metabolomiek

Metabolomiek, in Dutch often referred to as metabolomics, is the systematic study of the chemical fingerprints left by cellular processes, namely metabolites. It aims to comprehensively profile, quantify, and interpret low-molecular-weight compounds in biological samples such as blood, urine, tissue, or cell extracts. The field complements genomics, transcriptomics, and proteomics by capturing the functional state of a biological system.

Analytical platforms and approaches: The core technologies are mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, frequently coupled with chromatography (liquid chromatography LC, gas chromatography GC) to separate complex mixtures. Researchers conduct untargeted metabolomics, aiming to detect as many compounds as possible, or targeted metabolomics, focusing on predefined metabolite panels for quantitative accuracy.

Workflow: Study design, sample collection and preparation, data acquisition, preprocessing (noise filtering, peak detection, alignment), normalization, and statistical analysis. Metabolite identification is a critical step, often supported by spectral libraries and in silico tools, followed by pathway and network interpretation.

Data analysis: Uses multivariate statistics, machine learning, and pathway analyses to interpret metabolite changes in the context of biology. Quantification can be relative or absolute, and results rely on careful calibration and quality control.

Applications: Biomarker discovery for disease and treatment response, nutrition and food science, pharmacometabolomics, toxicology, plant and environmental metabolomics, and microbial metabolism studies.

Challenges: Metabolite diversity and chemical properties span wide polarity, concentration ranges; metabolite identification remains difficult; standardization and reproducibility across platforms; data sharing and annotation gaps; biological interpretation can be complex.

History and outlook: The field gained momentum in the 1990s and 2000s with advances in MS and NMR; ongoing efforts aim to improve standardization, annotation, and integration with other omics to enable systems biology and personalized medicine.