Estimating cell lineage from distributions of randomly introduced markers

Area of Science:

• Developmental Biology
• Computational Biology
• Genetics

Background:

• Traditional cell lineage analysis requires tracking markers through multiple cell divisions.
• Identifying all cells and introducing markers at each stage is labor-intensive and complex.
• Existing methods face challenges in reconstructing lineages for large numbers of cells or complex developmental processes.

Purpose of the Study:

• To develop a new computational method for estimating cell lineage from intercellular marker data at a single developmental stage.
• To leverage the principle of parsimony for efficient cell lineage reconstruction.
• To provide a robust method applicable to both small and large cell populations.

Main Methods:

• The study proposes a method based on analyzing distributions of intercellular markers observed at a single stage.
• It identifies the most likely cell lineage by finding the pattern requiring the minimum number of marker insertions (parsimony).
• For large cell numbers, a clustering method is employed, sequentially merging cell pairs with high marker correlation, validated by simulations.

Main Results:

• The proposed method successfully reconstructs cell lineage by minimizing marker insertions, analogous to phylogenetic reconstruction.
• Computer simulations confirmed the efficiency of the clustering method for accurate cell lineage estimation.
• The method was successfully applied to reconstruct the cell lineage of ascidians from experimental data.

Conclusions:

• The novel computational approach offers an efficient alternative for cell lineage reconstruction, particularly when analyzing marker distributions at a single time point.
• The parsimony-based and clustering methods provide scalable solutions for diverse biological systems.
• This technique has practical applications in developmental biology, exemplified by its use in ascidian lineage reconstruction.