NAME PRINCIPLE APPLICATION
1H-NMR rat/mouse liver profiling Identification and (relative) quantification of up to 52 metabolites from aqueous and lipid extracts • Phenotyping of genetically modified animals;
• Drug toxicity and pre-clinical drug candidate safety assessment
• Biomarker Discovery;
• Clinical studies (diagnose and therapeutic efficacy)
• Monitoring of diet-related health phenotyping.
1H-NMR rat/mouse brain profiling Identification and (relative) quantification of 64 metabolites from aqueous and lipid extracts • Drug neurotoxicity and pre-clinical drug candidate safety)
• Neurotoxicity studies
1H-NMR serum/plasma extract profiling Identificacion and (relative) quantification of up to 47 metabolites from serum/plasma extracts of human/animal models • Biomarker Discovery;
• Clinical studies (diagnose and therapeutic efficacy)
• Monitoring of diet-related health phenotyping
1H-NMR urine extract profiling Identification and (relative) quantification of up to 46 metabolites in human/model animal urine • Biomarker discovery
• Assessment of diet or drug intervention
• Drug toxicology studies
• Clinical studies (diagnose and therapeutic efficacy)
HR-MAS 1H-NMR of biopsies, intact tissues or cell cultures Metabolic fingerprinting and profiling of intact non-solid/non-liquid biological samples • Clinical diagnostic
• Biomarker discovery
GC-MS untargeted metabolomics on serum, urine or tissue extract samples Identification of features that are differently expressed in cases vs. control experiments and identification of key metabolites • Biomarker discovery
• Clinical studies (diagnose and therapeutic efficacy)
• Assessment of diet interventions
LC-MS untargeted metabolomics on serum, urine or tissue extract samples Identification of features that are differently expressed in cases vs. control experiments and identification of key metabolites • Biomarker discovery
• Clinical studies (diagnose and therapeutic efficacy)
• Assessment of diet interventions
Targeted LC-MS metabolomics Detection and quantification of predetermined metabolites in biofluids or sample extracts • Phenotypic and physiological effects
• Pre-clinical drug candidate safety assessment
• Pharmacometabonomics
• Disease diagnosis and therapeutic efficacy
Statistics and multivariate analysis Programming and implementation of supervised and unsupervised multivariate algorithms in metabolomics datasets • Basic univariate statistical test.
• Use of advanced multivariate and artificial intelligence algorithms for metabolomics data sets and turn them into useful clinical information (PCA, PLS-DA, ANNs).
• Identify metabolic relationships, mechanism, functions and pathways in the experimental data and mapping of relevant pathways.