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

Abstract

Analysing single-cell lineage relationships of an organism is crucial towards understanding the fundamental cellular dynamics that drive development. Clustered regularly interspaced short palindromic repeats (CRISPR)-based dynamic lineage tracing relies on recent advances in genome editing and sequencing technologies to generate inheritable, evolving genetic barcode sequences that enable reconstruction of such cell lineage trees, also referred to as phylogenetic trees. Recent work generated custom computational strategies to produce robust tree estimates from such data. We further capitalize on these advancements and introduce GESTALT analysis using Bayesian inference (GABI), which extends the analysis of genome editing of synthetic target arrays for lineage tracing (GESTALT) data to a fully integrated Bayesian phylogenetic inference framework in software BEAST 2. This implementation allows users to represent the uncertainty in reconstructed trees and enables their scaling in absolute time. Furthermore, based on such time-scaled lineage trees, the underlying processes of growth, differentiation and apoptosis are quantified through so-called phylodynamic inference, typically relying on a birth-death or coalescent model. After validating its implementation, we demonstrate that our methodology results in robust estimates of growth dynamics characteristic of early Danio rerio development. GABI's codebase is publicly available at https://github.com/azwaans/GABI.This article is part of the theme issue '"A mathematical theory of evolution": phylogenetic models dating back 100 years'.