Methods and applications of single-cell transcriptomics

Abstract

Single-cell transcriptomics has transformed biology by enabling deep interrogation of the RNA contents of individual cells. This has led to in-depth study of cellular heterogeneity and the role of cell state transitions in disease. Observational single-cell studies have moved towards hypothesis gen- eration about complex cellular processes; interventional studies enable mechanistic insights. Here, we use single cell transcriptomics to identify cell states underlying nasal polyp formation. We also interrogate how gene expression in the sinus changes in response to asthma treatment, combining single cell and bulk analyses for a more complete view. We then move beyond changes in individual cell types to uncover how cells relate to each other, using matrix decomposition to reveal multi-cell- type changes in gene expression in breast cancer, suggesting interaction signatures specific to breast cancer subtypes, and interactions predicting response to treatment. Our findings on drug response in these two disease cases were limited by a lack of robust statistical tools; to improve tools for inter- rogating perturbation response in single cells, we created an annotation-harmonized collection of single cell perturbation studies, then used this data resource to characterize the performance of E- statistics for evaluating perturbation similarity and efficacy. In total, this thesis contains two stories of using perturbation in patients to study disease, and one example of using a collection of datasets to improve methods used for interrogating biological systems.