A bioinformatic framework for immune repertoire diversity profiling enables detection of immunological status

A comprehensive evaluation of diversity measures for TCR repertoire profiling

BACKGROUND

T cells play a crucial role in adaptive immunity, as they monitor internal and external immunogenic signals through their specific receptors (TCRs). Using high-throughput sequencing, one can assess TCR repertoire in various clinical settings and describe it quantitatively by calculating a diversity index. Multiple diversity indices that capture the richness of TCRs and the evenness of their distribution have been proposed in the literature; however, there is no consensus on gold-standard measures and interpretation of each index is complex. Our goal was to examine the performance characteristics of 12 commonly used diversity indices in simulated and real-world data.

RESULTS

Simulated data were generated to evaluate how data richness and evenness affect index values using three nonparametric models. Fourteen real-world TCR datasets were obtained to examine differences in indices by analysis protocols and test their robustness to subsampling. Pielou, Basharin, d50, and Gini primarily describe evenness and highly correlate with one another. They are best suited for measuring the representation of TCR clones. Richness is best captured by S index, next Chao1 and ACE which also consider information on evenness. Shannon, Inv.Simspon, D3, D4, and Gini.Simpson measure richness and increasingly more information on evenness. More skewed TCR distributions provided more stable results in subsampling. Gini-Simpson, Pielou, and Basharin were the most robust in both simulated and experimental data.

CONCLUSIONS

Our results could guide investigators to select the best diversity index for their particular experimental question and draw attention to factors that can influence the accuracy and reproducibility of results.

TCREMP: A Bioinformatic Pipeline for Efficient Embedding of T-cell Receptor Sequences from Immune Repertoire and Single-cell Sequencing Data

T-cell receptors (TCRs) expressed by T-lymphocytes can recognize antigens presented on the surface of our cells to tell normal cells from infected and malignant ones. An extremely diverse set of TCRs is generated during T-lymphocyte formation and can be surveyed using high-throughput sequencing to tell the story of past and ongoing immune responses. Proper handling of TCR sequencing data and using it to trace antigen specificities is a challenging bioinformatic task. Here we present TCREMP (TCR embedding via Prototypes), a pipeline to digitize TCR amino acid sequences based on their similarity to commonly observed sequences that can provide a reference point for comparative analysis and aid in decoding antigen specificity prediction across TCR repertoire samples that one may find useful for adaptive immunity studies. Our results show that TCREMP embeddings can efficiently distinguish distinct antigen-specific T-cell populations based on single-cell and Major Histocompatibility Complex (MHCs)-multimer-sorted repertoire sequencing data. TCREMP is freely available at https://github.com/antigenomics/tcremp.

By integrating single-cell RNA sequencing and bulk RNA sequencing, plasma cells signature and tertiary lymphoid structures were verified to contribute to outcome in lung adenocarcinoma

Background

Tertiary lymphoid structures (TLS), consisting of T cell zones, B cell follicles, and germinal centers (GCs), are ectopic lymphoid tissue that form within non-lymphoid tissue. It has recently become a focus of attention. The TLS serve as an effective site for generating an anti-tumor inflammatory response by infiltrating immune cells, especially plasma cells. Thus, we aimed to explore the role of both TLS and plasma cells in influencing the prognosis of lung adenocarcinoma (LUAD).

Methods

Single-cell RNA sequencing (scRNA-seq) data were obtained from the Gene Expression Omnibus (GEO) database, and bulk RNA-seq data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Seurat R package was used to process scRNA-seq data and identify clusters by the marker genes with Kaplan-Meier (KM) curves plotted to predict the prognosis. Finally, hematoxylin and eosin (H&E) staining and multiplex immunofluorescence analysis were conducted to corroborate our suspicions.

Results

Seven clusters were identified in LUAD based on scRNA-seq data, with the number of B cells differing significantly between early and advanced cohorts. The plasma cells were also increased in advanced lung cancer (LC) and the number of TLS was significantly related to tumor stage. Then, via KM method, we confirmed that both plasma cells and TLS were associated with patient outcomes. Finally, H&E staining and multiplex immunofluorescence analysis verified the correlation between the two.

Conclusions

Plasma cells and TLS can effectively predict the prognosis of LUAD. In the tumor microenvironment (TME) of advanced tumors, plasma cells might be in a state of functional exhaustion. Comprehensive characterization of TLS and corresponding B‑cell pathways may help to activate the function of plasma cells and provide new strategies for cancer treatment.

Navigating the landscape: A comprehensive overview of computational approaches in therapeutic antibody design and analysis

Immunotherapy, harnessing components like antibodies, cells, and cytokines, has become a cornerstone in treating diseases such as cancer and autoimmune disorders. Therapeutic antibodies, in particular, have transformed modern medicine, providing a targeted approach that destroys disease-causing cells while sparing healthy tissues, thereby reducing the side effects commonly associated with chemotherapy. Beyond oncology, these antibodies also hold promise in addressing chronic infections where conventional therapeutics may fall short. However, antibodies identified through in vivo or in vitro methods often require extensive engineering to enhance their therapeutic potential. This optimization process, aimed at improving affinity, specificity, and reducing immunogenicity, is both challenging and costly, often involving trade-offs between critical properties. Traditional methods of antibody development, such as hybridoma technology and display techniques, are resource-intensive and time-consuming. In contrast, computational approaches offer a faster, more efficient alternative, enabling the precise design and analysis of therapeutic antibodies. These methods include sequence and structural bioinformatics approaches, next-generation sequencing-based data mining, machine learning algorithms, systems biology, immuno-informatics, and integrative approaches. These approaches are advancing the field by providing new insights and enhancing the accuracy of antibody design and analysis. In conclusion, computational approaches are essential in the development of therapeutic antibodies, significantly improving the precision and speed of discovery, optimization, and validation. Integrating these methods with experimental approaches accelerates therapeutic antibody development, paving the way for innovative strategies and treatments for various diseases ranging from cancers to autoimmune and infectious diseases.

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.

Cracking the code of adaptive immunity: The role of computational tools

In recent years, the advances in high-throughput and deep sequencing have generated a diverse amount of adaptive immune repertoire data. This surge in data has seen a proportional increase in computational methods aimed to characterize T cell receptor (TCR) repertoires. In this perspective, we will provide a brief commentary on the various domains of TCR repertoire analysis, their respective computational methods, and the ongoing challenges. Given the breadth of methods and applications of TCR analysis, we will focus our perspective on sequence-based computational methods.

Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning

The adaptive immune system holds invaluable information on past and present immune responses in the form of B and T cell receptor sequences, but we are limited in our ability to decode this information. Machine learning approaches are under active investigation for a range of tasks relevant to understanding and manipulating the adaptive immune receptor repertoire, including matching receptors to the antigens they bind, generating antibodies or T cell receptors for use as therapeutics, and diagnosing disease based on patient repertoires. Progress on these tasks has the potential to substantially improve the development of vaccines, therapeutics, and diagnostics, as well as advance our understanding of fundamental immunological principles. We outline key challenges for the field, highlighting the need for software benchmarking, targeted large-scale data generation, and coordinated research efforts.

