CompAIRR: ultra-fast comparison of adaptive immune receptor repertoires by exact and approximate sequence matching

T-cells specific for KSHV and HIV migrate to Kaposi sarcoma tumors and persist over time

Kaposi sarcoma-associated herpesvirus (KSHV) is the etiologic agent of Kaposi sarcoma (KS), which causes significant morbidity and mortality worldwide, particularly in people living with HIV (PLWH) and in sub-Saharan Africa where KSHV seroprevalence is high. Postulating that T-cells specific for KSHV and HIV would be attracted to KS tumors, we performed transcriptional profiling and T-cell receptor (TCR) repertoire analysis of tumor biopsies from 144 Ugandan adults with KS, 106 of whom were also living with HIV. We show that CD8+ T-cells and M2-polarized macrophages are the most common immune cells in KS tumors. The TCR repertoire of T-cells associated with KS tumors is shared across spatially and temporally distinct tumors from the same individual. Clusters of T-cells with predicted shared specificity for uncharacterized antigens, potentially encoded by KSHV or HIV, comprise ~25% of the T-cells in KS tumors. Single-cell RNA-sequencing of blood from a subset of 9 adults captured 4,283 unique αβ TCRs carried in 14,698 putative KSHV- or HIV-specific T-cells, which carried an antigen-experienced effector phenotype. T-cells engineered to express a representative sample of these TCRs showed high-avidity recognition of KSHV- or HIV-encoded antigens. These results suggest that a poly-specific, high-avidity KSHV- and HIV-specific T-cell response, potentially inhibited by M2 macrophages, migrates to and localizes with KS tumors. Further analysis of KSHV- and HIV-specific T-cells in KS tumors will provide insight into the pathogenesis of KS and could guide the development of specific immune therapy based on adoptive transfer or vaccination.

Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning

Machine learning (ML) has shown great potential in the adaptive immune receptor repertoire (AIRR) field. However, there is a lack of large-scale ground-truth experimental AIRR data suitable for AIRR-ML-based disease diagnostics and therapeutics discovery. Simulated ground-truth AIRR data are required to complement the development and benchmarking of robust and interpretable AIRR-ML methods where experimental data is currently inaccessible or insufficient. The challenge for simulated data to be useful is incorporating key features observed in experimental repertoires. These features, such as antigen or disease-associated immune information, cause AIRR-ML problems to be challenging. Here, we introduce LIgO, a software suite, which simulates AIRR data for the development and benchmarking of AIRR-ML methods. LIgO incorporates different types of immune information both on the receptor and the repertoire level and preserves native-like generation probability distribution. Additionally, LIgO assists users in determining the computational feasibility of their simulations. We show two examples where LIgO supports the development and validation of AIRR-ML methods: (i) how individuals carrying out-of-distribution immune information impacts receptor-level prediction performance and (ii) how immune information co-occurring in the same AIRs impacts the performance of conventional receptor-level encoding and repertoire-level classification approaches. LIgO guides the advancement and assessment of interpretable AIRR-ML methods.

Predictability of antigen binding based on short motifs in the antibody CDRH3

Adaptive immune receptors, such as antibodies and T-cell receptors, recognize foreign threats with exquisite specificity. A major challenge in adaptive immunology is discovering the rules governing immune receptor-antigen binding in order to predict the antigen binding status of previously unseen immune receptors. Many studies assume that the antigen binding status of an immune receptor may be determined by the presence of a short motif in the complementarity determining region 3 (CDR3), disregarding other amino acids. To test this assumption, we present a method to discover short motifs which show high precision in predicting antigen binding and generalize well to unseen simulated and experimental data. Our analysis of a mutagenesis-based antibody dataset reveals 11 336 position-specific, mostly gapped motifs of 3-5 amino acids that retain high precision on independently generated experimental data. Using a subset of only 178 motifs, a simple classifier was made that on the independently generated dataset outperformed a deep learning model proposed specifically for such datasets. In conclusion, our findings support the notion that for some antibodies, antigen binding may be largely determined by a short CDR3 motif. As more experimental data emerge, our methodology could serve as a foundation for in-depth investigations into antigen binding signals.

nf-core/airrflow: An adaptive immune receptor repertoire analysis workflow employing the Immcantation framework

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data with sequencing errors and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls, including a mixture of bulk and single-cell sequencing datasets. Using this dataset, we extended the convergence findings to 20 additional subjects, highlighting the applicability of nf-core/airrflow to validate findings in small in-house cohorts with reanalysis of large publicly available AIRR datasets.

