VDJView

VDJView analyzes single-cell multi-omics data to characterize T and B cell immune receptor diversity and to link antigen specificities with gene expression and clonal dynamics.

Key Features:

• Integration of multi-omics data: Integrates gene expression profiles, T-cell receptor (TCR) sequences, surface protein quantification, antigen specificities, and clinical metadata.
• Data source compatibility: Accepts plate-based sorted single-cell datasets and high-throughput platforms such as 10X Genomics scRNA-seq.
• Simultaneous or independent analysis: Enables analysis of gene expression, immune receptors, and clinical metadata either concurrently or separately.
• Comprehensive analytical functions: Provides quality control, filtering (e.g., tetramer non-specific cells), clustering, random sampling, and hypothesis testing to identify antigen-specific gene signatures linked to immune cell differentiation and clonal expansion.

Scientific Applications:

• Large-scale CD8+ T cell analysis: Applied to a dataset of 150,000 CD8+ T cells integrating gene expression, TCR sequences, 15 surface protein markers, and antigen specificities across viruses, cancer, and self-antigens to discover antigen-specific gene signatures correlated with differentiation and clonal expansion.
• Cancer immune profiling across subjects and tissues: Used on single-cell data from 11 subjects to reveal clonally expanded T and B cells in primary cancer tissues and metastatic lymph nodes and to identify distinct clustering patterns associated with breast cancer molecular subtypes.

Methodology:

Computational methods explicitly include integration of gene expression and TCR analysis tools, quality control, filtering (e.g., tetramer non-specific cells), clustering, random sampling, hypothesis testing, and visualization for dataset exploration.