Journal articles 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.

Background TP53 mutations are associated with unfavorable outcomes in various cancer types and present an obstacle to achieving sustained remission in acute myeloid leukemia (AML). Even with allogeneic hematopoietic stem cell transplantation (HSCT), the risk of relapse approximates 80%, with dismal long-term survival. This could imply methods of immune escape in TP53 mutated leukemias that allow them to evade the graft-versus-leukemia (GVL) effect, which is a critical mechanism of disease control with HSCT. This study investigates the dynamics of immune reconstitution at single-cell resolution in TP53-mutated AML at pre- and multiple post-HSCT timepoints in patients who either remain in long-term remission or relapse following HSCT. Methods We collected 26 longitudinal bone marrow aspirates and 1 peripheral blood sample from 12 patients with TP53-mutated AML who underwent HSCT at the Dana-Farber Cancer Institute between 2015-21. All patients received HLA-matched T-cell replete HSCT in morphologic remission, with standard tacrolimus/methotrexate-based prophylaxis and peripheral blood stem cells. We categorized the patients into two cohorts based on their treatment response: long-term remission (&gt;3.5 years, cohort 1, n=4) or relapse (cohort 2, n=8). We performed paired 5' single-cell RNA and T cell receptor (TCR) sequencing on sorted viable mononuclear from each sample and CD3+ T cells from selected samples using 10x Genomics procedures. In total, we analyzed 44 samples (27 mononuclear, 17 CD3+), resulting in 250,640 high-quality single-cell transcriptomes, including 137,649 T cells and NK cells. Data analysis was performed using Cell Ranger Multi (10x Genomics) and R 4.3 with the tidyverse 2.0, Seurat 5.1, and scRepertoire 1.1 packages. Results We focused our initial analysis on the 2-6-month post-HSCT period that is critical for immune reconstitution. This included eight samples from seven patients in the two cohorts (1: remission, 2: relapse, pink box in Figure 1). Of these 7 patients, 4 were in the relapse cohort, with these relapses occurring &gt;4 months post-HSCT. No significant difference in T-cell chimerism was seen between these 7 patients in this 2-6 month period. Based on canonical gene expression signatures, we annotated cell types and selected T cells for further evaluation (Figure 2). First, we found that the CD4/CD8 ratio differed between the cohorts, indicating a relative CD8+ T cell expansion in the relapse cohort (cohort 1: 1.69, cohort 2: 0.55, P&lt;0.0001). Second, we found lower TCR diversity in the relapse cohort (inverse Simpson index, cohort 1: 234.6, cohort 2: 83.0, P&lt;0.0001). This difference remained significant when subsampling the same number of T cells per patient and when restricting all samples to the 3 to 4-month timepoint. Considering the disparity in CD8 T cell numbers between the cohorts, we also analyzed repertoire diversity, specifically in CD8+ T cells, and found that the observed significance persisted. To further investigate TCR diversity, we overlaid T cell clonotypes (cells that share the same TCR sequence) in a UMAP visualization, which highlighted increased hyperexpanded CD8+ T cells in the relapse cohort (Figure 2). Finally, we found that greater clonotype expansion was correlated with the expression of key T cell genes, including CD8A, CD8B, PRF1, NKG7, and GZMA. These results suggest that cytotoxic CD8+ T cells link lower TCR diversity to the likelihood of disease relapse. Conclusions By performing deep analysis of single-cell gene expression linked to TCR clonotypes, we discover new insights into the biology of hematopoietic reconstitution after HSCT of patients with TP53-mutated AML. Despite having analyzed a limited number of samples, we find that patients who remain in long-term remission have significantly higher CD8+ TCR diversity between 2-6 months post-HSCT, while those who relapsed between 5-18 months post-HSCT showed lower TCR diversity and hyperexpanded CD8+ clones at the same timepoints. This work suggests that early post-HSCT TCR diversity could be a biomarker associated with GVL and warrants further study. In ongoing and future analyses, we will mine the current dataset for further insights into the factors influencing survival in TP53-mutated AML patients. We also plan to extend the measurement of TCR diversity as a predictor of survival for validation in a larger cohort.