Chickens with a Truncated Light Chain Transgene Express Single-Domain H Chain-Only Antibodies

H chain-only Igs are naturally produced in camelids and sharks. Because these Abs lack the L chain, the Ag-binding domain is half the size of a traditional Ab, allowing this type of Ig to bind to targets in novel ways. Consequently, the H chain-only single-domain Ab (sdAb) structure has the potential to increase the repertoire and functional range of an active humoral immune system. The majority of vertebrates use the standard heterodimeric (both H and L chains) structure and do not produce sdAb format Igs. To investigate if other animals are able to support sdAb development and function, transgenic chickens (Gallus gallus) were designed to produce H chain-only Abs by omitting the L chain V region and maintaining only the LC region to serve as a chaperone for Ab secretion from the cell. These birds produced 30-50% normal B cell populations within PBMCs and readily expressed chicken sequence sdAbs. Interestingly, the H chains contained a spontaneous CH1 deletion. Although no isotype switching to IgY or IgA occurred, the IgM repertoire was diverse, and immunization with a variety of protein immunogens rapidly produced high and specific serum titers. mAbs of high affinity were efficiently recovered by single B cell screening. In in vitro functional assays, the sdAbs produced by birds immunized against SARS-CoV-2 were also able to strongly neutralize and prevent viral replication. These data suggest that the truncated L chain design successfully supported sdAb development and expression in chickens.

Evaluation of short-circuited electrodes in combination with dark fermentation for promoting biohydrogen production process

Short-circuited electrodes, in combination with dark fermentation, were evaluated in a biohydrogen production process. The system is based on an innovative design of a non-compartmented electromicrobial bioreactor with a conductive tubular membrane as cathode and a graphite felt as anode. In particular, the electrode specialization occurred when the bioreactor was inoculated with manure as the whole medium and when a vacuum was applied in the tubular membrane, for allowing continuous extraction of gaseous species (H2, CH4, CO2) from the bioreactor. This specialization of the electrodes as anode and cathode was further confirmed by microbial ecology analysis of biofilms and by cyclic voltammetry measurements. In these experimental conditions, the potential of the electrochemical system (short-circuited electrodes) reached values as low as -320 mV vs. SHE, associated with a significant bioH2 production. Moreover, a higher bioH2 production occurred and a potential of the electrochemical system as low as -429 mV vs SHE was temporarily observed, when additional heat treatments of the whole manure were applied in order to remove methanogen microorganisms (i.e., hydrogen consumers). In the bioreactor, the higher production of bioH2 would be promoted by electrofermentation from the current flow observed between short-circuited anode and cathode.

Enhancing comparative T cell receptor repertoire analysis in small biological samples through pooling homologous cell samples from multiple mice

Accurate characterization and comparison of T cell receptor (TCR) repertoires from small biological samples present significant challenges. The main challenge is the low material input, which compromises the quality of bulk sequencing and hinders the recovery of sufficient TCR sequences for robust analyses. We aimed to address this limitation by implementing a strategic approach to pool homologous biological samples. Our findings demonstrate that such pooling indeed enhances the TCR repertoire coverage, particularly for cell subsets of constrained sizes, and enables accurate comparisons of TCR repertoires at different levels of complexity across T cell subsets with different sizes. This methodology holds promise for advancing our understanding of T cell repertoires in scenarios where sample size constraints are a prevailing concern.

Multimodal Single-Cell Sequencing of B Cells in Primary Sjögren's Syndrome

OBJECTIVE

B cells are important in the pathogenesis of primary Sjögren's syndrome (pSS). Patients positive for Sjögren's syndrome antigen A/Sjögren syndrome antigen B (SSA/SSB) autoantibodies are more prone to systemic disease manifestations and adverse outcomes. We aimed to determine the role of B cell composition, gene expression, and B cell receptor usage in pSS subgroups stratified for SSA/SSB antibodies.

METHODS

Over 230,000 B cells were isolated from peripheral blood of patients with pSS (n = 6 SSA-, n = 8 SSA+ single positive and n = 10 SSA/SSB+ double positive) and four healthy controls and processed for single-cell RNA sequencing (scRNA-seq) and single-cell variable, diversity, and joining (VDJ) gene sequencing (scVDJ-seq).

RESULTS

We show that SSA/SSB+ patients present the highest and lowest proportion of naïve and memory B cells, respectively, and the highest up-regulation of interferon-induced genes across all B cell subtypes. Differential usage of IGHV showed that IGHV1-69 and IGHV4-30-4 were more often used in all pSS subgroups compared with controls. Memory B cells from SSA/SSB+ patients displayed a higher proportion of cells with unmutated VDJ transcripts compared with other pSS patient groups and controls, indicating altered somatic hypermutation processes. Comparison with previous studies revealed heterogeneous clonotype pools, with little overlap in CDR3 sequences. Joint analysis using scRNA-seq and scVDJ-seq data allowed unsupervised stratification of patients with pSS and identified novel parameters that correlated to disease manifestations and antibody status.

CONCLUSION

We describe heterogeneity and molecular characteristics in B cells from patients with pSS, providing clues to intrinsic differences in B cells that affect the phenotype and outcome and allowing stratification of patients with pSS at improved resolution.

