Metabolomics DOI 10.1007/s11306-012-0428-2 ORIGINAL ARTICLE Metabolite profiling of small cerebrospinal fluid sample volumes with gas chromatography–mass spectrometry: application to a rat model of multiple sclerosis Leon Coulier • Bas Muilwijk • Sabina Bijlsma • Marek Noga • Marc Tienstra Amos Attali • Hans van Aken • Ernst Suidgeest • Tinka Tuinstra • Theo M. Luider • Thomas Hankemeier • Ivana Bobeldijk • Received: 14 February 2012 / Accepted: 17 April 2012 Ó Springer Science+Business Media, LLC 2012 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 lL, with similar coverage compared to the standard procedure using C100 lL 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 Electronic supplementary material The online version of this article (doi:10.1007/s11306-012-0428-2) contains supplementary material, which is available to authorized users. L. Coulier (&) S. Bijlsma I. Bobeldijk TNO, Utrechtseweg 48, 3704 HE Zeist, The Netherlands e-mail: [email protected] B. Muilwijk M. Tienstra TNO Triskelion BV, Zeist, The Netherlands M. Noga T. Hankemeier Leiden/Amsterdam Center for Drug Research, Leiden, The Netherlands A. Attali H. van Aken E. Suidgeest T. Tuinstra Abbott Healthcare Products B.V., Weesp, The Netherlands T. M. Luider Erasmus Medical Center, Rotterdam, The Netherlands T. Hankemeier Netherlands Metabolomics Centre, Leiden, The Netherlands applied in rat model of multiple sclerosis where statistical analysis of 93 metabolites, of which 73 were (tentatively) identified, in 10 lL 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. Keywords Metabolomics GC–MS Cerebrospinal fluid Multiple sclerosis Animal model EAE model 1 Introduction Cerebrospinal fluid (CSF) is an important biomarker compartment, especially for diseases related to the central nervous system. Although the sampling of CSF is much more invasive compared to other biofluids, such as plasma and urine, it is thought that due to its close contact with the extracellular fluid of the brain, the composition of CSF gives a good reflection of the biological process in the brain (Romeo et al. 2005). Analysis of metabolites in CSF has been common practice in clinical chemistry for many years, especially for the diagnosis of inborn errors of metabolism (Moolenaar et al. 2002) Analysis of CSF is mainly done in an untargeted mode using NMR (Moolenaar et al. 2002) or in targeted mode by MS-based or enzymatic methods (Baran et al. 2009). Recently, there has been an increasing attention for the analysis of metabolites in CSF by MS-based methods (Wikoff et al. 2008; Wishart et al. 2008; Carrasco-Pancorbo et al. 2009; Myint et al. 2009; Crews et al. 2009; Kosicek et al. 2010; Koek et al. 2010; Woulikainen et al. 2009; Noga et al. 2011; Locasale et al. 2012) but only very few 123 L. Coulier et al. publications exist in which MS-based methods are applied in metabolomics studies using CSF as a biomarker compartment (Wikoff et al. 2008; Myint et al. 2009; Noga et al. 2011; Wuolikainen et al. 2011; Locasale et al. 2012). Gas chromatography–mass spectrometry (GC–MS) analysis of oximated and silylated metabolites is an excellent example of a broad profiling method that is commonly used for metabolomics purposes as was recently reviewed by Koek et al. (2011). This method is usually applied to plasma and urine and only a few examples exist where GC–MS was applied to CSF (Wishart et al. 2008; Carrasco-Pancorbo et al. 2009; Koek et al. 2010; Woulikainen et al. 2009; Noga et al. 2011; Wuolikainen et al. 2011; Stoop et al. 2010). The advantage of the GC–MS method is its broad coverage of classes of metabolites that can be detected (Wishart et al. 2008; Koek et al. 2010; Stoop et al. 2010). One of the drawbacks of the GC–MS method is that relatively large sample volumes are needed, i.e. 100–250 lL (Wishart et al. 2008; Carrasco-Pancorbo et al. 2009; Stoop et al. 2010). However, this is less than often used for NMR and for human studies sufficient CSF is normally available to provide for these volumes. For animal studies, especially rodents, the large CSF volume required is problematic and pooling of study samples, in order to reach sufficient volumes, is the only option for analysis. This, however, inevitably leads to