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In-depth exploration of cerebrospinal fluid by combining peptide ligand library treatment and label-free protein quantification.

Authors
  • Mouton-Barbosa, Emmanuelle1
  • Roux-Dalvai, Florence
  • Bouyssié, David
  • Berger, François
  • Schmidt, Eric
  • Righetti, Pier Giorgio
  • Guerrier, Luc
  • Boschetti, Egisto
  • Burlet-Schiltz, Odile
  • Monsarrat, Bernard
  • Gonzalez de Peredo, Anne
  • 1 Institut de Pharmacologie et de Biologie Structurale (IPBS), CNRS, 205 route de Narbonne, 31077 Toulouse, France. , (France)
Type
Published Article
Journal
Molecular & Cellular Proteomics
Publisher
American Society for Biochemistry and Molecular Biology
Publication Date
May 01, 2010
Volume
9
Issue
5
Pages
1006–1021
Identifiers
DOI: 10.1074/mcp.M900513-MCP200
PMID: 20093276
Source
Medline
License
Unknown

Abstract

Cerebrospinal fluid (CSF) is the biological fluid in closest contact with the brain and thus contains proteins of neural cell origin. Hence, CSF is a biochemical window into the brain and is particularly attractive for the search for biomarkers of neurological diseases. However, as in the case of other biological fluids, one of the main analytical challenges in proteomic characterization of the CSF is the very wide concentration range of proteins, largely exceeding the dynamic range of current analytical approaches. Here, we used the combinatorial peptide ligand library technology (ProteoMiner) to reduce the dynamic range of protein concentration in CSF and unmask previously undetected proteins by nano-LC-MS/MS analysis on an LTQ-Orbitrap mass spectrometer. This method was first applied on a large pool of CSF from different sources with the aim to better characterize the protein content of this fluid, especially for the low abundance components. We were able to identify 1212 proteins in CSF, and among these, 745 were only detected after peptide library treatment. However, additional difficulties for clinical studies of CSF are the low protein concentration of this fluid and the low volumes typically obtained after lumbar puncture, precluding the conventional use of ProteoMiner with large volume columns for treatment of patient samples. The method has thus been optimized to be compatible with low volume samples. We could show that the treatment is still efficient with this miniaturized protocol and that the dynamic range of protein concentration is actually reduced even with small amounts of beads, leading to an increase of more than 100% of the number of identified proteins in one LC-MS/MS run. Moreover, using a dedicated bioinformatics analytical work flow, we found that the method is reproducible and applicable for label-free quantification of series of samples processed in parallel.

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