The physiological landscape and specificity of antibody repertoires are consolidated by multiple immunizations

Diverse antibody repertoires spanning multiple lymphoid organs (i.e., bone marrow, spleen, lymph nodes) form the foundation of protective humoral immunity. Changes in their composition across lymphoid organs are a consequence of B-cell selection and migration events leading to a highly dynamic and unique physiological landscape of antibody repertoires upon antigenic challenge (e.g., vaccination). However, to what extent B cells encoding identical or similar antibody sequences (clones) are distributed across multiple lymphoid organs and how this is shaped by the strength of a humoral response remains largely unexplored. Here, we performed an in-depth systems analysis of antibody repertoires across multiple distinct lymphoid organs of immunized mice and discovered that organ-specific antibody repertoire features (i.e., germline V-gene usage and clonal expansion profiles) equilibrated upon a strong humoral response (multiple immunizations and high serum titers). This resulted in a surprisingly high degree of repertoire consolidation, characterized by highly connected and overlapping B-cell clones across multiple lymphoid organs. Finally, we revealed distinct physiological axes indicating clonal migrations and showed that antibody repertoire consolidation directly correlated with antigen specificity. Our study uncovered how a strong humoral response resulted in a more uniform but redundant physiological landscape of antibody repertoires, indicating that increases in antibody serum titers were a result of synergistic contributions from antigen-specific B-cell clones distributed across multiple lymphoid organs. Our findings provide valuable insights for the assessment and design of vaccine strategies.

Immunomolecular and reactivity landscapes of gut IgA subclasses in homeostasis and inflammatory bowel disease

The human gut includes plasma cells (PCs) expressing immunoglobulin A1 (IgA1) or IgA2, two structurally distinct IgA subclasses with elusive regulation, function, and reactivity. We show here that intestinal IgA1+ and IgA2+ PCs co-emerged early in life, comparably accumulated somatic mutations, and were enriched within short-lived CD19+ and long-lived CD19- PC subsets, respectively. IgA2+ PCs were extensively clonally related to IgA1+ PCs and a subset of them presumably emerged from IgA1+ precursors. Of note, secretory IgA1 (SIgA1) and SIgA2 dually coated a large fraction of mucus-embedded bacteria, including Akkermansia muciniphila. Disruption of homeostasis by inflammatory bowel disease (IBD) was associated with an increase in actively proliferating IgA1+ plasmablasts, a depletion in long-lived IgA2+ PCs, and increased SIgA1+SIgA2+ gut microbiota. Such increase featured enhanced IgA1 reactivity to pathobionts, including Escherichia coli, combined with depletion of beneficial A. muciniphila. Thus, gut IgA1 and IgA2 emerge from clonally related PCs and show unique changes in both frequency and reactivity in IBD.

PD1+CD4+ T cells promote receptor editing and suppress autoreactivity of CD19+CD21low B cells within the lower respiratory airways in adenovirus pneumonia

Human adenovirus (HAdV) pneumonia poses a major health burden for young children, however, factors that contribute to disease severity remain elusive. We analyzed immune cells from bronchoalveolar lavage (BAL) of children with HAdV pneumonia and found that CD19+CD21low B cells were significantly enriched in the BAL and were associated with increased autoantibody concentrations and disease severity. Myeloid cells, PD-1+CD4+ T helper cells and CD21low B cells formed tertiary lymphoid structures within the respiratory tracts. Myeloid cells promoted autoantibody production by expressing high amounts of B cell activating factor (BAFF). In contrast, PD-1+CD4+ T helper cells induced production of IgG1 and IgG3 antibodies but suppressed autoreactive IgGs by initiating B cell receptor editing. In summary, this study reveals cellular components involved in protective versus autoreactive immune pathways in the respiratory tract, and these findings provide potential therapeutic targets for severe HAdV lower respiratory tract infections.

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.

BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire

The T cell receptor (TCR) repertoire is pivotal to the human immune system, and understanding its nuances can significantly enhance our ability to forecast cancer-related immune responses. However, existing methods often overlook the intra- and inter-sequence interactions of T cell receptors (TCRs), limiting the development of sequence-based cancer-related immune status predictions. To address this challenge, we propose BertTCR, an innovative deep learning framework designed to predict cancer-related immune status using TCRs. BertTCR combines a pre-trained protein large language model with deep learning architectures, enabling it to extract deeper contextual information from TCRs. Compared to three state-of-the-art sequence-based methods, BertTCR improves the AUC on an external validation set for thyroid cancer detection by 21 percentage points. Additionally, this model was trained on over 2000 publicly available TCR libraries covering 17 types of cancer and healthy samples, and it has been validated on multiple public external datasets for its ability to distinguish cancer patients from healthy individuals. Furthermore, BertTCR can accurately classify various cancer types and healthy individuals. Overall, BertTCR is the advancing method for cancer-related immune status forecasting based on TCRs, offering promising potential for a wide range of immune status prediction tasks.

Benchmarking and integrating human B-cell receptor genomic and antibody proteomic profiling

Immunoglobulins (Ig), which exist either as B-cell receptors (BCR) on the surface of B cells or as antibodies when secreted, play a key role in the recognition and response to antigenic threats. The capability to jointly characterize the BCR and antibody repertoire is crucial for understanding human adaptive immunity. From peripheral blood, bulk BCR sequencing (bulkBCR-seq) currently provides the highest sampling depth, single-cell BCR sequencing (scBCR-seq) allows for paired chain characterization, and antibody peptide sequencing by tandem mass spectrometry (Ab-seq) provides information on the composition of secreted antibodies in the serum. Yet, it has not been benchmarked to what extent the datasets generated by these three technologies overlap and complement each other. To address this question, we isolated peripheral blood B cells from healthy human donors and sequenced BCRs at bulk and single-cell levels, in addition to utilizing publicly available sequencing data. Integrated analysis was performed on these datasets, resolved by replicates and across individuals. Simultaneously, serum antibodies were isolated, digested with multiple proteases, and analyzed with Ab-seq. Systems immunology analysis showed high concordance in repertoire features between bulk and scBCR-seq within individuals, especially when replicates were utilized. In addition, Ab-seq identified clonotype-specific peptides using both bulk and scBCR-seq library references, demonstrating the feasibility of combining scBCR-seq and Ab-seq for reconstructing paired-chain Ig sequences from the serum antibody repertoire. Collectively, our work serves as a proof-of-principle for combining bulk sequencing, single-cell sequencing, and mass spectrometry as complementary methods towards capturing humoral immunity in its entirety.

Complete response of metastatic microsatellite-stable BRAF V600E colorectal cancer to first-line oxaliplatin-based chemotherapy and immune checkpoint blockade

The randomized METIMMOX trial (NCT03388190) examined if patients with previously untreated, unresectable abdominal metastases from microsatellite-stable (MSS) colorectal cancer (CRC) might benefit from potentially immunogenic, short-course oxaliplatin-based chemotherapy alternating with immune checkpoint blockade (ICB). Three of 38 patients assigned to this experimental treatment had metastases from BRAF-mutant MSS-CRC, in general a poor-prognostic subgroup explored here. The ≥70-year-old females presented with ascending colon adenocarcinomas with intermediate tumor mutational burden (6.2-11.8 mutations per megabase). All experienced early disappearance of the primary tumor followed by complete response of all overt metastatic disease, resulting in progression-free survival as long as 20-35 months. However, they encountered recurrence at previously unaffected sites and ultimately sanctuary organs, or as intrahepatic tumor evolution reflected in the terminal loss of initially induced T-cell clonality in liver metastases. Yet, the remarkable first-line responses to short-course oxaliplatin-based chemotherapy alternating with ICB may offer a novel therapeutic option to a particularly hard-to-treat MSS-CRC subgroup.

