Quantitative Proteomics and Metabolomics Analysis of Normal Human Cerebrospinal Fluid Samples*

Abstract

The analysis of cerebrospinal fluid (CSF) is used in biomarker discovery studies for various neurodegenerative central nervous system (CNS) disorders. However, little is known about variation of CSF proteins and metabolites between patients without neurological disorders. A baseline for a large number of CSF compounds appears to be lacking. To analyze the variation in CSF protein and metabolite abundances in a number of well-defined individual samples of patients undergoing routine, non-neurological surgical procedures, we determined the variation of various proteins and metabolites by multiple analytical platforms. A total of 126 common proteins were assessed for biological variations between individuals by ESI-Orbitrap. A large spread in inter-individual variation was observed (relative standard deviations [RSDs] ranged from 18 to 148%) for proteins with both high abundance and low abundance. Technical variation was between 15 and 30% for all 126 proteins. Metabolomics analysis was performed by means of GC-MS and nuclear magnetic resonance (NMR) imaging and amino acids were specifically analyzed by LC-MS/MS, resulting in the detection of more than 100 metabolites. The variation in the metabolome appears to be much more limited compared with the proteome: the observed RSDs ranged from 12 to 70%. Technical variation was less than 20% for almost all metabolites. Consequently, an understanding of the biological variation of proteins and metabolites in CSF of neurologically normal individuals appears to be essential for reliable interpretation of biomarker discovery studies for CNS disorders because such results may be influenced by natural inter-individual variations. Therefore, proteins and metabolites with high variation between individuals ought to be assessed with caution as candidate biomarkers because at least part of the difference observed between the diseased individuals and the controls will not be caused by the disease, but rather by the natural biological variation between individuals. The analysis of cerebrospinal fluid (CSF) is used in biomarker discovery studies for various neurodegenerative central nervous system (CNS) disorders. However, little is known about variation of CSF proteins and metabolites between patients without neurological disorders. A baseline for a large number of CSF compounds appears to be lacking. To analyze the variation in CSF protein and metabolite abundances in a number of well-defined individual samples of patients undergoing routine, non-neurological surgical procedures, we determined the variation of various proteins and metabolites by multiple analytical platforms. A total of 126 common proteins were assessed for biological variations between individuals by ESI-Orbitrap. A large spread in inter-individual variation was observed (relative standard deviations [RSDs] ranged from 18 to 148%) for proteins with both high abundance and low abundance. Technical variation was between 15 and 30% for all 126 proteins. Metabolomics analysis was performed by means of GC-MS and nuclear magnetic resonance (NMR) imaging and amino acids were specifically analyzed by LC-MS/MS, resulting in the detection of more than 100 metabolites. The variation in the metabolome appears to be much more limited compared with the proteome: the observed RSDs ranged from 12 to 70%. Technical variation was less than 20% for almost all metabolites. Consequently, an understanding of the biological variation of proteins and metabolites in CSF of neurologically normal individuals appears to be essential for reliable interpretation of biomarker discovery studies for CNS disorders because such results may be influenced by natural inter-individual variations. Therefore, proteins and metabolites with high variation between individuals ought to be assessed with caution as candidate biomarkers because at least part of the difference observed between the diseased individuals and the controls will not be caused by the disease, but rather by the natural biological variation between individuals. The analysis of CSF 1The abbreviations used are:CSFcerebrospinal fluidCNScentral nervous systemRSDrelative standard deviationGC-MSgas chromatography MSNMRnuclear magnetic resonanceSRMselective reaction monitoringUPLCultra performance liquid chromatographyPCAprincipal component analysis. is indispensable in the diagnosis and understanding of various neurodegenerative CNS disorders (1.Frankfort S.V. Tulner L.R. van Campen J.P. Verbeek M.M. Jansen R.W. Beijnen J.H. Amyloid beta protein and tau in cerebrospinal fluid and plasma as biomarkers for dementia: a review of recent literature.Curr. Clin. Pharmacol. 2008; 3: 123-131Crossref PubMed Scopus (67) Google Scholar, 2.Helbok R. Broessner G. Pfausler B. Schmutzhard E. Chronic meningitis.J. Neurol. 