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

• Published in: PLoS computational biology Vol. 16; no. 6; p. e1007977
• Main Authors: Nouri, Nima, Kleinstein, Steven H.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 23.06.2020; Public Library of Science (PLoS)
• Subjects: Adaptive immunity; Antigens; B cells; B-cell receptor; B-Lymphocytes - immunology; Biology and Life Sciences; Cell division; Cloning; Clustering; Complementarity Determining Regions; Computational Biology - methods; Computer simulation; DNA sequencing; Genes; Genetic aspects; Health informatics; High-Throughput Nucleotide Sequencing - methods; Humans; Identification and classification; Medicine; Medicine and Health Sciences; Methods; Mutation; Next-generation sequencing; Pathogens; Physiological aspects; Receptors; Receptors, Antigen, B-Cell - genetics; Receptors, Antigen, B-Cell - immunology; Recombination, Genetic; Research and Analysis Methods; Somatic cells; Somatic hypermutation; Source code; Iran; United States > US; Connecticut
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1007977

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 B cell receptor (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 junction 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 junction sequence to improve the ability to identify clonally related sequences. We develop independent distance functions that capture junction similarity and shared mutations, and combine these in a spectral clustering framework to infer the BCR clonal relationships. Using both simulated and experimental data, we show that this model improves both the sensitivity and specificity for identifying B cell clones. 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 AGPLv3 license.