Immune fingerprinting through repertoire similarity

Predictability of B cell clonal persistence and immunosurveillance in breast cancer

B cells and T cells are important components of the adaptive immune system and mediate anticancer immunity. The T cell landscape in cancer is well characterized, but the contribution of B cells to anticancer immunosurveillance is less well explored. Here we show an integrative analysis of the B cell and T cell receptor repertoire from individuals with metastatic breast cancer and individuals with early breast cancer during neoadjuvant therapy. Using immune receptor, RNA and whole-exome sequencing, we show that both B cell and T cell responses seem to coevolve with the metastatic cancer genomes and mirror tumor mutational and neoantigen architecture. B cell clones associated with metastatic immunosurveillance and temporal persistence were more expanded and distinct from site-specific clones. B cell clonal immunosurveillance and temporal persistence are predictable from the clonal structure, with higher-centrality B cell antigen receptors more likely to be detected across multiple metastases or across time. This predictability was generalizable across other immune-mediated disorders. This work lays a foundation for prioritizing antibody sequences for therapeutic targeting in cancer.

The dengue-specific immune response and antibody identification with machine learning

Dengue virus poses a serious threat to global health and there is no specific therapeutic for it. Broadly neutralizing antibodies recognizing all serotypes may be an effective treatment. High-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) and bioinformatic analysis enable in-depth understanding of the B-cell immune response. Here, we investigate the dengue antibody response with these technologies and apply machine learning to identify rare and underrepresented broadly neutralizing antibody sequences. Dengue immunization elicited the following signatures on the antibody repertoire: (i) an increase of CDR3 and germline gene diversity; (ii) a change in the antibody repertoire architecture by eliciting power-law network distributions and CDR3 enrichment in polar amino acids; (iii) an increase in the expression of JNK/Fos transcription factors and ribosomal proteins. Furthermore, we demonstrate the applicability of computational methods and machine learning to AIRR-seq datasets for neutralizing antibody candidate sequence identification. Antibody expression and functional assays have validated the obtained results.

Inferring the T cell repertoire dynamics of healthy individuals

The adaptive immune system is a diverse ecosystem that responds to pathogens by selecting cells with specific receptors. While clonal expansion in response to particular immune challenges has been extensively studied, we do not know the neutral dynamics that drive the immune system in the absence of strong stimuli. Here, we learn the parameters that underlie the clonal dynamics of the T cell repertoire in healthy individuals of different ages, by applying Bayesian inference to longitudinal immune repertoire sequencing (RepSeq) data. Quantifying the experimental noise accurately for a given RepSeq technique allows us to disentangle real changes in clonal frequencies from noise. We find that the data are consistent with clone sizes following a geometric Brownian motion and show that its predicted steady state is in quantitative agreement with the observed power-law behavior of the clone-size distribution. The inferred turnover time scale of the repertoire increases with patient age and depends on the clone size in some individuals.

Distinguishing between monozygotic twins' blood samples through immune repertoire sequencing

Monozygotic (MZ) twins with highly similar genomic DNA sequences can not be distinguished by conventional forensic DNA testing. The immune repertoire (IR) reflects an individual's immune history, which is unique between individuals, has been applied to individualized treatment in precision medicine. However, the application of IR in forensic genetics has not been reported to date. In this study, the diversity in the complementary determining region 3 (CDR3) of both the T-cell receptor β chain (TCRβ) and B-cell receptor heavy chain (also known as immunoglobulin heavy chain, IGH) in four pairs of MZ twins were analyzed. The results showed that the amino acid sequences length distribution frequency of TCRβ CDR3 had 4-10 differences, and the nucleic acid sequences length distribution frequency of TCRβ CDR3 had 2-7 differences between MZ twins. The shared difference of four pairs of MZ twins focused on the length distribution frequency of 34 bp nucleotide sequences in TCRβ. By analyzing the usage frequency of V and J genes in TCRβ and IGH CDR3 DNA sequence rearrangements, we also found that there were biases between each pair of MZ twins, and the usage frequency of TRBJ2-3 showed common differences between each pair of MZ twins. Furthermore, each pair of MZ twins had its own unique V-J genes combination mode in TCRβ and IGH CDR3 DNA sequences. This study, for the first time, suggested that IR can be used as a potential biological marker to distinguish MZ twins.

