Title
Metabolite profiling of small cerebrospinal fluid sample volumes with gas chromatography-mass spectrometry: Application to a rat model of multiple sclerosis
Author
Coulier, L.
Muilwijk, B.
Bijlsma, S.
Noga, M.
Tienstra, M.
Attali, A.
van Aken, H.
Suidgeest, E.
Tuinstra, T.
Luider, T.M.
Hankemeier, T.
Bobeldijk, I.
Publication year
2013
Abstract
Analysis of metabolites in biofluids by gas chromatography-mass spectrometry (GC-MS) after oximation and silylation is a key method in metabolomics. The GC-MS method was modified by a modified vial design and sample work-up procedure in order to make the method applicable to small volumes of cerebrospinal fluid (CSF), i. e. 10 μL, with similar coverage compared to the standard procedure using ≥100 μL of CSF. The data quality of the modified GC-MS method was assessed by analyzing a study sample set in an animal model for multiple sclerosis, including repetitively analysed quality control rat CSF samples. Automated normalization and intra- and inter-batch correction significantly improved the data quality with the majority of metabolites showing a relative standard deviation <20 %. The modified GC-MS method was successfully applied in rat model of multiple sclerosis where statistical analysis of 93 metabolites, of which 73 were (tentatively) identified, in 10 μL of rat CSF showed statistically significant differences in metabolite profiles of rats at the onset and peak of experimental autoimmune encephalomyelitis compared to rats in the control group. The modified GC-MS method presented proved to be a valid and valuable metabolomics method when only limited sample volumes are available. © 2012 Springer Science+Business Media, LLC.
Subject
Life Triskelion BV
QS - Quality & Safety
EELS - Earth, Environmental and Life Sciences TNO Bedrijven
Healthy for Life
Health
Healthy Living
Animal model
Cerebrospinal fluid
EAE model
GC-MS
Metabolomics
Multiple sclerosis
To reference this document use:
http://resolver.tudelft.nl/uuid:3aec1301-baa7-4643-87d3-a180a5e68de7
DOI
https://doi.org/10.1007/s11306-012-0428-2
TNO identifier
469527
ISSN
1573-3882
Source
Metabolomics, 9 (1), 78-87
Document type
article