Improving sample classification by harnessing the potential of 1H-NMR signal chemical shifts

Abstract NMR spectroscopy is a technology that is widely used in metabolomic studies. The information that these studies most commonly use from NMR spectra is the metabolite concentration. However, as well as concentration, pH and ionic strength information are also made available by the chemical sh...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Daniel Cañueto, Reza M. Salek, Xavier Correig, Nicolau Cañellas
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0556d9a60b024e1b8dc514fc94efb195
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0556d9a60b024e1b8dc514fc94efb195
record_format	dspace
spelling	oai:doaj.org-article:0556d9a60b024e1b8dc514fc94efb1952021-12-02T15:07:52ZImproving sample classification by harnessing the potential of 1H-NMR signal chemical shifts10.1038/s41598-018-30351-72045-2322https://doaj.org/article/0556d9a60b024e1b8dc514fc94efb1952018-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-30351-7https://doaj.org/toc/2045-2322Abstract NMR spectroscopy is a technology that is widely used in metabolomic studies. The information that these studies most commonly use from NMR spectra is the metabolite concentration. However, as well as concentration, pH and ionic strength information are also made available by the chemical shift of metabolite signals. This information is typically not used even though it can enhance sample discrimination, since many conditions show pH or ionic imbalance. Here, we demonstrate how chemical shift information can be used to improve the quality of the discrimination between case and control samples in three public datasets of different human matrices. In two of these datasets, chemical shift information helped to provide an AUROC value higher than 0.9 during sample classification. In the other dataset, the chemical shift also showed discriminant potential (AUROC 0.831). These results are consistent with the pH imbalance characteristic of the condition studied in the datasets. In addition, we show that this signal misalignment dependent on sample class can alter the results of fingerprinting approaches in the three datasets. Our results show that it is possible to use chemical shift information to enhance the diagnostic and predictive properties of NMR.Daniel CañuetoReza M. SalekXavier CorreigNicolau CañellasNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-8 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Daniel Cañueto Reza M. Salek Xavier Correig Nicolau Cañellas Improving sample classification by harnessing the potential of 1H-NMR signal chemical shifts
description	Abstract NMR spectroscopy is a technology that is widely used in metabolomic studies. The information that these studies most commonly use from NMR spectra is the metabolite concentration. However, as well as concentration, pH and ionic strength information are also made available by the chemical shift of metabolite signals. This information is typically not used even though it can enhance sample discrimination, since many conditions show pH or ionic imbalance. Here, we demonstrate how chemical shift information can be used to improve the quality of the discrimination between case and control samples in three public datasets of different human matrices. In two of these datasets, chemical shift information helped to provide an AUROC value higher than 0.9 during sample classification. In the other dataset, the chemical shift also showed discriminant potential (AUROC 0.831). These results are consistent with the pH imbalance characteristic of the condition studied in the datasets. In addition, we show that this signal misalignment dependent on sample class can alter the results of fingerprinting approaches in the three datasets. Our results show that it is possible to use chemical shift information to enhance the diagnostic and predictive properties of NMR.
format	article
author	Daniel Cañueto Reza M. Salek Xavier Correig Nicolau Cañellas
author_facet	Daniel Cañueto Reza M. Salek Xavier Correig Nicolau Cañellas
author_sort	Daniel Cañueto
title	Improving sample classification by harnessing the potential of 1H-NMR signal chemical shifts
title_short	Improving sample classification by harnessing the potential of 1H-NMR signal chemical shifts
title_full	Improving sample classification by harnessing the potential of 1H-NMR signal chemical shifts
title_fullStr	Improving sample classification by harnessing the potential of 1H-NMR signal chemical shifts
title_full_unstemmed	Improving sample classification by harnessing the potential of 1H-NMR signal chemical shifts
title_sort	improving sample classification by harnessing the potential of 1h-nmr signal chemical shifts
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/0556d9a60b024e1b8dc514fc94efb195
work_keys_str_mv	AT danielcanueto improvingsampleclassificationbyharnessingthepotentialof1hnmrsignalchemicalshifts AT rezamsalek improvingsampleclassificationbyharnessingthepotentialof1hnmrsignalchemicalshifts AT xaviercorreig improvingsampleclassificationbyharnessingthepotentialof1hnmrsignalchemicalshifts AT nicolaucanellas improvingsampleclassificationbyharnessingthepotentialof1hnmrsignalchemicalshifts
_version_	1718388362949165056

Improving sample classification by harnessing the potential of 1H-NMR signal chemical shifts

Ejemplares similares