Self-supervised learning of T cell receptor sequences exposes core properties for T cell membership

The T cell receptor (TCR) repertoire is an extraordinarily diverse collection of TCRs essential for maintaining the body's homeostasis and response to threats. In this study, we compiled an extensive dataset of more than 4200 bulk TCR repertoire samples, encompassing 221,176,713 sequences, alongside 6,159,652 single-cell TCR sequences from over 400 samples. From this dataset, we then selected a representative subset of 5 million bulk sequences and 4.2 million single-cell sequences to train two specialized Transformer-based language models for bulk (CVC) and single-cell (scCVC) TCR repertoires, respectively. We show that these models successfully capture TCR core qualities, such as sharing, gene composition, and single-cell properties. These qualities are emergent in the encoded TCR latent space and enable classification into TCR-based qualities such as public sequences. These models demonstrate the potential of Transformer-based language models in TCR downstream applications.

Decoding Immuno-Competence: A Novel Analysis of Complete Blood Cell Count Data in COVID-19 Outcomes

BACKGROUND

While 'immuno-competence' is a well-known term, it lacks an operational definition. To address this omission, this study explored whether the temporal and structured data of the complete blood cell count (CBC) can rapidly estimate immuno-competence. To this end, one or more ratios that included data on all monocytes, lymphocytes and neutrophils were investigated.

MATERIALS AND METHODS

Longitudinal CBC data collected from 101 COVID-19 patients (291 observations) were analyzed. Dynamics were estimated with several approaches, which included non-structured (the classic CBC format) and structured data. Structured data were assessed as complex ratios that capture multicellular interactions among leukocytes. In comparing survivors with non-survivors, the hypothesis that immuno-competence may exhibit feedback-like (oscillatory or cyclic) responses was tested.

RESULTS

While non-structured data did not distinguish survivors from non-survivors, structured data revealed immunological and statistical differences between outcomes: while survivors exhibited oscillatory data patterns, non-survivors did not. In survivors, many variables (including IL-6, hemoglobin and several complex indicators) showed values above or below the levels observed on day 1 of the hospitalization period, displaying L-shaped data distributions (positive kurtosis). In contrast, non-survivors did not exhibit kurtosis. Three immunologically defined data subsets included only survivors. Because information was based on visual patterns generated in real time, this method can, potentially, provide information rapidly.

DISCUSSION

The hypothesis that immuno-competence expresses feedback-like loops when immunological data are structured was not rejected. This function seemed to be impaired in immuno-suppressed individuals. While this method rapidly informs, it is only a guide that, to be confirmed, requires additional tests. Despite this limitation, the fact that three protective (survival-associated) immunological data subsets were observed since day 1 supports many clinical decisions, including the early and personalized prognosis and identification of targets that immunomodulatory therapies could pursue. Because it extracts more information from the same data, structured data may replace the century-old format of the CBC.

Detecting T-cell clonal expansions and quantifying clone survival using deep profiling of immune repertoires

An individual's T-cell repertoire constantly changes under the influence of external and internal factors. Cells that do not receive a stimulatory signal die, while those that encounter and recognize a pathogen or receive a co-stimulatory signal divide, resulting in clonal expansions. T-cell clones can be traced by monitoring the presence of their unique T-cell receptor (TCR) sequence, which is assembled de novo through a process known as V(D)J rearrangement. Tracking T cells can provide valuable insights into the survival of cells after hematopoietic stem cell transplantation (HSCT) or cancer treatment response and can indicate the induction of protective immunity by vaccination. In this study, we report a bioinformatic method for quantifying the T-cell repertoire dynamics from TCR sequencing data. We demonstrate its utility by measuring the T-cell repertoire stability in healthy donors, by quantifying the effect of donor lymphocyte infusion (DLI), and by tracking the fate of the different T-cell subsets in HSCT patients and the expansion of pathogen-specific clones in vaccinated individuals.

Systematic evaluation of B-cell clonal family inference approaches

The reconstruction of clonal families (CFs) in B-cell receptor (BCR) repertoire analysis is a crucial step to understand the adaptive immune system and how it responds to antigens. The BCR repertoire of an individual is formed throughout life and is diverse due to several factors such as gene recombination and somatic hypermutation. The use of Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) using next generation sequencing enabled the generation of full BCR repertoires that also include rare CFs. The reconstruction of CFs from AIRR-seq data is challenging and several approaches have been developed to solve this problem. Currently, most methods use the heavy chain (HC) only, as it is more variable than the light chain (LC). CF reconstruction options include the definition of appropriate sequence similarity measures, the use of shared mutations among sequences, and the possibility of reconstruction without preliminary clustering based on V- and J-gene annotation. In this study, we aimed to systematically evaluate different approaches for CF reconstruction and to determine their impact on various outcome measures such as the number of CFs derived, the size of the CFs, and the accuracy of the reconstruction. The methods were compared to each other and to a method that groups sequences based on identical junction sequences and another method that only determines subclones. We found that after accounting for data set variability, in particular sequencing depth and mutation load, the reconstruction approach has an impact on part of the outcome measures, including the number of CFs. Simulations indicate that unique junctions and subclones should not be used as substitutes for CF and that more complex methods do not outperform simpler methods. Also, we conclude that different approaches differ in their ability to correctly reconstruct CFs when not considering the LC and to identify shared CFs. The results showed the effect of different approaches on the reconstruction of CFs and highlighted the importance of choosing an appropriate method.

The multilevel extensive diversity across the cynomolgus macaque captured by ultra-deep adaptive immune receptor repertoire sequencing

The extent to which AIRRs differ among and within individuals remains elusive. Via ultra-deep repertoire sequencing of 22 and 25 tissues in three cynomolgus macaques, respectively, we identified 84 and 114 novel IGHV and TRBV alleles, confirming 72 (85.71%) and 100 (87.72%) of them. The heterogeneous V gene usage patterns were influenced, in turn, by genetics, isotype (for BCRs only), tissue group, and tissue. A higher proportion of intragroup shared clones in the intestinal tissues than those in other tissues suggests a close intra-intestinal adaptive immunity network. Significantly higher mutation burdens in the public clones and the inter-tissue shared IgM and IgD clones indicate that they might target the shared antigens. This study reveals the extensive heterogeneity of the AIRRs at various levels and has broad fundamental and clinical implications. The data generated here will serve as an invaluable resource for future studies on adaptive immunity in health and diseases.

Comparison of T-cell receptor diversity of people with myalgic encephalomyelitis versus controls

OBJECTIVE

Myalgic Encephalomyelitis (ME; sometimes referred to as Chronic Fatigue Syndrome) is a chronic disease without laboratory test, detailed aetiological understanding or effective therapy. Its symptoms are diverse, but it is distinguished from other fatiguing illnesses by the experience of post-exertional malaise, the worsening of symptoms even after minor physical or mental exertion. Its frequent onset after infection suggests autoimmune involvement or that it arises from abnormal T-cell activation.

RESULTS

To test this hypothesis, we sequenced the genomic loci of α/δ, β and γ T-cell receptors (TCR) from 40 human blood samples from each of four groups: severely affected people with ME; mildly or moderately affected people with ME; people diagnosed with Multiple Sclerosis, as disease controls; and, healthy controls. Seeking to automatically classify these individuals' samples by their TCR repertoires, we applied P-SVM, a machine learning method. However, despite working well on a simulated data set, this approach did not allow statistically significant partitioning of samples into the four subgroups. Our findings do not support the hypothesis that blood samples from people with ME frequently contain altered T-cell receptor diversity.