BackgroundBecause conventional single-cell strategies rely on dissociating tissues into suspensions that lose spatial context,1 we developed Slide-TCR-seq to sequence both whole transcriptomes and TCRs with 10µm-spatial resolution, & applied it to renal cell carcinoma (ccRCC) treated with immune checkpoint inhibitors (ICI).MethodsSlide-TCR-seq combines Slide-seqV22 3—a 10µm-resolution spatial approach utilizing mRNA capture and DNA-barcoded beads—with sensitive targeted capture of TCR sequences (rhTCRseq,4 previously developed by our group), thereby enabling amplification of segments extending from upstream of CDR3 to the 3’-end of the TCR transcript (figure 1A). We tested Slide-TCR-seq first on OT-I murine spleen and then applied this methodology to 3 patients‘ pre-αPD-1 ccRCC samples5 and a post-αPD-1 metastasis to investigate the spatial, functional, and clonotypic organization of T cells in relationship to tumor using RCTD,6 spatial enrichment, and spatial expression analyses.ResultsUsing Slide-TCR-seq, we first recapitulated native spatial structure of OT-I mouse spleen (figure 1B-G). TCRα/β CDR3 sequences were detected on 37.1% of beads with Trac/Trbc2 constant sequences—comparable to other scTCRseq methods. Because the clonal and spatial context of TILs have been increasingly implicated in immunotherapy resistance, we used Slide-TCR-seq to analyze a lung ccRCC metastasis following αPD-1 therapy. We employed unsupervised clustering to delineate the tumor, intervening boundary, and lung compartments, and RCTD analyses to spatially map individual cell types; together recapitulating the architecture observed in corresponding histology (figure 2). We identified 1,132 unique clonotypes, with distinct spatial distributions spanning the tissue compartments. Eight clonotypes were significantly enriched in tumor, whereas 5 were depleted (all p<0.05) (figure 3). We then analyzed the relationships between the T cells’ clonotype, gene expression, and tumor infiltration depth among clonotypes. Using a T-cell geneset associated with poor response to ICI,7 we dichotomized T-clonotype beads by geneset expression, and found spatial segregation of this geneset’s expression both within and across clonotypes (figure 4). TCR-4—the most significantly tumor-enriched clonotype—and TCR-2 displayed high expression of the poor ICI response geneset near the tumor’s edge, but low expression deeper in the tumor compartment; indicating that there are transcriptionally distinct subpopulations of these clonotypes, which depended on the extent of their tumor infiltration.Abstract 76 Figure 1Slide-TCR-seq spatially localizes T cell receptors and transcriptome information. a. Schematic of Slide-TCR-seq, in which tissue is placed onto an in situ barcoded bead array. cDNA libraries prepared with Slide-seqV2 are split prior to fragmentation with one portion used for targeted amplification via rhTCRseq optimized for use with Slide-seq libraries. Slide-TCR-seq provides gene expression, cell type, and clonotype information in space. b. Serial sections of the OT-1 mouse spleen with hematoxylin and eosin stain show characteristic architecture of red pulp and white pulp separation. c. Spatial reconstruction of Slide-TCR-seq array for a corresponding section of OT-I mouse spleen, with RCTD immune cell type assignment. NK = natural killer. d. Gene expression gaussian-filtered heatmap for visualizing the spatial distribution of gene markers for marginal zone (Marco), red blood cells (RBCs; Gypa), and CD8 T cells (Cd8a). e and f. Comparing the spatial distribution of constant (left) and variable (right) sequences for TCRα (e) and TCRβ (f), with superimposed density plot. g. The fraction of beads that capture CDR3 variable sequences (y-axis) when constant UMIs are captured (x-axis) for TCRα (left, light blue) and TCRβ (right, dark blue), with the number of corresponding beads along the top axis. All scale bars: 500 µm.Abstract 76 Figure 2Slide-TCR-seq identifies spatial differences between T cell clonotypes in renal cell carcinoma. (a) H&E stain of a ccRCC metastasis to the lung following PD-1 blockade therapy. (b) The compartment assignment of lung (green), immune cell