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

Area of Science:

• Computational Biology
• Evolutionary Biology
• Genomics

Background:

• Phylodynamic inference (PI) analyzes population dynamics using phylogenetic trees.
• Single-cell lineage tracing enables cellular-level PI but faces challenges in somatic tissues due to cell differentiation and evolution.
• Existing PI frameworks are not directly applicable to complex somatic tissue dynamics.

Purpose of the Study:

• To introduce scPhyloX, a computational framework for modeling structured cell populations.
• To infer tissue development and tumor evolution dynamics using single-cell phylogenetic trees.
• To enable the inference of time-varying parameters for dynamic biological processes.

Main Methods:

• Leveraging single-cell phylogenetic trees to model structured cell populations.
• Developing a computational framework (scPhyloX) to infer tissue development and tumor evolution.
• Implementing methods to infer time-varying parameters for dynamic biological processes.

Main Results:

• scPhyloX accurately infers dynamics in simulated scenarios of tissue development, disease treatment, and tumor growth.
• Analysis of real datasets revealed insights into fly organ development (stem cell overshoot).
• Identified clonal expansion in human aging and subclonal selection in colorectal cancer.

Conclusions:

• scPhyloX provides a novel computational approach for studying somatic tissue development and evolution.
• The framework enhances understanding of dynamic biological processes in multicellular organisms.
• Applications range from developmental biology to cancer research, offering new avenues for investigation.