Combined Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry Approaches for Metabolomics

Letertre, Marine P.M.; Dervilly, Gaud; Giraudeau, Patrick

doi:10.1021/acs.analchem.0c04371

Cited by 85 publications

(69 citation statements)

References 146 publications

(359 reference statements)

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“…Major improvements in TA B L E 1 Technological platforms used to resolve polar and lipid metabolite diversity. The most common and successful applications are described [31][32][33][34][35][36] Flow [37] Complex lipids: Glycerolipids, glycerophospholipids, sphingolipids [38][39][40] Surface [41][42][43] Fatty acids Complex lipids: Glycerolipids, glycerophospholipids, sphingolipids [44][45][46][47][48] Gas [49][50][51] Fatty acids, oxysterols, oxylipins, steroids, aldehydes [24,[52][53][54][55][56] Liquid [57][58][59][60][61] Fatty acids, oxysterols, oxylipins, steroids, acyl carnitines [56,[62][63][64] Complex lipids: Glycerolipids, glycerophospholipids, sphingolipids [65,66] Capillary electrophoresis mass spectrometry (CE-MS) [67][68][69][70]…”

Section: "One Size Doesn't Fit All"-resolving Metabolite Chemical DIVmentioning

confidence: 99%

“…Among the technological platforms used to resolve chemical diversity, nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) constitute two main, widely applied technologies for broad‐range metabolite and lipid analysis. [ 66 ] While NMR has the advantage of being quantitative, non‐destructive and highly reproducible— mainly due to the lack of direct interaction between the sample and the instrument, its important drawbacks are the lack of sensitivity and spectral resolution or overlapping signals making unambiguous metabolite identification and quantification difficult when analyzing complex biological matrices. [ 27–29 ] NMR is widely used for the analysis of urinary metabolites, present in high concentrations, in the context of large‐scale population studies, in view of its high robustness over time.…”

Section: “One Size Doesn't Fit All”—resolving Metabolite Chemical DIVmentioning

confidence: 99%

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Metabolomics meets lipidomics: Assessing the small molecule component of metabolism

2020

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Metabolomics, including lipidomics, is emerging as a quantitative biology approach for the assessment of energy flow through metabolism and information flow through metabolic signaling; thus, providing novel insights into metabolism and its regulation, in health, healthy ageing and disease. In this forward-looking review we provide an overview on the origins of metabolomics, on its role in this postgenomic era of biochemistry and its application to investigate metabolite role and (bio)activity, from model systems to human population studies. We present the challenges inherent to this analytical science, and approaches and modes of analysis that are used to resolve, characterize and measure the infinite chemical diversity contained in the metabolome (including lipidome) of complex biological matrices. In the current outbreak of metabolic diseases such as cardiometabolic disorders, cancer and neurodegenerative diseases, metabolomics appears to be ideally situated for the investigation of disease pathophysiology from a metabolite perspective.

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Section: "One Size Doesn't Fit All"-resolving Metabolite Chemical DIVmentioning

confidence: 99%

Section: “One Size Doesn't Fit All”—resolving Metabolite Chemical DIVmentioning

confidence: 99%

Metabolomics meets lipidomics: Assessing the small molecule component of metabolism

2020

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“…From an analytical method development stand point, interesting developments such as plasma pseudotargeted metabolomics method using ultra-high-performance liquid chromatography–mass spectrometry (UHPLC-MS) (Zheng et al 2020 ) and the need for combined use of nuclear magnetic resonance spectroscopy and mass spectrometry approaches in metabolomics (Letertre et al 2020 ) are notable. For volume-limited samples, solutions such as sub-nanoliter metabolomics via LC–MS/MS such as pulsed MS ion generation method known as triboelectric nanogenerator inductive nanoelectrospray ionization (TENGi nanoESI) MS (Li et al 2020 ) was introduced.…”

Section: Introductionmentioning

confidence: 99%

New software tools, databases, and resources in metabolomics: updates from 2020

Misra

2021

Metabolomics

149

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Background Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success. Aim of the review In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community. Key scientific concepts of review In Table 1 the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.

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“…Stable-isotope labeling experiments are widely used to investigate metabolic networks in the fields of systems biology [1][2] , biotechnology [3][4] and biomedical research [5][6] . The most effective approach is to combine 13 C-labeling strategies with a detailed analysis of isotope incorporation into metabolites, as measured by mass spectrometry (MS) and/or nuclear magnetic resonance (NMR) spectroscopy 7 . MS provides global isotopic information by quantifying the proportions of molecules with different numbers of tracer isotopes (isotopologue distributions) [8][9] , while NMR provides positional information on tracer incorporation at specific positions in the molecules (isotopomer distributions) [10][11][12][13] by exploiting the 1 H and 13 C nuclei via non-decoupled experimentssuch as homonuclear 1 H-1 H-TOCSY and heteronuclear 1 H- 13 C-HSQC experiments.…”

mentioning

confidence: 99%

“…Integrating measurements from different approaches should expand the range of quantifiable isotopic species 7 , thus improving the coverage of isotopic space. This is exploited in 13 C-fluxomics studies, where different datasets are frequently integrated using isotopic models of metabolic networks to improve flux quantification 4,[15][16] .…”

mentioning

confidence: 99%

IsoSolve: an integrative framework to improve isotopic coverage and consolidate isotopic measurements by MS and/or NMR

Millard

Sokol

Kohlstedt

et al. 2021

Preprint

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Stable-isotope labeling experiments are widely used to investigate the topology and functioning of metabolic networks. Label incorporation into metabolites can be quantified using a broad range of mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy methods, but in general, no single approach can completely cover isotopic space, even for small metabolites. The number of quantifiable isotopic species could be increase, and the coverage of isotopic space improved, by integrating measurements obtained by different methods; however, this approach has remained largely unexplored because no framework able to deal with partial, heterogeneous isotopic measurements has yet been developed. Here, we present a generic computational framework based on symbolic calculus that can integrate any isotopic dataset by connecting measurements to the chemical structure of the molecules. As a test case, we apply this framework to isotopic analyses of amino acids, which are ubiquitous to life, central to many biological questions, and can be analyzed by a broad range of MS and NMR methods. We demonstrate how this integrative framework helps to i) clarify and improve the coverage of isotopic space, ii) evaluate the complementarity and redundancy of different techniques, iii) consolidate isotopic datasets, iv) design experiments, and v) guide future analytical developments. This framework, which can be applied to any labeled element, isotopic tracer, metabolite, and analytical platform, has been implemented in IsoSolve (available at https://github.com/MetaSys-LISBP/IsoSolve and https://pypi.org/project/IsoSolve), an open source software that can be readily integrated into data analysis pipelines.

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Combined Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry Approaches for Metabolomics

Cited by 85 publications

References 146 publications

Metabolomics meets lipidomics: Assessing the small molecule component of metabolism

Metabolomics meets lipidomics: Assessing the small molecule component of metabolism

New software tools, databases, and resources in metabolomics: updates from 2020

IsoSolve: an integrative framework to improve isotopic coverage and consolidate isotopic measurements by MS and/or NMR

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