Quantitative Measurement of Clonal Evolution in Human Colon

PhD thesisThe clonal history of a cell is recorded within its (epi)genome via the accumulation of heritable changes. Studying the patterns of these heritable changes, termed lineage tracing markers, allows for the reconstruction of a tissue’s clonal architecture and the dynamics of clone replacements. In this thesis, I attempt to quantitatively measure clonal dynamics within normal human tissue from a single time-point, uncovering new homeostatic mechanisms in the intestine, and developing a new technique for quantifying somatic cell evolutionary dynamics at high temporal resolution across human tissues. Intestinal crypt fission provides a mechanism for mutations fixed in a single crypt to colonise the colon. In this thesis, evidence is presented that human colonic crypts also undergo fusion, a hitherto unknown process in human by which two crypts fuse to form a single daughter crypt. The existence of this balancing homeostatic process upon the distribution of mutant patch sizes was explored, allowing the estimate of the fission rate to be updated. The spatial distribution of crypt fission/fusion events exhibited spatial clustering, further emphasising the complex nature of the human gut epithelium. I present evidence that fluctuating DNA methylation can be used as molecular clocks in cells, where ongoing (de)methylation cause repeated “flip-flops” between methylated and unmethylated states. Endogenous fluctuating CpG sites were identified using standard methylation arrays, and a mathematical model was developed to quantitatively measure human adult stem cell dynamics from individual colon, small intestine and endometrial glands. The mathematical framework developed above is inappropriate for studying large polyclonal systems, such as haematopoietic stem cells. A flexible, stochastic modelling approach was developed that allows for the quantification of clonal dynamics for both fixed and growing populations of arbitrary size. This thesis demonstrates that mathematical interpretation of clone size data reveals clonal dynamics in human tissues without requiring longitudinal data