LinRace: single cell lineage reconstruction using paired lineage barcode and gene expression data

Area of Science:

• Developmental biology
• Stem cell biology
• Genomics

Background:

• Understanding single-cell division and differentiation is crucial in developmental and stem cell biology.
• CRISPR/Cas9 lineage tracing enables simultaneous gene expression and barcode readouts for cell lineage reconstruction.
• Existing lineage reconstruction methods primarily use barcode data, with emerging approaches incorporating gene expression for improved accuracy.

Approach:

• LinRace (Lineage Reconstruction with asymmetric cell division model) integrates lineage barcodes and gene expression data.
• It employs an asymmetric cell division model and a framework combining Neighbor Joining and maximum-likelihood heuristics.
• This approach models gene expression changes across cell divisions to enhance lineage inference.

Key Points:

• LinRace achieves higher accuracy in cell division tree reconstruction compared to existing methods on both simulated and real data.
• The method successfully infers cell states of ancestral cells, a capability rarely offered by current tools.
• It provides insights into how progenitor cells generate diverse cell populations with varied functionalities.

Conclusions:

• LinRace offers a novel computational method for accurate lineage reconstruction by leveraging both genetic and expression data.
• The ability to determine ancestral cell states opens new avenues for studying cell fate determination and differentiation pathways.
• This tool advances the analysis of cellular dynamics in developmental processes and tissue regeneration.