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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: Nov 5, 2025

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

Interpretation of T cell states from single-cell transcriptomics data using reference atlases.

Massimo Andreatta^1,2, Jesus Corria-Osorio¹, Sören Müller³

¹Department of Oncology, Lausanne Branch, Ludwig Institute for Cancer Research, CHUV and University of Lausanne, Lausanne, Epalinges, Switzerland.

Nature Communications

|May 21, 2021

Summary

This summary is machine-generated.

This study introduces ProjecTILs, an algorithm that standardizes immune cell subtype analysis across studies and diseases. It enables accurate mapping of new data and identification of novel cell states, improving T cell heterogeneity research.

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Area of Science:

Immunology
Computational Biology
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) reveals extensive immune cell diversity.
Standardized definition of immune cell subtypes and states across studies remains a challenge.

Purpose of the Study:

To develop a robust method for defining T cell subtypes and states across diverse conditions.
To create reference T cell atlases for cancer and viral infections.
To enable accurate projection of new scRNA-seq data onto reference atlases.

Main Methods:

Multi-study integration of scRNA-seq data.
Development and application of the ProjecTILs algorithm for atlas projection.
Meta-analysis of tumor-infiltrating T cells from human and mouse cohorts.

Main Results:

ProjecTILs accurately embeds new scRNA-seq data into reference atlases without structural changes.
The algorithm identifies novel cell states deviating from the reference.
ProjecTILs predicts cell perturbation effects and identifies altered gene programs.
Significant conservation of T cell subtypes between human and mouse was observed.

Conclusions:

ProjecTILs provides a consistent framework for describing T cell heterogeneity across studies, diseases, and species.
This approach enhances the comparability and interpretability of scRNA-seq data in immunology.
The findings support a unified understanding of T cell responses in cancer and viral infections.