boundary (orange), and tumor (blue) by applying K-nearest neighbors to cell types determined by unsupervised clustering from Slide-TCR-seq of a sequential tissue section. (c) Spatial reconstruction of cell type identifies using RCTD anaysis of the Slide-TCR-seq data. (d) Spatial localization of T cell clonotypes (n=447 clonotypes, colored by clonotype) from the the Slide-TCR-seq data.Abstract 76 Figure 3Top: y-axis Significance of clonotype spatial distributions compared against all other clonotypes with at least ten beads per array from the ccRCC lung metastasis plotted against an x-axis of magnitude of tumor enrichment or depletion (data from n=3 replicate arrays, two one-tailed K-S tests). Bottom: Visualization of selected significant clonotypes, ordered by tumor enrichment, in tissue compartments for a single array (T cells within the tumor compartment are displayed as opaque, T cells within other compartments are displayed as translucent).Abstract 76 Figure 4Spatial and molecular heterogeneity in clonotype gene expression and tumor infiltration. a. The three axes — spatial localization, gene expression, and T cell clonotype — that Slide-TCR-seq can relate. b. Top: distribution of poor response to immune checkpoint inhibitor treatment (’PRI’) geneset7 expression across all clonotypes in the tumor region of the same post-PD1 inhibitor RCC lung metastasis from figures 2–3 (from a single replicate) with kernel density estimation. Yellow = clonotypes with lower than median PRI expression; purple = clonotypes with PRI expression greater than or equal to the median value. Bottom: localization of low (yellow) and high (purple) PRI geneset expression clonotypes within the tumor region (light blue) from the Slide-TCR-seq array shows their distinct spatial separation (light blue = tumor region, orange = boundary region, green = lung region). Scale bar: 500 µm. c. Smoothed histograms comparing the distance infiltrated into tumor by two-tailed K-S test comparing low (yellow) and high (purple) expression clonotypes, as dichotomized by median expression of PRI. d. Comparing distance infiltrated into tumor by two-tailed K-S test between low and high PRI expression T cells across those clonotypes with at least 20 beads (n=7 clonotypes).ConclusionsSlide-TCR-seq effectively integrates spatial transcriptomics with TCR detection at 10µm resolution, thereby relating T cells’ clonality and gene expression to their spatial organization in tumors. Our findings suggest that a clonotype’s T cells may exhibit mixed responses to ICI depending on their spatial localization. The heterogeneity among clonotypes, in both gene expression and organization, underscores the importance of studying the TCR repertoire with spatial resolution.AcknowledgementsWe are grateful to Irving A. Barrera-Lopez, Zoe N. Garcia, and Aziz Al’Khafaji for technical assistance.ReferencesGohil S, Iorgulescu JB, Braun D, Keskin D, Livak K. Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy. Nat Rev Clin Oncol 2021; 18:244–256.Stickels RR, Murray E, Kumar P, Li J, Marshall JL, Di Bella DJ, Arlotta P, Macosko EZ, Chen F. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat Biotechnol 2021 Mar;39(3):313–319.Rodriques SG, Stickels RR, Goeva A, Martin CA, Murray E, Vanderburg CR, Welch J, Chen LM, Chen F, Macosko EZ. Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. 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Robust decomposition of cell type mixtures in spatial transcriptomics. Nat Biotechnol 2021 Feb 18. doi: 10.1038/s41587-021-00830-w. Epub ahead of print. PMID: 33603203.Sade-Feldman M, Yizhak K, Bjorgaard SL, Ray JP, de Boer CG, Jenkins RW, Lieb DJ, Chen JH, Frederick DT, Barzily-Rokni M, Freeman SS, Reuben A, Hoover PJ, Villani AC, Ivanova E, Portell A, Lizotte PH, Aref AR, Eliane JP, Hammond MR, Vitzthum H, Blackmon SM, Li B, Gopalakrishnan V, Reddy SM, Cooper ZA, Paweletz CP, Barbie DA, Stemmer-Rachamimov A, Flaherty KT, Wargo JA, Boland GM, Sullivan RJ, Getz G, Hacohen N. Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma. Cell 2018 Nov 1;175(4):998–1013Ethics ApprovalThis study was approved by MGB/DFCI/Broad institution’s Ethics Board; approval number 2019P000017.

