Glioblastoma (GBM) is the most common and aggressive primary brain tumor and is comprised of transcriptionally heterogeneous cells and a complex microenvironment. Despite decades of research effort, few treatments significantly benefit clinical outcomes. This may be, in part, due to the lack of tools to directly measure functional responses of these heterogeneous cell types under therapy. This thesis aims to advance the understanding of cell type-specific therapeutic response by the development and application of integrated single cell imaging and RNA sequencing technology.
Chapter 1 provides an overview of GBM and its heterogeneity, how investigation of cell type-specific phenotypes would benefit the development of GBM treatments, and current sequencing and imaging technologies to examine cell phenotypes with single-cell resolution. Chapter 2 presents a new microfluidic technology for joint single cell imaging and RNA sequencing that can link imaging-based phenotypes and transcriptional identity of the same individual cells with high throughput, molecular capture efficiency, linking accuracy, and user-friendliness. Chapters 3 and 4 present applications of this technology in investigating cell type-specificities of GBM treatments.
Chapter 3 focuses on the specificities of 5-aminolevulinic acid (5-ALA), an FDA approved fluorogenic agent, used in fluorescence guided surgery and reveals 5-ALA labeling is not specific to transformed glioma cells, which encourages further studies to systematically compare its performance with potential alternatives. Chapter 4 focuses on the specificities of drug responses by presenting a functional drug screening approach that directly links cell states measured by apoptosis indicators with transcriptional states, which greatly enhances the interpretability of single cell-resolved drug perturbation assays.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/v4pw-yx81 |
| Date | January 2023 |
| Creators | Liu, Zhouzerui |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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