Application and challenges of TCR and BCR sequencing to investigate T- and B-cell clonality in elastase-induced experimental murine abdominal aortic aneurysm

Background

An abdominal aortic aneurysm (AAA) is a life-threatening cardiovascular disease. Although its pathogenesis is still poorly understood, recent evidence suggests that AAA displays autoimmune disease characteristics. Particularly, T cells responding to AAA-related antigens in the aortic wall may contribute to an initial immune response. Single-cell RNA (scRNA) T cell receptor (TCR) and B cell receptor (BCR) sequencing is a powerful tool for investigating clonality. However, difficulties such as limited numbers of isolated cells must be considered during implementation and data analysis, making biological interpretation challenging. Here, we perform a representative single-cell immune repertoire analysis in experimental murine AAA and show a reliable bioinformatic processing pipeline highlighting opportunities and limitations of this approach.

Methods

We performed scRNA TCR and BCR sequencing of isolated lymphocytes from the infrarenal aorta of male C57BL/6J mice 3, 7, 14, and 28 days after AAA induction via elastase perfusion of the aorta. Sham-operated mice at days 3 and 28 and non-operated mice served as controls.

Results

Comparison of complementarity-determining region (CDR3) length distribution of 179 B cells and 796 T cells revealed neither differences between AAA and control nor between the disease stages. We found no clonal expansion of B cells in AAA. For T cells, we identified several clones in 11 of 16 AAA samples and one of eight control samples. Immune receptor repertoire comparison indicated that only a few clones were shared between the individual AAA samples. The most frequently used V-genes in the TCR beta chain in AAA were TRBV3, TRBV19, and the splicing variant TRBV12-2 + TRBV13-2.

Conclusion

We found no clonal expansion of B cells but evidence for clonal expansion of T cells in elastase-induced AAA in mice. Our findings imply that a more precise characterization of TCR and BCR distribution requires a more extensive number of lymphocytes to prevent undersampling and potentially detect rare clones. Thus, further experiments are necessary to confirm our findings. In summary, this paper examines TCR and BCR sequencing results, identifies limitations and pitfalls, and offers guidance for future studies.

Identification of TCR repertoire patterns linked with anti-cancer immunotherapy

The highly diverse T cell receptor (TCR) repertoire is a crucial component of the adaptive immune system that aids in the protection against a wide variety of pathogens. This TCR repertoire, comprising the collection of all TCRs in an individual, is a valuable source of information on both recent and ongoing T cell activation. Cancer cells, like pathogens, have the ability to trigger an adaptive immune response. However, because cancer cells use a variety of strategies to escape immune responses, this is often insufficient to completely eradicate them. As a result, immunotherapy is a promising treatment option for cancer patients. This treatment is expected to increase T cell activation and subsequently alter the TCR repertoire composition in these patients. Monitoring TCR repertoires before and after immunotherapy can therefore provide additional insight into T cell responses and might identify cancer-associated TCR sequences. Here we present a computational strategy to identify those changes in the TCR repertoire that occur after treatment with immunotherapy. Since this method allows the identification of TCR patterns that might be treatment-associated, it can help future research by revealing those patterns that are related with response. This TCR analysis workflow is illustrated using public data from three different cancer patients who received anti-PD-1 treatment.

Shared bias in H chain V-J pairing in naive and memory B cells

Introduction

H chain rearrangement in B cells is a two-step process where first DH binds JH, and only then VH is joined to the complex. As such, there is no direct rearrangement between VH and JH.

Results

Nevertheless, we here show that the VHJH combinations frequency in humans deviates from the one expected based on each gene usage frequency. This bias is observed mainly in functional rearrangements, and much less in out-of-frame rearrangements. The bias cannot be explained by preferred binding for DH genes or a preferred reading frame. Preferred VH JH combinations are shared between donors.

Discussion

These results suggest a common structural mechanism for these biases. Through development, thepreferred VH JH combinations evolve during peripheral selection to become stronger, but less shared. We propose that peripheral Heavy chain VH JH usage is initially shaped by a structural selection before the naive B cellstate, followed by pathogen-induced selection for host specific VH-JH pairs.

Rigorous benchmarking of T-cell receptor repertoire profiling methods for cancer RNA sequencing

The ability to identify and track T-cell receptor (TCR) sequences from patient samples is becoming central to the field of cancer research and immunotherapy. Tracking genetically engineered T cells expressing TCRs that target specific tumor antigens is important to determine the persistence of these cells and quantify tumor responses. The available high-throughput method to profile TCR repertoires is generally referred to as TCR sequencing (TCR-Seq). However, the available TCR-Seq data are limited compared with RNA sequencing (RNA-Seq). In this paper, we have benchmarked the ability of RNA-Seq-based methods to profile TCR repertoires by examining 19 bulk RNA-Seq samples across 4 cancer cohorts including both T-cell-rich and T-cell-poor tissue types. We have performed a comprehensive evaluation of the existing RNA-Seq-based repertoire profiling methods using targeted TCR-Seq as the gold standard. We also highlighted scenarios under which the RNA-Seq approach is suitable and can provide comparable accuracy to the TCR-Seq approach. Our results show that RNA-Seq-based methods are able to effectively capture the clonotypes and estimate the diversity of TCR repertoires, as well as provide relative frequencies of clonotypes in T-cell-rich tissues and low-diversity repertoires. However, RNA-Seq-based TCR profiling methods have limited power in T-cell-poor tissues, especially in highly diverse repertoires of T-cell-poor tissues. The results of our benchmarking provide an additional appealing argument to incorporate RNA-Seq into the immune repertoire screening of cancer patients as it offers broader knowledge into the transcriptomic changes that exceed the limited information provided by TCR-Seq.

The prevalence of IgG antibodies against milk and milk antigens in patients with multiple sclerosis

Introduction

Multiple sclerosis (MS) is a chronic demyelinating disease of the central nervous system (CNS). The pathophysiology of MS is complex and is said to be influenced by multiple environmental determinants, including diet. We and others have previously demonstrated how consumption of bovine milk can aggravate disease severity in MS patients, which can be explained by molecular mimicry between milk antigens and those expressed within the CNS. In this study we set out to identify alternatives to drinking cow milk which might be less detrimental to MS patients who have a genetic predisposition towards developing antibody titers against bovine milk antigens that cross-react with CNS antigens.

Methods

To this end, we screened 35 patients with MS and 20 healthy controls for their IgG reactivity against an array of animal-sourced milk, plant-based alternatives as well as individual antigens from bovine milk.

Results

We demonstrate that MS patients have a significantly higher IgG response to animal-sourced milk, especially cow milk, in comparison to healthy donors. We also show that the reactivity to cow milk in MS patients can be attributed to reactivity against different bovine milk antigens. Finally, our correlation data indicate the co-existence of antibodies to individual bovine milk antigens and their corresponding cross-reactive CNS antigens.

Discussion

Taken together, we suggest screening of blood from MS patients for antibodies against different types of milk and milk antigens in order to establish a personalized diet regimen.

