The adaptive immune system stores invaluable information about current and past immune responses and may serve as an ultrasensitive biosensor. Given the immune system’s critical role in a wide variety of disease types, this has broad implications for biomedicine. Machine and deep learning is being leveraged to decipher how information is encoded in adaptive immune receptor repertoires to enable prediction from adaptive immune responses and fast-track vaccine, therapeutics, and diagnostics development. Recent advances include predicting the presence of immunity post-vaccination or infection, predicting the presence of disease, and designing antibody-based therapeutics.
Much is still not understood about the human adaptive immune response to SARS-CoV-2, the causative agent of COVID-19. In this paper, we demonstrate the use of machine learning to classify SARS-CoV-2 epitope specific T-cell clonotypes in T-cell receptor (TCR) sequencing data. We apply these models to public TCR data and show how they can be used to study T-cell longitudinal profiles in COVID-19 patients to characterize how the adaptive immune system reacts to the SARS-CoV-2 virus. Our findings confirm prior knowledge that SARS-CoV-2 reactive T-cell diversity increases over the course of disease progression.
Monitoring the T cell receptor (TCR) repertoire in health and disease can provide key insights into adaptive immune responses, but the accuracy of current TCR sequencing (TCRseq) methods is unclear. In this study, we systematically compared the results of nine commercial and academic TCRseq methods, including six rapid amplification of complementary DNA ends (RACE)-polymerase chain reaction (PCR) and three multiplex-PCR approaches, when applied to the same T cell sample.
Colonization by the microbiota causes a marked stimulation of B cells and induction of immunoglobulin, but mammals colonized with many taxa have highly complex and individualized immunoglobulin repertoires. Here we use a simplified model of defined transient exposures to different microbial taxa in germ-free mice to deconstruct how the microbiota shapes the B cell pool and its functional responsiveness. We followed the development of the immunoglobulin repertoire in B cell populations, as well as single cells by deep sequencing.
During a pandemic, data combined with the right context and meaning can be transformed into knowledge for informing public health responses. Timely and accurate collection, reporting and sharing of data with the research community, public health practitioners, clinicians and policy makers will inform assessment of the likely impact of a pandemic to implement efficient and effective response strategies.
Polymorphisms in human immunoglobulin heavy chain variable genes and their upstream regions
Germline variations in immunoglobulin genes influence the repertoire of B cell receptors and antibodies, and such polymorphisms may impact disease susceptibility. However, the knowledge of the genomic variation of the immunoglobulin loci is scarce. Here, we report 25 potential novel germline IGHV alleles as inferred from rearranged naïve B cell cDNA repertoires of 98 individuals.
VDJbase: an adaptive immune receptor genotype and haplotype database
VDJbase is a publicly available database that offers easy searching of data describing the complete sets of gene sequences (genotypes and haplotypes) inferred from adaptive immune receptor repertoire sequencing datasets. VDJbase is designed to act as a resource that will allow the scientific community to explore the genetic variability of the immunoglobulin (Ig) and T cell receptor (TR) gene loci.
RAbHIT is an R Haplotype Antibody Inference Tool, that implements a novel algorithm to infer V(D)J haplotypes by adapting a Bayesian framework. RAbHIT offers inference of haplotype and gene deletions. It may be applied to sequences from naïve and non-naïve B-cells, sequenced by different library preparation protocols.
immuneSIM: tunable multi-feature simulation of B- and T-cell receptor repertoires for immunoinformatics benchmarking
immuneSIM enables in silico generation of single and paired chain human and mouse B- and T-cell repertoires with user-defined tunable properties to provide the user with experimental-like (or aberrant) data to benchmark their repertoire analysis methods.
High frequency of shared clonotypes in human B cell receptor repertoires
The human genome contains approximately 20 thousand protein-coding genes1, but the size of the collection of antigen receptors of the adaptive immune system that is generated by the recombination of gene segments with non-templated junctional additions (on B cells) is unknown—although it is certainly orders of magnitude larger.
Reproducibility and Reuse of Adaptive Immune Receptor Repertoire Data
High-throughput sequencing (HTS) of immunoglobulin (B-cell receptor, antibody) and T-cell receptor repertoires has increased dramatically since the technique was introduced in 2009 (1–3). This experimental approach explores the maturation of the adaptive immune system and its response to antigens, pathogens, and disease conditions in exquisite detail.
Adaptive Immune Receptor Repertoire Community recommendations for sharing immune-repertoire sequencing data
Antigen specificity is a cardinal feature of adaptive immunity that underlies immune homeostasis and control of pathogenic attack in higher vertebrates.
Mosaic deletion patterns of the human antibody heavy chain gene locus shown by Bayesian haplotyping
Analysis of antibody repertoires by high-throughput sequencing is of major importance in understanding adaptive immune responses. Our knowledge of variations in the genomic loci encoding immunoglobulin genes is incomplete, resulting in conflicting VDJ gene assignments and biased genotype and haplotype inference.