A Bayesian phylodynamic inference framework for single-cell CRISPR/Cas9 lineage tracing barcode data with dependent...

Area of Science:

• Developmental Biology
• Computational Biology
• Genomics

Background:

• Understanding single-cell lineage relationships is key to deciphering developmental cellular dynamics.
• CRISPR-based dynamic lineage tracing uses genome editing to create genetic barcodes for reconstructing cell lineage trees (phylogenetic trees).
• Previous computational strategies have improved tree estimation from lineage tracing data.

Purpose of the Study:

• To introduce GESTALT analysis using Bayesian inference (GABI), a framework for phylogenetic inference using Genome Editing of Synthetic Target Arrays for Lineage Tracing (GESTALT) data.
• To enable representation of tree uncertainty and scaling in absolute time.
• To quantify developmental processes like growth, differentiation, and apoptosis using phylodynamic inference.

Main Methods:

• Integration of GESTALT data analysis into the BEAST 2 software using Bayesian inference.
• Application of birth-death or coalescent models for phylodynamic inference on time-scaled lineage trees.
• Validation of the GABI methodology on early *Danio rerio* development.

Main Results:

• GABI provides a fully integrated Bayesian phylogenetic inference framework for lineage tracing data.
• The method allows for robust estimation of uncertainty in reconstructed cell lineage trees.
• Time-scaled lineage trees enabled robust quantification of growth dynamics in early *Danio rerio* development.

Conclusions:

• GABI offers a powerful tool for robust phylogenetic inference and phylodynamic analysis of CRISPR-based lineage tracing data.
• The developed methodology accurately estimates developmental growth dynamics.
• The GABI codebase is publicly available for broader scientific use.