This post provides information regarding technical aspects of the Biocrates metabolomics platform.
Biocrates is an Austrian metabolomics company that produces the targeted quantitative metabolomics kit p150. This kit runs on a number of mass spectrometry platform and measures 163 metabolites, covering a biologically relevant panel of amino acids, sugars, acylcarnitines and phospholipids, using flow injection electrospray ionization tandem mass spectrometry (ESI-MS/MS). A more recent version of that kit (p180) includes a more complete set of amino acids and a number of biogenic amines by adding an LC-MS/MS analysis step.
We used the Biocrates kits in a number of studies to qualify its robustness and sensitivity to experimental parameters.Most of these analyses were run at the metabolomics platform of the Helmholtz Center in Munich. The following links point to peer-reviewed papers discussing the respective aspects of the Biocrates kit:
- Using dried blood spots as a matrix
- Serum versus plasma
- Extraction from different tissues
- Pre-analytical sample quality
- Alterations during prolonged cryostorage
We also used the Biocrates p150 and p180 kits in genome-wide association stuies (GWAS) with metabolomics.
- A genome-wide perspective of genetic variation in human metabolism (Nature Genetics, 2010)
- Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels (Nature Communications, 2015)
The Supplemental Table 4 of our Nature Genetics paper provides a full list of the metabolites of the p150 kit together with the experimental variance (CV) for 45 technical replicates measured on 9 different kit plates (5 replicate samples/kit).
Since a genetic variation is by definition causative for any observed association with a phenotype, any GWAS hit with a metabolic phenotype indicates that this phenotype is carrying biologically meaningful information. Thus – every measure of a metabolite that has been seen in a GWAS is clearly more than background noise of the instrument (whether the biochemical annotation of that metabolite is correct is another question). Therefore, I consider genetic associations with metabolites as being part of the quality control measures of any metabolomics platform.
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