Protein variability in cerebrospinal fluid and its possible implications for neurological protein biomarker research
Open accessAbstract
Cerebrospinal fluid is investigated in biomarker studies for various neurological disorders of the central nervous system due to its proximity to the brain. Currently, only a limited number of biomarkers have been validated in independent studies. The high variability in the protein composition and protein abundance of cerebrospinal fluid between as well as within individuals might be an important reason for this phenomenon. To evaluate this possibility, we investigated the inter- and intraindividual variability in the cerebrospinal fluid proteome globally, with a specific focus on disease biomarkers described in the literature. Cerebrospinal fluid from a longitudinal study group including 12 healthy control subjects was analyzed by label-free quantification (LFQ) via LC-MS/MS. Data were quantified via MaxQuant. Then, the intra- and interindividual variability and the reference change value were calculated for every protein. We identified and quantified 791 proteins, and 216 of these proteins were abundant in all samples and were selected for further analysis. For these proteins, we found an interindividual coefficient of variation of up to 101.5% and an intraindividual coefficient of variation of up to 29.3%. Remarkably, these values were comparably high for both proteins that were published as disease biomarkers and other proteins. Our results support the hypothesis that natural variability greatly impacts cerebrospinal fluid protein biomarkers because high variability can lead to unreliable results. Thus, we suggest controlling the variability of each protein to distinguish between good and bad biomarker candidates, e.g., by utilizing reference change values to improve the process of evaluating potential biomarkers in future studies.
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CSF offers an attractive alternative to plasma when studying the CNS because many CSF proteins are relevant to CNS diseases.
“CSF offers an attractive alternative to plasma when studying the central nervous system (CNS) because many CSF proteins are relevant to CNS diseases (14).”
From Low overlap of plasma and CSF protein quantitative trait loci affects protein discovery for neurological disease (2026) · cites this paper as [14]
The Introduction says some CSF proteins originate in the CNS and that CSF has been investigated for biomarkers of neurological disease. It supports CSF’s relevance to CNS research, but does not describe CSF as an attractive alternative to plasma; its accessibility comparison is with brain biopsy. (LLM, conf=0.83)
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Michalski, A., Cox, J., & Mann, M. (2011) · Journal of Proteome Research · doi:10.1021/pr101060v · Cited by 685 · see citing works
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Research works printed but not linked: 52
- [1]Reiber H. Dynamics of brain-derived proteins in cerebrospinal fluid. Clin Chim Acta. 2001; 310(2): 173– 86. Epub 2001/08/11.. 11498083
- [2]Reiber H. Proteins in cerebrospinal fluid and blood: barriers, CSF flow rate and source-related dynamics. Restor Neurol Neurosci. 2003; 21(3–4): 79– 96. Epub 2003/10/08.. 14530572
- [3]Simon MJ, Iliff JJ. Regulation of cerebrospinal fluid (CSF) flow in neurodegenerative, neurovascular and neuroinflammatory disease. Biochim Biophys Acta. 2016; 1862(3): 442– 51. Epub 2015/10/27. 10.1016/j.bbadis.2015.10.014; PubMed Central PMCID: PMCPMC4755861. 26499397 PMC4755861 · DOI
- [4]Spector R, Robert Snodgrass S, Johanson CE. A balanced view of the cerebrospinal fluid composition and functions: Focus on adult humans. Exp Neurol. 2015; 273: 57– 68. Epub 2015/08/08. 10.1016/j.expneurol.2015.07.027. 26247808 · DOI