Abalign: a comprehensive multiple sequence alignment platform for B-cell receptor immune repertoires

The utilization of high-throughput sequencing (HTS) for B-cell receptor (BCR) immune repertoire analysis has become widespread in the fields of adaptive immunity and antibody drug development. However, the sheer volume of sequences generated by these experiments presents a challenge in data processing. Specifically, multiple sequence alignment (MSA), a critical aspect of BCR analysis, remains inadequate for handling massive BCR sequencing data and lacks the ability to provide immunoglobulin-specific information. To address this gap, we introduce Abalign, a standalone program specifically designed for ultrafast MSA of BCR/antibody sequences. Benchmark tests demonstrate that Abalign achieves comparable or even better accuracy than state-of-the-art MSA tools, and shows remarkable advantages in terms of speed and memory consumption, reducing the time required for high-throughput analysis from weeks to hours. In addition to its alignment capabilities, Abalign offers a broad range of BCR analysis features, including extracting BCRs, constructing lineage trees, assigning VJ genes, analyzing clonotypes, profiling mutations, and comparing BCR immune repertoires. With its user-friendly graphic interface, Abalign can be easily run on personal computers instead of computing clusters. Overall, Abalign is an easy-to-use and effective tool that enables researchers to analyze massive BCR/antibody sequences, leading to new discoveries in the field of immunoinformatics. The software is freely available at http://cao.labshare.cn/abalign/.

Exploring the impact of clonal definition on B-cell diversity: implications for the analysis of immune repertoires

The adaptive immune system has the extraordinary ability to produce a broad range of immunoglobulins that can bind a wide variety of antigens. During adaptive immune responses, activated B cells duplicate and undergo somatic hypermutation in their B-cell receptor (BCR) genes, resulting in clonal families of diversified B cells that can be related back to a common ancestor. Advances in high-throughput sequencing technologies have enabled the high-throughput characterization of B-cell repertoires, however, the accurate identification of clonally related BCR sequences remains a major challenge. In this study, we compare three different clone identification methods on both simulated and experimental data, and investigate their impact on the characterization of B-cell diversity. We observe that different methods lead to different clonal definitions, which affects the quantification of clonal diversity in repertoire data. Our analyses show that direct comparisons between clonal clusterings and clonal diversity of different repertoires should be avoided if different clone identification methods were used to define the clones. Despite this variability, the diversity indices inferred from the repertoires' clonal characterization across samples show similar patterns of variation regardless of the clonal identification method used. We find the Shannon entropy to be the most robust in terms of the variability of diversity rank across samples. Our analysis also suggests that the traditional germline gene alignment-based method for clonal identification remains the most accurate when the complete information about the sequence is known, but that alignment-free methods may be preferred for shorter sequencing read lengths. We make our implementation freely available as a Python library cdiversity.

Signatures of T cell immunity revealed using sequence similarity with TCRDivER algorithm

Changes in the T cell receptor (TCR) repertoires have become important markers for monitoring disease or therapy progression. With the rise of immunotherapy usage in cancer, infectious and autoimmune disease, accurate assessment and comparison of the "state" of the TCR repertoire has become paramount. One important driver of change within the repertoire is T cell proliferation following immunisation. A way of monitoring this is by investigating large clones of individual T cells believed to bind epitopes connected to the disease. However, as a single target can be bound by many different TCRs, monitoring individual clones cannot fully account for T cell cross-reactivity. Moreover, T cells responding to the same target often exhibit higher sequence similarity, which highlights the importance of accounting for TCR similarity within the repertoire. This complexity of binding relationships between a TCR and its target convolutes comparison of immune responses between individuals or comparisons of TCR repertoires at different timepoints. Here we propose TCRDivER algorithm (T cell Receptor Diversity Estimates for Repertoires), a global method of T cell repertoire comparison using diversity profiles sensitive to both clone size and sequence similarity. This approach allowed for distinction between spleen TCR repertoires of immunised and non-immunised mice, showing the need for including both facets of repertoire changes simultaneously. The analysis revealed biologically interpretable relationships between sequence similarity and clonality. These aid in understanding differences and separation of repertoires stemming from different biological context. With the rise of availability of sequencing data we expect our tool to find broad usage in clinical and research applications.

Reactivity Graph Yields Interpretable IgM Repertoire Signatures as Potential Tumor Biomarkers

Combining adaptive and innate immunity induction modes, the repertoire of immunoglobulin M (IgM) can reflect changes in the internal environment including malignancies. Previously, it was shown that a mimotope library reflecting the public IgM repertoire of healthy donors (IgM IgOme) can be mined for efficient probes of tumor biomarker antibody reactivities. To better explore the interpretability of this approach for IgM, solid tumor-related profiles of IgM reactivities to linear epitopes of actual tumor antigens and viral epitopes were studied. The probes were designed as oriented planar microarrays of 4526 peptide sequences (as overlapping 15-mers) derived from 24 tumor-associated antigens and 209 cancer-related B cell epitopes from 30 viral antigens. The IgM reactivity in sera from 21 patients with glioblastoma multiforme, brain metastases of other tumors, and non-tumor-bearing neurosurgery patients was thus probed in a proof-of-principle study. A graph representation of the binding data was developed, which mapped the cross-reactivity of the mixture of IgM (poly)specificities, delineating different antibody footprints in the features of the graph-neighborhoods and cliques. The reactivity graph mapped the major features of the IgM repertoire such as the magnitude of the reactivity (titer) and major cross-reactivities, which correlated with blood group reactivity, non-self recognition, and even idiotypic specificities. A correlation between an aspect of this image of the IgM IgOme, namely, small cliques reflecting rare self-reactivities and the capacity of subsets of the epitopes to separate the diagnostic groups studied was found. In this way, the graph representation helped the feature selection in its filtering step and provided reduced feature sets, which, after recursive feature elimination, produced a classifier containing 51 peptide reactivities separating the three diagnostic groups with an unexpected efficiency. Thus, IgM IgOme approaches to repertoire studies is greatly augmented when self/viral antigens are used and the data are represented as a reactivity graph. This approach is most general, and if it is applicable to tumors in immunologically privileged sites, it can be applied to any solid tumors, for instance, breast or lung cancer.

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.

Dynamic kernel matching for non-conforming data: A case study of T cell receptor datasets

Most statistical classifiers are designed to find patterns in data where numbers fit into rows and columns, like in a spreadsheet, but many kinds of data do not conform to this structure. To uncover patterns in non-conforming data, we describe an approach for modifying established statistical classifiers to handle non-conforming data, which we call dynamic kernel matching (DKM). As examples of non-conforming data, we consider (i) a dataset of T-cell receptor (TCR) sequences labelled by disease antigen and (ii) a dataset of sequenced TCR repertoires labelled by patient cytomegalovirus (CMV) serostatus, anticipating that both datasets contain signatures for diagnosing disease. We successfully fit statistical classifiers augmented with DKM to both datasets and report the performance on holdout data using standard metrics and metrics allowing for indeterminant diagnoses. Finally, we identify the patterns used by our statistical classifiers to generate predictions and show that these patterns agree with observations from experimental studies.

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.

