Genedata has collaborated on analysis of mass spectrometry data with researchers at the Comprehensive Cancer Center Proteomics Facility of the University of Alabama at Birmingham (UAB).
The collaboration made use of the Genedata's Expressionist system to identify protein biomarkers for critical human diseases using animal models.
The collaboration spanned three studies to date and covers research into pancreatic cancer, prostate cancer and cystic fibrosis. In each of these research areas the Genedata Expressionist Refiner MS and Analyst modules were used for pre-processing and statistical analyses on LC-MS spectra.
Using the Expressionist system, researchers first created matched peak lists across all samples with the Refiner MS module and then performed uni- and multivariate statistical analyses with the Analyst module to identify peaks differentiating normal and disease states. After statistical analysis, researchers used standard techniques to identify the peptides found in the differentiating peaks. By doing the peak processing and analyses up front and using raw MS data, researchers were able to identify biomarker peptides that they would have otherwise missed had they only captured secondary MS traces running instruments in a more typical data-dependent mode.
Professor James Mobley, director of the Comprehensive Cancer Center Proteomics Facility, said: 'Using Expressionist in a systems biology approach advanced our research in two fundamental ways. First, because of the reproducibility inherent in the system, we were able to identify key biomarkers with a relatively small cohort. Second, the reproducibility of this approach also increased the confidence in our resulting protein biomarker hits and enabled us to identify key associated pathways that were otherwise difficult to discern. The ability to compare samples prior to identification coupled with the sheer processing power and accuracy of the Expressionist system will enable our research to go into areas that might otherwise remain unexplored.'