Academic literature on the topic 'Immune repertoire visualization'

Abstract VDJServer is a comprehensive, web-accessible system for analysis of immune repertoire sequencing data. VDJServer provides a complete analysis workflow from pre-processing of sequence reads, to V(D)J assignment, to repertoire characterization and comparison. Recent enhancements in VDJServer include: --Automatic parallelization of analysis tools to handle very large data sets running on a high-performance supercomputer--Import and export subject and sample metadata. User-defined sample groups allows for sophisticated group analysis and comparison.--Extensive analysis functionality such as gene segment usage, CDR3 patterns, clonality, diversity measures, somatic mutation patterns, B cell lineage trees, and quantification of selection pressure. Analysis is performed for both samples and sample groups.--Interactive charting of analysis data provides exploratory visualization for ad-hoc comparison of samples and sample groups. Charts can be downloaded as image files for use in presentations and publications. All analysis data can be downloaded in standard TSV format for use with external tools.--Novel process workflow metadata that is automatically captured by VDJServer. Hiding the complexities of command line tools and their parameters, yet providing complete transparency of the analysis workflow for reproducibility. VDJServer allows users to upload antigen receptor repertoire sequences and execute a customizable workflow for all steps in the analysis. Data and analysis results can be privately shared with other users for collaborative projects. VDJServer is funded by the NIAID and is freely available.

Abstract T cells and the T-cell receptor (TCR) repertoire play pivotal roles in immune response and immunotherapy. TCR sequencing (TCR-Seq) technology has enabled accurate profiling TCR repertoire and currently a large number of TCR-Seq data are available in public. Based on the urgent need to effectively re-use these data, we developed TCRdb, a comprehensive human TCR sequences database, by a uniform pipeline to characterize TCR sequences on TCR-Seq data. TCRdb contains more than 277 million highly reliable TCR sequences from over 8265 TCR-Seq samples across hundreds of tissues/clinical conditions/cell types. The unique features of TCRdb include: (i) comprehensive and reliable sequences for TCR repertoire in different samples generated by a strict and uniform pipeline of TCRdb; (ii) powerful search function, allowing users to identify their interested TCR sequences in different conditions; (iii) categorized sample metadata, enabling comparison of TCRs in different sample types; (iv) interactive data visualization charts, describing the TCR repertoire in TCR diversity, length distribution and V-J gene utilization. The TCRdb database is freely available at http://bioinfo.life.hust.edu.cn/TCRdb/ and will be a useful resource in the research and application community of T cell immunology.