Abstract Abstract SCI-51 Lymph nodes provide specialized stromal environments that support the maintenance and homeostasis of T and B lymphocyte populations and are also staging grounds for lymphocyte effector responses against pathogens and transformed cells. They serve as immune information hotspots by collecting lymph fluid from peripheral tissues, especially our external and internal epithelial body surfaces, thus displaying a condensed representation of foreign and self-antigens at these sites in addition to integrating innate alarm signals that report tissue damage or pathogen invasion. Naïve B and T cells constantly traffic through these environments via the blood stream and efferent lymphatic vessels, which allows for efficient matching of their antigen receptor repertoires with the regional antigenic landscape. Depending on the absence or presence of signs of a potential threat to the organism, the result may be either tolerance or immunity towards the origin of these antigens. The architecture of lymph nodes is optimized to facilitate the presentation of lymph-borne antigen in various forms and to guide naïve lymphocytes in their search for 'their' cognate antigen in the form in which they are able to 'see' it. It also facilitates the cellular crosstalk with other immune cell populations that shape and regulate an ensuing adaptive response if cognate antigen is encountered in an immunogenic context. Our conception of how these various tasks are accomplished has recently been enriched through new methodological approaches that include the dynamic in situ or in vivo visualization of cellular and molecular processes using modern microscopy technology. We will review some recent insights into the function of lymph nodes derived from these studies. Disclosures No relevant conflicts of interest to declare.

Abstract Background Next-generation sequencing (NGS) is nowadays the most used high-throughput technology for DNA sequencing. Among others NGS enables the in-depth analysis of immune repertoires. Research in the field of T cell receptor (TCR) and immunoglobulin (IG) repertoires aids in understanding immunological diseases. A main objective is the analysis of the V(D)J recombination defining the structure and specificity of the immune repertoire. Accurate processing, evaluation and visualization of immune repertoire NGS data is important for better understanding immune responses and immunological behavior. Results ImmunoDataAnalyzer (IMDA) is a pipeline we have developed for automatizing the analysis of immunological NGS data. IMDA unites the functionality from carefully selected immune repertoire analysis software tools and covers the whole spectrum from initial quality control up to the comparison of multiple immune repertoires. It provides methods for automated pre-processing of barcoded and UMI tagged immune repertoire NGS data, facilitates the assembly of clonotypes and calculates key figures for describing the immune repertoire. These include commonly used clonality and diversity measures, as well as indicators for V(D)J gene segment usage and between sample similarity. IMDA reports all relevant information in a compact summary containing visualizations, calculations, and sample details, all of which serve for a more detailed overview. IMDA further generates an output file including key figures for all samples, designed to serve as input for machine learning frameworks to find models for differentiating between specific traits of samples. Conclusions IMDA constructs TCR and IG repertoire data from raw NGS reads and facilitates descriptive data analysis and comparison of immune repertoires. The IMDA workflow focus on quality control and ease of use for non-computer scientists. The provided output directly facilitates the interpretation of input data and includes information about clonality, diversity, clonotype overlap as well as similarity, and V(D)J gene segment usage. IMDA further supports the detection of sample swaps and cross-sample contamination that potentially occurred during sample preparation. In summary, IMDA reduces the effort usually required for immune repertoire data analysis by providing an automated workflow for processing raw NGS data into immune repertoires and subsequent analysis. The implementation is open-source and available on https://bioinformatics.fh-hagenberg.at/immunoanalyzer/.