reduction of the statistical power (Noga et al. 2011). The reason for the relatively large sample volumes used, is the sample work-up which includes different (derivatization) steps resulting in a relatively large end volume necessary to do all these steps in a robust way. As a result, a larger sample volume is necessary at the start in order to detect sufficient metabolites. Recently, Koek et al. (2010) described an elegant way of analyzing very small CSF volumes, i.e. 1 lL, with GC–MS. Although the results are promising, this approach does not seem to be suitable yet for large scale metabolomics studies. Here we report an analytical method that can be used for metabolomics studies when only a limited amount of sample volume is available. The performance of the modified GC–MS method will be shown using rat CSF samples, i.e. 10 lL, in an acute experimental autoimmune encephalomyelitis (EAE) rat model which is a standard model for studying processes related to neuroinflammation and blood– brain-barrier disruptions used to investigate mechanisms potentially involved in multiple sclerosis (MScl). 2 Materials and methods Smolinska et al. (2011). In brief, Male Lewis rats (Harlan Laboratories B.V., the Netherlands) were inoculated on day 0 by injection of a 100 lL saline based emulsion containing 50 lL complete Freund’s adjuvant H37 RA (CFA, Difco Laboratories, Detroit, MI), 500 lL Mycobacterium tuberculosis type H37RA (Difco) and 20 lg guinea pig myelin basic protein (MBP) in the pad of the left hind paw of isoflurane anaesthetized animals. Next to these MBP challenged rats (EAE group), one control group was included: a group of rats receiving the same emulsion without MBP (CFA group). Each group consisted of 30 animals. Disease symptoms and weights of all animals were recorded daily. Of each group half of the animals was sacrificed to collect CSF on day 10 (day of onset of disease in EAE group) and the other half on day 14 (peak of disease in EAE group). Due to the disturbed physiological state of the animals of the EAE group at day 14, the success rate of CSF sampling for these animals was significantly lower. Table 1 shows the final number of CSF samples of each group that was used for statistical analysis (total number of CSF samples analyzed in the complete rat EAE study was 90). The animal experiments described were approved by the local Ethical Committee for Animal Experiments Solvay Pharmaceuticals, Weesp, the Netherlands (study number S0900.5.0012). 2.2 GC–MS For small CSF volumes (10 lL) custom made 12 9 32 mm Teflon vials (200 lL, concical) produced by Savillex (Eden Prairie, MN, USA) and distributed by Grace (Breda, the Netherlands). Small volumes of rat CSF (10 lL) were deproteinized by adding 40 lL methanol and subsequently centrifuged for 10 min at 11,800 g. The supernatant was dried under N2 followed by derivatization with 10 lL ethoxyamine.HCl (c = 56 mg/mL (0.58 M) in pyridine) for 90 min at 40 °C and 20 lL methyl-N-(trimethylsilyl)-trifluoroacetamide (MSTFA) for 50 min at 40 °C. During the different steps in the sample work-up, i.e. before deproteinization, derivatization and injection, different (deuterated) internal standards (dicyclohexylphthalate (38 lM), alanine-d4 (135 lM), leucine-d3 (97 lM), succinic acid-d4 (95 lM), phenylalanine-d5 (76 lM), Table 1 Experimental design of the rat EAE study and the final number of CSF samples of each group used for statistical analysis Treatment groups Day 10 Onset disease Day 14 Peak disease CFA 14 11 EAE 13 6 2.1 Rat EAE study Rat CSF study and CSF sampling was carried out using procedures as previously described Noga et al. (2011) and 123 Multiple sclerosis glutamic acid-d3 (87 lM), ribose-13C5 (79 lM), citric acid-d4 (63 lM), cholic acid-d4 (31 lM)) were added. The final volume was 50 lL. 1 lL of the derivatized samples was injected in splitless mode on a HP5-MS 30 m 9 0.25 mm 9 0.25 lm capillary column (Agilent Technologies, Palo Alto, CA) using a temperature gradient from 70 to 320 °C at a rate of 5 °C/min. GC–MS analysis was performed using an Agilent 6890 gas chromatograph coupled to an Agilent 5973 mass selective detector. MS detection was used in electron impact mode and full scan monitoring mode (m/z 15–800). The electron impact for the generation of ions was 70 eV. A total of 90 rat CSF samples from the EAE study described above were analyzed by GC–MS. The samples were randomly distributed over three batches, i.e. three batches of 30 samples. One larger volume of rat CSF (80 lL) obtained from one rat in the same experiment was used as quality control (QC) sample and was analyzed in sextuplicate (two QC samples analyzed in triplicate) in all three batches, similar to the procedure described by van der Greef et al. (2007). 