Systems immunology spanning tumors, lymph nodes, and periphery

The immune system defines a complex network of tissues and cell types that orchestrate responses across the body in a dynamic manner. The local and systemic interactions between immune and cancer cells contribute to disease progression. Lymphocytes are activated in lymph nodes, traffic through the periphery, and impact cancer progression through their interactions with tumor cells. As a result, therapeutic response and resistance are mediated across tissues, and a comprehensive understanding of lymphocyte dynamics requires a systems-level approach. In this review, we highlight experimental and computational methods that can leverage the study of leukocyte trafficking through an immunomics lens and reveal how adaptive immunity shapes cancer.

KA-Search, a method for rapid and exhaustive sequence identity search of known antibodies

Antibodies with similar amino acid sequences, especially across their complementarity-determining regions, often share properties. Finding that an antibody of interest has a similar sequence to naturally expressed antibodies in healthy or diseased repertoires is a powerful approach for the prediction of antibody properties, such as immunogenicity or antigen specificity. However, as the number of available antibody sequences is now in the billions and continuing to grow, repertoire mining for similar sequences has become increasingly computationally expensive. Existing approaches are limited by either being low-throughput, non-exhaustive, not antibody specific, or only searching against entire chain sequences. Therefore, there is a need for a specialized tool, optimized for a rapid and exhaustive search of any antibody region against all known antibodies, to better utilize the full breadth of available repertoire sequences. We introduce Known Antibody Search (KA-Search), a tool that allows for the rapid search of billions of antibody variable domains by amino acid sequence identity across either the variable domain, the complementarity-determining regions, or a user defined antibody region. We show KA-Search in operation on the [Formula: see text]2.4 billion antibody sequences available in the OAS database. KA-Search can be used to find the most similar sequences from OAS within 30 minutes and a representative subset of 10 million sequences in less than 9 seconds. We give examples of how KA-Search can be used to obtain new insights about an antibody of interest. KA-Search is freely available at https://github.com/oxpig/kasearch .

T-Cell Receptor Repertoire Sequencing in the Era of Cancer Immunotherapy

T cells are integral components of the adaptive immune system, and their responses are mediated by unique T-cell receptors (TCR) that recognize specific antigens from a variety of biological contexts. As a result, analyzing the T-cell repertoire offers a better understanding of immune responses and of diseases like cancer. Next-generation sequencing technologies have greatly enabled the high-throughput analysis of the TCR repertoire. On the basis of our extensive experience in the field from the past decade, we provide an overview of TCR sequencing, from the initial library preparation steps to sequencing and analysis methods and finally to functional validation techniques. With regards to data analysis, we detail important TCR repertoire metrics and present several computational tools for predicting antigen specificity. Finally, we highlight important applications of TCR sequencing and repertoire analysis to understanding tumor biology and developing cancer immunotherapies.

simAIRR: simulation of adaptive immune repertoires with realistic receptor sequence sharing for benchmarking of immune state prediction methods

BACKGROUND

Machine learning (ML) has gained significant attention for classifying immune states in adaptive immune receptor repertoires (AIRRs) to support the advancement of immunodiagnostics and therapeutics. Simulated data are crucial for the rigorous benchmarking of AIRR-ML methods. Existing approaches to generating synthetic benchmarking datasets result in the generation of naive repertoires missing the key feature of many shared receptor sequences (selected for common antigens) found in antigen-experienced repertoires.

RESULTS

We demonstrate that a common approach to generating simulated AIRR benchmark datasets can introduce biases, which may be exploited for undesired shortcut learning by certain ML methods. To mitigate undesirable access to true signals in simulated AIRR datasets, we devised a simulation strategy (simAIRR) that constructs antigen-experienced-like repertoires with a realistic overlap of receptor sequences. simAIRR can be used for constructing AIRR-level benchmarks based on a range of assumptions (or experimental data sources) for what constitutes receptor-level immune signals. This includes the possibility of making or not making any prior assumptions regarding the similarity or commonality of immune state-associated sequences that will be used as true signals. We demonstrate the real-world realism of our proposed simulation approach by showing that basic ML strategies perform similarly on simAIRR-generated and real-world experimental AIRR datasets.

CONCLUSIONS

This study sheds light on the potential shortcut learning opportunities for ML methods that can arise with the state-of-the-art way of simulating AIRR datasets. simAIRR is available as a Python package: https://github.com/KanduriC/simAIRR.

Reference-based comparison of adaptive immune receptor repertoires

B and T cell receptor (immune) repertoires can represent an individual's immune history. While current repertoire analysis methods aim to discriminate between health and disease states, they are typically based on only a limited number of parameters. Here, we introduce immuneREF: a quantitative multidimensional measure of adaptive immune repertoire (and transcriptome) similarity that allows interpretation of immune repertoire variation by relying on both repertoire features and cross-referencing of simulated and experimental datasets. To quantify immune repertoire similarity landscapes across health and disease, we applied immuneREF to >2,400 datasets from individuals with varying immune states (healthy, [autoimmune] disease, and infection). We discovered, in contrast to the current paradigm, that blood-derived immune repertoires of healthy and diseased individuals are highly similar for certain immune states, suggesting that repertoire changes to immune perturbations are less pronounced than previously thought. In conclusion, immuneREF enables the population-wide study of adaptive immune response similarity across immune states.