Abstract Background T-cell and B-cell repertoire analysis is used in oncology research, to understand the etiology of complex disease phenotypes, for the identification of biomarkers predictive of disease burden, outcome, and response to treatment, and for research in diagnosis and recurrence monitoring. Key predictors include secondary and tertiary repertoire features not reported by existing sequencing software solutions. For example, due to ongoing somatic hypermutation in mature B-cell receptors, the underlying sequence of a given clone can accumulate base differences and appear as several distinct clones with smaller frequencies, thereby hampering the ability of analysis software to detect its presence as a single dominant clone with the highest frequency. This has particularly detrimental implications for research in disorders such as follicular lymphoma and may require clonal lineage analysis for proper mitigation. Therefore, to aid the downstream analytics of biomarker identification and the study of complex disease, we developed fully automated analysis solutions that directly compute and report several key features (clonal lineage, amongst several others described below) pertinent to this area of research. Results We developed the Oncomine™ TCR Beta-SR, TCR Gamma-SR, BCR IGH-SR and BCR IGKL-SR workflows on Ion Reporter™ to characterize T-cell (β, γ chains) and B-cell (heavy and light (κ, δ) chains) repertoires. These workflows generate output tables and visualizations for primary repertoire features such as detected clones (viz., unique rearrangements in the receptor DNA sequence), their frequencies, as well as their somatic hypermutation levels in the case of B-cells (Figure 1a & 1b) for clonality assessment and rare clone detection. The software also quantifies and reports several secondary and tertiary repertoire features in a sample, such as clonal diversity, evenness of the clonal population, and B-cell lineage groupings useful in identifying related sub-clones. It includes spectratyping format plots to simultaneously assess the above features as a function of v-gene usage and CDR3 length combinations (Figure 1c & 1d), thereby providing users a complete snapshot of the repertoire, and also the capability to quickly determine CDR3 lengths and V-gene usage of highly expanded or mutated clones. A separate CDR3 lengths histogram is included, as well as a heatmap that depicts the distributions/intensity of Variable-Joining gene combinations (Figure 1e & 1f). Furthermore, the TCR workflows also report (i) convergence frequencies (fraction of clones with different nucleotide sequences, but identical amino acid sequences), and (ii) haplotype grouping for an analyzed sample, based on V-gene allele genotyping and clustering (Figure 1g). In addition, the long read Oncomine™ BCR IGH-LR workflow uniquely reports the isotype class for every detected clone, and includes a visualization of total reads, clones and lineages in the sample represented by isotype (Figure 1h). Conclusion The Oncomine™ immune repertoire workflows for T-cell and B-cell receptor sequencing were designed to be of high utility in distinct areas of malignancy research, and we expect them to greatly simplify complex downstream analyses. The unique capabilities of the workflows to automatically report secondary and tertiary repertoire features such as (i) clonal lineages for improved dominant clone detection in blood cancers, (ii) TCR clone convergence for prediction of response to immune checkpoint inhibitors [1,2], (iii) TCR haplotype grouping for evaluation of risk factors for autoimmunity and immune-related adverse events [3], and (iv) isotype classification in BCRs for studying pan-cancer immune evasion mechanisms, demonstrate the clear advantages of using these automated workflows over other existing solutions. For research use only. References 1) Looney TJ et al. (2020) TCR Convergence in Individuals Treated With Immune Checkpoint Inhibition for Cancer. Front. Immunol. 10:2985. 2) Naidus et al. (2021) Early changes in the circulating T cells are associated with clinical outcomes after PD-L1 blockade by durvalumab in advanced NSCLC patients. Cancer Immunology, Immunotherapy 70:2095-2102 3) Looney TJ et al. (2019) Haplotype Analysis of the T-Cell Receptor Beta (TCRB) Locus by Long-amplicon TCRB Repertoire Sequencing. Journal of Immunotherapy and Precision Oncology. 2 (4): 137-143. Figure 1 Figure 1. Disclosures Sarda: Thermo Fisher Scientific: Current Employment. Lowman: Thermo Fisher Scientific: Current Employment. Toro: Thermo Fisher Scientific: Current Employment. Pickle: Thermo Fisher Scientific: Current Employment. Looney: Thermo Fisher Scientific: Ended employment in the past 24 months; Singular Genomics: Current Employment. Hyland: Thermo Fisher Scientific: Current Employment.

Abstract VDJbase is a publicly available database that offers easy searching of data describing the complete sets of gene sequences (genotypes and haplotypes) inferred from adaptive immune receptor repertoire sequencing datasets. VDJbase is designed to act as a resource that will allow the scientific community to explore the genetic variability of the immunoglobulin (Ig) and T cell receptor (TR) gene loci. It can also assist in the investigation of Ig- and TR-related genetic predispositions to diseases. Our database includes web-based query and online tools to assist in visualization and analysis of the genotype and haplotype data. It enables users to detect those alleles and genes that are significantly over-represented in a particular population, in terms of genotype, haplotype and gene expression. The database website can be freely accessed at https://www.vdjbase.org/, and no login is required. The data and code use creative common licenses and are freely downloadable from https://bitbucket.org/account/user/yaarilab/projects/GPHP.

Abstract Recent technical advances have transformed the field of immunology. We are now capable of measuring features of immune responses, including B- and T-cell specificity and repertoire, serum and intracellular cytokines, and more, on a scale never imagined before. As a consequence, the generation of big data sets has become routine and there is an urgent need for an analysis platform to facilitate data exploration and integration across assays and studies. Here we present ImmuneSpace, the data repository and analysis platform of the Human Immunology Project Consortium (HIPC). The HIPC program, funded by the NIH, is a multi-center collaborative effort to characterize the status of the immune system in different populations under diverse stimulations and disease states. This ongoing effort has generated large amounts of varied high-throughput, high-dimensional biological data (flow cytometry, CyTOF, RNA-Seq, Luminex, among others). All data generated to date by HIPC, along with other selected datasets generated by other NIAID funded projects, have been made publicly available through ImmuneSpace and are ready to be explored using visualization and analysis tools built in ImmuneSpace. To this end, we hope that ImmuneSpace will act as a central immunological hub, allowing experimentalists, statisticians, and bioinformaticians to freely retrieve, explore and compare data across assays and across studies generated within and outside of HIPC.

