Metabolomics & Lipidomics

Metabolomics & Lipidomics

img-metabolomics

Nowadays, metabolomics and lipidomics are dynamically evolving parts of analytical biochemistry dealing with the study of metabolome / lipidome. 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. Lipidomics deals with a study of water-insoluble biomolecules (lipids) that are present in a given biological system.

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 as well as lipidome. These changes are more sensitive than alternation in transcriptome or a proteome. Even a small change in protein expression can have a huge effect on the activity of the biochemical pathway and the concentration of the relevant metabolites and lipids.

The targeted or untargeted approach is used for sample analysis. The first one is based on quantitative or semi-quantitative measurement of certain groups of metabolites/lipids (e.g. substrates and products of basic biochemical pathways; compounds belonging to a particular chemical group). The untargeted metabolomic/lipidomic analysis is characterized by a semi-quantitative analysis of a broad spectrum of metabolites (thousands of features).

Our knowledge

  • 30 years’ experience with multicomponent analysis by mass spectrometry
  • 10 years’ experience with metabolomics
  • robust and standardized metabolomic pipeline based on advanced mass spectrometry
  • development of new analytical methods
  • flexible data processing and thorough statistical analysis
  • data evaluation and biochemical interpretation
  • study of drug metabolization
  • structural elucidation of unknown compounds

Methods


Design of experiment

  • One of the most critical steps in metabolomic/lipidomic project.
  • It is crucial to plan experiments together with statisticians, analytical chemists, biochemists and (eventually) physicians to correctly adjust numbers and type of subjects/samples. Samples must be taken representatively and precisely. 
  • The targeted approach is usually chosen for a better understanding of pathobiochemical mechanisms of the disease. Untargeted methods are used to search for new potential biomarkers. 

Sample preparation

  • For metabolomics, the stop metabolic processes and changes in metabolite profiles are required.
  • Fast preparation by organic solvents or cell quenching are usually used. 
  • We are experienced with preparation of urine (dilution on creatinine concentration); 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 for detail information into biology; pathobiochemistry; cellular processes and understanding biochemical pathways based on HILIC UHPLC/QqQ (TripleQuad 6500, Sciex), allows relative quantitation of 365 compounds in one run (Amino acids | Organic acids | Purine and pyrimidine bases, ribosides and nucleotides | Sugar phosphates | Acylcarnitines | Fatty acids | Carbohydrates | Amines). Untargeted metabolomics and lipidomics for discovering compounds as potential biomarkers based on RP UHPLC/HR-MS (Orbitrap Elite, Thermo) allow profiling of 1000+ metabolites and phospholipids. Targeted lipidomics allows relative quantitation of 1000+ compounds in positive and negative mode (UHPLC/QqQ, TripleQuad 6500, Sciex, C8 column).

Data handling

  • MultiQuant 
  • Compound discoverer 3.0 
  • R software – LOESS for QC processing
  • Data transformation (clr, ilr, log, PQR)

Statistical analysis

  • Search for the most discriminant metabolites/lipids between groups
  • Univariate statistical methods (Boxplots, Volcano plots)
  • Multivariate data analysis (PCA, Hierarchical Cluster Analysis, PLS-DA, OPLS-DA, Heat-maps, Random forest)
  • Correlation analysis
  • ROC curve

Interpretation of findings

  • HMDB, SMPDB, Metlin, mzCloud, ChemSpider, Kegg, PubChem, Lipids maps, Metagene, BRENDA
  • Biochemical interpretation
  • Structure identification