Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data

Abstract

Motivation: Adaptive immune receptor repertoire sequencing (AIRR-Seq) offers the possibility of identifying and tracking B cell clonal expansions during adaptive immune responses. Members of a B cell clone are descended from a common ancestor and share the same initial V(D)J rearrangement, but their BCR sequence may differ due to the accumulation of somatic hypermutations (SHMs). Clonal relationships are learned from AIRR-seq data by analyzing the BCR sequence, with the most common methods focused on the highly diverse CDR3 region. However, clonally related cells often share SHMs which have been accumulated during affinity maturation. Here, we investigate whether shared SHMs in the V and J segments of the BCR can be leveraged along with the CDR3 sequence to improve the ability to identify clonally related sequences. We develop independent distance functions that capture shared mutations and CDR3 similarity, and combine these in a spectral clustering framework. Using simulated data, we show that this model improves both the sensitivity and specificity for identifying clonal relationships. Availability: Source code for this method is freely available in the SCOPer (Spectral Clustering for clOne Partitioning) R package (version 0.2 or newer) in the Immcantation framework: www.immcantation.org under the CC BY-SA 4.0 license.

Unraveling B cell trajectories at single cell resolution

“…B cell receptors (BCRs)]. Sequencing the full BCR heavy and light-chain sequences at the single cell level in B cells and plasma cells has improved our understanding of clonality, somatic hypermutation (SHM), class switch recombination, and antigen receptor repertoires [18] (Box 1). Combined with scRNA-seq, a growing amount of data has allowed an understanding of B cell biology from different perspectives.…”

High-affinity, neutralizing antibodies to SARS-CoV-2 can be made without T follicular helper cells

“…Each sequence was annotated with the corresponding germline V(D)J sequence, masking N/P and D-segment regions. Spectral clustering was performed using SCOPer (v1.2.0) (61) to identify clonally-related sequences, based on the level of junction region homology and the mutation profiles in the V-J segments. Following clonal clustering, consensus germline sequences for each clone were reconstructed as above.…”

Immune Literacy: Reading, Writing, and Editing Adaptive Immunity

“…However, setting a fixed distance cut-off for clonal definition is far from ideal, as it cannot account for different levels of clonal diversification within a repertoire. Therefore, much effort is put into more advanced and alternative strategies that make use of different grouping criteria or thresholds to infer clones, including ones based on probabilistic models or spectral clustering with adaptive thresholds ( Ralph and Matsen, 2016 ; Nouri and Kleinstein, 2018 ; 2020 ). Just recently, Lindenbaum and colleagues introduced an alignment-free method bypassing initial V- and J-gene assignments ( Lindenbaum et al., 2020 ).…”