2.3 Data pre-processing Data pre-processing was performed by composing target lists of peaks detected in the samples based on retention time and mass spectra. These peaks were integrated for all samples. The peak areas were subsequently normalized using internal standards and corrected for intra- and interbatch effects using the QC samples according to the procedure described by van der Kloet et al. (2009). Finally the corrected data were autoscaled and the 10 % rule was applied, which removes all metabolites with non-zero values for less than 10 % of the sample, prior to statistical analysis. 2.4 Statistical analysis 2.4.1 Univariate data-analysis An ANOVA model was built including group as factor. Data were log-transformed and statistical outliers (defined as an observation for which the absolute residual was three times higher than the square root of the model error) were removed. In all statistical tests performed, the null hypothesis (no group difference) was rejected at the 0.05 level of probability. For each variable, partial tests between group levels were performed using multiple comparison correction (Tukey–Kramer). Since many variables were tested, the Benjamini-Hochberg procedure was used to control the false discovery rate (FDR). Statistical analyses were performed using the SAS statistical software package (SAS version 9.1.3, SAS Institute, Cary, NC, USA). 2.4.2 Multivariate data-analysis All multivariate analyses were performed in the Matlab environment (R2008b, 1984–2008, The Mathworks Inc.) and the PLS toolbox for Matlab (version 5.0.3 (r6466), 1995–2008 Eigenvector Research Inc.). Principal component analysis (PCA) (Jolliffe 1986) was used to screen the metabolomics data sets in order to detect outliers or certain patterns present in the data (time patterns, similarity between samples etc.). Partial least squares discriminant analysis (PLS-DA) (Stahle and Wold 1987) was applied to correlate metabolomics data to class assignments. For good PLS-DA models, the top metabolites with the difference between the two groups were ranked based on the absolute value of their regression coefficient. The validity of the PLS-DA model was tested using a 10-fold double cross validation (DCV) procedure (Smit et al. 2007). In a PLS-DA model the double cross error multiplied by 100 percent indicates the percentage of misclassification. Besides the DCV, a permutation test was performed to test the predictivity of the model. In the permutation test the class assignments were re-ordered 250 times and every time a new model was built. If the model was predictive, the DCV error of the models after permutation of the class assignments was higher than the original model. 3 Results and discussion 3.1 GC–MS modification method Typical sample volumes used for GC–MS are 100 lL or more for e.g. CSF (Wishart et al. 2008; Wuolikainen et al. 2011; Stoop et al. 2010). As a result of the different sample work-up steps, especially the derivatization steps, i.e. oximation and silylation, the final volume is *150 lL. This volume is necessary to have an excess of derivatization reagents, sufficient homogenization in the vial and sufficient volume height in the vial for injection. When less sample volume is available the same procedure can be used but this will result in significant dilution of the sample, leading to a strong decrease in sensitivity and thus low coverage. Therefore a new vial design was developed in which the depth of the insert of the vial as well as the diameter was reduced leading to a reduction of the total volume of the insert, i.e. from 500 to 200 lL (see Fig. 1). In addition, the volume of the derivatization reagents was reduced resulting