T cell receptor repertoire sequencing reveals chemotherapy-driven clonal expansion in colorectal liver metastases

BACKGROUND

Colorectal liver metastasis (CLM) is a leading cause of colorectal cancer mortality, and the response to immune checkpoint inhibition (ICI) in microsatellite-stable CRC has been disappointing. Administration of cytotoxic chemotherapy may cause increased density of tumor-infiltrating T cells, which has been associated with improved response to ICI. This study aimed to quantify and characterize T-cell infiltration in CLM using T-cell receptor (TCR) repertoire sequencing. Eighty-five resected CLMs from patients included in the Oslo CoMet study were subjected to TCR repertoire sequencing. Thirty-five and 15 patients had received neoadjuvant chemotherapy (NACT) within a short or long interval, respectively, prior to resection, while 35 patients had not been exposed to NACT. T-cell fractions were calculated, repertoire clonality was analyzed based on Hill evenness curves, and TCR sequence convergence was assessed using network analysis.

RESULTS

Increased T-cell fractions (10.6% vs. 6.3%) were detected in CLMs exposed to NACT within a short interval prior to resection, while modestly increased clonality was observed in NACT-exposed tumors independently of the timing of NACT administration and surgery. While private clones made up >90% of detected clones, network connectivity analysis revealed that public clones contributed the majority of TCR sequence convergence.

CONCLUSIONS

TCR repertoire sequencing can be used to quantify T-cell infiltration and clonality in clinical samples. This study provides evidence to support chemotherapy-driven T-cell clonal expansion in CLM in a clinical context.

Unraveling the Transcriptomic Signatures of Homologous Recombination Deficiency in Ovarian Cancers

Homologous recombination deficiency (HRD) is a crucial driver of tumorigenesis by inducing impaired repair of double-stranded DNA breaks. Although HRD possibly triggers the production of numerous tumor neoantigens that sufficiently stimulate and activate various tumor-immune responses, a comprehensive understanding of the HRD-associated tumor microenvironment is elusive. To investigate the effect of HRD on the selective enrichment of transcriptomic signatures, 294 cases from The Cancer Genome Atlas-Ovarian Cancer project with both RNA-sequencing and SNP array data are analyzed. Differentially expressed gene analysis and network analysis are performed to identify HRD-specific signatures. Gene-sets associated with mitochondrial activation, including enhanced oxidative phosphorylation (OxPhos), are significantly enriched in the HRD-high group. Furthermore, a wide range of immune cell activation signatures is enriched in HRD-high cases of high-grade serous ovarian cancer (HGSOC). On further cell-type-specific analysis, M1-like macrophage genes are significantly enriched in HRD-high HGSOC cases, whereas M2-macrophage-related genes are not. The immune-response-associated genomic features, including tumor mutation rate, neoantigens, and tumor mutation burdens, correlated with HRD scores. In conclusion, the results of this study highlight the biological properties of HRD, including enhanced energy metabolism, increased tumor neoantigens and tumor mutation burdens, and consequent exacerbation of immune responses, particularly the enrichment of M1-like macrophages in HGSOC cases.

Advances in antibody discovery from human BCR repertoires

Antibodies make up an important and growing class of compounds used for the diagnosis or treatment of disease. While traditional antibody discovery utilized immunization of animals to generate lead compounds, technological innovations have made it possible to search for antibodies targeting a given antigen within the repertoires of B cells in humans. Here we group these innovations into four broad categories: cell sorting allows the collection of cells enriched in specificity to one or more antigens; BCR sequencing can be performed on bulk mRNA, genomic DNA or on paired (heavy-light) mRNA; BCR repertoire analysis generally involves clustering BCRs into specificity groups or more in-depth modeling of antibody-antigen interactions, such as antibody-specific epitope predictions; validation of antibody-antigen interactions requires expression of antibodies, followed by antigen binding assays or epitope mapping. Together with innovations in Deep learning these technologies will contribute to the future discovery of diagnostic and therapeutic antibodies directly from humans.

Perturbations in the T cell receptor β repertoire during malaria infection in children: A preliminary study

The changes occurring in the T cell repertoire during clinical malaria infection in children remain unknown. In this study, we undertook the first detailed comparative study of the T cell repertoire in African children with and without clinical malaria to test the hypothesis that clonotypic expansions that occur during P. falciparum infection will contribute to the generation of a T cell repertoire that is unique to each disease state. We profiled the complementarity-determining region 3 (CDR3) of the TCRβ chain sequences from children with Plasmodium falciparum infections (asymptomatic, uncomplicated and severe malaria) and compared these with sequences from healthy children. Interestingly, we discovered that children with symptomatic malaria have a lower TCR diversity and frequency of shared (or “public”) TCR sequences compared to asymptomatic children. Also, TCR diversity was inversely associated with parasitemia. Furthermore, by clustering TCR sequences based on their predicted antigen specificities, we identified a specificity cluster, with a 4-mer amino acid motif, that is overrepresented in the asymptomatic group compared to the diseased groups. Further investigations into this finding may help in delineating important antigenic targets for vaccine and therapeutic development. The results show that the T cell repertoire in children is altered during malaria, suggesting that exposure to P. falciparum antigens disrupts the adaptive immune response, which is an underlying feature of the disease.

AIRRscape: An interactive tool for exploring B-cell receptor repertoires and antibody responses

The sequencing of antibody repertoires of B-cells at increasing coverage and depth has led to the identification of vast numbers of immunoglobulin heavy and light chains. However, the size and complexity of these Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) datasets makes it difficult to perform exploratory analyses. To aid in data exploration, we have developed AIRRscape, an R Shiny-based interactive web browser application that enables B-cell receptor (BCR) and antibody feature discovery through comparisons among multiple repertoires. Using AIRR-seq data as input, AIRRscape starts by aggregating and sorting repertoires into interactive and explorable bins of germline V-gene, germline J-gene, and CDR3 length, providing a high-level view of the entire repertoire. Interesting subsets of repertoires can be quickly identified and selected, and then network topologies of CDR3 motifs can be generated for further exploration. Here we demonstrate AIRRscape using patient BCR repertoires and sequences of published monoclonal antibodies to investigate patterns of humoral immunity to three viral pathogens: SARS-CoV-2, HIV-1, and DENV (dengue virus). AIRRscape reveals convergent antibody sequences among datasets for all three pathogens, although HIV-1 antibody datasets display limited convergence and idiosyncratic responses. We have made AIRRscape available as a web-based Shiny application, along with code on GitHub to encourage its open development and use by immuno-informaticians, virologists, immunologists, vaccine developers, and other scientists that are interested in exploring and comparing multiple immune receptor repertoires.

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.

Apyrase-mediated amplification of secretory IgA promotes intestinal homeostasis

Secretory immunoglobulin A (SIgA) interaction with commensal bacteria conditions microbiota composition and function. However, mechanisms regulating reciprocal control of microbiota and SIgA are not defined. Bacteria-derived adenosine triphosphate (ATP) limits T follicular helper (Tfh) cells in the Peyer's patches (PPs) via P2X7 receptor (P2X7R) and thereby SIgA generation. Here we show that hydrolysis of extracellular ATP (eATP) by apyrase results in amplification of the SIgA repertoire. The enhanced breadth of SIgA in mice colonized with apyrase-releasing Escherichia coli influences topographical distribution of bacteria and expression of genes involved in metabolic versus immune functions in the intestinal epithelium. SIgA-mediated conditioning of bacteria and enterocyte function is reflected by differences in nutrient absorption in mice colonized with apyrase-expressing bacteria. Apyrase-induced SIgA improves intestinal homeostasis and attenuates barrier impairment and susceptibility to infection by enteric pathogens in antibiotic-induced dysbiosis. Therefore, amplification of SIgA by apyrase can be leveraged to restore intestinal fitness in dysbiotic conditions.

