Metabolomics: new analytical methods for metabolome research
- 15.07.2014
- English Articles
- Tobias Demetrowitsch
- Karin Schwarz
Peer-reviewed | Manuscript received: August 6, 2013 | Revision accepted: February 25, 2014
1 Investigation of human metabolism
Fundamental knowledge of human metabolism provides an important foundation for nutritional sciences and modern medicine. Changes in body fluids (e. g. blood plasma and urine) indicate that there have been changes in the conditions of the organism. These are often signs of disease. For a long time, measurements on urine were the central instrument for the diagnosis of diseases without obvious injuries. Even in the fifth century BC, Hippokrates and Hermogenes diagnosed diseases on the basis of the properties of urine [1].
In the 1950s, the concept of systematic characterisation of the metabolomic pattern of urine was introduced. The urine of ca. 200,000 individuals was examined by paper and thin layer chromatography [2]. A specific metabolic pattern was found for different diseases.
In the 1960s and 1970s, additional promising results were provided by using gas chromatography and mass spectrometry (see section 3). Thus Horning and Horning (1971) listed additional characteristic properties of urine, including increased concentrations of aromatic compounds in various diseases [3]. This approach has been continuously improved by progressive improvements in different analytical techniques, electronic process management and data processing.
Summary
Metabolome research is based on so-called “omic” techniques, which are particularly used to investigate problems in system biology. The central analytical procedure in metabolomics is mass spectrometry − mostly in combination with chromatographic procedures. The evaluation may either be targeted (hypothesis-supported) or untargeted (hypothesis-free). As these evaluation approaches in combination with mass spectrometry generate high data density, computerised procedures are inevitable. Targets, core statements and the analytical techniques are described.
Keywords: mass spectrometry, metabolomics, data processing
