CRISPR druppel sequenering om tumorheterogeniteit te decoderen

Cellular identity and heterogeneity are key to multiple aspects of normal development and physiology, but are disrupted in diseases such as cancer, driving treatment resistance. Cellular identities are encoded in the epigenome, which controls the stability of gene expression programmes. However, the ways in the machinery controls cellular heterogeneity are unknown, in part because of a historical lack of methods to study cellular heterogeneity at its fundamental unit, the cell. Recent development of single-cell omics technologies, particularly CRISPR droplet sequencing, now allows us to systematically study heterogeneity at the cellular level. In this thesis, multiple omics technologies including single-cell sequencing are applied to understand cellular heterogeneity. The work includes the development of GiRAFR, a novel computational tool for improving guide RNA detection in single-cell CRISPR screens. We then perform a large-scale CROP-seq screen of epigenetic enzymes in lung cancer to identify regulators of transcriptional heterogeneity, applying computational methods to quantify stochastic and deterministic heterogeneity. Finally, we apply in vivo CROP-seq to screen metabolic genes in an immunotherapy-resistant mouse pancreatic cancer model, identifying metabolic regulators of immune cell fitness. Together, this work establishes computational and experimental frameworks to quantify and understand heterogeneity in single-cell omics datasets and demonstrates their application to cancer biology.