Inferring Cell Differentiation Dynamics with Unobserved Progenitors.

Area of Science:

• Developmental Biology
• Computational Biology
• Systems Biology

Background:

• Cell differentiation maps are crucial for understanding development but challenging to determine.
• Single-cell lineage tracing provides cell division history but not direct differentiation events.
• Existing methods for inferring differentiation maps have limitations, including unrealistic assumptions and inability to handle unobserved cell types.

Purpose of the Study:

• To introduce TROUPE, a novel likelihood-based framework for inferring cell differentiation and growth dynamics.
• To address limitations of current methods by allowing for unobserved progenitor cell types and cell-type specific rates.
• To provide an efficient algorithm for estimating transition and growth rates and a model-selection scheme for unobserved types.

Main Methods:

• Developed TROUPE, a likelihood-based framework utilizing biologically motivated potency constraints.
• Implemented an efficient algorithm for maximum likelihood estimation of transition and growth rates.
• Designed a model-selection scheme to determine the number of unobserved cell types.

Main Results:

• TROUPE accurately recovers transition and growth rates in simulations, outperforming previous approaches, especially with unobserved cell types.
• The framework was applied to mammalian Trunk-Like Structures (TLS) development.
• TROUPE computed reasonable self-renewal and differentiation rates under standard and perturbed conditions.

Conclusions:

• TROUPE offers a robust method for inferring cell differentiation and growth dynamics from lineage trees.
• The framework effectively handles unobserved progenitor cell types, advancing developmental biology research.
• TROUPE provides valuable insights into cell fate decisions during development, applicable to various biological systems.