Chapter 5: Nuclear magnetic resonance in metabolomics

by Abdul-Hamid Emwas, Kacper Szczepski, Benjamin Gabriel Poulson, Ryan McKay, Leonardo Tenori, Edoardo Saccenti, Joanna Lachowicz, Mariusz Jaremko 

abstract

Nuclear magnetic resonance (NMR) is one of the most common and powerful techniques used in metabolomics. The inherent quantitative, non-destructive, and non-biased properties, together with minimal sample preparation/manipulation make NMR a potent approach to any investigative metabolic study involving biological systems. NMR spectroscopy offers several unique monitoring opportunities such as: extremely high reproducibility, relatively short experiment times, a wide range of available experiments (e.g. multi-dimensional and multi-nuclear based), and advanced highly automated robotic sample handling/exchange technologies enabling potentially hundreds of samples per instrument in a single day.

In this chapter, we will highlight primary advantages and limitations of NMR spectroscopy, introduce the most commonly applied NMR experiments in metabolomics, and review some of the recent advances with selected examples of novel applications, such as high-resolution magic-angle spinning (HR-MAS) for tissue samples, and pure shift NMR method as an example of a promising new approach that can be used to overcome overlapping of 1D NMR spectra. The main advantages of NMR spectroscopy with a particular focus on reproducibility are also presented.

Abdul-Hamid Emwas

King Abdullah University of Science and Technology. Core Labs. Thuwal 23955-6900, Kingdom of Saudi Arabia

Abdul-Hamid Emwas received a M.Sc. in Biophysical Chemistry University of Bergen and a Ph.D. in Physical Chemistry from University of New Brunswick. Currently he is a research scientist at NMR Core Lab of KAUST. Before that he worked at the Miami University, USA, CRUK Institute for Cancer Studies, and University of Waterloo.

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Ryan McKay

Department of Chemistry, University of Alberta, Edmonton, T6G 2W2 Canada

Born in Lethbridge, Alberta, Canada, with B.Sc. Univ. of Lethbridge, and Ph.D from the Univ. of Alberta.  Post doctoral positions at the Univ. of Minneapolis and Univ. of Calgary.
From 2001-2012 Scientific and Assistant Director of the National High Field NMR Centre, then 2012-present NMR Facility Manager (uab.ca/nmr), Department of Chemistry, Univ. of Alberta
Love seeing the light come on during teaching and getting students to help answer their own questions.  Hobbies include analog audio speaker DIY, D&D, bicycling, swimming, and soccer.

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Leonardo Tenori

University of Florence. Department of Chemistry and Magnetic Resonance Center (CERM). Via Luigi Sacconi 6, 50019 Sesto Fiorentino (Florence)

Leonardo Tenori received a MSc degree in Chemistry and a PhD degree in Structural Biology from the University of Florence, Italy. After the PhD, his primary interest turned to the applications of metabolomics via magnetic resonance spectroscopy (NMR) for the study of complex biological mixtures, both in the biomedicine field (analysis of human biofluids), and in the agri-food area (analysis of food matrices such as oil, wine, milk, coffee). He performs his research in the framework of the CERM/CIRMMP infrastructure, one of the largest and best equipped NMR facilities in word. Dr. Tenori's research interest is focused on the use of NMR spectroscopy to identify metabolites that vary in response to physio-pathological states. In the medical field, this mainly translates into identifying biomarkers (or marker panels, or metabolic fingerprints) that can be used for diagnosis, prognosis, patient stratification, or to investigate the molecular mechanisms underlying the onset of diseases.
 

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Edoardo Saccenti

Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE, Wageningen, The Netherlands

Edoardo Saccenti received a MSc degree in Physics and a PhD degree in Structural Biology from the University of Florence, Italy. His main research is multivariate statistics in particular: Principal components analysis and related methods with a focus on the problem of dimensionality assessment and its relationships with inferential statistics in the frame of Random Matrix Theory; power analysis and sample size determination in the context of PCA, PLS-DA and network inference; sparse component methodologies for data exploration and interpretation.

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Mariusz Jaremko

King Abdullah University of Science and Technology (KAUST), Biological and Environmental Sciences & Engineering Division (BESE), Thuwal, 23955-6900, Saudi Arabia

Positions and Employment

2017 – now Assistant Professor, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia;
2013-2017  Postdoctoral researcher, Max-Planck Institute for Biophysical Chemistry, Gottingen, Germany;
2010-2017  Numerous internships in the leading European and American Research Institutions;
2008-2012  Ph.D. studies in Biophysics at the Institute of Biochemistry and Biophysics PAS, Warsaw; Poland.



Honors


  • Laureate of the Polish National Chemistry Olympiad (2002/03)
  • Laureate (1st award) on the Polish Eliminations of the 15th European Union Contest of Young Scientists, 2003;
  • 2nd award on the 15th European Union contest for young Scientists, Budapest, 2003;
  • FEBS: Collaborative Experimental Scholarships for Central & Eastern Europe;
  • Stipends for Young Researchers, Foundation of Polish Science Start Programme 2010;
  • Conference Grant, Foundation of Polish Science Programme 2010;
  • Fellowship for the research training in USA, Foundation of Polish Science Programme 2010;
  • ICMRBS Travel Stipend 2010;
  • Ventures Programme, Foundation of Polish Science Programme 2010-2012;
  • Stipends for Young Researchers, Foundation of Polish Science Start Programme 2011;
  • FGMR Ernst Award 2013;
  • Finalist of the Galenus-von-Pergamon and Springer Medizin contest, 2014;
  • Erwin Neher Nobel Fellowship, 2015.

 Till the End of 2021 Dr. Mariusz Jaremko has published over 110 peer-reviewed papers, and a few book Chapters, and is also a co-author of one patent application.

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