Isotype-aware inference of B cell clonal lineage trees from single-cell sequencing data

Area of Science:

• Immunology
• Computational Biology
• Genomics

Background:

• Single-cell RNA sequencing (scRNA-seq) provides insights into B cell micro-evolution during adaptive immune responses.
• Existing phylogenetic methods primarily focus on somatic hypermutation (SHM), neglecting class switch recombination (CSR) in B cell evolution.
• Accurate reconstruction of B cell evolutionary history is crucial for understanding immune responses and diseases.

Purpose of the Study:

• To introduce TRIBAL (tree inference of B cell clonal lineages), an algorithm for reconstructing B cell evolutionary history.
• To enable optimal reconstruction of B cell lineages considering both SHM and CSR from scRNA-seq data.
• To improve B cell lineage tracing for applications such as vaccine response modeling and therapeutic antibody identification.

Main Methods:

• Development of the TRIBAL algorithm for phylogenetic reconstruction of B cell clonal lineages.
• Utilized scRNA-seq data to capture SHM and CSR processes.
• Comparative analysis with existing methods using simulations and real-world datasets.

Main Results:

• TRIBAL generates more comprehensive and accurate B cell lineage trees compared to existing methods in simulations.
• TRIBAL successfully recapitulates biological trends in an affinity maturation model using real-world data.
• Reconstructed evolutionary histories exhibit more parsimonious class switching compared to state-of-the-art methods.

Conclusions:

• TRIBAL significantly enhances B cell lineage tracing by integrating SHM and CSR.
• The algorithm provides a valuable tool for studying adaptive immunity, vaccine responses, and disease progression.
• TRIBAL aids in identifying potential therapeutic antibodies by elucidating B cell evolution.