Rapid Proteomic Profiling by MALDI-TOF Mass Spectrometry for Better Brain Tumor Classification
| Autor: | Graciane Petre, John Rendu, François Berger, Charles Coutton, Pierre F. Ray, Laurent Pelletier, Guillaume Nugue, Marie Bidart, Harmonie Durand, Margot Poulenard |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Proteomics Time Factors 030102 biochemistry & molecular biology Proteomic Profiling Brain Neoplasms Clinical Biochemistry Brain tumor Computational biology Biological classification Biology medicine.disease MALDI-TOF Mass Spectrometry Diagnosis Differential 03 medical and health sciences Matrix-assisted laser desorption/ionization 030104 developmental biology Cell Line Tumor Spectrometry Mass Matrix-Assisted Laser Desorption-Ionization medicine Humans Time-of-flight mass spectrometry Precision Medicine Grading (tumors) Glioblastoma |
| Zdroj: | Proteomics. Clinical applications. 14(5) |
| ISSN: | 1862-8354 |
| Popis: | Purpose Glioblastoma is one of the most aggressive primary brain cancers. The precise grading of tumors is important to adopt the best follow-up treatment but complementary methods to histopathological diagnosis still lack in achieving an unbiased and reliable classification. Experimental design To progress in the field, a rapid Matrix Assisted Laser Desorption Ionization - Time of Flight Mass spectrometry (MALDI-TOF MS) protocole, devised for the identification and taxonomic classification of microorganisms and based on the analysis of whole cell extracts, was applied to glioma cell lines. Results The analysis of different human glioblastoma cell lines permitted to identify distinct proteomic profiles thus demonstrating the ability of MALDI-TOF to distinguish different malignant cell types. Conclusions and clinical relevance In the study, the authors showed the ability of MALDI-TOF profiling to discriminate glioblastoma cell lines, demonstrating that this technique could be used in complement to histological tumor classification. The proposed procedure is rapid and inexpensive and could be used to improve brain tumors classification and help propose a personalized and more efficient treatment. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text