- [5]Gomez-Rio M, Caballero MM, Gorriz Saez JM, Minguez-Castellanos A. Diagnosis of Neurodegenerative Diseases: The Clinical Approach. Curr Alzheimer Res. 2016; 13(5): 469– 74. Epub 2015/11/17.. 26567736
- [6]Kalia LV, Lang AE. Parkinson's disease. Lancet. 2015; 386(9996): 896– 912. Epub 2015/04/24. 10.1016/S0140-6736(14)61393-3. 25904081 · DOI
- [7]Schrag A, Horsfall L, Walters K, Noyce A, Petersen I. Prediagnostic presentations of Parkinson's disease in primary care: a case-control study. Lancet Neurol. 2015; 14(1): 57– 64. Epub 2014/12/02. 10.1016/S1474-4422(14)70287-X. 25435387 · DOI · Paper page
- [8]Thal LJ, Kantarci K, Reiman EM, Klunk WE, Weiner MW, Zetterberg H, et al The Role of Biomarkers in Clinical Trials for Alzheimer Disease. Alzheimer Dis Assoc Disord. 2006; 20(1): 6– 15. 10.1097/01.wad.0000191420.61260.a8; PubMed Central PMCID: PMC1820855. 16493230 PMC1820855 · DOI
- [9]Doherty CM, Forbes RB. Diagnostic Lumbar Puncture. Ulster Med J. 2014; 83(2): 93– 102.; PubMed Central PMCID: PMC4113153. 25075138 PMC4113153
- [10]Olsson B, Lautner R, Andreasson U, Ohrfelt A, Portelius E, Bjerke M, et al CSF and blood biomarkers for the diagnosis of Alzheimer's disease: a systematic review and meta-analysis. Lancet Neurol. 2016; 15(7): 673– 84. Epub 2016/04/14. 10.1016/S1474-4422(16)00070-3. 27068280 · DOI · Paper page
- [11]Babic M, Svob Strac D, Muck-Seler D, Pivac N, Stanic G, Hof PR, et al Update on the core and developing cerebrospinal fluid biomarkers for Alzheimer disease. Croat Med J. 2014; 55(4): 347– 65. Epub 2014/08/29. 10.3325/cmj.2014.55.347; PubMed Central PMCID: PMCPMC4157375. 25165049 PMC4157375 · DOI
- [12]Halbgebauer S, Ockl P, Wirth K, Steinacker P, Otto M. Protein biomarkers in Parkinson's disease: Focus on cerebrospinal fluid markers and synaptic proteins. Mov Disord. 2016; 31(6): 848– 60. Epub 2016/05/03. 10.1002/mds.26635. 27134134 · DOI
- [13]Matejcikova Z, Mares J, Prikrylova Vranova H, Klosova J, Sladkova V, Dolakova J, et al Cerebrospinal fluid inflammatory markers in patients with multiple sclerosis: a pilot study. J Neural Transm (Vienna). 2015; 122(2): 273– 7. Epub 2014/06/05. 10.1007/s00702-014-1244-9. 24894698 · DOI
- [14]Ross CA, Aylward EH, Wild EJ, Langbehn DR, Long JD, Warner JH, et al Huntington disease: natural history, biomarkers and prospects for therapeutics. Nat Rev Neurol. 2014; 10(4): 204– 16. Epub 2014/03/13. 10.1038/nrneurol.2014.24. 24614516 · DOI · Paper page
- [15]Huhmer AF, Biringer RG, Amato H, Fonteh AN, Harrington MG. Protein analysis in human cerebrospinal fluid: Physiological aspects, current progress and future challenges. Dis Markers. 2006; 22(1–2): 3– 26. Epub 2006/01/18. 10.1155/2006/158797; PubMed Central PMCID: PMCPMC3850820. 16410649 PMC3850820 · DOI
- [16]Kang JH, Mollenhauer B, Coffey CS, Toledo JB, Weintraub D, Galasko DR, et al CSF biomarkers associated with disease heterogeneity in early Parkinson's disease: the Parkinson's Progression Markers Initiative study. Acta Neuropathol. 2016; 131(6): 935– 49. Epub 2016/03/30. 10.1007/s00401-016-1552-2; PubMed Central PMCID: PMCPMC5031365. 27021906 PMC5031365 · DOI
- [17]Hall S, Ohrfelt A, Constantinescu R, Andreasson U, Surova Y, Bostrom F, et al Accuracy of a panel of 5 cerebrospinal fluid biomarkers in the differential diagnosis of patients with dementia and/or parkinsonian disorders. Arch Neurol. 2012; 69(11): 1445– 52. Epub 2012/08/29. 10.1001/archneurol.2012.1654. 22925882 · DOI · Paper page
- [18]Mollenhauer B, Trautmann E, Sixel-Doring F, Wicke T, Ebentheuer J, Schaumburg M, et al Nonmotor and diagnostic findings in subjects with de novo Parkinson disease of the DeNoPa cohort. Neurology. 2013; 81(14): 1226– 34. Epub 2013/09/03. 10.1212/WNL.0b013e3182a6cbd5. 23997153 · DOI
