Transcriptional consequences of genetic variation on single cells in development and cancer
The study of genetic variation has long been central to understanding human disease. Genetic mutations, which constantly accumulate in cells through every cell division and may be inherited, can profoundly impact human development and health. A deeper understanding of their functional consequences, particularly those with adverse effects, may reveal insights to facilitate the development of new, highly-specific, and efficacious therapeutic strategies. While significant progress has been made, the complexity of genetic contributions to disease mechanisms means there is still much to uncover. A crucial starting point to study the impacts of mutations is the transcriptome. As the first and direct readout of the genetic code, the transcriptome offers a powerful lens to understand how genetic aberrations drive phenotypic changes. Decades of research and technological advancement have now enabled exploration of the transcriptional effects of genetic aberrations at unprecedented resolution, thereby delineating their contributions to disease. Motivated by this, my work throughout this thesis focuses on quantifying transcriptional changes as direct consequences of different genetic aberrations at single-cell resolution. I began with the development and benchmarking of alleleIntegrator, a computational tool designed to precisely identify cells carrying copy number variants (CNVs) in single-cell RNA sequencing (scRNA-seq) data. CNVs, frequently observed in human cancers, can serve as markers to track the development of cancer and subclones. By integrating the information from whole genome sequencing (WGS) data, alleleIntegrator definitively genotypes individual transcriptomes by assessing allelic imbalances of cancer-defining CNVs in RNA sequencing reads. This expression-independent approach provides a robust means for identifying cancer cells in scRNA-seq data. Next, to further explore the functional consequences of CNVs, I constructed three scRNA-seq atlases of fetal tissues (adrenal gland, kidney and liver) that carry constitutional whole-chromosome CNVs (i.e. aneuploidy). I investigated and demonstrated the impact of these CNVs on both the cell type composition and the transcriptome of various cell types within each tissue, with a particular focus on the fetal liver. This is especially pertinent as children with constitutional trisomy 21 (i.e. Down syndrome) exhibit a 150-fold increased risk of developing myeloid leukaemia (ML-DS), which arises through a pre-leukaemic state (TAM) originated in utero in the developing fetal liver. I examined the stepwise transcriptional evolution of ML-DS in primary human samples, at single-cell resolution, and showed that TAM-defining GATA1 mutations account for most of the ML-DS transcriptome. Finally, I investigated the transcriptional features associated with progressive disease in ML-DS, leveraging alleleIntegrator to identify the cells responsible for refractory disease in a case of refractory ML-DS. This thesis provides a framework for studying the transcriptional consequences of genetic variation at high-resolution by combining WGS and scRNA-seq. I believe that detailed insights into the molecular mechanisms underpinning disease are crucial for shaping the future of precision and regenerative medicine.
Genomics, Myeloid leukaemia in Down syndrome, ML-DS, Childhood cancers, Human development, Aneuploidy
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