2009; 256: 168-175Crossref PubMed Scopus (31) Google Scholar, 3.Lewczuk P. Hornegger J. Zimmermann R. Otto M. Wiltfang J. Kornhuber J. Neurochemical dementia diagnostics: assays in CSF and blood.Eur. Arch. Psychiatr. Clin. Neurosci. 2008; 258: 44-49Crossref PubMed Scopus (9) Google Scholar). CSF is a fluid that has different functions, such as the protection of the brain from outside forces, transport of biological substances, and excretion of toxic and waste substances. It is in close contact with the extracellular fluid of the brain. Therefore, the composition of CSF can reflect biological processes of the brain (4.Romeo M.J. Espina V. Lowenthal M. Espina B.H. Petricoin 3rd, E.F. Liotta L.A. CSF proteome: a protein repository for potential biomarker identification.Expert Rev. Proteomics. 2005; 2: 57-70Crossref PubMed Scopus (107) Google Scholar). By discovering the characterization of the proteome and metabolome of CSF we may gain better insight on the pathogenesis of CNS disorders. This would be significant because, for many of these disorders, the etiology is still unclear.CSF is produced in the ventricles of the brain and in the subarachnoidal spaces. Humans normally produce around 500 mL of CSF each day, and the total volume of CSF at a given time is approximately 150 mL. CSF reflects the composition of blood plasma, although the concentrations of most proteins and metabolites in CSF are lower. However, individual proteins and metabolites can act differently. Active transport from blood and secretion from the brain contribute to the specific composition of CSF. This composition can be disturbed in neurological disorders (5.Johnston I. Teo C. Disorders of CSF hydrodynamics.Childs Nerv. Syst. 2000; 16: 776-799Crossref PubMed Scopus (52) Google Scholar–6.Taniguchi M. Okayama Y. Hashimoto Y. Kitaura M. Jimbo D. Wakutani Y. Wada-Isoe K. Nakashima K. Akatsu H. Furukawa K. Arai H. Urakami K. Sugar chains of cerebrospinal fluid transferrin as a new biological marker of Alzheimer's disease.Dementia Geriatric Cog Dis. 2008; 26: 117-122Crossref PubMed Scopus (33) Google Scholar). Since CNS-specific proteins and metabolites are typically low in abundance compared with their levels in blood, this change in composition is more likely to be found in CSF because in blood the more abundant plasma proteins can completely mask the signal of the less abundant proteins. Also, if the disease markers do not cross the blood-brain-barrier, then the CSF is the only viable biofluid source. Therefore, CSF might be an excellent source for biomarker discovery for CNS disorders if we follow the hypothesis that neurological diseases induce alterations in CSF protein and metabolite levels.Analysis of metabolites in CSF has been common practice in clinical chemistry for decades to analyze biomarkers for inborn errors of metabolism. The approaches used are either metabolite profiling of CSF using NMR (7.Moolenaar S. von Engelke U. Hoenderop S. Morava E. van der Graaf M. Wevers R. Handbook of 1H-NMR spectroscopy in inborn errors of metabolism. SPS Publications, Heilbronn2002Google Scholar), or targeted analysis of one or a few metabolites using specific analytical methods (8.Baran R. Reindl W. Northen T.R. Mass spectrometry based metabolomics and enzymatic assays for functional genomics.Curr. Opin. Microbiol. 2009; 12: 547-552Crossref PubMed Scopus (53) Google Scholar). Metabolomics includes the analysis of metabolites in biofluids by NMR or MS-based approaches, i.e. LC-MS or GC-MS. Several metabolite profiling studies were performed on CSF using NMR, some of which were published only recently (9.Lindon J.C. Nicholson J.K. Everett J.R. NMR spectroscopy of biofluids.in: Webb G.A. Annual Reports on NMR Spectroscopy. Academic Press, London1999: 1-88Google Scholar,10.Lutz N.W. Viola A. Malikova I. Confort-Gouny S. Audoin B. Ranjeva J.P. Pelletier J. Cozzone P.J. Inflammatory multiple-sclerosis plaques generate characteristic metabolic profiles in cerebrospinal fluid.PLos One. 2007; 2: e595Crossref PubMed Scopus (75) Google Scholar). Surprisingly, very few metabolomics studies using MS-based methods have been performed on CSF to date (11.Kawashima H. Oguchi M. Ioi H. Amaha M. Yamanaka G. Kashiwagi Y. Takekuma K. Yamazaki Y. Hoshika A. Watanabe Y. Primary biomarkers in cerebral spinal fluid obtained from patients with influenza-associated encephalopathy analyzed by metabolomics.Int J Neurosci. 2006; 116: 927-936Crossref PubMed Scopus (23) Google Scholar,12.Myint K.T. Aoshima K. Tanaka S. Nakamura T. Oda Y. Quantitative profiling of polar cationic metabolites in human cerebrospinal fluid by reversed-phase nanoliquid chromatography/mass spectrometry.Analytical Chem. 2009; 81: 1121-1129Crossref PubMed Scopus (63) Google Scholar). One of the reasons is the fact that the human CSF metabolome has not yet been characterized very well. Many CSF metabolites remain unidentified, and for those that have been identified there is not much known about normal concentration ranges. A systematic categorization of the CSF metabolome is necessary and expected to be beneficial for future biomarker discoveries. Recently, Wishart et al. made a good start in exploring the human CSF metabolome with their computer-aided literature survey that resulted in 308 detectable metabolites in human CSF (13.Wishart D.S. Lewis M.J. Morrissey J.A. Flegel M.D. Jeroncic K. Xiong Y. Cheng D. Eisner R. Gautam B. Tzur D. Sawhney S. Bamforth F. Greiner R. Li L. The human cerebrospinal fluid metabolome.J. Chromatogr. 