Comparing algorithms that reconstruct cell lineage trees utilizing information on microsatellite mutations.

Area of Science:

• Cellular Biology
• Genetics
• Bioinformatics

Background:

• Cellular history is traced by cell lineage trees, mapping cell progeny from zygote to extant/dead cells.
• Somatic mutations create unique genomic signatures, enabling lineage reconstruction.
• Standard population genetics tools need adaptation for unique cell population features like stem cells and limited generations.

Purpose of the Study:

• To validate and quantify the utility of known lineage tree reconstruction algorithms using somatic mutations in microsatellite loci (MS).
• To assess algorithm performance on experimental data from human and mouse cells.
• To identify optimal algorithms and distance measures for cell lineage tree reconstruction.

Main Methods:

• Developed a method using mutations in highly variable microsatellite loci (MS, also called short tandem repeats, STR).
• Collected extensive data on somatic mutations in individual cells from healthy and diseased human and mouse tissues.
• Applied and evaluated multiple lineage tree reconstruction algorithms.

Main Results:

• Most tested algorithms successfully inferred simple biological scenarios.
• The Neighbor Joining algorithm with normalized absolute distance emerged as the best performer.
• The study provides a comprehensive dataset for further analysis.

Conclusions:

• Cell lineage tree reconstruction is feasible using somatic mutations in MS.
• Algorithm choice and distance measure significantly impact reconstruction accuracy.
• Neighbor Joining with normalized absolute distance is recommended for cell lineage tree reconstruction from MS data.