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Determination of Cerebrospinal Fluid Proteome Variations by Isobaric Labeling Coupled with Strong Cation-Exchange Chromatography and Tandem Mass Spectrometry

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Cerebrospinal Fluid (CSF) Proteomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2044))

Abstract

Cerebrospinal fluid (CSF) is in direct contact with the brain and represents a valuable source of mediators that reflect metabolic processes occurring in the central nervous system (CNS). In this sense, mass spectrometry (MS) methods have proven to be sensitive in quantifying the proteomic profiles of CSF, therefore being able to detect biomarker candidates for neurological disorders. In particular, a key development has been the use of multiplexing technologies to easily identify and quantify complex protein mixtures. This chapter describes a workflow suitable for the analysis of CSF proteome using isobaric labeling coupled to strong cation-exchange chromatography fractionation for its potential use as a biomarker discovery platform. In this case, the isobaric tags for relative and absolute quantitation (iTRAQ) label all proteins in a sample via free amines at the N-terminus and on the side chain of lysine residues. Then, the labeled samples are pooled and chromatographically fractionated. These fractions with the pooled samples are afterward analyzed by tandem mass spectrometry (MS/MS), and proteins are quantified by the relative intensities of the reporter ions in the MS/MS spectra, simultaneously obtaining the amino acid sequence. This method complements the neuroproteomic toolbox to identify new protein biomarkers not only for the early clinical diagnosis and disease staging of CNS-related disorders but also to elucidate the molecular mechanisms related to the pathophysiology of these symptoms.

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References

  1. Heemels MT (2016) Neurodegenerative diseases. Nature 539:179

    Article  Google Scholar 

  2. Dos Santos Picanco LC, Ozela PF et al (2018) Alzheimer’s disease: a review from the pathophysiology to diagnosis, new perspectives for pharmacological treatment. Curr Med Chem 25:3141–3159

    Article  CAS  Google Scholar 

  3. Lista S, Zetterberg H et al (2017) Evolving relevance of neuroproteomics in Alzheimer’s disease. Methods Mol Biol 1598:101–115

    Article  CAS  Google Scholar 

  4. Shahim P, Mansson JE et al (2013) Cerebrospinal fluid biomarkers in neurological diseases in children. Eur J Paediatr Neurol 17:7–13

    Article  Google Scholar 

  5. Blennow K, Hampel H et al (2010) Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat Rev Neurol 6:131–144

    Article  CAS  Google Scholar 

  6. Parnetti L, Castrioto A et al (2013) Cerebrospinal fluid biomarkers in Parkinson disease. Nat Rev Neurol 9:131–140

    Article  CAS  Google Scholar 

  7. Manek R, Moghieb A et al (2018) Protein biomarkers and neuroproteomics characterization of microvesicles/exosomes from human cerebrospinal fluid following traumatic brain injury. Mol Neurobiol 55:6112–6128

    Article  CAS  Google Scholar 

  8. Lun MP, Monuki ES et al (2015) Development and functions of the choroid plexus–cerebrospinal fluid system. Nat Rev Neurosci 16:445–457

    Article  CAS  Google Scholar 

  9. Li X, Li TQ et al (2014) The association between biomarkers in cerebrospinal fluid and structural changes in the brain in patients with Alzheimer’s disease. J Intern Med 275:418–427

    Article  CAS  Google Scholar 

  10. Nunez Galindo A, Kussmann M et al (2015) Proteomics of cerebrospinal fluid: throughput and robustness using a scalable automated analysis pipeline for biomarker discovery. Anal Chem 87:10755–10761

    Article  CAS  Google Scholar 

  11. Barkovits K, Linden A et al (2018) Characterization of cerebrospinal fluid via data-independent acquisition mass spectrometry. J Proteome Res 17:3418

    Article  CAS  Google Scholar 

  12. Carlyle BC, Trombetta BA et al (2018) Proteomic approaches for the discovery of biofluid biomarkers of neurodegenerative dementias. Proteomes 6:E32

    Article  Google Scholar 

  13. Ernoult E, Bourreau A et al (2010) A proteomic approach for plasma biomarker discovery with iTRAQ labelling and OFFGEL fractionation. J Biomed Biotechnol 2010:927917