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.

Development and application of a next-generation sequencing protocol and bioinformatics pipeline for the comprehensive analysis of the canine immunoglobulin repertoire

Profiling the adaptive immune repertoire using next generation sequencing (NGS) has become common in human medicine, showing promise in characterizing clonal expansion of B cell clones through analysis of B cell receptors (BCRs) in patients with lymphoid malignancies. In contrast, most work evaluating BCR repertoires in dogs has employed traditional PCR-based approaches analyzing the IGH locus only. The objectives of this study were to: (1) describe a novel NGS protocol to evaluate canine BCRs; (2) develop a bioinformatics pipeline for processing canine BCR sequencing data; and (3) apply these methods to derive insights into BCR repertoires of healthy dogs and dogs undergoing treatment for B-cell lymphoma. RNA from peripheral blood mononuclear cells of healthy dogs (n = 25) and dogs newly diagnosed with intermediate-to-large B-cell lymphoma (n = 18) with intent to pursue chemotherapy was isolated, converted into cDNA and sequenced by NGS. The BCR repertoires were identified and quantified using a novel analysis pipeline. The IGK repertoires of the healthy dogs were far less diverse compared to IGL which, as with IGH, was highly diverse. Strong biases at key positions within the CDR3 sequence were identified within the healthy dog BCR repertoire. For a subset of the dogs with B-cell lymphoma, clonal expansion of specific IGH sequences pre-treatment and reduction post-treatment was observed. The degree of expansion and reduction correlated with the clinical outcome in this subset. Future studies employing these techniques may improve disease monitoring, provide earlier recognition of disease progression, and ultimately lead to more targeted therapeutics.

An Analysis of Different Distance-Linkage Methods for Clustering Gene Expression Data and Observing Pleiotropy: Empirical Study

BACKGROUND

Large amounts of biological data have been generated over the last few decades, encouraging scientists to look for connections between genes that cause various diseases. Clustering illustrates such a relationship between numerous species and genes. Finding an appropriate distance-linkage metric to construct clusters from diverse biological data sets has thus become critical. Pleiotropy is also important for a gene's expression to vary and create varied consequences in living things. Finding the pleiotropy of genes responsible for various diseases has become a major research challenge.

OBJECTIVE

Our goal was to establish the optimal distance-linkage strategy for creating reliable clusters from diverse data sets and identifying the common genes that cause various tumors to observe genes with pleiotropic effect.

METHODS

We considered 4 linking methods-single, complete, average, and ward-and 3 distance metrics-Euclidean, maximum, and Manhattan distance. For assessing the quality of different sets of clusters, we used a fitness function that combines silhouette width and within-cluster distance.

RESULTS

According to our findings, the maximum distance measure produces the highest-quality clusters. Moreover, for medium data set, the average linkage method, and for large data set, the ward linkage method works best. The outcome is not improved by using ensemble clustering. We also discovered genes that cause 3 different cancers and used gene enrichment to confirm our findings.

CONCLUSIONS

Accuracy is crucial in clustering, and we investigated the accuracy of numerous clustering techniques in our research. Other studies may aid related works if the data set is similar to ours.

A Deep Learning Model for Accurate Diagnosis of Infection Using Antibody Repertoires

The adaptive immune receptor repertoire consists of the entire set of an individual's BCRs and TCRs and is believed to contain a record of prior immune responses and the potential for future immunity. Analyses of TCR repertoires via deep learning (DL) methods have successfully diagnosed cancers and infectious diseases, including coronavirus disease 2019. However, few studies have used DL to analyze BCR repertoires. In this study, we collected IgG H chain Ab repertoires from 276 healthy control subjects and 326 patients with various infections. We then extracted a comprehensive feature set consisting of 10 subsets of repertoire-level features and 160 sequence-level features and tested whether these features can distinguish between infected individuals and healthy control subjects. Finally, we developed an ensemble DL model, namely, DL method for infection diagnosis (https://github.com/chenyuan0510/DeepID), and used this model to differentiate between the infected and healthy individuals. Four subsets of repertoire-level features and four sequence-level features were selected because of their excellent predictive performance. The DL method for infection diagnosis outperformed traditional machine learning methods in distinguishing between healthy and infected samples (area under the curve = 0.9883) and achieved a multiclassification accuracy of 0.9104. We also observed differences between the healthy and infected groups in V genes usage, clonal expansion, the complexity of reads within clone, the physical properties in the α region, and the local flexibility of the CDR3 amino acid sequence. Our results suggest that the Ab repertoire is a promising biomarker for the diagnosis of various infections.

Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification

BACKGROUND

Machine learning (ML) methodology development for the classification of immune states in adaptive immune receptor repertoires (AIRRs) has seen a recent surge of interest. However, so far, there does not exist a systematic evaluation of scenarios where classical ML methods (such as penalized logistic regression) already perform adequately for AIRR classification. This hinders investigative reorientation to those scenarios where method development of more sophisticated ML approaches may be required.

RESULTS

To identify those scenarios where a baseline ML method is able to perform well for AIRR classification, we generated a collection of synthetic AIRR benchmark data sets encompassing a wide range of data set architecture-associated and immune state-associated sequence patterns (signal) complexity. We trained ≈1,700 ML models with varying assumptions regarding immune signal on ≈1,000 data sets with a total of ≈250,000 AIRRs containing ≈46 billion TCRβ CDR3 amino acid sequences, thereby surpassing the sample sizes of current state-of-the-art AIRR-ML setups by two orders of magnitude. We found that L1-penalized logistic regression achieved high prediction accuracy even when the immune signal occurs only in 1 out of 50,000 AIR sequences.

CONCLUSIONS

We provide a reference benchmark to guide new AIRR-ML classification methodology by (i) identifying those scenarios characterized by immune signal and data set complexity, where baseline methods already achieve high prediction accuracy, and (ii) facilitating realistic expectations of the performance of AIRR-ML models given training data set properties and assumptions. Our study serves as a template for defining specialized AIRR benchmark data sets for comprehensive benchmarking of AIRR-ML methods.

High-Throughput Sequencing of Complementarity Determining Region 3 in the Heavy Chain of B-Cell Receptor in Renal Transplant Recipients: A Preliminary Report

Background: Graft failure resulting from rejection or any other adverse event usually originates from an aberrant and/or exaggerated immune response and is often catastrophic in renal transplantation. So, it is essential to monitor patients’ immune status for detecting a rejection/graft failure early on. Methods: We monitored the sequence change of complementary determining region 3 (CDR3) in B-cell receptor (BCR) immunoglobulin heavy-chain (IGH) immune repertoire (iR) in 14 renal transplant patients using next-generation sequencing (NGS), correlating its diversity to various clinical events occurring after transplantation. BCR-IGH-CDR3 in peripheral blood mononuclear cells was sequenced along the post-transplantation course by NGS using the iRweb server. Results: Datasets covering VDJ regions of BCR-IGH-CDR3 indicated clonal diversity (D50) variations along the post-transplant course. Furthermore, principal component analysis showed the clustering of these sequence variations. A total of 544 shared sequences were identified before transplantation. D50 remained low in three patients receiving rituximab. Among them, one’s D50 resumed after 3 m, indicating graft tolerance. The D50 rapidly increased after grafting and decreased thereafter in four patients without rejection, decreased in two patients with T-cell-mediated rejection (TCMR) and exhibited a sharp down-sliding after 3 m in two patients receiving donations after cardiac death (DCD). In another two patients with TCMR, D50 was low just before individual episodes, but either became persistently low or returned to a plateau, depending on the failure or success of the immunosuppressive treatments. Shared CDR3 clonal expansions correlated to D50 changes. Agglomerative hierarchical clustering showed a commonly shared CDR3 sequence and at least two different clusters in five patients. Conclusions: Clonal diversity in BCR-IGH-CDR3 varied depending on clinical courses of 14 renal transplant patients, including B-cell suppression therapy, TCMR, DCD, and graft tolerance. Adverse events on renal graft failure might lead to different clustering of BCR iR. However, these preliminary data need further verification in further studies for the possible applications of iR changes as genetic expression biomarkers or laboratory parameters to detect renal graft failure/rejection earlier.