to a final volume of 50 lL instead of 150 lL. It should be noted that using 50 lL as final volume in the standard vial design lead to significant heterogeneity in the derivatization reaction as well as irreproducible injection due to bad homogenization and low volume heights. Therefore, the combination of the improved vial 123 L. Coulier et al. Fig. 1 Schematic representation of standard PTFE vials and optimized PTFE vial for small sample volumes “standard” 500µL vial optimized 200µL vial 5.4 mm 7,5 mm 5.4 mm 17 mm 4 mm ca 31 mm 50µL endvolume 20µL for oximation ca 45° 150µL endvolume 40µL for oximation ca 45° design and modified sample work-up procedure is essential to obtain reliable results for small volumes of CSF. Comparison of TIC GC–MS chromatogram and detected metabolites obtained for 100 lL human CSF with the procedure for large sample volumes (Stoop et al. 2010) and 10 lL rat CSF with the modified method shows that although the dilution factor, respectively 1.35 and 5, is still higher for small volumes, the coverage of metabolites is very similar (see Fig. 2 and Table S1 in supplementary material). This demonstrates that with the modified GC–MS method a broad range of metabolite classes can be detected in only 10 lL of CSF, the majority being identified. The number of metabolites detected is similar or even higher than reported for large volumes of human CSF by others (Wishart et al. 2008; Stoop et al. 2010; Wuolikainen et al. 2011) which shows that the modified GC–MS has similar or even better metabolite coverage for only 10 lL of CSF compared to other GC–MS methods in literature that use sample volumes C100 lL. Furthermore it can be seen that there is quite some overlap between metabolites detected in rat and human CSF by GC–MS. More than 85 % of the metabolites detected by the modified GC–MS in 10 lL of rat CSF were also detected in 100 lL of human CSF (Stoop et al. 2010) as can be seen in Table S1 in 123 supplementary material. In addition, comparison with the coverage of metabolites detected from 2 lL human CSF using the in-liner derivatization approach as published by Koek et al. (2010) shows clearly that the latter approach has significantly less metabolite coverage (see Table S1 in supplementary material). It should be stressed that the work by Koek et al. (2010) was a proof-of-principle and did not include only limited optimization, validation and confirmation of metabolite identities by analyzing reference compounds. 3.2 Data quality Besides good sensitivity and coverage, the quality of the data obtained with the modified GC–MS method is essential in order to apply the method in metabolomics studies. To this purpose the modified GC–MS method was applied to a series of 90 rat CSF samples (10 lL) analyzed in three batches of 30 samples. One rat CSF sample was used as QC sample, aliquoted and analyzed repeatedly in all three batches covering the complete analysis sequence. With this procedure the relative standard deviation of the raw area of all metabolites detected in all QC samples was calculated (see Fig. 3) (van der Greef et al. 2007). It can be Multiple sclerosis C A *10 7 0 DCHP (IS) pyroglutamic acid leucine serine 0 10.00 B alanine 2.1 myo-inositol glutamine urea lactic acid galactose glucose 3.2 1.6 *107 4.2 15.00 20.00 25.00 30.00 35.00 40.00 10.00 D *107 15.00 20.00 25.00 30.00 35.00 40.00 15.00 20.00 25.00 30.00 35.00 40.00 *107 1.2 0.6 0.6 0.3 0 10.00 15.00 20.00 25.00 30.00 35.00 40.00 0 10.00 Fig. 2 a TIC GC–MS chromatogram and b zoom of a obtained with 10 lL rat CSF using the modified GC–MS method, c TIC GC–MS chromatogram and d zoom of c obtained with 100 ll human CSF using the standard GC–MS method seen that the RSD is in general high, i.e. [10 %. Next, the raw peak areas were normalized using the most suitable internal standard for each metabolite, selected and applied by an automated procedure (van der Kloet et al. 2009). This procedure already leads to a significant improvement of the RSDs as can be seen in Fig. 3. The final step in the procedure is the intra- and inter-batch correction, a calibration method that takes into account offsets between batches due to small changes in the instrumental set-up, i.e. column and liner, as