Abstract Chronic idiopathic neutropenia (CIN) is an acquired disorder of granulopoiesis characterized by prolonged neutropenia, mainly affecting middle-age females of the HLA-DRB1*1302 type. The defective hematopoiesis in CIN can be mainly attributed to accelerated Fas-mediated death of the CD34+/CD33+ granulocytic progenitors, secondary to an inflammatory bone marrow (BM) microenvironment. Crucial to CIN pathogenesis are the increased numbers of activated T cells identified in both peripheral blood (PB) and BM of CIN patients, supporting an immune pathogenesis. Using Sanger sequencing, we previously reported that the T-cell receptor (TR) gene repertoire in CIN is skewed, indicating antigen selection in CIN ontogeny. However, the analytical depth afforded by Sanger sequencing is limited, hindering definitive conclusions. In order to obtain a truly comprehensive view into the role of antigen drive in CIN, using next generation sequencing (NGS) we interrogated the TR repertoire of 13 patients (8 females, 5 males) included in our previous study as well as a healthy female. TRBV-TRBD-TRBJ gene rearrangements were amplified according to the BIOMED2 protocol on either genomic DNA or cDNA isolated from CD8+ cells of PB (n=4) or BM (n=10) samples. PCR products were used as a substrate for paired-end sample preparation (Illumina) and subjected to NGS on the MiSeq Illumina Platform. The raw NGS data were preprocessed with a dedicated pipeline for this purpose, including: (i) quality filtering of each read; (ii) merging of paired-end reads via local alignment; (iii) preparation of fasta files for submission to the IMGT/High V-QUEST tool; and, (iv) IMGT/High V-QUEST metadata analysis, interpretation and visualization. Overall, 6,196,980 TRBV-TRBD-TRBJ gene rearrangements were analyzed (130,020-1,037,680 /case) of which 5,317,609 were productive since they used functional TRBV genes and also carried in-frame CDR3. Rearrangements with identical TRBV gene usage and CDR3 sequence were defined as clonotypes. For repertoire analyses, clonotypes rather than single rearrangement sequences were considered. Overall, 553,145 unique clonotypes were identified (median 39,510; range 7,732-172,253/case), of which 408,744 represented singletons. All clonotypes from the Sanger analysis were detected by NGS as well. Among the 46 functional TRBV genes identified, the most frequent were: TRBV29-1 (13.9%), TRBV19 (6.7%), TRBV12-3 (5.6%), TRBV6-5 (5.4%), TRBV27 (4.9%) and TRBV6-1 (4.0%), collectively accounting for 40,5% of the TRBV repertoire; the TRBV29-1 gene predominated in 9/13 CIN cases. All CIN cases were found to carry distinct expanded clonotypes (median 10,314; range 2,279-40,245/case). The predominant clonotype ranged in frequency from 5.25 to 20.2% of the total clonotypes observed in each case. This contrasts significantly (p<0.001) with a 0.47% frequency of the dominant clonotype in the healthy control. Cluster analysis of the sequences of all CIN cases identified 9034 different clonotypes shared by different patients and, thus, deemed as public. Notably, public clonotypes of a given CDR3 length could show high sequence similarity, further underscoring the restricted nature of the repertoire. As an example, 1632/2665 (61.2%) public clonotypes with 12 aminoacid-long CDR3 were grouped into 168 distinct communities, populated with 2-280 highly similar sequences, each linked with 1 aminoacid distance with at least another member of the community. Overall, the present study offers conclusive evidence that the TR repertoire in CIN is remarkably skewed. The finding of oligoclonal T-cell expansions and public clonotypes strongly indicates that antigen-driven immune responses are very likely implicated in the pathogenesis of CIN. Disclosures No relevant conflicts of interest to declare.

