Literature DB >> 21207299

Nuclear magnetic resonance (NMR)-based metabolomics.

Hector C Keun1, Toby J Athersuch.   

Abstract

Biofluids are by far the most commonly studied sample type in metabolic profiling studies, encompassing blood, urine, cerebrospinal fluid, cell culture media and many others. A number of these fluids can be obtained at a high sampling frequency with minimal invasion, permitting detailed characterisation of dynamic metabolic events. One of the attractive properties of solution-state metabolomics is the ability to generate profiles from these fluids following simple preparation, allowing the analyst to gain a naturalistic, largely unbiased view of their composition that is highly representative of the in vivo situation. Solution-state samples can also be generated from the extraction of tissue or cellular samples that can be tailored to target metabolites with particular properties. Nuclear magnetic resonance (NMR) provides an excellent technique for profiling these fluids and is especially adept at characterising complex solutions. Profiling biofluid samples by NMR requires appropriate preparation and experimental conditions to overcome the demands of varied sample matrices, including those with high protein, lipid or saline content, as well as the presence of water in aqueous samples.

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Year:  2011        PMID: 21207299     DOI: 10.1007/978-1-61737-985-7_19

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  11 in total

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Journal:  Diabetes       Date:  2015-03       Impact factor: 9.461

2.  Choline-releasing glycerophosphodiesterase EDI3 drives tumor cell migration and metastasis.

Authors:  Joanna D Stewart; Rosemarie Marchan; Michaela S Lesjak; Joerg Lambert; Roland Hergenroeder; James K Ellis; Chung-Ho Lau; Hector C Keun; Gerd Schmitz; Juergen Schiller; Mandy Eibisch; Christian Hedberg; Herbert Waldmann; Ekkehart Lausch; Berno Tanner; Jalid Sehouli; Jens Sagemueller; Hagen Staude; Eric Steiner; Jan G Hengstler
Journal:  Proc Natl Acad Sci U S A       Date:  2012-05-08       Impact factor: 11.205

3.  Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis.

Authors:  Masahiro Sugimoto; Masato Kawakami; Martin Robert; Tomoyoshi Soga; Masaru Tomita
Journal:  Curr Bioinform       Date:  2012-03       Impact factor: 3.543

4.  Metabolite signatures of doxorubicin induced toxicity in human induced pluripotent stem cell-derived cardiomyocytes.

Authors:  Umesh Chaudhari; James K Ellis; Vilas Wagh; Harshal Nemade; Jürgen Hescheler; Hector C Keun; Agapios Sachinidis
Journal:  Amino Acids       Date:  2017-04-18       Impact factor: 3.520

5.  Optimization of Synovial Fluid Collection and Processing for NMR Metabolomics and LC-MS/MS Proteomics.

Authors:  James R Anderson; Marie M Phelan; Luis M Rubio-Martinez; Matthew M Fitzgerald; Simon W Jones; Peter D Clegg; Mandy J Peffers
Journal:  J Proteome Res       Date:  2020-04-07       Impact factor: 4.466

6.  Microdialysis Workflow for Metabotyping Superficial Pathologies: Application to Burn Injury.

Authors:  Dominic Friston; Helen Laycock; Istvan Nagy; Elizabeth J Want
Journal:  Anal Chem       Date:  2019-05-11       Impact factor: 6.986

7.  The impact of p53 on aristolochic acid I-induced nephrotoxicity and DNA damage in vivo and in vitro.

Authors:  Mateja Sborchia; Eric G De Prez; Marie-Hélène Antoine; Lucie Bienfait; Radek Indra; Gabriel Valbuena; David H Phillips; Joëlle L Nortier; Marie Stiborová; Hector C Keun; Volker M Arlt
Journal:  Arch Toxicol       Date:  2019-10-10       Impact factor: 5.153

Review 8.  Insights into glucocorticoid responses derived from omics studies.

Authors:  Mengyuan Kan; Blanca E Himes
Journal:  Pharmacol Ther       Date:  2020-09-08       Impact factor: 12.310

9.  Hepatic metabolic effects of Curcuma longa extract supplement in high-fructose and saturated fat fed rats.

Authors:  Fabrice Tranchida; Zo Rakotoniaina; Laetitia Shintu; Léopold Tchiakpe; Valérie Deyris; Mehdi Yemloul; Pierre Stocker; Nicolas Vidal; Odile Rimet; Abel Hiol; Stefano Caldarelli
Journal:  Sci Rep       Date:  2017-07-19       Impact factor: 4.379

10.  Synovial Fluid Metabolites Differentiate between Septic and Nonseptic Joint Pathologies.

Authors:  James R Anderson; Marie M Phelan; Peter D Clegg; Mandy J Peffers; Luis M Rubio-Martinez
Journal:  J Proteome Res       Date:  2018-07-20       Impact factor: 4.466

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