The evolutionary and functional significance of germline immunoglobulin gene variation

The recombination between immunoglobulin (IG) gene segments determines an individual's naïve antibody repertoire and, consequently, (auto)antigen recognition. Emerging evidence suggests that mammalian IG germline variation impacts humoral immune responses associated with vaccination, infection, and autoimmunity - from the molecular level of epitope specificity, up to profound changes in the architecture of antibody repertoires. These links between IG germline variants and immunophenotype raise the question on the evolutionary causes and consequences of diversity within IG loci. We discuss why the extreme diversity in IG loci remains a mystery, why resolving this is important for the design of more effective vaccines and therapeutics, and how recent evidence from multiple lines of inquiry may help us do so.

Comparing T cell receptor repertoires using optimal transport

The complexity of entire T cell receptor (TCR) repertoires makes their comparison a difficult but important task. Current methods of TCR repertoire comparison can incur a high loss of distributional information by considering overly simplistic sequence- or repertoire-level characteristics. Optimal transport methods form a suitable approach for such comparison given some distance or metric between values in the sample space, with appealing theoretical and computational properties. In this paper we introduce a nonparametric approach to comparing empirical TCR repertoires that applies the Sinkhorn distance, a fast, contemporary optimal transport method, and a recently-created distance between TCRs called TCRdist. We show that our methods identify meaningful differences between samples from distinct TCR distributions for several case studies, and compete with more complicated methods despite minimal modeling assumptions and a simpler pipeline.

Resolving SARS-CoV-2 CD4+ T cell specificity via reverse epitope discovery

The current strategy to detect immunodominant T cell responses focuses on the antigen, employing large peptide pools to screen for functional cell activation. However, these approaches are labor and sample intensive and scale poorly with increasing size of the pathogen peptidome. T cell receptors (TCRs) recognizing the same epitope frequently have highly similar sequences, and thus, the presence of large sequence similarity clusters in the TCR repertoire likely identify the most public and immunodominant responses. Here, we perform a meta-analysis of large, publicly available single-cell and bulk TCR datasets from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected individuals to identify public CD4+ responses. We report more than 1,200 αβTCRs forming six prominent similarity clusters and validate histocompatibility leukocyte antigen (HLA) restriction and epitope specificity predictions for five clusters using transgenic T cell lines. Collectively, these data provide information on immunodominant CD4+ T cell responses to SARS-CoV-2 and demonstrate the utility of the reverse epitope discovery approach.

Machine Learning Approaches to TCR Repertoire Analysis

Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dramatically. Various data and database platforms are now driving the rapid progress of machine learning for immunological data analysis. Of various topics in immunology, T cell receptor repertoire analysis is one of the most important targets of machine learning for assessing the state and abnormalities of immune systems. In this paper, we review recent repertoire analysis methods based on machine learning and deep learning and discuss their prospects.

Dual UMIs and Dual Barcodes With Minimal PCR Amplification Removes Artifacts and Acquires Accurate Antibody Repertoire

Antibody repertoire sequencing (Rep-seq) has been widely used to reveal repertoire dynamics and to interrogate antibodies of interest at single nucleotide-level resolution. However, polymerase chain reaction (PCR) amplification introduces extensive artifacts including chimeras and nucleotide errors, leading to false discovery of antibodies and incorrect assessment of somatic hypermutations (SHMs) which subsequently mislead downstream investigations. Here, a novel approach named DUMPArts, which improves the accuracy of antibody repertoires by labeling each sample with dual barcodes and each molecule with dual unique molecular identifiers (UMIs) via minimal PCR amplification to remove artifacts, is developed. Tested by ultra-deep Rep-seq data, DUMPArts removed inter-sample chimeras, which cause artifactual shared clones and constitute approximately 15% of reads in the library, as well as intra-sample chimeras with erroneous SHMs and constituting approximately 20% of the reads, and corrected base errors and amplification biases by consensus building. The removal of these artifacts will provide an accurate assessment of antibody repertoires and benefit related studies, especially mAb discovery and antibody-guided vaccine design.

