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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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Related Experiment Video

Updated: Oct 21, 2025

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Probing T-cell response by sequence-based probabilistic modeling.

Barbara Bravi¹, Vinod P Balachandran², Benjamin D Greenbaum³

¹Laboratoire de Physique de l'Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, Paris, France.

Plos Computational Biology

|September 2, 2021

Summary

This summary is machine-generated.

This study introduces a machine learning method to identify antigen-specific T cell receptors (TCRs) from sequencing data. The approach accurately predicts TCR responses to specific antigens, aiding diagnostics and therapeutics.

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Published on: March 8, 2012

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Last Updated: Oct 21, 2025

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Published on: February 25, 2020

Generation of Human Alloantigen-specific T Cells from Peripheral Blood

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Use of Interferon-γ Enzyme-linked Immunospot Assay to Characterize Novel T-cell Epitopes of Human Papillomavirus

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Published on: March 8, 2012

Area of Science:

Immunology
Bioinformatics
Computational Biology

Background:

High-throughput sequencing enables detailed analysis of the human T cell receptor (TCR) repertoire.
Inferring antigen-specificity from TCR sequences holds promise for diagnostics and therapeutics.

Purpose of the Study:

To develop a sequence-based inference approach using machine learning to identify antigen-specific TCRs.
To extract TCR sequence motifs associated with antigen-specific responses.

Main Methods:

Utilized a Restricted Boltzmann Machine (RBM) machine learning approach.
Combined probabilistic models of TCR sequences with clone abundance data.
Developed patient-personalized TCR motifs for antigen response identification.

Main Results:

Successfully identified TCR motifs specific to individual tumor and infectious disease antigens.
Accurately discriminated between specific and non-specific TCR responses.
Demonstrated an interpretable model where TCRs recognizing the same antigen cluster, distinguishing responses to different viral epitopes.

Conclusions:

The developed model can identify condition-specific responding TCRs.
This approach aids in understanding TCR responses to neoantigens and epitopes.
Potential applications in developing personalized diagnostics and therapeutics based on TCR repertoire analysis.