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Metabolomics and Lipidomics

Metabolomics is the study of small molecule metabolites that are produced and consumed by chemical reactions throughout biological systems. Mass spectrometry based metabolomics facilitates the measurement of ionizable components in a biological sample and subsequent identification of these metabolites using high resolution accurate mass measurements, isotopic distribution calculations for molecular formula determination and fragmentation matching using tandem mass spectrometry fingerprints. Metabolomics studies provide a snapshot of the metabolite profile under various study conditions (e.g., control vs. diseased) and the results offer insight to the perturbation of biological pathways. Front end separations typically involve liquid chromatography and routine methods for untargeted studies are often divided between polar (e.g., amino acids, sugars, organic acids) and non-polar metabolites (e.g., lipids). Lipidomics focuses on the large-scale study of cellular lipids (the lipidome) in biological systems. Although the lipidome is a subset of the metabolome, the analytical approaches for lipidomics differ from those used for small polar metabolites.

An example of a data visualization output from a metabolomics study conducted by METRIC. This visualization shows a principal component analysis scores plot (A), variable importance in projections (VIP) plots (B and C), and hierarchical clustering heatmap (D) from MetaboAnalyst. Abbreviations: CF, control female; CM, control male; HF, high-dose female; HM, high-dose male.

From Kylie D Rock, Genevieve St Armour, Brian Horman, Allison Phillips, Matthew Ruis, Allison K Stewart, Dereje Jima, David C Muddiman, Heather M Stapleton, Heather B Patisaul, Effects of Prenatal Exposure to a Mixture of Organophosphate Flame Retardants on Placental Gene Expression and Serotonergic Innervation in the Fetal Rat Brain, Toxicological Sciences 2020 176 (1), 203–223.
  • High Resolution Accurate Mass (HRAM) Measurements
  • HRAM MSn  for Structural Characterization
  • CID or HCD fragmentation (ID-X)
  • AcquireX automated MS2 background exclusion and sample inclusion
  • UPLC, nanoLC, CE, RapidFire and Ion Mobility front end separations
  • Targeted Quantitative Analyses (QqQ or PRM)
  • Isotope Tracer Studies 
  • Data Analysis and Statistical Tools: Skyline, Compound Discoverer, LipidSearch, GNPS, SIRIUS, MetaboAnalyst, METASPACE and MSiReader
  • Biological Fluids and Tissues:
    • Tissue: brain, liver, skin, testes, ovaries, embryos, placentas
    • Biofluids and biosolids: plasma, serum, CSF, urine, feces, cecal/ileal content
  • Source Organisms:
    • Cell Culture (bacterial, fungal, human and murine cell-lines) – media or cell pellet
    • Plants (Arabidopsis, tomatoes) – roots, stem, leaves, etc
    • Model organisms (zebrafish, mouse, rat)
    • Other: horse, canine, feline, insect
  • Environmental Matrices: Water, Ice-Cores, POCIS extracts, Soil, Landfill Leachate

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  • Endogenous Metabolites:
    • Amino Acids
    • Organic Acids
    • Cofactors and Vitamins
    • Nucleotides
    • Peptides
    • Carbohydrates
    • Lipids
  • Exogenous Metabolites
    • Xenobiotics 
    • Pesticides
    • Herbicides
    • Insecticides
    • Industrial pollutants
    • Toxins
  • Untargeted: little to no hypothesis on potentially disrupted pathway; provides global knowledge and can lead into targeted studies
  • Targeted: Hypothesis driven (e.g. guided by gene expression data); Target can be a single compound, group of compounds, metabolite class, or pathway; Relative or absolute abundance