Single-cell phylodynamic inference of stem cell differentiation and tumor evolution.

Abstract

Phylodynamic inference (PI) quantifies population dynamics and evolutionary trajectories using phylogenetic trees. Single-cell lineage tracing enables phylogenetic tree reconstruction for thousands of cells in multicellular organisms, facilitating PI at the cellular level. However, cell differentiation and somatic evolution challenge the direct application of existing PI frameworks to somatic tissues. We introduce scPhyloX, a computational framework modeling structured cell populations by leveraging single-cell phylogenetic trees to infer tissue development and tumor evolution dynamics. A key advancement is its ability to infer time-varying parameters, capturing dynamic biological processes. Simulations demonstrate scPhyloX's accuracy in scenarios including tissue development, disease treatment, and tumor growth. Application to three real datasets reveals insights into somatic dynamics: cycling stem cell overshoot in fly organ development, clonal expansion of multipotent hematopoietic progenitors during human aging, and pronounced subclonal selection in early colorectal tumorigenesis. scPhyloX thus provides a computational approach for investigating somatic tissue development and evolution.