2008; 871: 164-173Google Scholar).The CSF proteome has been characterized to a much larger extent than the CSF metabolome and is currently the topic of investigations in several research groups worldwide. Recently, studies have been published with numerous identities and quantities of CSF proteins. Pan and co-workers were able to identify 2,594 proteins in well-characterized pooled human CSF samples using strict proteomics criteria with a combination of linear trap quadrupole LTQ-FT (Thermo Fisher Scientific, Bremen, Germany) and MALDI TOF/TOF equipment (14.Pan S. Zhu D. Quinn J.F. Peskind E.R. Montine T.J. Lin B. Goodlett D.R. Taylor G. Eng J. Zhang J. A combined dataset of human cerebrospinal fluid proteins identified by multi-dimensional chromatography and tandem mass spectrometry.Proteomics. 2007; 7: 469-473Crossref PubMed Scopus (106) Google Scholar). They were also able to quantify several proteins using a targeted LC MALDI TOF/TOF approach (15.Pan S. Rush J. Peskind E.R. Galasko D. Chung K. Quinn J. Jankovic J. Leverenz J.B. Zabetian C. Pan C. Wang Y. Oh J.H. Gao J. Zhang J. Montine T. Zhang J. Application of targeted quantitative proteomics analysis in human cerebrospinal fluid using a liquid chromatography matrix-assisted laser desorption/ionization time-of-flight tandem mass spectrometer (LC MALDI TOF/TOF) platform.J Proteome Res. 2008; 7: 720-730Crossref PubMed Scopus (58) Google Scholar). Hu et al. have studied the intra- and inter-individual variation in human CSF and found large variations in protein concentrations in six patients by means of two dimensional–gel electrophoresis (16.Hu Y. Malone J.P. Fagan A.M. Townsend R.R. Holtzman D.M. Comparative proteomic analysis of intra- and interindividual variation in human cerebrospinal fluid.Mol. Cell. Proteomics. 2005; 4: 2000-2009Abstract Full Text Full Text PDF PubMed Scopus (104) Google Scholar), focusing mainly on the variations within individuals at two different time-points. Although only a limited number of proteins was analyzed, the variation between the time-points was profound, exceeding 200% for seven proteins.Unique CSF biomarkers may contribute to a deeper understanding of the mechanisms of CNS disorders. However, for this assumption to come true, there are still challenges ahead. Although CSF is not as complex as blood (almost missing the cellular part and the clotting system present in blood), it is expected to consist of thousands of organic- and non-organic salts, sugars, lipids, and proteins. A large part of the CSF consists of a few highly abundant metabolites and proteins, which hamper, if no precautions are undertaken, the identification and quantification of metabolites and proteins that occur in lower amounts. The analysis of the CSF metabolome is complicated because of the diverse chemical nature of metabolites and the lower concentration of metabolites compared with blood. Analytical method development is still required because it is not possible to identify the entire range of CSF metabolites with one single analytical method. Although in proteome research efforts have been made to quantify proteins, metabolomics studies up to now either do not provide quantitative information or they only give information for the most abundant metabolites.Another challenge is the sample amount obtained by lumbar puncture to collect CSF. Lumbar puncture is an invasive method that is not performed as frequently as blood sampling. However, often after the analysis of various clinical parameters, only a limited amount of CSF sample is available for biomarker discovery. Metabolomics studies are hampered by limited CSF sample amount. Therefore, analytical methods are required that are suitable to handle relatively small sample volumes.The main objectives of this study were (1.Frankfort S.V. Tulner L.R. van Campen J.P. Verbeek M.M. Jansen R.W. Beijnen J.H. Amyloid beta protein and tau in cerebrospinal fluid and plasma as biomarkers for dementia: a review of recent literature.Curr. Clin. Pharmacol. 2008; 3: 123-131Crossref PubMed Scopus (67) Google Scholar) to analyze the variation in CSF protein and metabolite abundances in a number of well-defined individual samples by multiple analytical platforms; and (2.Helbok R. Broessner G. Pfausler B. Schmutzhard E. Chronic meningitis.J. Neurol. 