WAT3R

WAT3R processes and analyzes T-cell receptor (TCR) variable regions enriched from 3'-based single-cell RNA sequencing (scRNA-seq) data to reconstruct TRA and TRB clonotypes and link nucleotide and amino-acid sequences to cell barcodes for repertoire and differentiation-state analysis.


Key Features:

  • TCR Variable Region Enrichment: Processes cDNA from 3' scRNA-seq to capture and analyze TCR α and β chain variable regions encoded by TRA and TRB genes.
  • Clonotype Reconstruction: Links nucleotide and amino-acid sequences of TCRs to cell barcodes to reconstruct T-cell clonotypes alongside associated transcriptomes.
  • Specificity and Sensitivity: Detects TCR sequences predominantly in single T cells with low detection rates in non-T-cell populations.
  • Clonal Analysis: Enables analysis of TCR clone sizes and associations between clonotypes and differentiation states, for example larger clones in CD8 Memory T cells.

Scientific Applications:

  • Adaptive immunity profiling: Characterizes T-cell repertoire diversity and composition from 3' scRNA-seq data to study antigen-specific immune responses.
  • Clonotype–state association: Correlates TCR clonotypes with immune cell differentiation states and transcriptional phenotypes.
  • Immunotherapy research: Provides repertoire-level information relevant to assessing clonal expansion and potential therapeutic targets.

Methodology:

Processes 3' scRNA-seq cDNA to extract and analyze TRA/TRB variable-region sequences, links sequences to cells via barcodes, and reconstructs clonotypes for repertoire and differentiation-state analysis.

Topics

Details

License:
MIT
Cost:
Free of charge
Tool Type:
command-line tool, workflow
Operating Systems:
Mac, Linux, Windows
Programming Languages:
R, Python
Added:
7/6/2022
Last Updated:
7/6/2022

Operations

Data Inputs & Outputs

Publications

Ainciburu M, Morgan DM, DePasquale EAK, Love JC, Prósper F, van Galen P. WAT3R: recovery of T-cell receptor variable regions from 3′ single-cell RNA-sequencing. Bioinformatics. 2022;38(14):3645-3647. doi:10.1093/bioinformatics/btac382. PMID:35674381. PMCID:PMC9272805.

PMID: 35674381
Funding: - NIH: R00CA218832 - Glenn Foundation for Medical Research and American Federation for Aging Research: FPU18/05488

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