well as changes within a batch due to e.g. instability of derivatives (van der Kloet et al. 2009). After the batch correction, the RSDs are even further improved (see Fig. 3). Almost 75 % of the metabolites have an RSD \20 % (see also Table S2 in supplementary material for the RSDs of the individual metabolites). In general, the low abundant metabolites show a higher RSD. Note that the metabolites with extremely high RSDs often show missing values in the QC samples and are generally very low abundant in QC samples. However, these metabolites can be of relevance in the study sample when they are significantly up-regulated in specific groups. It can be concluded that the quality of the GC–MS data after correction is good and comparable to when 100 lL is used (see Table S2 in Supplementary material and Stoop et al. 2010). The modified GC–MS method for small CSF volumes seems therefore suitable for use in metabolomics studies. 3.3 EAE study Rat CSF samples from a rat EAE study were analyzed by the improved GC–MS method. The rat study was described earlier by Smolinska et al. (2011) and was a repetition of that reported recently by Noga et al. (2011). CSF samples were taken from rats from the EAE group (=rats challenged by myelin based protein) and the CFA group (=control group) at day 10 and 14. Due to the severe symptoms at day 14, the number of CSF samples for the EAE group at day 14 is significantly less than 15, i.e. 6, but large enough for further statistical analysis although the statistical power is less for this group compared to the other groups (see Table 1). After data-preprocessing 93 peaks were used for statistical analysis of which 73 could be identified or characterized and 20 were unknown (see Table S2 in Supplementary material). Principal component analysis (PCA) was applied for visual inspection of the CSF metabolite profiles of the four animal groups (see Fig. 4). No batch effects or outliers could be observed. The EAE day 14 group, i.e. peak of disease, is clearly separated from the other groups. The same seems partially true for the EAE day 10 samples, i.e. early onset of the disease. The two control groups, i.e. CFA day 10 and day 14, are not separated from each other. 123 L. Coulier et al. IS corrected Raw area < 10% <10% < >30% 10-20% >30% 20-30% 10-20% 20-30% QC corrected >30% < 10% <10% 20-30% 10-20% 20-30% >30% 10-20% Fig. 3 RSD (%) of metabolites in QC samples before and after data preprocessing and batch correction Various metabolites contribute to the separation of the full stage EAE animals and part of the early onset EAE animals from the other animals along PC1 (see Fig. S1 in supplementary material). Higher principal components did not show any additional separations (see Fig. S2 in supplementary material). PLS-DA models of the GC–MS data were calculated to verify whether correct classification of CSF samples for different treatment groups could be obtained. A good PLS-DA model could be obtained for EAE day 14 versus CFA day 14, i.e. peak of the disease versus control, with an overall correct classification of 94 %. Similar results were obtained for EAE day 14 versus EAE day 10, i.e. peak versus early onset of the disease, with an overall correct 123 classification of 89 %. Not surprisingly, the metabolites that contributed significantly to these models were very similar (see Table S3 in supplementary information). For EAE day 10 versus CFA day 10, i.e. onset of disease versus control, a PLS-DA model was obtained with an overall correct classification of 75 %. There is little or no overlap between the list of metabolites contributing to the classification in this model and the first two models, which imply that different biological processes are playing a role in the onset and the peak of the disease. Table 2 summarizes statistically significant increases and decreases of metabolites based on univariate statistics. Most of these metabolites also contribute to the PLS-DA models (see Table S3 in supplementary information). Multiple sclerosis Scores on PC# 2 (explained variance 17.48 %) Scores for PC# 1 versus PC# 2 6 + CFA day 10 o EAE day 10 4 +x CFA day 14 X EAE day 14 2 0 -2 -4 -6 -8 -5 0 5 10 Scores on PC# 1 (explained variance 