MOTIVATION: B-cell receptors (BCRs) and gene expression profiles are two distinct yet complementary modalities of B cells. However, most analyses treat them independently. Here, we present CoMBCR, a B-cell embedding tool that co-learns BCRs and gene expressions, representing data within a unified latent space for downstream analysis. RESULTS: We applied CoMBCR to 126,791 B cells from diverse datasets with matched BCRs and gene expressions. First, CoMBCR outperforms the methods solely encoding BCRs in capturing B-cell biological features, achieving at least 0.1 improvement in Matthews Correlation Coefficient on a SARS-CoV-2 binding prediction task. Second, CoMBCR reveals active immune responses and CDR3 motif preferences through modality gap analysis in SARS-CoV-2-specific memory B cells. Moreover, when supported by spatial transcriptomics data, CoMBCR accurately traces the developmental trajectories of malignant B cells and uncovers transcriptional patterns associated with their survival within lymphoma patients. AVAILABILITY AND IMPLEMENTATION: The CoMBCR software is publicly available under the MIT License at https://github.com/deepomicslab/CoMBCR.git. CONTACT: [email protected].

MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: 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.

The specificity of a T-cell receptor (TCR) repertoire determines personalized immune capacity. Existing methods have modeled the qualitative aspects of TCR specificity, while the quantitative aspects remained unaddressed. We developed a package, TCRanno, to quantify the specificity of TCR repertoires. We created deep-learning-based, epitope-aware vector embeddings to infer individual TCR specificity. Then we aggregated clonotype frequencies of TCRs to obtain a quantitative profile of repertoire specificity at epitope, antigen and organism levels. Applying TCRanno to 4195 TCR repertoires revealed quantitative changes in repertoire specificity upon infections, autoimmunity and cancers. Specifically, TCRanno found cytomegalovirus-specific TCRs in seronegative healthy individuals, supporting the possibility of abortive infections. TCRanno discovered age-accumulated fraction of severe acute respiratory syndrome coronavirus 2 specific TCRs in pre-pandemic samples, which may explain the aggressive symptoms and age-related severity of coronavirus disease 2019. TCRanno also identified the encounter of Hepatitis B antigens as a potential trigger of systemic lupus erythematosus. TCRanno annotations showed capability in distinguishing TCR repertoires of healthy and cancers including melanoma, lung and breast cancers. TCRanno also demonstrated usefulness to single-cell TCRseq+gene expression data analyses by isolating T-cells with the specificity of interest.