TRUST4: immune repertoire reconstruction from bulk and single-cell RNA-seq data

Abstract

We introduce the TRUST4 open-source algorithm for reconstruction of immune receptor repertoires in αβ/γδ T cells and B cells from RNA-sequencing (RNA-seq) data. Compared with competing methods, TRUST4 supports both FASTQ and BAM format and is faster and more sensitive in assembling longereven full-length-receptor repertoires. TRUST4 can also call repertoire sequences from single-cell RNA-seq (scRNA-seq) data without V(D)J enrichment, and is compatible with both SMART-seq and 5′ 10x Genomics platforms.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. Following antigen recognition, BCRs also undergo somatic hypermutations (SHMs) to further improve antigen-binding affinity. 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. Alternatively, RNA-seq data contain expressed TCR and BCR sequences in tissues or peripheral blood mononuclear cells (PBMC). 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.Previously we developed the TRUST algorithm 6-8 , utilized to de novo assemble immune receptor repertories directly from tissue or blood RNA-seq data. 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. 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 . 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 . 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. 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. 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. 15 ). Therefore, algorithms that can infer full-length immune receptor repertoires can facilitate better receptor-antigen interaction modeling.

A m6Avalue predictive of prostate cancer stemness, tumor immune landscape and immunotherapy response

“…Neoantigens are attractive candidates for developing cancer vaccines, but their recognition by the immune system depends on efficient presentation. Due to a lack of global characterization of neoepitopes in TCGA-PRAD samples, we took another computational approach to infer neoantigen load in m 6 A_Clusters by reconstituting both the B- and T-cell receptor repertoires via a newly developed TRUST4 algorithm ( 67 ). To our surprise, the number of complementary-determining region 3 (CDR3) clones decreased from C1 to C3, so as the CDR1 and CDR2 on the V sequence (Figure 3H ).…”

A mutation-based gene set predicts survival benefit after immunotherapy across multiple cancers and reveals the immune response landscape

“…Tumor-infiltrating immune cells play a crucial role in patient prognosis and cancer treatment efficacy [5][6][7][8]. In the tumor microenvironment, the composition of immune cells is related to cancer heterogeneity and creates complexity that is interesting but challenging when studying the dynamic interactions between cancer and immune cells [8].…”

Natural Barcodes for Longitudinal Single Cell Tracking of Leukemic and Immune Cell Dynamics

“…Finally, cost-effective targeted approaches with multiplexed PCR can be an option for analysis of rare cells or little starting material ( 47 , 58 ). Different analysis pipelines for these platforms have been developed that include cellranger (5’ scRNA-seq with VD(J) enrichment) ( 59 ), MiXCR ( 60 ) or TRUST4 ( 61 ).…”