Principle component analysis combined with matrix-assisted laser desorption ionization mass spectrometry for rapid diagnosing the sera of patients with major depression

  • Tiao Lai Huang*
  • , Yi Tzu Cho
  • , Hong Su
  • , Jentaie Shiea
  • *Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

22 Scopus citations

Abstract

Background: Previously, we demonstrated that 6. M HCl hydrolysis followed by matrix-assisted laser desorption/ionization (MALDI-TOF) mass spectrometry is a useful technique to detect potential protein biomarkers in the sera collected from major depression (MD) patients and from healthy controls. Methods: In this study, the effects of various organic acids in hydrolyzing proteins in serum were first examined. The organic matrixes commonly used in MALDI analysis were also examined for characterizing the hydrolyzed peptides. Finally, principle component analysis (PCA) was used to analyze the MALDI mass spectra of acid-hydrolyzed serum samples. Results: It was found that 20% TFA and sinapinic acid were the optimal reagents for hydrolysis and MALDI matrix. Samples collected from MD patients and healthy controls were readily classified through PCA analysis. A receiver operating characteristic (ROC) curve based on the ratio of the intensities of the two fragment ions (m/. z 8606 and 9287) indicated by PCA plot was also constructed. The area under the curve was 0.845; the sensitivity and specificity were both 80%. Conclusions: An analytical platform employing trifluoroacetic acid to hydrolyze serum proteins followed by MALDI-TOF/MS and PCA analysis was developed to rapidly differentiate the sera between MD patients and healthy controls.

Original languageEnglish
Pages (from-to)175-181
Number of pages7
JournalClinica Chimica Acta
Volume424
DOIs
StatePublished - 23 09 2013

Keywords

  • Acid hydrolysis
  • Major depression
  • Matrix-assisted laser desorption ionization/time-of-flight mass spectrometry
  • Principle component analysis
  • Protein biomarker

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