20.18 %) Fig. 4 PCA scores plot of GC–MS metabolomics data showing the separation of EAE day 14 samples (X) and partial separation of EAE day 10 samples (o) from the control samples (CFA) The amino acids listed in Table 2 confirm some of the results found earlier (Noga et al. 2011; Smolinska et al. 2011). The observed effect, i.e. up- or down-regulation, of the amino acids is also similar between the two studies. Smolinska et al. (2011) found lysine to be up-regulated in the EAE day 14 group, similar to this study (see Table 2). Comparison of Table 2 and the results found by Noga et al. (2011) shows that both studies see the up-regulation of isoleucine, leucine, lysine, phenylalanine, threonine, valine, O-phosphorylethanolamine and taurine for rats in the EAE day 14 group, i.e. full stage EAE and/or peak of the disease. The other amino acids reported by Noga et al. (2011) are mainly low abundant compounds for which the GC–MS method lacks sensitivity. It should be noted that the method used by Noga et al. (2011) is specifically developed for the analysis of (low abundant) amino acids but it does not cover other classes of metabolites. With PLS-DA more amino acids were found relevant, like asparagine and alanine (see Table S3 in supplementary information). Interestingly, only alanine shows down-regulation at both the onset of the disease as well as at the peak of the disease. Down-regulation of alanine was also reported in a rat EAE model (Noga et al. 2011, Smolinska et al. 2011) as well as in MScl (Sinclair et al. 2010). The only major difference between this study and that of Noga et al. (2011) was glutamic acid which shows an increase in this study for the diseased animals but a decrease in the study by Noga et al. (2011). This observation contributes to the controversy on glutamic acid as biomarker in CSF. It is reported that glutamic acid in CSF is highly susceptible to sampling variables and in particular to storage temperature (Woulikainen et al. 2009). Although biological interpretation of the results obtained in this study was not the primary goal, it is interesting to see what the added value of the modified GC–MS method is with respect to metabolite coverage. Some metabolites that were only detected by GC–MS support the findings of Noga et al. (2011), like glycerol (lipid metabolism) and uric acid (oxidative stress). Many other classes of metabolites are listed in Table 2, including carbohydrates, organic acids, polyols and purine/pyrimidine bases, demonstrating the complementary metabolite coverage of the GC–MS method. Typical patterns can be observed in Fig. 5 where uric acid and mannose already show an increase for EAE day 10 indicative for an early marker for disease while fructose and myo-inositol show a significant decrease for EAE day 14, i.e. markers for the peak of the disease. The rats of the EAE group start to loose weight and start to develop disease symptoms around day 10, while at day 14 the peak of the disease is observed generally resulting in a minimal weight and a maximum score on disease symptoms (Noga et al. 2011; Smolinska et al. 2011). The up-regulation of ketone bodies, like acetoacetic acid and 3-hydroxybutyric acid, can be related to the decreasing physical state of the rats during the progression of the disease including loss of weight, lower food intake and increasing illness (Laffel 1999). Interestingly, some metabolites, like citric acid, glyceric acid, fructose and myo-inositol showed down-regulation (see Table 2 and Fig. 5). For example, citric acid shows a strong decrease for the progression of the disease although at the onset of the disease a significant increase of citric acid is observed. Reduced citric acid levels were also reported by Smolinska et al. (2011) in a similar rat EAE study and by Sinclair et al. (2010) for MScl. The sugars and polyols are especially an interesting group of metabolites while glucose, mannose and fucose or 6-deoxyglucose show a clear up-regulation while fructose and myo-inositol show down-regulation for disease progression and at the peak of the disease. Glucose concentrations in CSF are generally determined by blood glucose levels. Higher glucose concentrations in CSF are attributed to increased