Individualized VDJ recombination predisposes the available Ig sequence space

The process of recombination between variable (V), diversity (D), and joining (J) immunoglobulin (Ig) gene segments determines an individual's naive Ig repertoire and, consequently, (auto)antigen recognition. VDJ recombination follows probabilistic rules that can be modeled statistically. So far, it remains unknown whether VDJ recombination rules differ between individuals. If these rules differed, identical (auto)antigen-specific Ig sequences would be generated with individual-specific probabilities, signifying that the available Ig sequence space is individual specific. We devised a sensitivity-tested distance measure that enables inter-individual comparison of VDJ recombination models. We discovered, accounting for several sources of noise as well as allelic variation in Ig sequencing data, that not only unrelated individuals but also human monozygotic twins and even inbred mice possess statistically distinguishable immunoglobulin recombination models. This suggests that, in addition to genetic, there is also nongenetic modulation of VDJ recombination. We demonstrate that population-wide individualized VDJ recombination can result in orders of magnitude of difference in the probability to generate (auto)antigen-specific Ig sequences. Our findings have implications for immune receptor-based individualized medicine approaches relevant to vaccination, infection, and autoimmunity.

Characterization of SARS-CoV-2 public CD4+ αβ T cell clonotypes through reverse epitope discovery

The amount of scientific data and level of public sharing produced as a consequence of the COVID-19 pandemic, as well as the speed at which these data were produced, far exceeds any previous effort against a specific disease condition. This unprecedented situation allows for development and application of new research approaches. One of the major technical hurdles in immunology is the characterization of HLA-antigen-T cell receptor (TCR) specificities. Most approaches aim to identify reactive T cells starting from known antigens using functional assays. However, the need for a reverse approach identifying the antigen specificity of orphan TCRs is increasing. Utilizing large public single-cell gene expression and TCR datasets, we identified highly public CD4 ⁺ T cell responses to SARS-CoV-2, covering >75% of the analysed population. We performed an integrative meta-analysis to deeply characterize these clonotypes by TCR sequence, gene expression, HLA-restriction, and antigen-specificity, identifying strong and public CD4 ⁺ immunodominant responses with confirmed specificity. CD4 ⁺ COVID-enriched clonotypes show T follicular helper functional features, while clonotypes depleted in SARS-CoV-2 individuals preferentially had a central memory phenotype. In total we identify more than 1200 highly public CD4+ T cell clonotypes reactive to SARS-CoV-2. TCR similarity analysis showed six prominent TCR clusters, for which we predicted both HLA-restriction and cognate SARS-CoV-2 immunodominant epitopes. To validate our predictions we used an independent cohort of TCR repertoires before and after vaccination with ChAdOx1 , a replication-deficient simian adenovirus-vectored vaccine, encoding the SARS-CoV-2 spike protein. We find statistically significant enrichment of the predicted spike-reactive TCRs after vaccination with ChAdOx1 , while the frequency of TCRs specific to other SARS-CoV-2 proteins remains stable. Thus, the CD4-associated TCR repertoire differentiates vaccination from natural infection. In conclusion, our study presents a novel reverse epitope discovery approach that can be used to infer HLA- and antigen-specificity of orphan TCRs in any context, such as viral infections, antitumor immune responses, or autoimmune disease.

HIGHLIGHTS

Identification of highly public CD4+ T cell responses to SARS-CoV-2Systematic prediction of exact immunogenic HLA class II epitopes for CD4+ T cell responseMethodological framework for reverse epitope discovery, which can be applied to other disease contexts and may provide essential insights for future studies and clinical applications.