Abstract Non-neoplastic lymphadenopathy (NNL) associated with the human immunodeficiency virus (HIV) infection may develop concurrently with the onset of HIV viremia (acute retroviral syndrome) that can persist beyond the acute phase. Histopathological findings at this early phase mainly pertain to hyperplastic changes with large lymphoid follicles; with time, the number of lymphoid follicles diminishes, while plasma cells increase; at the extreme is a pattern characterized by sclerosis of the germinal centers in the residual follicles. HIV-specific CD8+ T cell responses have been reported and certain viral protein epitopes have been identified e.g. the p24 protein, a component of the HIV particle capsid. Overall, these findings reflect an ongoing immune response that is still incompletely characterized at the molecular level, particularly as it concerns the composition of the T cell receptor (TR) gene repertoire. In order to obtain a comprehensive view into the role of antigen selection in shaping T cell responses in HIV-associated NNL [HIV(+) NNL], we studied in-depth the TR repertoire in: (i) lymph node biopsy samples from 12 patients with HIV(+) NNL, (ii) lymph node samples from 5 non-HIV patients with reactive lymphadenopathy [HIV(-) RL]; and, (iii) peripheral blood samples from 4 healthy, HIV-seronegative individuals without lymphadenopathy [healthy controls, HIV(-) HC]. Genomic DNA was isolated from either paraffin-embedded lymph nodes (for patients with lymphadenopathy) or blood mononuclear cells (for healthy individuals). TRBV-TRBD-TRBJ gene rearrangements were amplified according to the BIOMED2 protocol. PCR products were subjected to next generation sequencing (NGS) on the MiSeq Illumina Platform. NGS data analysis, interpretation and visualization was performed by a validated, in-house bioinformatics pipeline. Overall, we obtained: (i) 1,440,305 (mean: 120,025) productive rearrangement sequences in the HIV(+) NNL group; (ii) 702,533 (mean: 140,506) productive sequences in the HIV(-) RL group; and, (iii) 539,981 (mean: 134,995) productive sequences in HIV(-) HC cases. Rearrangements with identical TRBV gene usage and CDR3 sequence were defined as clonotypes. In total, we identified 15,553 unique clonotypes in patients with HIV(+) NNL (mean: 1,296, range: 337-6,212), 53,874 in HIV(-) RL (mean: 10,774, range: 3,336-16,304) and 220,069 clonotypes in HIV(-) HC cases (mean; 55,017, range: 35,430-68,916), indicating significant repertoire restriction in the former group. Indeed, this group was characterized by an increased level of oligoclonality compared to the other two groups: the mean values of the sum of relative frequencies for the 10 most frequent clonotypes were 80%, 19.6% and 16.5%, respectively. Seven of 12 HIV(+) NNL cases carried the same dominant clonotype (TRBV29-1, SVDPSGTGGEGYT) that was also found in the remaining 5 patients of this group, albeit at lower frequencies; in contrast, it was completely absent in the HIV(-) RL and HIV(-) HC groups. Regarding the TRBV gene repertoire, the TRBV29-1 gene was overrepresented (p<0.005) in the HIV(+) NNL group, whereas the TRBV6-5 and TRBV19 genes were frequent in both groups of patients with lymphadenopathy (HIV-associated or not); finally, the TRBV5-1 was underrepresented (p<0.005) in patients with lymphadenopathy (HIV-associated or not) compared to HIV(-) HC cases. Comparison of the present TR gene sequence dataset against public databases identified 2 clonotypes with an established reactivity against the p24 protein that were present in 2 different patients with HIV(+) NNL of the present cohort. In conclusion, the TR gene repertoire of patients with HIV(+) NNL displays increased level of clonality, distinct TRBV gene repertoire as well as a widely shared, specific dominant clonotype compared to HIV(-) RL cases or HIV(-) healthy controls. These findings allude to an antigen-driven, HIV-specific immune process, a claim also supported by the detection of clonotypes with established anti-HIVp24 reactivity in at least a fraction of the analyzed patients. Disclosures Gemenetzi: Gilead: Research Funding. Agathangelidis:Gilead: Research Funding. Stamatopoulos:Janssen: Honoraria, Research Funding; Gilead: Honoraria, Research Funding; Abbvie: Honoraria, Research Funding. Hadzidimitriou:Gilead: Research Funding; Janssen: Honoraria, Research Funding; Abbvie: Research Funding.

