Services do NOT include
A dataset (Excel based) containing relative metabolites levels across all samples. In some cases, accurate quantification is possible, where in-house standards are available.
Sample Requirements - input of users
In agreement with service provider (Users are encouraged to engage with the service provider in experimental design and choosing the correct biology to target). Sample requirements are dependent on the chosen metabolomics platforms. General guidelines include:
Good study design including biological replicates
On average cell pellets containing 100 000 to 1 000 000 cells. (Depended on the cell type used)
Cell quenching procedures must be discussed with the service provider prior to sample collection
Collected samples must be stored at -80°C
In Vivo (Animal models, Human):
Sample handling and collection must be described. All samples must be collected on the same protocol and at the same institution and stored at -80°C.
Metadata to include: Gender, BMI, Known medication (drug usage), Fasting status of sample, Known medical conditions. (Examples include: metabolic syndrome and diabetes)
Different metabolomics platforms require different sample volume.
Description of service
Modality of access under this proposal: Users can provide human or animal serum, plasma or tissue samples, or cell pellets or supernatants from cell models. These must be extracted prior to analysis either by LU or by the user after appropriate training, including techniques to stop metabolic activity during sampling. The user will choose the metabolomics platforms and experimental design with advice from LU. Users are also advised to reach out to the BMFL.
6 months, from sample delivery
Dr. Amy Harms firstname.lastname@example.org
0031 71 527 5682
Metabolomics can be a valuable tool to combine with animal model studies provided by TRANSVAC2. Complimentary services in Immunecorrelates and systems biology (TNA7).
Dr. Amy Harms and Thomas Hankemeier
Noga, M.J., et al. Metabolomics of cerebrospinal fluid reveals changes in the central nervous system metabolism in a rat model of multiple sclerosis. Metabolomics 2012; 8: 253-263. (doi:10.1007/s11306-011-0306-3)
Chunxiu Hu et al., RPLC-Ion-Trap-FTMS Method for Lipid Profiling of Plasma: Method Validation and Application to p53 Mutant Mouse Model. J. Proteome Res., 2008, 7 (11), pp 4982–4991. doi: 10.1021/pr800373m.
Strassburg et al., Quantitative profiling of oxylipins through comprehensive LC-MS/MS analysis: application in cardiac surgery. ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2012; 5:1413-1426. doi: 10.1007/s00216-012-6226-x.
GC (optimized version based on a method developed)
Koek MM, van der Kloet FM, Kleemann R, Kooistra T, Verheij ER, Hankemeier T. Semi-automated non-target processing in GC × GC–MS metabolomics analysis: applicability for biomedical studies. Metabolomics. 2011;7(1):1-14. (doi:10.1007/s11306-010-0219-6)
QC correction method
Van der Kloet, F.M. et al., Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping. , J. Proteome Res., 2009, 8 (11), pp 5132–5141. doi: 10.1021/pr900499r.