The human cerebrospinal fluid metabolome☆
Introduction
Metabolomics is an emerging area of “omics” research that involves the global or near global analysis of the small molecule metabolites (<1500 Da) found in living organisms (i.e. the metabolome). While still in its infancy we are already beginning to see applications of metabolomics in many fields, including disease diagnostics [1], pharmaceutical research and development [2], and agriculture and food safety [3]. These applications are leading to the discovery of many useful biomarkers and the development of a number of improved screening assays. Continued advances in detection and separation technologies certainly suggest that the potential range of metabolomics applications will continue to grow. However, a common criticism about this field is the fact that in any given metabolomics study, relatively few metabolites are identified or quantified. In other words, metabolomics is not as quantitative as the other “omics” sciences. With the release of the first draft of the Human Metabolome [4], we believe an important step has been taken to make metabolomics studies much more quantitative. In an effort to lay an even more solid foundation to quantitative metabolomics we have started to systematically determine the detectable metabolic composition of clinically important biofluids and tissue types. Based on its relative metabolic simplicity and its potential importance to central nervous system (CNS) diseases, we have selected cerebrospinal fluid (CSF) as our first biofluid to be comprehensively characterized. Presented herein is the most complete catalogue of the human CSF metabolome to date.
CSF is the secretion product of the central nervous system that fills the ventricles and the subarachnoid space of the brain and spinal column [5], [6]. Apart from it's role in protecting the brain from physical shock, CSF also has a function in circulating nutrients and chemicals filtered from the blood along with waste management by removing organic acids either by active transport or bulk flow from the extracellular fluid in the brain to the subarachnoid compartment, and ultimately into the venous blood stream and the lymphatic system [5], [6], [7]. Since the composition of CSF is directly dependent upon metabolite production rates in the brain [7], analysis of the CSF metabolome can offer biochemical insights into central nervous system disorders, such as brain injury [8], Parkinson's disease [9] and multiple sclerosis [10].
Over the past 50 years several different routes have been pursued to characterize the CSF metabolome including: (1) modern metabolomic or metabolic profiling approaches; (2) referential clinical chemistry studies and (3) targeted metabolite identification studies. In terms of metabolite profiling methods, several different groups have applied 1H NMR [11], [12], [13], [14], gas chromatography–mass spectrometry (GC–MS) [7], [13], [14] and amino acid analysis [15], [16] to characterize a significant portion of the CSF metabolome. Large numbers of referential clinical chemistry studies, largely focusing on a single metabolite at a time, were also conducted on CSF in the 1960s and 1970s [17], [18]. The intent of these studies was to determine reference concentrations for many easily detected compounds. Information on these compounds and their concentration ranges has been compiled in a number of well known clinical chemistry texts [19]. With improvements to instrumentation sensitivity and separation capacity, dozens of other targeted metabolite studies have been conducted on CSF that have led to the identification and quantification of many previously undetectable CSF metabolites. Unfortunately, this information is not located in any central repository and is rather scattered across numerous journals and periodicals [4].
In order to facilitate future CSF research, it is important to establish a comprehensive, electronically accessible database of the detectable metabolites in human CSF. In this report we present a catalogue of detectable metabolites (including their concentrations and disease associations) that can be found in human cerebral spinal fluid. This catalogue was assembled using a combination of both experimental and literature-based research. Experimentally, we used nuclear magnetic resonance (NMR), gas chromatography–mass spectrometry, Fourier transform–mass spectrometry (FTMS) and liquid chromatography (LC) to separate, identify, quantify and validate CSF metabolites. To compliment these “global” metabolic profiling efforts, our team also surveyed and extracted metabolite and disease-association data from more than 2000 books and journal articles that had been identified through computer-aided literature mining. In undertaking this effort we wished to address four key questions: (1) what compounds can be or have ever been identified in CSF? (2) What are the concentration ranges for these metabolites? (3) What portion of the CSF metabolome can be routinely identified and/or quantified in CSF using conventional, untargeted metabolomics methods? (4) What analytical methods (NMR, GC–MS, LC–MS) are best suited for comprehensively characterizing the CSF metabolome? Comprehensive tables containing the compounds, concentrations, spectra, protocols and links to disease associations that were uncovered or identified from this work are freely available at http://www.csfmetabolome.ca.
Section snippets
CSF collection
Lumbar CSF samples were collected from 50 patients screened for meningitis in accordance with guidelines established by the University of Alberta Health Research Ethics Board. As part of the disease screening procedure, CSF samples were required to be stored at 4 °C for 2 days, after which they were placed in a freezer for long-term storage at −80 °C. Studies with CSF and other biofluids indicate that these fluids are quite stable at low (<5 °C) temperatures [16], [20], [21]. Samples that were
Results and discussion
In this study, we have attempted to perform a quantitative, “base-line” characterization of the human CSF metabolome using a combination of both experimental and literature-based approaches. The literature-based data proved to be critical to the identification of a number of previously unidentified or misidentified peaks in our experimental data sets. Likewise, the experimental data allowed correction or confirmation of a number of questionable literature-derived values. The combination of both
Conclusion
We began this study in an effort to address four key questions: (1) what compounds can be or have ever been identified in CSF? (2) What are the concentration ranges for these metabolites? (3) What portion of the CSF metabolome can be routinely identified and/or quantified in CSF using conventional, untargeted metabolomics methods? (4) What analytical methods (NMR, GC–MS, LC–MS) are best suited for comprehensively characterizing the CSF metabolome? Our computer-aided literature survey allowed us
Acknowledgements
This research was supported by Genome Alberta, Genome Canada and the University of Alberta. The authors would like to thank Connie Sobsey, Jun Peng and Ryan Fradette for their help in preparing the figures and Dr. Xingye Su for help with implementing the HILIC technique.
References (32)
- et al.
Brain Res.
(2006) - et al.
J. Neurol. Sci.
(1997) - et al.
J. Neurol. Sci.
(1996) - et al.
J. Pharm. Biomed. Anal.
(1993) - et al.
Clin. Chim. Acta
(1989) - et al.
Anal. Biochem.
(1984) Neuropharmacology
(1972)- et al.
Brain Res. Brain Res. Protoc.
(1998) - et al.
FEBS Lett.
(2005) - et al.
J. Emerg. Med.
(2000)
Analyst
FEBS J.
J. Agric. Food Chem.
Nucleic Acids Res.
Geigy Scientific Tables
J. Inherit. Metab. Dis.
Cited by (269)
Mass spectrometry for biomarkers, disease mechanisms, and drug development in cerebrospinal fluid metabolomics
2024, TrAC - Trends in Analytical ChemistryMetabolomics in neurodegenerative disorders—Parkinson's disease
2024, Comprehensive Analytical ChemistryApplication of metabolomics in diagnostics and differentiation of meningitis: A narrative review with a critical approach to the literature
2023, Biomedicine and PharmacotherapyIntegrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective
2023, Journal of Pharmaceutical AnalysisShort chain fatty acids: Microbial metabolites for gut-brain axis signalling
2022, Molecular and Cellular Endocrinology
- ☆
This paper is part of a special volume entitled “Hyphenated Techniques for Global Metabolite Profiling”, guest edited by Georgios Theodoridis and Ian D. Wilson.