    Article  Google Scholar 

  14. Thompson A, Schafer J et al (2003) Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem 75:1895–1904

    Article  CAS  Google Scholar 

  15. Patton SM, Coe CL et al (2012) Quantitative proteomic analyses of cerebrospinal fluid using iTRAQ in a primate model of iron deficiency anemia. Dev Neurosci 34:354–365

    Article  CAS  Google Scholar 

  16. Yang Y, Mu J et al (2015) iTRAQ-based quantitative proteomic analysis of cerebrospinal fluid reveals NELL2 as a potential diagnostic biomarker of tuberculous meningitis. Int J Mol Med 35:1323–1332

    Article  CAS  Google Scholar 

  17. Lehnert S, Jesse S et al (2012) iTRAQ and multiple reaction monitoring as proteomic tools for biomarker search in cerebrospinal fluid of patients with Parkinson’s disease dementia. Exp Neurol 234:499–505

    Article  CAS  Google Scholar 

  18. Yan X, Mai L et al (2017) CSF-based analysis for identification of potential serum biomarkers of neural tube defects. Neurosci Bull 33:436–444

    Article  CAS  Google Scholar 

  19. Wang C, Zhao D et al (2017) Proteome analysis of potential synaptic vesicle cycle biomarkers in the cerebrospinal fluid of patients with Sporadic Creutzfeldt-Jakob disease. Mol Neurobiol 54:5177–5191

    Article  CAS  Google Scholar 

  20. Chen Y, Liu X et al (2016) Proteomic analysis of cerebrospinal fluid in amyotrophic lateral sclerosis. Exp Ther Med 11:2095–2106

    Article  CAS  Google Scholar 

  21. Zhang K, Tang C et al (2018) Isobaric tags for relative and absolute quantification (iTRAQ)-based untargeted quantitative proteomic approach to identify change of the plasma proteins by salbutamol abuse in beef cattle. J Agric Food Chem 66:378–386

    Article  CAS  Google Scholar 

  22. Wang X, Peng Y et al (2017) Identification of extracellular matrix protein 1 as a potential plasma biomarker of ESCC by proteomic analysis using iTRAQ and 2D-LC-MS/MS. Proteomics Clin Appl 11

    Google Scholar 

  23. Song F, Poljak A et al (2014) Plasma protein profiling of Mild Cognitive Impairment and Alzheimer’s disease using iTRAQ quantitative proteomics. Proteome Sci 12:5

    Article  Google Scholar 

  24. Li W, Zheng H et al (2018) Exploration of differentially expressed plasma proteins in patients with lung adenocarcinoma using iTRAQ-coupled 2D LC-MS/MS. Clin Respir J 12:2036–2045

    Article  CAS  Google Scholar 

  25. Luczak M, Marczak L et al (2014) Optimization of plasma sample pretreatment for quantitative analysis using iTRAQ labeling and LC-MALDI-TOF/TOF. PLoS One 9:e101694

    Article  Google Scholar 

  26. Houel S, Abernathy R et al (2010) Quantifying the impact of chimera MS/MS spectra on peptide identification in large-scale proteomics studies. J Proteome Res 9:4152–4160

    Article  CAS  Google Scholar 

  27. Michalski A, Cox J et al (2011) More than 100,000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC-MS/MS. J Proteome Res 10:1785–1793

    Article  CAS  Google Scholar 

  28. Ow SY, Salim M et al (2009) iTRAQ underestimation in simple and complex mixtures: “the good, the bad and the ugly”. J Proteome Res 8:5347–5355

    Article  CAS  Google Scholar 

  29. Karp NA, Huber W et al (2010) Addressing accuracy and precision issues in iTRAQ quantitation. Mol Cell Proteomics 9:1885–1897

    Article  CAS  Google Scholar 

  30. Ow SY, Salim M et al (2011) Minimising iTRAQ ratio compression through understanding LC-MS elution dependence and high-resolution HILIC fractionation. Proteomics 11:2341–2346

    Article  CAS  Google Scholar 

  31. Yuan X, Desiderio DM (2005) Proteomics analysis of prefractionated human lumbar cerebrospinal fluid. Proteomics 5:541–550

    Article  CAS  Google Scholar 

  32. Maccarrone G, Birg I et al (2004) In-depth analysis of the human CSF proteome using protein prefractionation. Clin Proteomics 1:333–364

    Article  CAS  Google Scholar 

  33. Chan KC, Issaq HJ (2013) Fractionation of peptides by strong cation-exchange liquid chromatography. Methods Mol Biol 1002:311–315

