Marina Ainciburu1,2,3, Duncan M Morgan4,5, Erica A K DePasquale2,3, J Christopher Love3,4,5, Felipe Prósper1, Peter van Galen2,3. 1. Program of Hemato-Oncology, University of Navarra, Pamplona, 31008, Spain. 2. Division of Hematology, Brigham and Women's Hospital, Boston, MA, 02115, USA. 3. Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA. 4. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. 5. Koch Institute for Integrative Cancer Research,Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Abstract
SUMMARY: Diversity of the T cell receptor (TCR) repertoire is central to adaptive immunity. The TCR is composed of α and β chains, encoded by the TRA and TRB genes, of which the variable regions determine antigen specificity. To generate novel biological insights into the complex functioning of immune cells, combined capture of variable regions and single-cell transcriptomes provides a compelling approach. Recent developments enable the enrichment of TRA and TRB variable regions from widely used technologies for 3'-based single-cell RNA-sequencing (scRNA-seq). However, a comprehensive computational pipeline to process TCR-enriched data from 3' scRNA-seq is not available. Here we present an analysis pipeline to process TCR variable regions enriched from 3' scRNA-seq cDNA. The tool reports TRA and TRB nucleotide and amino acid sequences linked to cell barcodes, enabling the reconstruction of T cell clonotypes with associated transcriptomes. We demonstrate the software using peripheral blood mononuclear cells (PBMCs) from a healthy donor and detect TCR sequences in a high proportion of single T cells. Detection of TCR sequences is low in non-T cell populations, demonstrating specificity. Finally, we show that TCR clones are larger in CD8 Memory T cells than in other T cell types, indicating an association between T cell clonotypes and differentiation states. AVAILABILITY AND IMPLEMENTATION: The Workflow for Association of T cell receptors from 3' single-cell RNA-seq (WAT3R), including test data, is available on GitHub (https://github.com/mainciburu/WAT3R), Docker Hub (https://hub.docker.com/r/mainciburu/wat3r), and a workflow on the Terra platform (https://app.terra.bio). The test dataset is available on GEO (accession number GSE195956). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
SUMMARY: Diversity of the T cell receptor (TCR) repertoire is central to adaptive immunity. The TCR is composed of α and β chains, encoded by the TRA and TRB genes, of which the variable regions determine antigen specificity. To generate novel biological insights into the complex functioning of immune cells, combined capture of variable regions and single-cell transcriptomes provides a compelling approach. Recent developments enable the enrichment of TRA and TRB variable regions from widely used technologies for 3'-based single-cell RNA-sequencing (scRNA-seq). However, a comprehensive computational pipeline to process TCR-enriched data from 3' scRNA-seq is not available. Here we present an analysis pipeline to process TCR variable regions enriched from 3' scRNA-seq cDNA. The tool reports TRA and TRB nucleotide and amino acid sequences linked to cell barcodes, enabling the reconstruction of T cell clonotypes with associated transcriptomes. We demonstrate the software using peripheral blood mononuclear cells (PBMCs) from a healthy donor and detect TCR sequences in a high proportion of single T cells. Detection of TCR sequences is low in non-T cell populations, demonstrating specificity. Finally, we show that TCR clones are larger in CD8 Memory T cells than in other T cell types, indicating an association between T cell clonotypes and differentiation states. AVAILABILITY AND IMPLEMENTATION: The Workflow for Association of T cell receptors from 3' single-cell RNA-seq (WAT3R), including test data, is available on GitHub (https://github.com/mainciburu/WAT3R), Docker Hub (https://hub.docker.com/r/mainciburu/wat3r), and a workflow on the Terra platform (https://app.terra.bio). The test dataset is available on GEO (accession number GSE195956). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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