Third, TRUST4 explicitly represents highly similar reads in the contig consensus, thus accommodating somatic hypermutations and improving memory efficiency ( Methods). Second, TRUST4 prioritizes candidate read assembly by abundance and assembles all candidate reads with partial overlaps against contigs, thus increasing algorithm speed. First, TRUST4 supports fast extraction of TCR/BCR candidate reads from either FASTQ or BAM files. In this study, we redesigned the TRUST algorithm to TRUST4 with substantially enhanced features and improved performance for immune repertoire reconstruction (Fig. To analyze immune repertoires using the 10x Genomics platform, researchers currently need to prepare extra libraries to amplify TCR/BCR sequences. In contrast to SMART-seq, droplet-based scRNA-seq platforms such as 10x Genomics 20, while yielding sparser transcript coverage per cell, can process orders of magnitude more cells at lower cost. Several algorithms, including MiXCR 11, BALDR 16, BASIC 17 and VDJPuzzle 18, have been developed to construct full-length paired TCRs or BCRs from the SMART-seq scRNA-seq platform 19. With the advance of scRNA-seq technologies, researchers can study immune cell gene expression and receptor repertoire sequences simultaneously. Therefore, algorithms that can infer full-length immune receptor repertoires can facilitate better receptor–antigen interaction modeling. 14), and four out of nine positions contributing most to 4A8 antibody binding to the SARS-CoV-2 spike protein are in CDR1 and CDR2 (ref. For example, five out of six mutations predicted in a recent study to influence antibody affinity in the acidic tumor environment are located in CDR1 and CDR2 (ref. These methods focus on reconstruction of complementary-determining region 3 (CDR3), with limited ability to assemble full-length V(D)J receptor sequences, although CDR1 and CDR2 on the V sequence still contribute considerably to antigen recognition and binding. Recent years have also seen other computational methods introduced for immune repertoire construction from RNA-seq data, including V’DJer 10, MiXCR 11, CATT 12 and ImRep 13. Although less sensitive than TCR-seq and BCR-seq, TRUST is able to identify the abundantly expressed and potentially more clonally expanded TCRs/BCRs in the RNA-seq data that are more likely to be involved in antigen binding 9. When applied to The Cancer Genome Atlas (TCGA) tumor RNA-seq data, TRUST revealed profound biological insights into the repertoires of tumor-infiltrating T cells 6 and B cells 8, as well as their associated tumor immunity.
Previously we developed the TRUST algorithm 6, 7, 8, utilized to de novo assemble immune receptor repertories directly from tissue or blood RNA-seq data. However, because repertoire sequences from V(D)J recombination and SHM are different from the germline, they are often eliminated in the read-mapping step. Alternatively, RNA-seq data contain expressed TCR and BCR sequences in tissues or peripheral blood mononuclear cells (PBMC). Repertoire sequencing has been increasingly adopted in infectious disease 1, allergy 2, autoimmune 3, tumor immuology 4 and cancer immunotherapy 5 studies, but it is an expensive assay and consumes valuable tissue samples. Following antigen recognition, BCRs also undergo somatic hypermutations (SHMs) to further improve antigen-binding affinity. Both T and B cells can generate diverse receptor (TCR and BCR, respectively) repertoires, through somatic V(D)J recombination, to recognize various external antigens or tumor neoantigens.
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