img-metabolomicsNowadays, metabolomics is a dynamically evolving part of analytical biochemistry dealing with the study of metabolomes. A metabolome is defined as a set of low-molecular-weight compounds (metabolites) present in the biological matrices which are involved in all the metabolic processes necessary for the normal functioning of the cell and the whole organism. Metabolomics appropriately complements “older” fields such as transcriptomics, proteomics and genomics. Physiological changes that manifest themselves as a result of deviation in gene expression are characterized by a change of a metabolome. Thus a metabolome is more sensitive than a transcriptome or a proteome. Even a small change in protein expression can have a huge effect on the activity of the metabolic pathway and the concentration of the relevant metabolites.

In summary, we can say that metabolomics is the systematic and data-driven study of the temporal interactions between the complement of low-molecular-weight (bio-)chemicals (metabolites) that are abundant within living organisms, tissues and cells.



Design of Experimentimg-methods

  • One of the most critical steps in metabolomics project
  • It is crutial to plan the project and experiments together with statisticians, analytical chemists and biochemists to correctly adjust numbers of samples and to precisely take the samples.

Sample preparation

  • For metabolomics the stop metabolic processes and changes in metabolite profiles is required.
  • Fast preparation by organic solvents or cell quenching are the methods of sample prep.
  • We are experienced with urine (dilution on creatinine); plasma and serum (deproteination); cells (lysis & deproteination); dry blood spot (deproteination/extraction); sweat (deproteination); breath condensate (dilution); tissues (homogenisation & deproteination)

Methods of analysis

  • Targeted metabolomics:
    • LC/QqQ by MRM transitions and polarity switching on ANP column under alkaline conditions (400 metabolites)
    • GCxGC/TOF by full scan of derivatized samples
    • FIA/QqQ by MRM transitions in positive polarity (60 metabolites)
  • Untargeted metabolomics:
    • LC/Orbitrap by HR-AM full scan in positive and negative polarity on ANP column under alkaline conditions (hunderds-thousands metabolites)

Data handling

  • MultiQuant – peak area integration
  • “peak picking” by XCMS, isotope & adducts identification (clustering features) CAMERA
  • R software – QC processing
  • Data transformation (clr, ilr, log)

Statistical analysis

  • Multivariate data analysis: PCA, Hierarchical Cluster Analysis, PLS-DA, OPLS-DA, Heat-maps
  • Most significant metabolites

Interpretation of findings

  • HMDB, Metlin, mzCloud, ChemSpider, Kegg
  • Biochemical description