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 in cancer, but also in infectious diseases like COVID-19. 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. Researchers from the Medical University of Innsbruck have developed 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.
The Scirpy workflow
After defining clonotypes via CDR3-sequence similarity, scirpy offers a wide range of visualization options to explore clonotype expansion, abundance, and VDJ gene usage. Finally, clonotype information can be integrated with transcriptomic data, leveraging the scanpy workflow. Top panel: Exemplary clonotype network. Each node represents a cell, colored by sample. Edges connect cells belonging to the same clonotype. Middle panel: Clonal expansion of different T cell subsets visualized as bar chart. The bars colored in blue, orange, and green represent the fractions of cells belonging to clonotypes with one, two or more than two cells, respectively. Lower panel: UMAP embedding based on gene expression. Colored dots represent the cells belonging to the most abundant clonotypes.
Availability – Scirpy source code and documentation are available at https://github.com/icbi-lab/scirpy.