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Maximum likelihood inference of time-scaled cell lineage trees with mixed-type missing data using LAML.

Gillian Chu¹, Uyen Mai¹, Henri Schmidt¹

¹Department of Computer Science, Princeton University, 08540, Princeton, NJ, USA.

|July 3, 2025

Summary

We developed Lineage Analysis via Maximum Likelihood (LAML) to accurately reconstruct cell lineage trees from dynamic lineage tracing data. This method reveals distinct metastasis progression timelines in lung adenocarcinoma.

Keywords:

Cell lineage Lineage tracing Maximum likelihood using LAML Phylogeny Time-resolved

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Area of Science:

Computational Biology
Genomics
Cancer Research

Background:

Dynamic lineage tracing technologies are crucial for understanding cell division and development.
Existing methods for inferring cell lineage trees face challenges in accuracy and scalability.

Purpose of the Study:

To introduce Lineage Analysis via Maximum Likelihood (LAML), a novel computational method for inferring time-resolved cell lineage trees.
To develop the Probabilistic Mixed-type Missing model to accurately describe dynamic lineage tracing data.
To assess LAML's performance against existing methods using simulated and experimental data.

Main Methods:

Development of the Probabilistic Mixed-type Missing model to capture key features of dynamic lineage tracing.
Implementation of the Lineage Analysis via Maximum Likelihood (LAML) algorithm for tree inference.
Validation using simulated datasets and a mouse model of lung adenocarcinoma.

Main Results:

LAML accurately reconstructs cell lineage tree topologies with branch lengths representing experimental time.
LAML demonstrates superior accuracy and scalability compared to existing methods on simulated data.
LAML identified distinct temporal patterns of cell migration to metastatic sites in lung adenocarcinoma.

Conclusions:

LAML provides a robust and scalable approach for inferring cell lineage trees from dynamic lineage tracing data.
The method accurately captures temporal dynamics of cellular processes, including metastasis.
LAML offers valuable insights into the progression of diseases like lung adenocarcinoma.