AffMB: affinity maturation analysis with SHM-guided B-cell lineage trees.

Area of Science:

• Immunology
• Computational Biology
• Genetics

Background:

• B-cell lineage trees model immunoglobulin gene evolution during affinity maturation.
• Current methods often fail to ensure accurate parent-to-child inheritance and mutation accumulation.
• Existing tools may not be compatible with diverse B-cell receptor sequencing data inputs.

Purpose of the Study:

• To develop a novel toolkit, AffMB (Affinity Maturation of B-cell receptor), for constructing B-cell lineage trees.
• To accurately track affinity maturation by generating SHM-ordered, inheritance-based lineage trees.
• To enable analysis of both single-cell and bulk B-cell receptor sequencing data.

Main Methods:

• Developed AffMB, an open-source Python package.
• Implemented an algorithm for SHM-ordered, inheritance-based B-cell lineage tree generation.
• Utilized contig FASTA or AIRR rearrangement TSV file formats for input.

Main Results:

• AffMB successfully generates and visualizes B-cell lineage trees reflecting affinity maturation.
• The SHM-ordered inheritance tree algorithm demonstrated superior performance compared to benchmarks in simulations.
• Application to BNT162b2 vaccination data enabled inference of immune responses and identification of potential high-affinity antibody sequences.

Conclusions:

• AffMB provides a robust and accurate method for analyzing B-cell receptor evolution.
• The toolkit addresses limitations of existing methods in tracking inheritance and mutation accumulation.
• AffMB facilitates the identification of high-affinity antibodies and understanding of immunization responses.