99%

Quantitative NMR Studies of Multiple Compound Mixtures

2017

Annual Reports on NMR Spectroscopy

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“…Nevertheless, this criterion does not seem very discriminant and cannot be used to choose the optimal sequence. For a more complete analysis of variability in profiling by 1 H-NMR, we recommend the excellent work of Sokolenko et al (2013).…”

Section: Repeatabilitymentioning

confidence: 99%

“…But in the case of metabolic samples, additional complexity arises from the need to suppress the solvent signal, which strongly affects the metabolite peak areas for different reasons that are described in this review. As shown by Sokolenko et al (2013), the water suppression scheme is one of the elements which most impacts the spectrum overall quality. It probably explains why an extremely large number of solvent suppression schemes have been proposed in the literature, starting from the continuous-wave presaturation proposed by Hoult in 1976 and including increasingly complex pulse sequences (Liu et al 1999;McKay 2009;Zheng and Price 2010), the most used of which are described in detail in this manuscript.…”

Section: Introductionmentioning

confidence: 99%

Optimizing water suppression for quantitative NMR-based metabolomics: a tutorial review

2015

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In nuclear magnetic resonance (NMR)-based metabolomics, the water suppression scheme is one of the elements that most impact the overall quality of the spectrum. The choice of the solvent suppression scheme and of the associated parameters has therefore a high impact on the accuracy of the resulting spectra. As a consequence, potential users of 1 H NMR quantitative metabolomics would certainly benefit from a set of practical tools and recommendations to choose the experimental parameters leading-for a specific metabolomics question-to the most accurate and precise analysis of 1 H NMR spectra with solvent suppression. This tutorial review is structured into four parts which address the following questions: (1) why suppress the water signal?(2) what are the difficulties in suppressing the water signal? (3) which methods are available to suppress the water signal? (4) which criteria are pertinent to optimize and compare the different methods? These four parts are completed by an experimental section describing in details all the pulse sequences and parameters used in this paper. For each method, the performances greatly depend on the chosen parameters. For instance, the robustness of the NOESY-1D block is significantly modified when the mixing time is changed. Therefore, we propose simple protocols that can be exploited to evaluate and optimize the performances of a water suppression method.

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Understanding the variability of compound quantification from targeted profiling metabolomics of 1D-1H-NMR spectra in synthetic mixtures and urine with additional insights on choice of pulse sequences and robotic sampling

Cited by 32 publications

References 40 publications

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

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

Quantitative NMR Studies of Multiple Compound Mixtures

Optimizing water suppression for quantitative NMR-based metabolomics: a tutorial review

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