Following tumor progression step-by-step with CRISPR/Cas9-based single-cell lineage tracing technologies and improved computational methods
Cellular lineages underlie several important biological phenomena, from embyrogenesis to tumor development. Traditional approaches for studying for these lineages have been limited in their throughput or resolution, and have thus have been largely incapable of profiling lineage dynamics in complex organisms. Recently, advances in microfluidic devices, sequencing technologies, and molecular biology have facilitated a genomics revolution enabling researchers to profile molecular species at single-cell resolution. Simultaneously, progress in precise genome editing with CRISPR/Cas9 technologies have been coupled with the revolution in single-cell genomics to provide single-cell-resolution lineage tracing technologies.
In this thesis, I first describe computational methodology for inferring models of cell lineages, or phylogenies, from the CRISPR/Cas9-based lineage tracing technology. Using both simulated and real data, I demonstrate that our methodology is both scalable and accurate in comparison to other algorithms. I additionally detail the functionality of our end-to-end software suite, Cassiopeia, and speculate on lineage tracing data analysis best practices.
Next, I describe a series of applications of a CRISPR/Cas9-based lineage tracing technology and our computational tools to in vivo cancer models. In one application, I describe the first report of using such technologies to investigate the transcriptional drivers of metastatic dynamics in a xenograft model of non-small-cell lung cancer. Next, I describe work in a genetically engineered mouse model of non-small-cell lung cancer in which we characterize the phylodynamics and evolutionary trajectories that govern a primary tumor as it evolves from a single, transformed cell to a complex, metastatic tumor.
Finally, I conclude by contextualizing how the work presented in this thesis fits into the larger picture of lineage tracing technologies and in vivo tumor studies and by speculating on how this informs future work.