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T Cell Activation and Clonal Selection

T Cell Activation and Clonal Selection

T cells are integral to our adaptive immune system, recognizing and effectively responding to foreign antigens. T cell activation and clonal selection are pivotal in orchestrating this immune response. This article elucidates these mechanisms, detailing the roles of cluster of differentiation (CD) markers, major histocompatibility complex (MHC) molecules, costimulatory signals, and the process of clonal selection.
Naive T cells that have not yet encountered an antigen express two primary CD...

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T cell receptor-centric perspective to multimodal single-cell data analysis.

Kerry A Mullan^1,2,3, My Ha^2,4,5, Sebastiaan Valkiers^1,2,3

¹Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium.

Science Advances

|November 29, 2024

Summary

This summary is machine-generated.

This study advocates for a T cell receptor (TCR)-first approach in single-cell analysis, revealing limitations in current methods. A TCR-first strategy enhances T cell repertoire understanding for improved immunotherapy and diagnostics.

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T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing

T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing

Published on: January 12, 2021

Area of Science:

Immunology
Computational Biology
Genomics

Background:

Single-cell analysis currently relies heavily on gene expression, underutilizing the T cell receptor (TCR).
Existing methods face limitations in accurately annotating T cell types within complex repertoires.
A TCR-centric approach is needed to fully leverage T cell data in various disease contexts.

Purpose of the Study:

To propose and validate a TCR-first strategy for single-cell T cell repertoire analysis.
To address limitations in unsupervised cell-type annotation by introducing a semi-supervised or TCR arrangement-based standard.
To demonstrate the utility of this approach in identifying treatment effects and novel T cell clusters.

Main Methods:

Curation of a large T cell atlas (500,000 cells) from 12 human studies across multiple diseases.
Development and application of a semi-supervised method or TCR arrangement for robust cell-type annotation.
Utilizing the STEGO.R tool to analyze treatment-related dynamics and T cell clusters.

Main Results:

Identified significant limitations in unsupervised cell-type annotation methods.
Proposed a more robust annotation standard using TCR arrangement or semi-supervised learning.
Demonstrated the identification of treatment-specific T cell dynamics and novel public T cell clusters with potential antigen specificity using a TCR-first approach.

Conclusions:

A paradigm shift to a TCR-first approach is essential for comprehensive T cell repertoire analysis.
This strategy can uncover critical T cell features previously overlooked by gene expression-focused methods.
The TCR-first approach holds promise for advancing immunotherapy and diagnostic strategies.