High-throughput strategies for proteomic biomarker discovery and clinical diagnostics using software and biosensors

• Main Author: Bhatia, Vivek N
• Language: en_US
• Published: Boston University 2018
• Online Access: https://hdl.handle.net/2144/31507

Thesis (M.S.)--Boston University === PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at [e-mail]. Thank you. === In the landscape of the failing blockbuster drug model of research and development, biomarkers are critical for creating the next inflection point in biotechnology and molecular medicine. They will enable stratified, personalized medicine drug development approaches by helping clinicians differentiate between different stages, types, and origins of diseases. Because of the inherent difficulty associated with discovering biomarkers in complex matrices and translating them to the clinic, we have developed differential proteomics software to aid in data mining of complex proteomics data. We have also developed an optical/mass spectrometry based biosensor to aid in the discovery and measurement of biomarkers in these matrices. We have created software to support finding differences between proteomics data sets, e.g., mass spectrometry results measuring differences in protein composition from the serum of sick and healthy patients. The Software Tool for Rapid Annotation of Proteins (STRAP) annotates a protein list with gene ontology information to aid interpretation of biological mass spectrometry data. The Software Tool for Rapid Annotation of Protein Post-Translational Modifications (STRAP PTM) uses PTM counting to find potential sites of global, differential post-translational modifications in large proteomics data sets. We demonstrate the utility of both packages through differential analysis of proteomics data sets. We have also developed the IM-chip, a different type of affinity array that uses a combination of Interferometric Reflectance Imaging Sensor (IRIS) and Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI-MS) technologies for analyte quantification and characterization, respectively. The IM-chip affords a label-free multiplexing biosensor that allows one to parallelize biomarker discovery with clinical assay development. The utility of most traditional affinity assays is otherwise limited to cases where the target is known a priori, whether for detection, diagnosis, or therapy. Possible applications of the 1M-chip have demonstrated several protein capture experiments. Data presented here includes experiments with insulin and the putative cancer biomarker prostate specific antigen (PSA). Further discussion is presented on how the software packages and sensor technology may be used in combination. === 2031-01-01