- [19]Byrne LM, Wild EJ. Cerebrospinal Fluid Biomarkers for Huntington's Disease. J Huntingtons Dis. 2016; 5(1): 1– 13. Epub 2016/04/01. 10.3233/JHD-160196. 27031730 · DOI
- [20]Stoop MP, Coulier L, Rosenling T, Shi S, Smolinska AM, Buydens L, et al Quantitative proteomics and metabolomics analysis of normal human cerebrospinal fluid samples. Mol Cell Proteomics. 2010; 9(9): 2063– 75. Epub 2010/09/03. 10.1074/mcp.M900877-MCP200; PubMed Central PMCID: PMCPMC2938111. 20811074 PMC2938111 · DOI
- [21]Rosen C, Hansson O, Blennow K, Zetterberg H. Fluid biomarkers in Alzheimer's disease—current concepts. Mol Neurodegener. 2013; 8: 20 Epub 2013/06/27. 10.1186/1750-1326-8-20; PubMed Central PMCID: PMCPMC3691925. 23800368 PMC3691925 · DOI
- [22]Simonsen AH, Herukka SK, Andreasen N, Baldeiras I, Bjerke M, Blennow K, et al Recommendations for CSF AD biomarkers in the diagnostic evaluation of dementia. Alzheimers Dement. 2017; 13(3): 274– 84. Epub 2017/03/28. 10.1016/j.jalz.2016.09.008. 28341066 · DOI · Paper page
- [23]Kalia LV, Kalia SK, Lang AE. Disease-modifying strategies for Parkinson's disease. Mov Disord. 2015; 30(11): 1442– 50. Epub 2015/07/25. 10.1002/mds.26354. 26208210 · DOI
- [24]Hu Y, Malone JP, Fagan AM, Townsend RR, Holtzman DM. Comparative proteomic analysis of intra- and interindividual variation in human cerebrospinal fluid. Mol Cell Proteomics. 2005; 4(12): 2000– 9. Epub 2005/10/04. 10.1074/mcp.M500207-MCP200. 16199891 · DOI
- [25]Begcevic I, Brinc D, Drabovich AP, Batruch I, Diamandis EP. Identification of brain-enriched proteins in the cerebrospinal fluid proteome by LC-MS/MS profiling and mining of the Human Protein Atlas. Clin Proteomics. 2016; 13: 11 Epub 2016/05/18. 10.1186/s12014-016-9111-3; PubMed Central PMCID: PMCPMC4868024. 27186164 PMC4868024 · DOI
- [26]Trombetta BA, Carlyle BC, Koenig AM, Shaw LM, Trojanowski JQ, Wolk DA, et al The technical reliability and biotemporal stability of cerebrospinal fluid biomarkers for profiling multiple pathophysiologies in Alzheimer's disease. PLoS One. 2018; 13(3): e0193707 Epub 2018/03/06. 10.1371/journal.pone.0193707; PubMed Central PMCID: PMCPMC5837100. 29505610 PMC5837100 · DOI
- [27]Fraser CG. Inherent biological variation and reference values. Clin Chem Lab Med. 2004; 42(7): 758– 64. Epub 2004/08/26. 10.1515/CCLM.2004.128. 15327011 · DOI
- [28]Fraser CG, Harris EK. Generation and application of data on biological variation in clinical chemistry. Crit Rev Clin Lab Sci. 1989; 27(5): 409– 37. Epub 1989/01/01. 10.3109/10408368909106595. 2679660 · DOI
- [29]Meijers WC, van der Velde AR, Muller Kobold AC, Dijck-Brouwer J, Wu AH, Jaffe A, et al Variability of biomarkers in patients with chronic heart failure and healthy controls. Eur J Heart Fail. 2017; 19(3): 357– 65. Epub 2016/10/22. 10.1002/ejhf.669; PubMed Central PMCID: PMCPMC5347881. 27766733 PMC5347881 · DOI · Paper page
- [30]Plum S, Helling S, Theiss C, Leite REP, May C, Jacob-Filho W, et al Combined enrichment of neuromelanin granules and synaptosomes from human substantia nigra pars compacta tissue for proteomic analysis. Journal of Proteomics. 2013; 94: 202– 6. 10.1016/j.jprot.2013.07.015 WOS:000330493400015. 23917253 PMC4083096 · DOI
- [31]Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nature Biotechnology. 2008; 26(12): 1367– 72. 10.1038/nbt.1511 WOS:000261591300022. 19029910 · DOI · Paper page
- [32]UniProt: a hub for protein information. Nucleic Acids Res. 2015; 43(Database issue): D204– 12. Epub 2014/10/29. 10.1093/nar/gku989; PubMed Central PMCID: PMCPMC4384041. 25348405 PMC4384041 · DOI · Paper page
- [33]Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen JV, Mann M. Andromeda: A Peptide Search Engine Integrated into the MaxQuant Environment. Journal of Proteome Research. 2011; 10(4): 1794– 805. 10.1021/pr101065j WOS:000288924000033. 21254760 · DOI
