Inferring cell differentiation maps from lineage tracing data

TL;DR

This paper introduces a new algorithm called Carta that improves the inference of cell differentiation maps from lineage tracing data, revealing developmental features missed by existing methods.

Contribution

The novel contribution is a quantitative framework and algorithm, Carta, that optimally balances map complexity and unobserved transitions in lineage data.

Findings

Carta identifies convergent differentiation and new intermediate progenitors in developmental models.

The method outperforms existing approaches in capturing progenitor differentiation dynamics.

It reveals features of mammalian trunk development and mouse hematopoiesis not detected before.

Abstract

During development, cells differentiate through a hierarchy of increasingly restricted cell types, a process that is summarized by a cell differentiation map. Recent technologies profile lineages and cell types at scale, but existing methods to infer cell differentiation maps from these data rely on heuristic models with restrictive assumptions about the developmental process. Here we introduce a quantitative framework to evaluate cell differentiation maps and develop an algorithm, called Carta, that infers an optimal differentiation map from single-cell lineage tracing data. The key insight in Carta is to balance the tradeoff between the complexity of the map and the number of unobserved cell type transitions on the lineage tree. We show that, in models of mammalian trunk development and mouse hematopoiesis, Carta identifies important features of development that are not revealed by other methods, including convergent differentiation of cell types, progenitor differentiation dynamics and new intermediate progenitors.

Carta leverages lineage tracing data to infer the optimal trajectory of cell differentiation.

Figures