9520 Background: Ex vivo experimental systems are often unable to fully capture complex intercellular communication between tumor cells and surrounding tissues - a critical feature in understanding cancer development and immune evasion. Imaging modality such as bioluminescence lacks the resolution necessary to discern subtle structural differences and heterogeneity in the tumor niche. Microscopic examination of fixed specimens is devoid of the 3-dimensional context or evolution of tumor progression within the same host. Methods: New insights have come from studies involving the use of intravital 2-photon laser scanning microscopy (2P-LSM), which allows deep visualization (>300um) with single-cell resolution (<1um), thus enables direct observation of cellular behavior in intact tissues at a suitable dynamic spatial-time resolution. We study the role of tumor niche in shaping immune repertoire and develop strategies to modify tumor niche to enhance anti-tumor immunity. Results: One example is our study of glioblastoma multiforme (GBM), which contains a cellular hierarchy with a CD133+ sub-population representing self-renewing and tumorigenic GBM stem cells (GSCs). In a xeno-transplant model, GSC was capable of tumor initiation in the mouse brain. To directly test the relative tumorigenic potential of GSCs (CD133+) and non-GSCs (CD133-), we inoculated paired tumor populations from the same primary GBM tumor cells and monitored tumor competition by serial 2P-LSM through implanted cranial windows. Serial 2P-LSM imaging shows that after 35 days, GBM formation was driven exclusively by GSCs but not non-GSCs. To interrogate tumor-associated immune responses, we inoculated syngeneic mouse glioma tumors into C57BL/6 mice. Using this and CNS-inflammatory models, we have begun to undercover the role of perivascular antigen-presenting cells and microglia in guiding the recruitment of CNS-bound lymphocytes. Conclusions: Our data provide the first direct functional evidence that CSCs are responsible for tumor propagation in GBM, and represent an in vivo experimental platform to monitor immunotherapeutic interventions.

Abstract T and B cells are known to play an important role in transplant rejection. Nevertheless, the factors that lead to rejection are not yet fully understood. We have developed a general bioinformatics pipeline for processing T cell receptor (TCR) and immunoglobulin (IG) repertoire next-generation sequencing (NGS) data for comparing immune repertoire properties between multiple groups of samples. Using such a pipeline can help to identify properties of the immune repertoire increasing the risk of allograft rejection after transplantation. Our pipeline is implemented in Python 3.7. Various methods for processing, analyzing and comparing multiple immune repertoire NGS samples are provided. The pipeline returns detailed information about the sequencing quality and provides calculations and visualizations in regard of clonality, diversity, clonotype overlap as well as V(D)J gene analysis and similarity analysis of TCR and IG repertoires. The functionality of the pipeline will be demonstrated on immune repertoire sequencing data from eight kidney transplant patients with sequential samples. For all patients mixed lymphocyte reactions (MLRs) have been performed to identify alloreactive T cells. With our clonotype overlap analysis module we were able to identify alloreactive clonotypes and to subsequently track changes in the alloreactive repertoire after transplantation.

Abstract Summary Advances in single-cell technologies have enabled the investigation of T-cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T-cell receptors. Here, we propose single-cell immune repertoires in Python (Scirpy), a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data. Availability and implementation Scirpy source code and documentation are available at https://github.com/icbi-lab/scirpy. Supplementary information Supplementary data are available at Bioinformatics online.

Le séquençage de nouvelle génération a permis aux chercheurs de réaliser des analyses approfondies du paysage du répertoire immunologique. Cependant, une préoccupation importante dans ces études est le coût informatique de l'analyse de millions de séquences avec une complexité, une variabilité et une capacité de mutation inhérentes, imposant des défis informatiques et nécessitant le développement de méthodes efficaces. Ce défi est encore plus évident dans le contexte clinique qui n'a pas nécessairement accès à des professionnels ayant des compétences informatiques ou des ressources informatiques robustes. Ainsi, l'objectif principal de cette thèse est de développer un ensemble d'outils dédiés qui seront utilisés dans l'environnement clinique, pour le diagnostic médical et les soins aux patients, et dans l'environnement de recherche, pour effectuer une analyse approfondie et à grande échelle du répertoire. Nous avons conçu et implémenté de multiples algorithmes et les avons rassemblés dans un pipeline interactif de visualisation du répertoire BCR afin de faciliter le processus d'intégration de l'analyse du répertoire BCR dans les pratiques médicales
Next-generation sequencing has enabled researchers to conduct in-depth analyses of the immunological repertoire landscape. However, a significant concern in these studies is the computational cost of analyzing millions of sequences with inherent complexity, variability, and mutational capacity, imposing computational challenges and necessitating the development of efficient methods. This challenge is even more evident in the clinical context that does not necessarily have access to professionals with computing skills or robust computational resources. Thus, the main goal of this thesis is to develop a set of dedicated and integrated tools to be used in the clinical environment, for medical diagnostic and patient care, and in the research environment, to perform large-scale and in-depth repertoire analysis. We have designed and implemented multiple algorithms and gathered them in a user-friendly interactive BCR repertoire visualization pipeline to facilitate the process of integrating BCR repertoire analysis into medical practices