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Maximum Likelihood Inference of Time-scaled Cell Lineage Trees with Mixed-type Missing Data.

Uyen Mai¹, Gillian Chu¹, Benjamin J Raphael¹

¹Department of Computer Science, Princeton University, Princeton, NJ 08544, USA.

Biorxiv : the Preprint Server for Biology

|March 18, 2024

Summary

This study introduces the Probabilistic Mixed-type Missing (PMM) model and LAML algorithm for dynamic lineage tracing, improving cell lineage tree inference from CRISPR mutations and revealing distinct metastasis epochs.

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Area of Science:

Computational Biology
Genomics
Developmental Biology

Background:

Dynamic lineage tracing uses CRISPR and single-cell sequencing to study cell divisions.
Inferring cell lineage trees from CRISPR mutations presents unique computational challenges.
Existing phylogenetic models do not adequately address CRISPR data's non-modifiable mutations and high missing data rates.

Purpose of the Study:

To develop a novel computational model and algorithm for accurate cell lineage tree inference from dynamic lineage tracing data.
To address the specific properties of CRISPR-induced mutations, including non-modifiability and decreasing mutation rates over time.
To improve the accuracy of phylogenetic tree reconstruction in the context of complex biological systems like cancer metastasis.

Main Methods:

Introduction of the Probabilistic Mixed-type Missing (PMM) model to capture unique features of CRISPR lineage tracing data.
Development of the LAML (Lineage Analysis via Maximum Likelihood) algorithm, combining Expectation Maximization (EM) with heuristic tree search.
Joint estimation of tree topology, branch lengths, and missing data parameters using PMM and LAML.

Main Results:

LAML infers more accurate tree topologies and time-scaled branch lengths than existing methods on simulated data.
The PMM model and LAML algorithm outperform standard phylogenetic models, especially with high heritable missing data.
Analysis of lung adenocarcinoma data reveals LAML-inferred phylogenetic distances concordant with gene expression, suggesting more plausible tumor progression dynamics.

Conclusions:

LAML provides a robust framework for inferring cell lineage trees from dynamic lineage tracing data.
The model accurately captures the complexities of CRISPR-induced mutations and missing data, leading to improved biological insights.
Application to lung cancer data identified distinct metastasis epochs, offering new perspectives on tumor progression and metastasis.