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  Rapidly signal‐enhanced metabolites for atomic scale monitoring of living cells with magnetic resonance

Ding, Y., Korchak, S., Mamone, S., Jagtap, A. P., Stevanato, G., Sternkopf, S., et al. (2022). Rapidly signal‐enhanced metabolites for atomic scale monitoring of living cells with magnetic resonance. Chemistry–Methods, 2(7): e202200023. doi:10.1002/cmtd.202200023.

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 Creators:
Ding, Y.1, Author           
Korchak, S.1, Author           
Mamone, S.1, Author           
Jagtap, A. P.1, Author           
Stevanato, G.1, Author           
Sternkopf, S.1, Author           
Moll, D.1, Author           
Schroeder, H.1, Author           
Becker, S.2, Author           
Fische, André, Author
Gerhardt, Ellen, Author
Outeiro, Tiago F., Author
Opazo, Felipe, Author
Griesinger, C.2, Author                 
Glöggler, S.1, Author           
Affiliations:
1Research Group of NMR Signal Enhancement, Max Planck Institute for Multidisciplinary Sciences, Max Planck Society, ou_3350277              
2Department of NMR Based Structural Biology, Max Planck Institute for Multidisciplinary Sciences, Max Planck Society, ou_3350124              

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 Abstract: Nuclear magnetic resonance (NMR) is widely applied from analytics to biomedicine although it is an inherently insensitive phenomenon. Overcoming sensitivity challenges is key to further broaden the applicability of NMR and, for example, improve medical diagnostics. Here, we present a rapid strategy to enhance the signals of 13C-labelled metabolites with para-hydrogen and, in particular, 13C-pyruvate, an important molecule for the energy metabolism. We succeeded to obtain an average of 27 % 13C polarization of 1-13C-pyruvate in water which allowed us to introduce two applications for studying cellular metabolism. Firstly, we demonstrate that the metabolism of 1-13C-pyruvate can serve as a biomarker in cellular models of Parkinson's disease and, secondly, we introduce the opportunity to combine real-time metabolic analysis with protein structure determination in the same cells. Based on the here presented results, we envision the use of our approach for future biomedical studies to detect diseases.

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Language(s): eng - English
 Dates: 2022-04-072022-07
 Publication Status: Published in print
 Pages: 6
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1002/cmtd.202200023
 Degree: -

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Project name : S. G. thanks the Deutsche Forschungsgemeinschaft (DFG) for funding (grants 418416679, 426677227 and 450146057). This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant agreement No. 949180). T. F. O. is supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – EXC 2067/1-390729940
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Project name : HyperULFNMR
Grant ID : 949180
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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Title: Chemistry–Methods
Source Genre: Journal
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Pages: 6 Volume / Issue: 2 (7) Sequence Number: e202200023 Start / End Page: - Identifier: ISSN: 2628-9725
ISSN: 2628-9725