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 not absolutely quantitative, i.e. the metabolite data are not expressed as nmol/ml plasma or nmol/mg tissue. Instead, the metabolite levels are expressed as normalised peak areas, which values 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 metabolites which are currently not identified. The number of identified metabolites is increasing, but still the majority of detected peaks are classifiedas unknowns.  All major compound classes can be detected with the described approach, the main ones being, depending on the type of sample, amino acids, fatty acids, phenols, sugars, organic acids, sterols, lipids, flavonoids, oligolignols and anthocyanins.

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 yest (other matrices have to be communicated with SMC)


  • 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
  • Generating a list of compounds 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 pooled sample for possible future extended identification (extended identification is not included)
  • Report and feedback
  • Archiving of raw data
  • SMC will not be listed as co-authors on a future publication but will be acknowledged