Additional corresponding author: Dr. Odile Burlet-Schiltz, E-mail: Odile.Schiltz@ipbs.fr
Label-free quantitative proteomic analysis of human plasma-derived microvesicles to find protein signatures of abdominal aortic aneurysms
Version of Record online: 15 AUG 2014
© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
PROTEOMICS - Clinical Applications
Special Issue: Cardiovascular Disease: Clinical and Translational Proteomics
Volume 8, Issue 7-8, pages 620–625, August 2014
How to Cite
Martinez-Pinna, R., de Peredo, A. G., Monsarrat, B., Burlet-Schiltz, O. and Martin-Ventura, J. L. (2014), Label-free quantitative proteomic analysis of human plasma-derived microvesicles to find protein signatures of abdominal aortic aneurysms. Prot. Clin. Appl., 8: 620–625. doi: 10.1002/prca.201400010
- Issue online: 15 AUG 2014
- Version of Record online: 15 AUG 2014
- Accepted manuscript online: 31 MAY 2014 02:09AM EST
- Manuscript Accepted: 26 MAY 2014
- Manuscript Revised: 25 APR 2014
- Manuscript Received: 31 JAN 2014
- EC, FAD project. Grant Numbers: FP-7, F2-2008-200647
- Spanish MICIN. Grant Number: SAF2013/42525
- Ministerio de Sanidad y Consumo, Instituto de Salud Carlos III, Redes RIC. Grant Number: RD12/0042/00038
- biobancos. Grant Number: RD09/0076/00101
- Fundación Conchita Rábago and in part by the Région Midi-Pyrénées, European funds
- French Ministry of Research
- Abdominal aortic aneurysm;
To find potential biomarkers of abdominal aortic aneurysms (AAA), we performed a differential proteomic study based on human plasma-derived microvesicles.
Exosomes and microparticles isolated from plasma of AAA patients and control subjects (n = 10 each group) were analyzed by a label-free quantitative MS-based strategy. Homemade and publicly available software packages have been used for MS data analysis.
The application of two kinds of bioinformatic tools allowed us to find differential protein profiles from AAA patients. Some of these proteins found by the two analysis methods belong to main pathological mechanisms of AAA such as oxidative stress, immune-inflammation, and thrombosis.
Conclusions and clinical relevance
Data analysis from label-free MS-based experiments requires the use of sophisticated bioinformatic approaches to perform quantitative studies from complex protein mixtures. The application of two of these bioinformatic tools provided us a preliminary list of differential proteins found in plasma-derived microvesicles not previously associated to AAA, which could help us to understand the pathological mechanisms related to this disease.