- [35]Cox J, Hein MY, Luber CA, Paron I, Nagaraj N, Mann M. Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ. Molecular & Cellular Proteomics. 2014; 13(9): 2513– 26. 10.1074/mcp.M113.031591 WOS:000341785800025. 24942700 PMC4159666 · DOI · Paper page
- [36]Turewicz M, Kohl M, Ahrens M, Mayer G, Uszkoreit J, Naboulsi W, et al BioInfra.Prot: A comprehensive proteomics workflow including data standardization, protein inference, expression analysis and data publication. J Biotechnol. 2017; 261: 116– 25. Epub 2017/06/14. 10.1016/j.jbiotec.2017.06.005. 28606611 · DOI
- [37]Abdi F, Quinn JF, Jankovic J, McIntosh M, Leverenz JB, Peskind E, et al Detection of biomarkers with a multiplex quantitative proteomic platform in cerebrospinal fluid of patients with neurodegenerative disorders. J Alzheimers Dis. 2006; 9(3): 293– 348. Epub 2006/08/18.. 16914840
- [38]Yin GN, Lee HW, Cho JY, Suk K. Neuronal pentraxin receptor in cerebrospinal fluid as a potential biomarker for neurodegenerative diseases. Brain Res. 2009; 1265: 158– 70. Epub 2009/04/17. 10.1016/j.brainres.2009.01.058. 19368810 · DOI · Paper page
- [39]Comabella M, Fernandez M, Martin R, Rivera-Vallve S, Borras E, Chiva C, et al Cerebrospinal fluid chitinase 3-like 1 levels are associated with conversion to multiple sclerosis. Brain. 2010; 133(Pt 4): 1082– 93. Epub 2010/03/20. 10.1093/brain/awq035. 20237129 · DOI
- [40]Fang Q, Strand A, Law W, Faca VM, Fitzgibbon MP, Hamel N, et al Brain-specific Proteins Decline in the Cerebrospinal Fluid of Humans with Huntington Disease*S⃞. Mol Cell Proteomics. 82009. p. 451– 66. 18984577 PMC2649809
- [41]Kroksveen AC, Guldbrandsen A, Vedeler C, Myhr KM, Opsahl JA, Berven FS. Cerebrospinal fluid proteome comparison between multiple sclerosis patients and controls. Acta Neurol Scand Suppl. 2012;(195): 90– 6. Epub 2013/01/04. 10.1111/ane.12029. 23278663 · DOI · Paper page
- [42]de Bont JM, den Boer ML, Reddingius RE, Jansen J, Passier M, van Schaik RH, et al Identification of apolipoprotein A-II in cerebrospinal fluid of pediatric brain tumor patients by protein expression profiling. Clin Chem. 2006; 52(8): 1501– 9. Epub 2006/06/10. 10.1373/clinchem.2006.069294. 16762995 · DOI
- [43]Ward MA, Proteome Sciences plc L, Soininen H, University of Kuopio K, O'Brien D, Proteome Sciences plc L, et al Evaluation of CSF cystatin C, beta-2-microglobulin, and VGF as diagnostic biomarkers of Alzheimer's disease using SRM. Alzheimer's & Dementia: The Journal of the Alzheimer's Association. 2011; 7(4). 10.1016/j.jalz.2011.05.405 · DOI
- [44]Heywood WE, Galimberti D, Bliss E, Sirka E, Paterson RW, Magdalinou NK, et al Identification of novel CSF biomarkers for neurodegeneration and their validation by a high-throughput multiplexed targeted proteomic assay. Mol Neurodegener. 2015; 10: 64 Epub 2015/12/03. 10.1186/s13024-015-0059-y; PubMed Central PMCID: PMCPMC4666172. 26627638 PMC4666172 · DOI
- [45]Paterson RW, Heywood WE, Heslegrave AJ, Magdalinou NK, Andreasson U, Sirka E, et al A targeted proteomic multiplex CSF assay identifies increased malate dehydrogenase and other neurodegenerative biomarkers in individuals with Alzheimer's disease pathology. Transl Psychiatry. 2016; 6(11): e952 Epub 2016/11/16. 10.1038/tp.2016.194; PubMed Central PMCID: PMCPMC5314115. 27845782 PMC5314115 · DOI
- [46]Jia Y, Wu T, Jelinek CA, Bielekova B, Chang L, Newsome S, et al Development of protein biomarkers in cerebrospinal fluid for secondary progressive multiple sclerosis using selected reaction monitoring mass spectrometry (SRM-MS). Clin Proteomics. 92012. p. 9. 22846148 PMC3466133
- [47]Magdalinou NK, Noyce AJ, Pinto R, Lindstrom E, Holmen-Larsson J, Holtta M, et al Identification of candidate cerebrospinal fluid biomarkers in parkinsonism using quantitative proteomics. Parkinsonism Relat Disord. 2017; 37: 65– 71. Epub 2017/02/19. 10.1016/j.parkreldis.2017.01.016. 28214264 · DOI · Paper page
