AIRR Community Guide to Planning and Performing AIRR-Seq Experiments.

The development of high-throughput sequencing of adaptive immune receptor repertoires (AIRR-seq of IG and TR rearrangements) has provided a new frontier for in-depth analysis of the immune system. The last decade has witnessed an explosion in protocols, experimental methodologies, and computational tools. In this chapter, we discuss the major considerations in planning a successful AIRR-seq experiment together with basic strategies for controlling and evaluating the outcome of the experiment. Members of the AIRR Community have authored several chapters in this edition, which cover step-by-step instructions to successfully conduct, analyze, and share an AIRR-seq project.

Reproducibility of 10x Genomics single cell RNA sequencing method in the immune cell environment.

10x Genomics is a highly accessible single cell RNA sequencing platform that allows for simultaneous gene expression analysis and identification of receptor chain combinations in cells of the adaptive immune system. Here, we asked whether the gene and receptor expression measurements in peripheral blood mononuclear cells (PBMC) are influenced by technical, cell freezing, FACS-processing, and day to day biological variation. No differentially expressed gene was observed between 1. triplicates aliquots taken from the same vial of frozen PBMC; 2. triplicate vials of frozen PBMC; and 3. triplicate aliquots taken from the same vial of frozen PBMC and processed separately for FACS staining and sorting of different PBMC populations. A small number of differentially expressed genes were observed between PBMC sampled, isolated and frozen from the same donor on different days, and these differences were more pronounced in the memory B cells than other cell populations. T cell receptors were recovered in all replicates when at least 5 cells per clonotype were identified. These findings show high reproducibility of 10x Genomics single cell RNA sequencing data in the immune cell context.

scRepertoire: An R-based toolkit for single-cell immune receptor analysis.

Single-cell sequencing is an emerging technology in the field of immunology and oncology that allows researchers to couple RNA quantification and other modalities, like immune cell receptor profiling at the level of an individual cell. A number of workflows and software packages have been created to process and analyze single-cell transcriptomic data. These packages allow users to take the vast dimensionality of the data generated in single-cell-based experiments and distill the data into novel insights. Unlike the transcriptomic field, there is a lack of options for software that allow for single-cell immune receptor profiling. Enabling users to easily combine mRNA and immune profiling, scRepertoire was built to process data derived from 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat R package. The scRepertoire R package and processed data are open source and available on GitHub and provides in-depth tutorials on the capability of the package.

Gene Expression-Based Identification of Antigen-Responsive CD8+ T Cells on a Single-Cell Level.

CD8+ T cells are important effectors of adaptive immunity against pathogens, tumors, and self antigens. Here, we asked how human cognate antigen-responsive CD8+ T cells and their receptors could be identified in unselected single-cell gene expression data. Single-cell RNA sequencing and qPCR of dye-labeled antigen-specific cells identified large gene sets that were congruently up- or downregulated in virus-responsive CD8+ T cells under different antigen presentation conditions. Combined expression of TNFRSF9, XCL1, XCL2, and CRTAM was the most distinct marker of virus-responsive cells on a single-cell level. Using transcriptomic data, we developed a machine learning-based classifier that provides sensitive and specific detection of virus-responsive CD8+ T cells from unselected populations. Gene response profiles of CD8+ T cells specific for the autoantigen islet-specific glucose-6-phosphatase catalytic subunit-related protein differed markedly from virus-specific cells. These findings provide single-cell gene expression parameters for comprehensive identification of rare antigen-responsive cells and T cell receptors.

Cytoplasmic ends of tetraspanin 7 harbour epitopes recognised by autoantibodies in type 1 diabetes.

AIMS/HYPOTHESIS: The beta cell protein tetraspanin 7 is a target of autoantibodies in individuals with type 1 diabetes. The aim of this study was to identify autoantibody epitope-containing regions and key residues for autoantibody binding. METHODS: Autoantibody epitope regions were identified by immunoprecipitation of luciferase-tagged single or multiple tetraspanin 7 domains using tetraspanin 7 antibody-positive sera. Subsequently, amino acids (AAs) relevant for autoantibody binding were identified by single AA mutations. RESULTS: In tetraspanin 7 antibody-positive sera, antibody binding was most frequent to tetraspanin 7 proteins that contained the NH2-terminal cytoplasmic domain 1 (C1; up to 39%) or COOH-terminal C3 (up to 22%). Binding to C3 was more frequent when the domain was expressed along with the flanking transmembrane domain, suggesting that conformation is likely to be important. Binding to external domains was not observed. Single AA mutations of C3 identified residues Y246, E247 and R239 as critical for COOH-terminal binding of 9/10, 10/10 and 8/10 sera tested, respectively. Mutation of cysteines adjacent to the transmembrane domain at either residues C235 or C236 resulted in both decreased (8/178 and 15/178 individuals, respectively; >twofold decrease) and increased (30/178 and 13/178 individuals, respectively; >twofold increase) binding in participant sera vs wild-type protein. CONCLUSIONS/INTERPRETATION: We hypothesise that conformation and, potentially, modification of protein terminal ends of tetraspanin 7 may be important for autoantibody binding in type 1 diabetes.