2009; 256: 168-175Crossref PubMed Scopus (31) Google Scholar) to integrate metabolomics and proteomics to present biological variations in metabolite and protein abundances and compare these with technical variations with the currently used analytical methods. The results will facilitate and increase the application of CSF for future biomarker discovery studies in the field of neurodegenerative diseases and neuro-oncology.EXPERIMENTAL PROCEDURESCSF SamplingCSF samples were obtained by lumbar puncture in the Erasmus University Medical Centre (Rotterdam, the Netherlands). An experienced medical doctor selected 10 samples, which were taken from patients receiving spinal anesthesia before non-neurological surgery. These subjects had no neurological diseases, were not using any medication, and were considered to have neurologically normal CSF. Immediately after sampling, the CSF samples were centrifuged (10 min at 3.000 rpm) to discard cellular elements. The samples were subsequently used for routine CSF diagnostics. This included quantification of total protein concentration by routine clinical chemistry measurements and quantification of the cell count (< 5 white blood cells per mL). The remaining volume of the samples was aliquoted and stored at −80 °C immediately after centrifugation. As a standard procedure, the samples were checked for blood contamination, and any sample in which a hemoglobin or apolipoprotein B100 peptide was identified with a significant score by nanoLC-Orbitrap MS was excluded from the study.For pooling of the samples (n = 10), the originally obtained samples were thawed on ice and 0.75 mL from each of the samples was joined, resulting in a 7.5 mL pooled CSF sample. This pooled CSF sample was vortexed for 30 seconds and then subdivided into 75 portions of 100 μL in sterile cryogenic vials (Nalgene Nunc Int., Rochester, NY). The portions were immediately frozen at -80 °C. The characteristics of the pooled sample are described in Table 1. This pooled sample was used to assess the technical variation in the proteomics experiments by measuring it five times. For the measurements of the individual patients only nine CSF samples were used because there was insufficient volume of one sample.The 28 CSF samples from the validation sample set were also taken by an experienced anesthesiologist from patients receiving spinal anesthesia before non-neurological surgery, but these samples were taken at another hospital (Sint Franciscus Gasthuis, Rotterdam, the Netherlands). These subjects had no neurological diseases, were not using any medication, and were considered to have neurologically normal CSF.The CSF samples used in the experimental sample set were selected by an experienced neurologist and taken from patients undergoing tests for clinical diagnosis. These samples, taken from multiple sclerosis and headache patients were subjected to the same, strict post-sampling procedure as the samples mentioned previously. In these samples no significant difference in protein concentration between the two groups was observed; therefore, there was no leakage in the blood-CSF barrier. All CSF samples used in this study were observed in the morning at approximately10 a.m. The Medical Ethical Committees of the Erasmus University Medical Centre in Rotterdam, The Netherlands, and the Sint Franciscus Gasthuis in Rotterdam, The Netherlands, approved the study protocol and all study participants gave written consent. The average age and protein concentration of the samples in all three sample sets is listed in Table 2, and age, gender and protein concentration of the individual samples is listed in the Supplementary Material.ProteomicsSample Preparation for NanoLC-Orbitrap MS and MALDI-FT-ICR MSFor measurement of proteins in CSF, samples were enzymatically digested with trypsin to obtain peptides. An amount of 50 μL Rapigest (Waters, Milford, MA) in 50 mmol/L ammonium bicarbonate and 1 μL 100 mmol/L DTT was added to 50 μL CSF. The mixture was heated at 60 °C for 30 minutes, upon which it was cooled down to room temperature in approximately 20 minutes. Iodoacetamide (5 μL of 0.3 mmol/L solution) was added and this mixture was left for 30 minutes in dark at room temperature. Trypsin was added (10 μL, 0.1 mg/mL) and all samples, processed in one batch, were incubated overnight at 37 °C. To stop digestion, 2 μL of a 50% TFA/50% water solution was added. The sample was then incubated for 45 minutes at 37 °C.NanoLC-Orbitrap MS AnalysisThese measurements were performed on an Ultimate 3000 nanoLC system (Dionex, Germering, Germany) online coupled to a hybrid linear ion trap/Orbitrap MS (LTQ Orbitrap XL; Thermo Fisher Scientific, Bremen, Germany). Five microliters of digest were loaded onto a C18 trap column (C18 PepMap, 300 μm ID x 5 mm, 5 μm particle size, 100 Å pore size; Dionex, Amsterdam, The Netherlands) and desalted for 10 minutes using a flow rate of 20 μL/min 0.1% TFA. Then the trap column was switched online with the analytical column (PepMap C18, 75 μm ID x 150 mm, 3 μm particle