This Tutorial Review addresses the principal steps from the sample preparation, acquisition and processing of spectra, data analysis and biomarker discovery and methodologies used in NMR-based metabolomics applied for pointing to key metabolites of diseases.

BackgroundThe objective of this study was to identify molecular alterations in the human blood serum related to bipolar disorder, using nuclear magnetic resonance (NMR) spectroscopy and chemometrics.MethodsMetabolomic profiling, employing 1H-NMR, 1H-NMR T2-edited, and 2D-NMR spectroscopy and chemometrics of human blood serum samples from patients with bipolar disorder (n = 26) compared with healthy volunteers (n = 50) was performed.ResultsThe investigated groups presented distinct metabolic profiles, in which the main differential metabolites found in the serum sample of bipolar disorder patients compared with those from controls were lipids, lipid metabolism-related molecules (choline, myo-inositol), and some amino acids (N-acetyl-l-phenyl alanine, N-acetyl-l-aspartyl-l-glutamic acid, l-glutamine). In addition, amygdalin, α-ketoglutaric acid, and lipoamide, among other compounds, were also present or were significantly altered in the serum of bipolar disorder patients. The data presented herein suggest that some of these metabolites differentially distributed between the groups studied may be directly related to the bipolar disorder pathophysiology.ConclusionsThe strategy employed here showed significant potential for exploring pathophysiological features and molecular pathways involved in bipolar disorder. Thus, our findings may contribute to pave the way for future studies aiming at identifying important potential biomarkers for bipolar disorder diagnosis or progression follow-up.Electronic supplementary materialThe online version of this article (doi:10.1186/s40345-017-0088-2) contains supplementary material, which is available to authorized users.

Candidatus Liberibacter spp. is the pathogen associated with Huanglongbing (HLB), a disease with an economic impact in the order of billions of dollars to the worldwide citrus industry. A key point to reduce HLB economic losses lies on early detection on asymptomatic stages of the infection by new detection methods as it is still not possible to cultivate Candidatus Liberibacter spp. in vitro, and the polymerase chain reaction (PCR) method used nowadays is not manageable in large scale. In this study, we search for metabolic biomarkers from Citrus sinensis leaves in different disease stages using a combined approach of NMR spectroscopy and chemometrics. Chemometric clustering was observed, providing excellent tools for class discrimination, with high accuracy, therefore enabling metabolic profile differentiation on disease early stages. Around 20 different key biomarkers, metabolites responsible for the clustering of each group, were identified using 2D NMR experimental data.