Alignment of single-cell trajectory trees with CAPITAL.

Area of Science:

• Computational Biology
• Single-cell Genomics
• Bioinformatics

Background:

• Aligning single-cell RNA sequencing (scRNA-seq) trajectories is crucial for understanding cell differentiation and development.
• Current methods primarily support linear trajectory alignment, limiting the analysis of complex, branching processes.
• Comparing pseudotime trajectories across datasets, especially between species, remains a significant challenge.

Purpose of the Study:

• To introduce CAPITAL (comparative analysis of pseudotime trajectory inference with tree alignment), a new computational method.
• To enable the global alignment and comparison of complex, branching single-cell trajectories.
• To facilitate the identification of conserved gene expression dynamics across different scRNA-seq datasets.

Main Methods:

• Development of a novel tree alignment algorithm for pseudotime trajectory inference.
• Implementation of CAPITAL for automated comparison of branching trajectories.
• Validation using synthetic datasets and authentic bone marrow cell scRNA-seq data.

Main Results:

• CAPITAL successfully achieved accurate and robust alignments of trajectory trees.
• The method demonstrated effective comparison of branching trajectories from different datasets.
• Analysis revealed conserved gene expression dynamics and gene-gene correlations across species.

Conclusions:

• CAPITAL provides a robust solution for aligning complex, branching pseudotime trajectories in scRNA-seq data.
• The method enhances comparative analysis of developmental processes across datasets and species.
• CAPITAL opens new avenues for studying conserved gene regulatory mechanisms in cell differentiation.