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

TL;DR

AffMB is a new tool that builds accurate B-cell lineage trees to study how antibodies improve their binding during immune responses.

Contribution

AffMB introduces a SHM-guided lineage tree algorithm that ensures mutation inheritance and outperforms existing methods.

Findings

AffMB's algorithm outperformed state-of-the-art methods in simulated data.

AffMB successfully analyzed real-world BNT162b2 vaccination data to infer immune responses.

The tool can identify potential high-affinity antibody sequences from sequencing data.

Abstract

B-cell lineage trees describe the evolutionary process of immunoglobulin genes during affinity maturation. Existing methods for building B-cell lineage trees generally do not guarantee the parent-to-child inheritance and accumulation of advantageous mutations under successive rounds of somatic hypermutation (SHM) and selection, and are often incompatible with repertoire input.

To address previous limitations, we developed AffMB (Affinity Maturation of B-cell receptor), a comprehensive toolkit for tracking affinity maturation through the generation and visualization of SHM-ordered, inheritance-based B-cell lineage trees from single-cell or bulk B-cell receptor sequencing data. The SHM-ordered inheritance tree algorithm outperformed state-of-the-art benchmarks in simulations. When applied to single-cell data from BNT162b2 vaccination (n = 42), AffMB demonstrated the ability to infer immunization responses and showed the feasibility of identifying potential high-affinity antibody sequences.

AffMB is an open-source Python package that supports contig FASTA or AIRR rearrangement TSV inputs. The source code for AffMB is freely available at https://github.com/deepomicslab/AffMB.

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