Abstract Motivation T and B cell receptors (TCRs and BCRs) play a pivotal role in the adaptive immune system by recognizing an enormous variety of external and internal antigens. Understanding these receptors is critical for exploring the process of immunoreaction and exploiting potential applications in immunotherapy and antibody drug design. Although a large number of samples have had their TCR and BCR repertoires sequenced using high-throughput sequencing in recent years, very few databases have been constructed to store these kinds of data. To resolve this issue, we developed a database. Results We developed a database, the Pan Immune Repertoire Database (PIRD), located in China National GeneBank (CNGBdb), to collect and store annotated TCR and BCR sequencing data, including from Homo sapiens and other species. In addition to data storage, PIRD also provides functions of data visualization and interactive online analysis. Additionally, a manually curated database of TCRs and BCRs targeting known antigens (TBAdb) was also deposited in PIRD. Availability and implementation PIRD can be freely accessed at https://db.cngb.org/pird.

Antibodies are critical effector molecules of the humoral immune system. Upon infection or vaccination, populations of antibodies are generated which bind to various regions of the invading pathogen or exogenous agent. Defining the reactivity and breadth of this antibody response provides an understanding of the antigenic determinants and enables the rational development and assessment of vaccine candidates. High-resolution analysis of these populations typically requires advanced techniques such as B cell receptor repertoire sequencing, mass spectrometry of isolated immunoglobulins, or phage display libraries that are dependent upon equipment and expertise which are prohibitive for many labs. High-density peptide microarrays representing diverse populations of putative linear epitopes (immunoarrays) are an effective alternative for high-throughput examination of antibody reactivity and diversity. While a promising technology, widespread adoption of immunoarrays has been limited by the need for, and relative absence of, user-friendly tools for consideration and visualization of the emerging data. To address this limitation, we developed EPIphany, a software platform with a simple web-based user interface, aimed at biological users, that provides access to important analysis parameters, data normalization options, and a variety of unique data visualization options. This platform provides researchers the greatest opportunity to extract biologically meaningful information from the immunoarray data, thereby facilitating the discovery and development of novel immuno-therapeutics.

Abstract Background CD4+ T cells play critical roles in the pathogenesis of IgG4-related disease (IgG4-RD). The aim of this study was to investigate the TCR repertoire of peripheral blood CD4+ T cells in IgG4-RD. Methods The peripheral blood was collected from six healthy controls and eight IgG4-RD patients. TCR β-chain libraries of CD4+ T cells were constructed by 5′-rapid amplification of cDNA ends (5′-RACE) and sequenced by Illumina Miseq platform. The relative similarity of TCR repertoires between samples was evaluated according to the total frequencies of shared clonotypes (metric F), correlation of frequencies of shared clonotypes (metric R), and total number of shared clonotypes (metric D). Results The clonal expansion and diversity of CD4+ T cell repertoire were comparable between healthy controls and IgG4-RD patients, while the proportion of expanded and coding degenerated clones, as an indicator of antigen-driven clonal expansion, was significantly higher in IgG4-RD patients. There was no significant difference in TRBV and TRBJ gene usage between healthy controls and IgG4-RD patients. The complementarity determining region 3 (CDR3) length distribution was skewed towards longer fragments in IgG4-RD. Visualization of relative similarity of TCR repertoires by multi-dimensional scaling analysis showed that TCR repertoires of IgG4-RD patients were separated from that of healthy controls in F and D metrics. We identified 11 IgG4-RD-specific CDR3 amino acid sequences that were expanded in at least 2 IgG4-RD patients, while not detected in healthy controls. According to TCR clonotype networks constructed by connecting all the CDR3 sequences with a Levenshtein distance of 1, 3 IgG4-RD-specific clusters were identified. We annotated the TCR sequences with known antigen specificity according to McPAS-TCR database and found that the frequencies of TCR sequences associated with each disease or immune function were comparable between healthy controls and IgG4-RD patients. Conclusion According to our study of CD4+ T cells from eight IgG4-RD patients, TCR repertoires of IgG4-RD patients were different from that of healthy controls in the proportion of expanded and coding degenerated clones and CDR3 length distribution. In addition, IgG4-RD-specific TCR sequences and clusters were identified in our study.

