![]() It offers a B cell receptor (BCR) and TCR contig annotation pipeline, integrative analysis with single-cell RNA-seq data and a V(D)J feature space for differential V(D)J usage and pseudotime trajectory inference. To that end, we developed Dandelion, a holistic analysis framework for understanding single-cell lymphocyte biology. There remain opportunities for new methods to realize the full potential of paired scRNA-seq and scVDJ-seq data. Tools for joint embedding of single-cell gene expression and AIR complementarity-determining region 3 (CDR3) sequences have also been developed (for example, CoNGA 21 and mvTCR 22). There are also tools for predicting antigen specificity of T cell receptors (TCRs for example, TcellMatch 17), annotating TCRs with epitopes (for example, Platypus 18 and Immunarch 19) and extraction of significant motifs and motif groups (for example, ALICE 20). ![]() Single-cell AIR software are often designed to interact with a companion single-cell gene expression software, for example, scirpy 13 with scanpy 14 and scRepertoire 15 with Seurat 16, providing valuable analysis and visualization options. The functions include re-annotation of AIR genes, quality control checks, matching contigs to cells, clonotype definition, mutation quantification, diversity estimation and many more (Extended Data Fig. There are established packages that can deal with single-cell AIR repertoire data and they provide a variety of methods for downstream analyses (nonexhaustive list of popular tools is shown in Extended Data Fig. This has led to the standardization of repertoire data representation across AIR analysis domains. The Adaptive Immune Receptor Repertoire (AIRR) community was formed in 2015 to help address challenges related to AIR data analysis 10, 11, 12. This includes annotations of variable (V), diversity (D) and joining (J) genes, which are recombined and selected during B/T cell development 9. However, unlike these modalities, which largely consist of continuous data, AIR data consist of a mixture of categorical and continuous data, posing additional challenges for integration. This includes the integration of paired single-cell RNA sequencing (scRNA-seq) and assay for transposase-accessible chromatin with high-throughput sequencing data or cellular indexing of transcriptomes and epitopes by sequencing data 7, 8. Multi-omics analysis has enabled the study of cellular biology across data modalities at an unprecedented resolution. Paired adaptive immune receptor (AIR) sequencing with mRNA expression in the same cell allows for direct linkage of AIR repertoire with cellular phenotypes, which is a powerful way to understand lymphocyte development and function 3, 4, 5, 6. Single-cell genomics has advanced our understanding of human immunology 1, 2. Dandelion analysis of other cell compartments provided insights into the origins of human B1 cells and ILC/NK cell development, illustrating the power of our approach. The application of Dandelion improved the alignment of human thymic development trajectories of double-positive T cells to mature single-positive CD4/CD8 T cells, generating predictions of factors regulating lineage commitment. ![]() We devised a strategy to create an AIR feature space that can be used for both differential V(D)J usage analysis and pseudotime trajectory inference. It enables the application of standard V(D)J analysis workflows to single-cell datasets, delivering improved V(D)J contig annotation and the identification of nonproductive and partially spliced contigs. Here we introduce Dandelion, a computational pipeline for scVDJ-seq analysis. Assessment of single-cell gene expression (single-cell RNA sequencing) and adaptive immune receptor (AIR) sequencing (scVDJ-seq) has been invaluable in studying lymphocyte biology. ![]()
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