- [48]Vafadar-Isfahani B, Ball G, Coveney C, Lemetre C, Boocock D, Minthon L, et al Identification of SPARC-like 1 protein as part of a biomarker panel for Alzheimer's disease in cerebrospinal fluid. J Alzheimers Dis. 2012; 28(3): 625– 36. Epub 2011/11/03. 10.3233/JAD-2011-111505. 22045497 · DOI
- [49]Kroksveen AC, Aasebo E, Vethe H, Van Pesch V, Franciotta D, Teunissen CE, et al Discovery and initial verification of differentially abundant proteins between multiple sclerosis patients and controls using iTRAQ and SID-SRM. J Proteomics. 2013; 78: 312– 25. Epub 2012/10/13. 10.1016/j.jprot.2012.09.037. 23059536 · DOI
- [50]Stoop MP, Dekker LJ, Titulaer MK, Burgers PC, Sillevis Smitt PA, Luider TM, et al Multiple sclerosis-related proteins identified in cerebrospinal fluid by advanced mass spectrometry. Proteomics. 2008; 8(8): 1576– 85. Epub 2008/03/21. 10.1002/pmic.200700446. 18351689 · DOI
- [51]Schutzer SE, Liu T, Natelson BH, Angel TE, Schepmoes AA, Purvine SO, et al Establishing the Proteome of Normal Human Cerebrospinal Fluid. PLoS One. 52010. PMC2881861 20552007
- [52]Perrin RJ, Payton JE, Malone JP, Gilmore P, Davis AE, Xiong C, et al Quantitative Label-Free Proteomics for Discovery of Biomarkers in Cerebrospinal Fluid: Assessment of Technical and Inter-Individual Variation. PLoS One. 82013. PMC3659127 23700471
- [53]Ahrens M, Turewicz M, Casjens S, May C, Pesch B, Stephan C, et al Detection of patient subgroups with differential expression in omics data: a comprehensive comparison of univariate measures. PLoS One. 2013; 8(11): e79380 Epub 2013/11/28. 10.1371/journal.pone.0079380; PubMed Central PMCID: PMCPMC3838370. 24278130 PMC3838370 · DOI
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Western, D., Yang, C., Timsina, J., Liu, M., Wang, C., Campbell, M. C., Kotzbauer, P., Perlmutter, J. S., Schindler, S. E., Morris, J. C., et al. (2026) · Science Translational Medicine · doi:10.1126/scitranslmed.adz2016 · Cited by 1 · see citing works
- Authors
- Schilde, L. M., Kösters, S., Steinbach, S., Schork, K., Eisenacher, M., Galozzi, S., Turewicz, M., Barkovits, K., Mollenhauer, B., Marcus, K., & May, C.
- Affiliations
- Ruhr University Bochum (DE)
- Universitätsmedizin Göttingen (DE)
- Corresponding author
- Marcus, Katrin
- ORCID
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-11-29
- Volume
- 13
- Issue
- 11
- Pages
- e0206478
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC6264484
- OpenAlex
- W2903178772
- Cited by
- 75
- References
- 1
- Citations by year
- 2026: 8 · 2025: 14 · 2024: 13 · 2023: 10 · 2022: 10 · 2021: 3 · 2020: 14 · 2019: 1 · 2018: 1 · 2017: 1
- FWCI
- 2.01
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Dokumentenrepositorium der RUB (Ruhr University Bochum) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Deutsche Parkinson Gesellschaft
- The HUPO Brain Proteome Project
- PURE, a project of Northrhine Westfalia a federal German state
- Bundesministerium für Bildung und Forschung (FKZ 031 A 534A, FKZ 01DN14023)
- Medical Faculty Ruhr-University Bochum (FoRUM)
- Horizon 2020 (NISCI) (681094)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Health Sciences › Medicine › Physiology › Clinical Laboratory Practices and Quality Control
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Cerebrospinal fluid and hydrocephalus
- Keywords
- cerebrospinal fluid proteome, protein variation, interindividual variability, within-person variability, reference change value, biomarker validation, coefficient of variation, label-free quantification
- MeSH terms
- Adult, Aged, Aged, 80 and over, Cerebrospinal Fluid Proteins, Female, Humans, Longitudinal Studies, Male, Middle Aged, Nervous System Diseases, Biomarkers, Proteomics, Healthy Volunteers
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