International Orthopaedics

, Volume 36, Issue 1, pp 57–64

Analysis of synovial fluid in knee joint of osteoarthritis:5 proteome patterns of joint inflammation based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

Authors

  • Xiaohua Pan
    • Department of Orthopedics and TraumatologyThe Second Clinical Medical College of Jinan University (Shenzhen People’s Hospital)
  • Liling Huang
    • Clinical Medical Research CenterThe Second Clinical Medical College of Jinan University (Shenzhen People’s Hospital)
  • Jiakai Chen
    • Department of Orthopedics and TraumatologyThe Second Clinical Medical College of Jinan University (Shenzhen People’s Hospital)
    • Clinical Medical Research CenterThe Second Clinical Medical College of Jinan University (Shenzhen People’s Hospital)
  • Xiaofen Chen
    • Department of Orthopedics and TraumatologyThe Second Clinical Medical College of Jinan University (Shenzhen People’s Hospital)
Original Paper

DOI: 10.1007/s00264-011-1258-y

Cite this article as:
Pan, X., Huang, L., Chen, J. et al. International Orthopaedics (SICOT) (2012) 36: 57. doi:10.1007/s00264-011-1258-y

Abstract

Purpose

The purpose of this study was to use matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) in osteoarthritis research. Our aim was to find differentially expressed disease-related and condition-specific peptide in synovial fluid in the knee joint of patients suffering from osteoarthritis (OA), and to develop and validate the peptide classification model for OA diagnosis.

Methods

Based on the American College of Rheumatology criteria, 30 OA cases and ten healthy donors were enrolled and underwent analysis. Magnetic beads-based weak cation exchange chromatography (MB-WCX) was performed for sample processing, and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) was conducted for peptide profile. ClinProt software 2.2 was used for data analysis and a genetic algorithm was created for class prediction.

Results

Two peptide peaks were found which may be characterised as the potential diagnostic markers for OA. Two other significantly different peptide peaks were found in OA patients at a medium stage compared to the early and late stages. A genetic algorithm (GA) was used to establish differential diagnosis models of OA. As a result, the algorithm models marked 100% of OA, and of 97.92% of medium-stage OA.

Conclusion

This study demonstrated that use of proteomics methods to identify potential biomarkers of OA is possible, and the identified potential biomarkers may be potential markers for diagnosis and monitoring the progression of OA.

Copyright information

© Springer-Verlag 2011