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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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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

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Published on: November 19, 2018

Can we predict T cell specificity with digital biology and machine learning?

Dan Hudson^1,2, Ricardo A Fernandes³, Mark Basham²

¹MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.

Nature Reviews. Immunology

|February 9, 2023

Summary

Predicting T cell receptor (TCR) antigen specificity is crucial for understanding immunity. This perspective calls for interdisciplinary efforts to develop computational models for mapping TCRs to their antigens, overcoming current limitations in data and model performance.

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Area of Science:

Immunology
Computational Biology
Machine Learning

Background:

T cell receptors (TCRs) are vital for cellular immunity.
Predicting TCR-antigen specificity computationally is a significant challenge.
Current models struggle with limited data and generalize poorly beyond known binders.

Purpose of the Study:

To advocate for a coordinated interdisciplinary approach to predict TCR-antigen specificity.
To outline the requirements for predictive models of TCR-antigen binding.
To explore how emerging technologies can address current limitations.

Main Methods:

Review of current limitations in TCR-antigen prediction.
Discussion of requirements for predictive modeling.
Exploration of potential solutions using single-cell technology and machine learning.

Main Results:

Current datasets are insufficient for comprehensive TCR-antigen mapping.
State-of-the-art models have limited predictive power outside of training data.
Advances in digital biology offer promising avenues for improvement.

Conclusions:

Predicting TCR-antigen specificity requires significant interdisciplinary collaboration.
New computational models leveraging advanced technologies are needed.
Successful prediction will advance systems immunology and understanding of immunogenicity.