Lipids are small molecules that can be divided into a number of different classes, e.g., fatty acyls, glycerolipids, glycerophospholipids, sphingolipids and sterol lipids (see Lipid Maps for classification and nomenclature). For lipid analysis a dedicated extraction protocol is used such as Folch extraction. Lipid analysis can be performed by LC-MS for all major classes of lipids and GC-MS when FA composition is of interest. Phosphatidylcholines (PC, 1-2 fatty acid chains), sphingomyelin (SM) and triacylglycerides (TG) are mainly detected in human plasma, serum and CSF. Other phospholipids might be present, but in lower concentrations. The analysis is based on MS-profiling and is not absolutely quantitative, i.e. the metabolite data is not expressed as nmol/ml plasma or nmol/mg tissue. Instead, the metabolite data is expressed as normalised peak areas, and the values can be compared between the analysed samples. Targeted data processing using an in-house database can be applied to identify the total fatty acid (FA) chain lengths and the total amount of double bonds of the compound for each of the lipid classes (PC + SM + TG + DG + PE + PS + MGDG + DGDG + Cer).

Untargeted data processing of the obtained LCMS chromatograms in both positive and negative ionization modes aiming for deeper identification of the lipid molecules is also possible to perform afterwards. Untargeted data processing and identification of the length the FA:s for a specific lipid are time-consuming and not included in the standard lipid profiling.

See this example

NUMBER OF SAMPLES: 50-140 samples (analysis of fewer or more samples has to be discussed with SMC)
MATRICES: Plasma, serum, CSF, tissue and cells (other matrices have to be communicated with SMC)


  • Sample preparation for LC-MS (Folch extraction, with chloroform/methanol phase separation)
  • Analysis by LC-MS (C18 column separation and QTOF MS detection using both positive and negative ion modes in ESI)
  • Quality assurance of the analysis
  • Targeted data processing, initially for five classes out of PC, SM, TG, DG, PE, PS, MGDG, DGDG and Cer in positive ion mode (~150 lipids, depending on matrix)
  • Initial statistical evaluation of the generated data according to the question asked in the project (multivariate and univariate data analysis)
  • Storage of pooled sample for possible future extended identification (extended identification is not included in the price)
  • Report and feedback
  • Archiving of raw data
  • SMC will not be listed as co-authors on a future publication but will be acknowledged