permeability of the blood–brain barrier (BBB) (Fishman 1993). Fructose and myo-inositol levels are considerably higher in CSF than in plasma, hence down-regulation of fructose and myo-inositol as observed in this study indicates increased permeability of the BBB leading to an outflow of these metabolites from CSF to blood (Fishman 1993). The up- and down-regulation of other sugars and polyols in Table S3 support this observation. These findings also correspond with the increase in BBB permeability that was reported by Rosenling et al. (2012) based on proteome analysis of rat CSF in a similar rat EAE study. Decreasing myo-inositol levels were also reported in CSF of persons with gliomas (Locasale et al. 2012). 123 L. Coulier et al. Table 2 Metabolites detected by GC–MS that changed statistically significant, single and double symbols denote respectively 95 and 99 % significance levels of the observed EAE effect (- = decrease, ? = increase) EAE day 10 versus CFA day 10 Early onset EAE day 14 versus EAE day 10 Progression EAE day 14 versus CFA day 14 Full stage Citric acid ?? 4-Hydroxyglutamate semialdehyde ?? 49CSF ?? Glycerol-3-phosphate ?? Pyroglutamic acid ?? Valine ?? 50CSF ?? 49CSF ?? Glutamic acid ?? Mannose ? Fucose or 6-deoxyglucose ?? Glycerol ?? Taurine ? Pseudouridine ?? Pseudouridine ?? Glycerol ? Leucine ?? 02CSF ?? Cholesterol ? Threonine ?? Threonine ?? Tryptophan - Glucose ?? ?? Mannose ?? 3-Methyl-2-hydroxybutanoic acid Uric acid Citric acid -- 18CSF ? 3-Hydroxybutanoic acid ?? 4-Hydroxyglutamate semialdehyd ? Glutamic acid ?? Glyceric acid -- Phosphate ? Acetoacetic acid ? 18CSF ? 2,3-Dihydroxybutanoic acid ? Myo-inositol - Phenylalanine ? Valine ? 28CSF - Acetoacetic acid ? Fructose - 02CSF ? Isoleucine ? 2-Hydroxybutanoic acid ? 20CSF 2-hydroxybutanoic acid ?? ?? 3-hydroxybutanoic acid ?? Cholesterol ?? Glucose ?? Pyroglutamic acid ?? Leucine ?? Lysine ?? Phosphorylethanolamine ?? Phosphate ?? Mannose ?? Fucose or 6-deoxyglucose ?? Uracil ?? Taurine ?? Other possible interesting metabolites in Table 2 and Table S3 that are up-regulated in CSF at the peak of the disease are uracil, pseudouridine and dihydrouracil that can be related to pyrimidine metabolism and cholesterol that is also up-regulated at the early onset of the disease. Biological interpretation of these finding was hampered due to lack of data in literature. Interestingly, several unknown peaks were observed that showed significant up-regulation both with univariate and multivariate statistics (see Table 2 and Table S3). Identification of these unknown metabolites can lead to new biomarkers and improved biological interpretation. 123 ?? 4 Conclusion A modified vial design and sample preparation made it possible to analyse small sample volumes of CSF, i.e. 10 lL, by GC–MS without losing sensitivity compared to the volumes usually used for GC–MS, i.e. C100 lL. The improved GC–MS method was applied to a rat model of MScl showing good data quality as determined from the relative standard deviation of metabolites in QC samples after internal standard and QC correction. Changes in CNS metabolism could successfully detected by the modified GC–MS method. Compared to earlier work on similar Multiple sclerosis -3 x 10 0.16 5 Uric acid Mannose 0.15 0.14 0.13 0.12 Values Values 4 3 2 0.11 0.1 0.09 0.08 1 0.07 0 0.06 CFA day 10 EAE day 10 CFA day 14 EAE day 14 CFA day 10 EAE day 10 Fructose 0.05 CFA day 14 EAE day 14 Myo-inositol 3 0.045 2.5 Values Values 0.04 0.035 0.03 2 0.025 1.5 0.02 0.015 1 CFA day 10 EAE day 10 CFA day 14 EAE day 14 CFA day 10 EAE day 10 CFA day 14 EAE day 14 Fig. 5 Box plots showing the relative peak areas of uric acid, mannose, fructose and myo-inositol in all experimental groups. Note the different trends observed for these metabolites between experimental groups studies, the application of GC–MS to CSF shows strong potential with respect to the biological interpretation due to its coverage of different classes of metabolites, like amino acids, sugars, polyols, organic acids. Acknowledgments This study was financially supported by Top Institute Pharma project D4-102. References Baran, R., Reindl, W., & Northen, T. 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