Clinical research applications of proteomics increasingly rely on MS-based approaches. Biomarker discovery, development, verification, validation, and quantification are all accessed using these approaches. Although different data collection modes exist for biomarker assessment, researchers are focusing on the emerging DIA mode in Combining Data-Independent Analysis (DIA) for Broad-Scale Phenotyping and Targeted Tandem Mass Spectrometry Quantification of Specific Biomarkers.
Rather than sampling a very well-defined set of peptides with tandem mass spectrometry, DIA sampling allows a broad acquisition of peptides that can be stored and queried at later time points. The ability to re-visit the DIA samples with new or emerging questions and mine that dataset using spectral library matching is the advantage of the DIA workflow over existing techniques (Sajic et al., 2015). Koomen discusses how he uses a database of all the mutant peptides that might be relevant to cancer and later come back and query the existing datasets to see if you’re able to observe those peptides.
For research use only. Not for use in diagnostic procedures.
John Koomen, Associate Member and Scientific Director of Proteomics at the H. Lee Moffitt Cancer Center and Research Institute in Tampa, Florida, focuses research in his lab on developing methods for proteomic analysis and evaluating their effectiveness and the validity of the results with functional investigations. After defining the biological problem of interest, existing analytical strategies are reviewed. Modified versions of existing approaches or novel techniques are then investigated and subsequently applied. Currently, we are examining chemical cleavage mechanisms for proteomics research and isolated organ perfusion coupled with proteomics for candidate biomarker discovery.
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