- Last Updated: Friday, 04 September 2020 13:31
The standard metabolomics analysis at SMC is performed using both GC-MS and LC-MS (positive and negative electrospray ionisation). The analysis is based on untargeted MS-analysis and is qualitative, i.e. the metabolite levels are expressed as normalised peak areas that can be compared between the analysed samples. The standard metabolomics analysis will result in a data table consisting of peak areas of identified metabolites and of detected putative non-identified metabolites. The number of identified metabolites is increasing, but still the majority of detected peaks are classified as unknowns. Major compound classes as amino acids, fatty acids, phenols, sugars, organic acids, sterols, lipids, flavonoids, oligolignols, and anthocyanins are all examples of classes that can be detected with the described approach.
NUMBER OF SAMPLES: 50-200 samples (analysis of fewer or more samples has to be discussed with SMC)
MATRICES: Plasma, Serum, CSF, Arabidopsis, Populus, bacteria, human tissues, cell cultures, and yeast (other matrices have to be communicated with SMC).
SMC is an independent partner and will be mentioned in the acknowledgements if the data is published
INCLUDED IN STANDARD METABOLOMICS ANALYSIS
- Sample preparation for GC-MS and LC-MS (eg. methanol/water extraction)
- Analysis by both GC-MS and LC-MS (C18 using positive/negative ion mode ESI)
- Quality assurance of the analysis
- Generation of a compound list for GC-MS (~100 - depending on matrix) and LC-MS (~100 - depending on matrix)
- Generation of a table of relevant unknown integrated peak areas (annotated with retention time/index and mass)
- Initial statistical evaluation of the generated data according to the question asked in the project (multivariate and univariate data analysis)
- Initial identification of unknown compounds deemed relevant for the project (around 10-20 metabolites)
- Storage of a pooled sample for possible future extended identification (extended identification is not included)
- Report and feedback
- Archiving of raw data