Developing proteomics pipelines to improve depth, throughput, and accuracy

Harvard Medical School, Boston MA

Investigators

Abstract

ABSTRACT Mass spectrometry (MS)-based proteomics is emerging as an essential tool for addressing important biological questions. Although many advancements have been made in the field over several decades, efforts are still needed with respect to sample preparation and data acquisition processes. In this research proposal, I will address challenges that limit throughput, depth, and accuracy in proteome-wide protein abundance, post translational modification (PTM), and cysteine profiling. Technological advancements in MS instrumentation steadily improve sensitivity, resolution, and speed of mass-to-charge measurements. Here, I will develop platforms to establish and optimize new MS-based proteomics technologies using these advances to address the challenges listed above. Leveraging state-of-the-art technologies, I will design high-throughput MS-based proteomics pipelines to profile proteomes and PTMs with unprecedented depth. The focus will be on sample preparation and data acquisition, both of which are continuously evolving. As the scale in both the number and the breadth of proteomics experiments increases, sample preparation becomes the key bottleneck. As such, a greater emphasis is necessary to streamline and automate all possible aspects of sample preparation to improve throughput and efficiency. As an example of the pipeline development process, I will test the hypothesis that select kinase inhibitors induce general and cell-specific alterations in the proteome, phosphoproteome, as well as protein inter- and/or intra-molecular changes that can be inferred by differential cysteine accessibility. The workflows will be established in a 96-well format with 8 cell lines and 11 kinase inhibitors (plus a control) after both 4hr and 24hr of treatment and will be performed in triplicate. Both isobaric tagging and data independent acquisition strategies will be investigated. In addition, I will also continue my efforts to ensure accurate quantification and thorough benchmarking of instrument performance with an updated quality control standard for isobaric labeling experiments and an associated web-based, automated database searching application. Ultimately, I aim to provide the community with innovative pipelines for high- throughout, accurate, and deep proteome, PTM, and cysteine profiling, while producing datasets (of my own and of collaborators) that are informative, biologically relevant, and readily accessible. The newly established pipelines may be adapted by the community to explore further various aspects for their own studies and can be used as templates to design experiments in line with their own research interests. For trainees, optimization projects are pedagogically ideal as they stress an understanding of the first principles behind a specific workflow and typically result in publishable data. In summary, the proposal that I have outlined above will result in a sustainable research program that can develop cutting-edge proteomics pipelines, while simultaneously address a wide range of biological questions for a scientific community which has varied research interests and diverse backgrounds.