and 100 Å pore size; Dionex, Amsterdam, The Netherlands) and peptides were eluted with the following binary gradients of solvent A and B: 0–25% solvent B in 120 minutes and 25–50% solvent B in further 60 minutes, where solvent A consisted of 2% acetonitrile and 0.1% formic in water and solvent B consisted of 80% acetonitrile and 0.08% formic acid in water. Column flow rate was set to 300 nL/min. For MS detection, a data-dependent acquisition method was used: high resolution survey scan from 400 - 1800 Th. was performed in the Orbitrap (value of target of automatic gain control (AGC) 106, resolution 30,000 at 400 m/z; lock mass was set to 445.120025 u (protonated (Si(CH3)2O)6) (17.Olsen J.V. de Godoy L.M. Li G. Macek B. Mortensen P. Pesch R. Makarov A. Lange O. Horning S. Mann M. Parts per million mass accuracy on an Orbitrap mass spectrometer via lock mass injection into a C-trap.Mol. Cell. Proteomics. 2005; 4: 2010-2021Abstract Full Text Full Text PDF PubMed Scopus (1233) Google Scholar)). Based on this survey scan, the five most intensive ions were consecutively isolated (automatic gain control target set to 104 ions) and fragmented by collision-activated dissociation applying 35% normalized collision energy in the linear ion trap. After precursors were selected for they were excluded for further for three minutes. were identified using the by (Thermo Fisher Scientific, Bremen, and (Thermo Fisher Scientific, Bremen, the criteria into with of and for and The used was the human of as and of as and were The number of was the mass for ions was 10 and the mass for ions was The for mass with the mass of the identified peptides was set at 2 on the pooled average age and average protein age = protein concentration = concentration = in a new The Orbitrap was subsequently analyzed using the LC-MS in which the LC were and the biological variation between the samples was to assess variation between individuals in this A and the of at least three per peptide were used as a for was assessed by the of all peptides of a The for the total ion of all peptides of a protein was compared between the and the of this was considered to be the inter-individual variation as in the Technical variation was assessed by the on the five of the pooled MS CSF samples were to the protocol we van Quantitative matrix-assisted laser ion resonance peptide profiling and identification of Proteome Res. 2009; PubMed Scopus Google Scholar), in which the samples were digested and desalted using C18 a acid the samples were all on an MALDI-FT-ICR mass spectrometer using a as by and et and and ion with an source Chem. 2000; PubMed Scopus Google Scholar, peptide analysis by matrix-assisted laser desorption/ionization mass spectrometry.Analytical PubMed Scopus Google Scholar, Application of acquisition in mass spectrometry.Analytical Chem. 2000; PubMed Scopus Google Scholar). mass was using a Quantitative MALDI-FT-ICR has been to quantify human 1 in cell de R. Quantitative analysis of in cell using mass spectrometry.Analytical Chem. 2008; PubMed Scopus Google Scholar) as as peptides in CSF van Quantitative matrix-assisted laser ion resonance peptide profiling and identification of Proteome Res. 2009; PubMed Scopus Google Scholar), that quantitative MALDI-FT-ICR methods are which is to the fact that variation in in MALDI-FT-ICR MS is much more than The of the of of each sample was then compared with concentration levels obtained by routine clinical chemistry deviations of the of the were between and for all on the three sample the sample set (n = the validation sample set (n = and the experimental sample sets (n = and protein concentration are in The age and protein concentration of all individual patients is listed in the Supplementary sample sample sample set sample set concentration in a new in an compare the results on the variation of protein abundances found in the neurologically normal individual CSF samples to an experimental an was performed on a larger set of A total of CSF samples, obtained from patients with either multiple sclerosis or was These samples, those of the multiple sclerosis from were from neurological the variation in protein which are known to be in diseases such as multiple sclerosis F. 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T. metabolomics with chromatography/mass spectrometry.Analytical Chem. 2006; PubMed Scopus Google Scholar). the different in the sample i.e. before and different were added at a of approximately 20 The volume was 45 μL for the sample set and μL for the validation sample set and 1 μL of the samples was in on a 30 x x μm column using a temperature from °C to °C at a rate of 5 GC-MS analysis was performed using an coupled to an mass MS detection was used in and scan The for the of ions was was performed in for the sample For sample set samples were in For both sample a pooled human CSF sample was analyzed in to the analytical in the analysis of metabolites by