    Article  CAS  Google Scholar 

  34. Trinh HV, Grossmann J et al (2013) iTRAQ-based and label-free proteomics approaches for studies of human adenovirus infections. Int J Proteomics 2013:581862

    Article  Google Scholar 

  35. Latosinska A, Vougas K et al (2015) Comparative analysis of label-free and 8-plex iTRAQ approach for quantitative tissue proteomic analysis. PLoS One 10:e0137048

    Article  Google Scholar 

  36. Chenau J, Michelland S et al (2008) Peptides OFFGEL electrophoresis: a suitable pre-analytical step for complex eukaryotic samples fractionation compatible with quantitative iTRAQ labeling. Proteome Sci 6:9

    Article  Google Scholar 

  37. Andrews GL, Simons BL et al (2011) Performance characteristics of a new hybrid quadrupole time-of-flight tandem mass spectrometer (TripleTOF 5600). Anal Chem 83:5442–5446

    Article  CAS  Google Scholar 

  38. Bradshaw RA, Burlingame AL et al (2006) Reporting protein identification data: the next generation of guidelines. Mol Cell Proteomics 5:787–788

    Article  CAS  Google Scholar 

  39. Unwin RD, Griffiths JR et al (2010) Simultaneous analysis of relative protein expression levels across multiple samples using iTRAQ isobaric tags with 2D nano LC-MS/MS. Nat Protoc 5:1574–1582

    Article  CAS  Google Scholar 

  40. Scheerlinck E, Dhaenens M et al (2015) Minimizing technical variation during sample preparation prior to label-free quantitative mass spectrometry. Anal Biochem 490:14–19

    Article  CAS  Google Scholar 

  41. Thingholm TE, Palmisano G et al (2010) Undesirable charge-enhancement of isobaric tagged phosphopeptides leads to reduced identification efficiency. J Proteome Res 9:4045–4052

    Article  CAS  Google Scholar 

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Acknowledgements

This work was funded by grants from the Spanish Ministry of Economy and Competitiveness (MINECO) (Ref. SAF2014-59340-R), Department of Economic Development from Government of Navarra (Ref. PC023-PC024, PC025, PC081-82 and PI059), and Obra Social la Caixa to ES. AGM was supported by PEJ-2014-A-61949 (MINECO) and a predoctoral fellowship from Public University of Navarra (UPNA). MLM was supported by a predoctoral fellowship from the Public University of Navarra (UPNA). The Proteomics Unit of Navarrabiomed is a member of Proteored, PRB3-ISCIII and is supported by grant PT17/0019, of the PE I+D+i 2013-2016, funded by ISCIII and ERDF. This project is part of the HUPO Brain Proteome Project and is lined up with the Spanish Initiative on the Human Proteome Project (SpHPP).

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Authors and Affiliations

  1. Proteomics Unit, Clinical Neuroproteomics Laboratory, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Proteored-ISCIII, Pamplona, Spain

    Mercedes Lachén-Montes, Andrea González-Morales, Joaquín Fernández-Irigoyen & Enrique Santamaría

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  1. Mercedes Lachén-Montes
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  2. Andrea González-Morales
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  3. Joaquín Fernández-Irigoyen
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  4. Enrique Santamaría
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Corresponding author

Correspondence to Enrique Santamaría .

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Editors and Affiliations

  1. Proteomics Unit, Clinical Neuroproteomics Laboratory, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Proteored-ISCIII, Pamplona, Spain

    Enrique Santamaría

  2. Proteomics Unit, Clinical Neuroproteomics Laboratory, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Proteored-ISCIII, Pamplona, Spain

    Joaquín Fernández-Irigoyen

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Lachén-Montes, M., González-Morales, A., Fernández-Irigoyen, J., Santamaría, E. (2019). Determination of Cerebrospinal Fluid Proteome Variations by Isobaric Labeling Coupled with Strong Cation-Exchange Chromatography and Tandem Mass Spectrometry. In: Santamaría, E., Fernández-Irigoyen, J. (eds) Cerebrospinal Fluid (CSF) Proteomics. Methods in Molecular Biology, vol 2044. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9706-0_10

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  • DOI: https://doi.org/10.1007/978-1-4939-9706-0_10

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9705-3

  • Online ISBN: 978-1-4939-9706-0

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