Biological Inference from Single Cell RNA-Sequencing
By: Hanna M. Levitin
Publisher: [publisher not identified]
Published: 2020
Place of publication: [New York, N.Y.?]
Language: eng
Description: Tissues are heterogeneous communities of cells that work together to achieve a higher-order function. Large-scale single cell RNA-sequencing (scRNA-seq) offers an unprecedented opportunity to systematically map the transcriptional programs underlying this diversity. However, extracting biological signal from noisy, high-dimensional scRNA-seq data requires carefully designed, statistically robust methodology that makes appropriate assumptions both for the data and for the biological question of interest. This thesis explores computational approaches to finding biological signal in scRNA-seq datasets. Chapter 2 focuses on preprocessing and cell-centric approaches to downstream analysis that have become a mainstay of analytical pipelines for scRNA-seq, and includes dissections of lineage diversity in high grade glioma and in the largest neural stem cell niche in the adult mouse brain. Notably, the former study suggests that heterogeneity in high grade glioma arises from at least two distinct biological processes: aberrant neural development and mesenchymal transformation.

Chapter 3 presents a flexible approach for de novo discovery of gene expression programs without an a priori structure across cells, revealing subtle properties of a spatially sampled high grade glioma that would not have been apparent with previous approaches. Chapter 4 leverages our prior work and a unique tissue resource to build a unified reference map of human T cell functional states across tissues and ages. We discover and validate a novel pan-T cell activation marker and a previously undescribed kinetic intermediate in CD4+ T cell activation. Finally, ongoing work defines key programs of gene expression in tissue-associated T cells in infants and adults and predicts their candidate regulators.
Notes: Department: Cellular, Molecular and Biomedical Studies.

Thesis advisor: Peter A. Sims.

Thesis (Ph.D.)--Columbia University, 2020.