Modern cancer immunotherapy involves the conditioning of endogenous T cells to fight cancerous bodies that have managed to resist or avoid detection. Recently approved antibody drugs target the immune checkpoint pathway in T cells to prevent their tolerance to cancer antigens. There exists a compelling need, especially in the drug discovery world, to develop better assays for screening and to study the underlying mechanisms of these new antibody drugs.
The core motivation of my work is to develop a primary cell assay for the immune checkpoint pathway using 1-D imaging cytometry. The assay is focused on high throughput and high content screening. It takes advantage of our novel 1-D imaging cytometer platform. The assay is designed to artificially induce anergy in primary human T cells and systemically study their drug response. An automated statistical method quantifies the functional phenotypes of both healthy and anergic T cells into a single descriptive readout. Reducing localization of biomarkers into a single ‘activity score’ readout has many advantages for drug screening and characterization. Additional assays were developed to study T cell activation dynamics and other signaling events during the immune checkpoint pathway.
Our 1-D instrument leverages both the high throughput aspects of traditional flow cytometry and the high spatial content of 2-D imaging cytometers. The PMC data analysis emphasizes an unbiased approach to analyze flow cytometry data, which eliminates the subjective manual gating of current cytometric methods. This is crucial to developing more accurate and reliable assays with minimal supervision and need for expert operators. The high-throughput and high-content capabilities presented enable new types of assays previously not possible with human primary T cells. Adoption of physiological relevant primary cell assays has potential to revolutionize large-scale drug screening and future applications in personalized medicine.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/27049 |
| Date | 02 November 2017 |
| Creators | Wang, Steve S. |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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