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T cell fate and clonality inference from single-cell transcriptomes.

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Summary
This summary is machine-generated.

We created TraCeR, a new method to reconstruct T cell receptor (TCR) sequences from single-cell data. This tool connects T cell function to their specific receptors, revealing how T cell populations change during infection.

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Area of Science:

  • Immunology
  • Computational Biology
  • Genomics

Background:

  • Understanding T cell receptor (TCR) diversity is crucial for immunology.
  • Single-cell RNA sequencing (scRNA-seq) provides transcriptional profiles but often lacks paired TCR sequences.

Purpose of the Study:

  • To develop a computational method for reconstructing full-length, paired TCR sequences from scRNA-seq data.
  • To link T cell specificity with functional responses by analyzing clonal relationships and transcriptional profiles.

Main Methods:

  • Developed TraCeR, a computational tool for TCR sequence reconstruction.
  • Applied TraCeR to scRNA-seq data from a mouse Salmonella infection model.

Main Results:

  • Successfully reconstructed full-length, paired TCR sequences.
  • Demonstrated that T cell clonotypes span different activation states, from early CD4+ T cells to effector and memory cells.
  • Linked TCR sequences with cellular transcriptional profiles to understand clonal dynamics.

Conclusions:

  • TraCeR enables comprehensive analysis of T cell repertoires and their functional states.
  • Revealed the dynamic nature of T cell clonotypes during an immune response.
  • Provides a valuable tool for studying T cell immunity and disease.