Abstract
Over the past decade, liquid chromatography tandem mass spectrometry (LC MS/MS)-based workflows become standard for biomarker discovery in proteomics. These medium- to high-throughput (in terms of protein content) profiling approaches have been applied to clinical research. As a result, human proteomes have been characterized to a greater extent than ever before. However, proteomics in clinical research and biomarker discovery studies has generally been performed with small cohorts of subjects (or pooled samples from larger cohorts). This is problematic, as when aiming to identify novel biomarkers, small studies suffer from inherent and important limitations, as a result of the reduced biological diversity and representativity of human populations. Consequently, larger-scale proteomics will be key to delivering robust biomarker candidates and enabling translation to clinical practice.
Cerebrospinal fluid (CSF) is a highly clinically relevant body fluid, and an important source of potential biomarkers for brain-associated damage, such as that induced by traumatic brain injury and stroke, and brain diseases, such as Alzheimer’s disease and Parkinson’s disease. We have developed a scalable automated proteomic pipeline (ASAP2) for biomarker discovery. This workflow is compatible with larger clinical research studies in terms of sample size, while still allowing several hundred proteins to be measured in CSF by MS. In this chapter, we describe the whole proteomic workflow to analyze human CSF. We further illustrate our protocol with some examples from an analysis of hundreds of human CSF samples, in the specific context of biomarker discovery to characterize central nervous system disorders.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Prince M, Bryce R, Albanese E et al (2013) The global prevalence of dementia: a systematic review and metaanalysis. Alzheimers Dement 9(1):63–75. https://doi.org/10.1016/j.jalz.2012.11.007
Musiek ES, Holtzman DM (2015) Three dimensions of the amyloid hypothesis: time, space and ‘wingmen’. Nat Neurosci 18(6):800–806. https://doi.org/10.1038/nn.4018
Lista S, Faltraco F, Prvulovic D et al (2013) Blood and plasma-based proteomic biomarker research in Alzheimer’s disease. Prog Neurobiol 101–102:1–17. https://doi.org/10.1016/j.pneurobio.2012.06.007
Agrawal M, Biswas A (2015) Molecular diagnostics of neurodegenerative disorders. Front Mol Biosci 2:54. https://doi.org/10.3389/fmolb.2015.00054
Henriksen K, O’Bryant SE, Hampel H et al (2014) The future of blood-based biomarkers for Alzheimer’s disease. Alzheimers Dement 10(1):115–131. https://doi.org/10.1016/j.jalz.2013.01.013
Begcevic I, Brinc D, Drabovich AP et al (2016) Identification of brain-enriched proteins in the cerebrospinal fluid proteome by LC-MS/MS profiling and mining of the human protein atlas. Clin Proteomics 13:11. https://doi.org/10.1186/s12014-016-9111-3
Fang Q, Strand A, Law W et al (2009) Brain-specific proteins decline in the cerebrospinal fluid of humans with Huntington disease. Mol Cell Proteomics 8(3):451–466. https://doi.org/10.1074/mcp.M800231-MCP200
Dayon L, Núñez Galindo A, Corthésy J et al (2014) Comprehensive and scalable highly automated MS-based proteomic workflow for clinical biomarker discovery in human plasma. J Proteome Res 13(8):3837–3845. https://doi.org/10.1021/pr500635f
Núñez Galindo A, Kussmann M, Dayon L (2015) Proteomics of cerebrospinal fluid: throughput and robustness using a scalable automated analysis pipeline for biomarker discovery. Anal Chem 87(21):10755–10761. https://doi.org/10.1021/acs.analchem.5b02748
Dayon L, Núñez Galindo A, Cominetti O et al (2017) A highly automated shotgun proteomic workflow: clinical scale and robustness for biomarker discovery in blood. In: Greening D, Simpson R (eds) Serum/plasma proteomics. Methods in molecular biology, vol 1619. Humana Press, New York, NY, pp 433–449. https://doi.org/10.1007/978-1-4939-7057-5_30
Cominetti O, Núñez Galindo A, Corthésy J et al (2016) Proteomic biomarker discovery in 1000 human plasma samples with mass spectrometry. J Proteome Res 15(2):389–399. https://doi.org/10.1021/acs.jproteome.5b00901
Dayon L, Wojcik J, Núñez Galindo A et al (2017) Plasma proteomic profiles of cerebrospinal fluid-defined Alzheimer’s disease pathology in older adults. J Alzheimers Dis 60(4):1641–1652. https://doi.org/10.3233/JAD-170426
Oller Moreno S, Cominetti O, Núñez Galindo A et al (2018) The differential plasma proteome of obese and overweight individuals undergoing a nutritional weight loss and maintenance intervention. Proteomics Clin Appl 12(1):1600150. https://doi.org/10.1002/prca.201600150
Dayon L, Núñez Galindo A, Wojcik J et al (2018) Alzheimer disease pathology and the cerebrospinal fluid proteome. Alzheimers Res Ther 10(1):66. https://doi.org/10.1186/s13195-018-0397-4
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Núñez Galindo, A., Macron, C., Cominetti, O., Dayon, L. (2019). Analyzing Cerebrospinal Fluid Proteomes to Characterize Central Nervous System Disorders: A Highly Automated Mass Spectrometry-Based Pipeline for Biomarker Discovery. In: Brun, V., Couté, Y. (eds) Proteomics for Biomarker Discovery. Methods in Molecular Biology, vol 1959. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9164-8_6
Download citation
DOI: https://doi.org/10.1007/978-1-4939-9164-8_6
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-9163-1
Online ISBN: 978-1-4939-9164-8
eBook Packages: Springer Protocols