Autoreactive encephalitogenic T cells exist in the healthy immune repertoire but need a trigger to induce CNS inflammation. The underlying mechanisms remain elusive, whereby microbiota were shown to be involved in the manifestation of CNS autoimmunity. Here, we used intravital imaging to explore how microbiota affect the T cells as trigger of CNS inflammation. Encephalitogenic CD4 + T cells transduced with the calcium-sensing protein Twitch-2B showed calcium signaling with higher frequency than polyclonal T cells in the small intestinal lamina propria (LP) but not in Peyer’s patches. Interestingly, nonencephalitogenic T cells specific for OVA and LCMV also showed calcium signaling in the LP, indicating a general stimulating effect of microbiota. The observed calcium signaling was microbiota and MHC class II dependent as it was significantly reduced in germfree animals and after administration of anti-MHC class II antibody, respectively. As a consequence of T cell stimulation in the small intestine, the encephalitogenic T cells start expressing Th17-axis genes. Finally, we show the migration of CD4 + T cells from the small intestine into the CNS. In summary, our direct in vivo visualization revealed that microbiota induced T cell activation in the LP, which directed T cells to adopt a Th17-like phenotype as a trigger of CNS inflammation.

Abstract The major histocompatibility complex (MHC) encodes a range of immune response genes, including the human leukocyte antigens (HLAs) in humans. These molecules bind peptide antigens and present them on the cell surface for T cell recognition. The repertoires of peptides presented by HLA molecules are termed immunopeptidomes. The highly polymorphic nature of the genres that encode the HLA molecules confers allotype-specific differences in the sequences of bound ligands. Allotype-specific ligand preferences are often defined by peptide-binding motifs. Individuals express up to six classical class I HLA allotypes, which likely present peptides displaying different binding motifs. Such complex datasets make the deconvolution of immunopeptidomic data into allotype-specific contributions and further dissection of binding-specificities challenging. Herein, we developed MHCpLogics as an interactive machine learning-based tool for mining peptide-binding sequence motifs and visualization of immunopeptidome data across complex datasets. We showcase the functionalities of MHCpLogics by analyzing both in-house and published mono- and multi-allelic immunopeptidomics data. The visualization modalities of MHCpLogics allow users to inspect clustered sequences down to individual peptide components and to examine broader sequence patterns within multiple immunopeptidome datasets. MHCpLogics can deconvolute large immunopeptidome datasets enabling the interrogation of clusters for the segregation of allotype-specific peptide sequence motifs, identification of sub-peptidome motifs, and the exportation of clustered peptide sequence lists. The tool facilitates rapid inspection of immunopeptidomes as a resource for the immunology and vaccine communities. MHCpLogics is a standalone application available via an executable installation at: https://github.com/PurcellLab/MHCpLogics.

The capuchin monkey (Sapajus apella), a New World monkey species, exhibits prominent characteristics that make it an ideal model for neuroscience research. These characteristics include its phylogenetic traits, telencephalization coefficient, anatomical structures and pathways, genetic profile, immune responses, cognitive abilities, and complex behavioral repertoires. Traditionally, methodologies for stereotactic neurosurgery in research models have relied on the use of brain atlases. However, this approach can lead to errors due to the considerable variation in brain size and shape among individual monkeys. To address this issue, we developed a protocol for deriving individual coordinates for each monkey using a straightforward and relatively inexpensive method involving MRI imaging. Our protocol utilizes a specially designed, 3D-printed stereotactic head-holder that is safe to use with an MR magnet, non-invasive placement of fiducial markers, and post-processing with open-source software. This approach enhances MRI data visualization, improves anatomical targeting, and refines the design of neurosurgical experiments. Our technique could also prove beneficial in other areas of neuroscience research that require accurate calculation of stereotaxic coordinates. Furthermore, it could be useful for other nonhuman primate species for which brain atlases are typically unavailable.