Metabolomics is the fourth ‘omics’ research field after genomics, transcriptomics and proteomics. The metabolome represents the entity of all ‘small’ molecules (< 1500 Dalton) and is most predictive of the phenotype of an organism.

Comparisons of the metabolome of well defined groups of individuals allow the identification of biomarkers, which are responsible for group differences.

Success story

ABF has succesfully developed and validated an untargeted metabolic profiling platform applicable for various matrices (plasma, urine, saliva).

We could demonstrate in a first show case study including smoking and non-smoking subjects, that the untargeted metabolomics approach using GC-TOF-MS is a powerful tool for exploring differences at the metabolite levels, which can potentially serve as biomarkers of early biological effects involved in disease generation. A series of biomarkers could be succesfully identified from different biological matrices and further verified and quantified using different targeted assays.

Related publications:

  • Plasma

Daniel C. Müller, Christian Degen, Gerhard Scherer, Gerhard Jahreis, Reinhard Niessner, Max Scherer (2014) Mass spectrometry based analysis of human plasma reveals significantly altered fatty acid and phospholipid species profiles in smokers. J Chromatogr B Analyt Technol Biomed Life Sci. 2014 Mar 3 (Epub ahead of print)

  • Saliva

Daniel C. Müller, Markus Piller, Reinhard Niessner, Max Scherer, Gerhard Scherer. Untargeted Metabolomic Profiling in Saliva of Smokers and Nonsmokers by a Validated GC-TOF-MS Method. J Proteome Res. 2014 Mar 7;13(3):1602-13.

  • Urine

Daniel C. Müller, Markus Piller, Reinhard Niessner, Gerhard Scherer, Max Scherer (2014). Untargeted metabolic fingerprinting in urine and plasma of smokers and non-smokers (in preparation)


The metabolomics service uses a combination of an untargeted fingerprinting platform by GC-TOF-MS in order to identify potential biomarkers responsible for a group separation, and depending on the outcome further targeted assays to verify and quantify specific biomarkers of interest.

Analytical workflow:

  • Untargeted metabolic profiling by GC-TOF-MS
  • Series of bioinformatics tools to process raw data making them accessible for subsequent statistic testing
  • Biostatistics to identify biomarkers responsible for group separation
  • Target hits identification
  • Depending on the outcome further evaluation and quantification of the affected biochmeical pathways with designated targeted methods (this step is optional and would require additional resources)


Reporting and biochemical data file:

  • The data file contains information about each sample processed including the biochemical name, biochemical pathway, retention time, m/z.
  • The statisitcs can be provided as illustrations (e.g. PLS-DA plot) and/or numbers (e.g. statistical significance, fold-change etc.)
  • ABF will provide a final report at the end of each project, including a comprehensive overview of the sample analysis, test sample results and eventual interpretation of the results


Getting started


For additional questions you may have or to receive a free quote please contact us.


Please note that ABF can also assist in designing and planning an appropriate metabolomics study!