Utility of Bulk T-Cell Receptor Repertoire Sequencing Analysis in Understanding Immune Responses to COVID-19

Measuring immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 19 (COVID-19), can rely on antibodies, reactive T cells and other factors, with T-cell-mediated responses appearing to have greater sensitivity and longevity. Because each T cell carries an essentially unique nucleic acid sequence for its T-cell receptor (TCR), we can interrogate sequence data derived from DNA or RNA to assess aspects of the immune response. This review deals with the utility of bulk, rather than single-cell, sequencing of TCR repertoires, considering the importance of study design, in terms of cohort selection, laboratory methods and analysis. The advances in understanding SARS-CoV-2 immunity that have resulted from bulk TCR repertoire sequencing are also be discussed. The complexity of sequencing data obtained by bulk repertoire sequencing makes analysis challenging, but simple descriptive analyses, clonal analysis, searches for specific sequences associated with immune responses to SARS-CoV-2, motif-based analyses, and machine learning approaches have all been applied. TCR repertoire sequencing has demonstrated early expansion followed by contraction of SARS-CoV-2-specific clonotypes, during active infection. Maintenance of TCR repertoire diversity, including the maintenance of diversity of anti-SARS-CoV-2 response, predicts a favourable outcome. TCR repertoire narrowing in severe COVID-19 is most likely a consequence of COVID-19-associated lymphopenia. It has been possible to follow clonotypic sequences longitudinally, which has been particularly valuable for clonotypes known to be associated with SARS-CoV-2 peptide/MHC tetramer binding or with SARS-CoV-2 peptide-induced cytokine responses. Closely related clonotypes to these previously identified sequences have been shown to respond with similar kinetics during infection. A possible superantigen-like effect of the SARS-CoV-2 spike protein has been identified, by means of observing V-segment skewing in patients with severe COVID-19, together with structural modelling. Such a superantigen-like activity, which is apparently absent from other coronaviruses, may be the basis of multisystem inflammatory syndrome and cytokine storms in COVID-19. Bulk TCR repertoire sequencing has proven to be a useful and cost-effective approach to understanding interactions between SARS-CoV-2 and the human host, with the potential to inform the design of therapeutics and vaccines, as well as to provide invaluable pathogenetic and epidemiological insights.

Tumor Treating Fields dually activate STING and AIM2 inflammasomes to induce adjuvant immunity in glioblastoma

Tumor Treating Fields (TTFields), an approved therapy for glioblastoma (GBM) and malignant mesothelioma, employ noninvasive application of low-intensity, intermediate-frequency, alternating electric fields to disrupt the mitotic spindle, leading to chromosome missegregation and apoptosis. Emerging evidence suggests that TTFields may also induce inflammation. However, the mechanism underlying this property and whether it can be harnessed therapeutically are unclear. Here, we report that TTFields induced focal disruption of the nuclear envelope, leading to cytosolic release of large micronuclei clusters that intensely recruited and activated 2 major DNA sensors - cyclic GMP-AMP synthase (cGAS) and absent in melanoma 2 (AIM2) - and their cognate cGAS/stimulator of interferon genes (STING) and AIM2/caspase 1 inflammasomes to produce proinflammatory cytokines, type 1 interferons (T1IFNs), and T1IFN-responsive genes. In syngeneic murine GBM models, TTFields-treated GBM cells induced antitumor memory immunity and a cure rate of 42% to 66% in a STING- and AIM2-dependent manner. Using single-cell and bulk RNA sequencing of peripheral blood mononuclear cells, we detected robust post-TTFields activation of adaptive immunity in patients with GBM via a T1IFN-based trajectory and identified a gene panel signature of TTFields effects on T cell activation and clonal expansion. Collectively, these studies defined a therapeutic strategy using TTFields as cancer immunotherapy in GBM and potentially other solid tumors.

Intratumoral plasma cells predict outcomes to PD-L1 blockade in non-small cell lung cancer

Inhibitors of the programmed cell death-1 (PD-1/PD-L1) signaling axis are approved to treat non-small cell lung cancer (NSCLC) patients, based on their significant overall survival (OS) benefit. Using transcriptomic analysis of 891 NSCLC tumors from patients treated with either the PD-L1 inhibitor atezolizumab or chemotherapy from two large randomized clinical trials, we find a significant B cell association with extended OS with PD-L1 blockade, independent of CD8⁺ T cell signals. We then derive gene signatures corresponding to the dominant B cell subsets present in NSCLC from single-cell RNA sequencing (RNA-seq) data. Importantly, we find increased plasma cell signatures to be predictive of OS in patients treated with atezolizumab, but not chemotherapy. B and plasma cells are also associated with the presence of tertiary lymphoid structures and organized lymphoid aggregates. Our results suggest an important contribution of B and plasma cells to the efficacy of PD-L1 blockade in NSCLC.

Haematopoietic Stem Cell Transplantation Results in Extensive Remodelling of the Clonal T Cell Repertoire in Multiple Sclerosis

Autologous haematopoietic stem cell transplantation (AHSCT) is a vital therapeutic option for patients with highly active multiple sclerosis (MS). Rates of remission suggest AHSCT is the most effective form of immunotherapy in controlling the disease. Despite an evolving understanding of the biology of immune reconstitution following AHSCT, the mechanism by which AHSCT enables sustained disease remission beyond the period of lymphopenia remains to be elucidated. Auto-reactive T cells are considered central to MS pathogenesis. Here, we analyse T cell reconstitution for 36 months following AHSCT in a cohort of highly active MS patients. Through longitudinal analysis of sorted naïve and memory T cell clones, we establish that AHSCT induces profound changes in the dominant T cell landscape of both CD4+ and CD8+ memory T cell clones. Lymphopenia induced homeostatic proliferation is followed by clonal attrition; with only 19% of dominant CD4 (p <0.025) and 13% of dominant CD8 (p <0.005) clones from the pre-transplant repertoire detected at 36 months. Recovery of a thymically-derived CD4 naïve T cell repertoire occurs at 12 months and is ongoing at 36 months, however diversity of the naïve populations is not increased from baseline suggesting the principal mechanism of durable remission from MS after AHSCT relates to depletion of putative auto-reactive clones. In a cohort of MS patients expressing the MS risk allele HLA DRB1*15:01, public clones are probed as potential biomarkers of disease. AHSCT appears to induce sustained periods of disease remission with dynamic changes in the clonal T cell repertoire out to 36 months post-transplant.