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Updated: Jan 24, 2026

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
Published on: November 29, 2024
Evaluation of sample preparation protocols for quantitative NMR-based metabolomics
Olga A Snytnikova1,2, Anastasiya A Khlichkina3,4, Renad Z Sagdeev3
1International Tomography Center SB RAS, Institutskaya 3a, Novosibirsk, 630090, Russia. snytnikova_olga@tomo.nsc.ru.
Introduction:
Quantification of metabolites in biological fluids and tissues by NMR spectroscopy is challenged by the presence of abundant macromolecules and lipoproteins in samples, which give broad signals in the NMR spectra. To improve the quality of NMR spectra the different protocols for protein and lipid removal from the sample are used.
Objectives:
This work is aimed at the evaluation of the effectiveness of various methods of purification of blood serum from proteins and lipids for 1H NMR metabolomic profiling.
Methods:
The advantages and limitations of different methods of the sample preparation for NMR-based quantitative metabolomics have been compared, including ultrafiltration, methanol and ethanol extractions with and without additional lipid removal, and methanol-chloroform extraction.
Results:
The concentrations of 30 abundant metabolites extracted from human blood serum have been measured. It is found that ultrafiltration provides the best lipid removal, but causes significant and inhomogeneous metabolite losses. Ethanol and methanol extractions demonstrate similar performance with the minimal metabolite losses, and are ideal for fluids and tissues with low lipid content. The additional purification of alcohol extracts from lipids allows for the significant improving of NMR spectra, but causes additional metabolite losses.
Conclusions:
The methanol-chloroform extraction seems to be an optimal method for tissues with the high lipid content, providing a satisfactory lipid removal and low metabolite losses. The ultrafiltration leads to large losses of metabolites (up to 60%) and for this reason is not suitable for quantitative analysis.
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