Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

T Cell Activation and Clonal Selection01:22

T Cell Activation and Clonal Selection

812
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...
812
Cells of the Adaptive Immune Response01:23

Cells of the Adaptive Immune Response

1.0K
The T and B lymphocytes of the adaptive immune system develop from common lymphoid progenitor cells in the bone marrow. These progenitors give rise to precursors that eventually develop into both T and B lymphocytes. As these precursors mature, they gain the ability to detect and respond to foreign antigens in the body, a process known as immunocompetence. Additionally, these precursors acquire self-tolerance, a process that ensures they do not react to self-antigens. This intricate system...
1.0K
T Cell Types and Functions01:24

T Cell Types and Functions

1.1K
When T cells with CD4 markers are activated, they give rise to two types of effector cells: helper T cells and regulatory T cells. Meanwhile, T cells with CD8 markers differentiate into effector cytotoxic T cells. The differentiation of CD4 T cells into helper T cell subsets, such as Th1, Th2, and Th17 cells, is dependent on the antigen type, antigen-presenting cell, and regulatory cytokines.
Th1 cells stimulate dendritic cells to express necessary co-stimulatory molecules on their surfaces for...
1.1K
Immunological Memory01:23

Immunological Memory

671
Immunological memory, a pivotal pillar of the adaptive immune system, is responsible for the body's ability to remember and respond more swiftly and effectively to previously encountered pathogens. This remarkable feature is what makes vaccines so effective in preventing diseases.
What is Immunological Memory?
Immunological memory is an integral function of the immune system that allows it to recognize and react more rapidly and effectively to pathogens previously encountered. This feature...
671

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Systems biology during 20 years of PLoS Computational Biology.

PLoS computational biology·2026
Same author

A multiscale, Bayesian inference approach to augment mechanistic models of cell signaling with machine-learning predictions of binding affinity.

PLoS computational biology·2026
Same author

Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer.

NPJ systems biology and applications·2025
Same author

Integrating mechanism-based T cell phenotypes into a model of tumor-immune cell interactions.

APL bioengineering·2024
Same author

Merging Metabolic Modeling and Imaging for Screening Therapeutic Targets in Colorectal Cancer.

bioRxiv : the preprint server for biology·2024
Same author

Information-Theoretic Analysis of a Model of CAR-4-1BB-Mediated NFκB Activation.

Bulletin of mathematical biology·2023

Related Experiment Video

Updated: Jul 20, 2025

Tumor Transplantation for Assessing the Dynamics of Tumor-Infiltrating CD8+ T Cells in Mice
07:36

Tumor Transplantation for Assessing the Dynamics of Tumor-Infiltrating CD8+ T Cells in Mice

Published on: June 12, 2021

6.6K

A data-driven Boolean model explains memory subsets and evolution in CD8+ T cell exhaustion.

Geena V Ildefonso1, Stacey D Finley2,3,4

  • 1Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, California, USA.

NPJ Systems Biology and Applications
|July 31, 2023
PubMed
Summary

This study models gene expression changes in CD8+ T cells to understand T cell exhaustion (TCE). The findings reveal the sequence of gene expression patterns leading to TCE, aiding in developing strategies to inhibit this state.

More Related Videos

Measuring Mitochondrial Function of Naïve and Effector CD8 T Cells
06:07

Measuring Mitochondrial Function of Naïve and Effector CD8 T Cells

Published on: March 28, 2025

361
Murine Superficial Lymph Node Surgery
04:36

Murine Superficial Lymph Node Surgery

Published on: May 21, 2012

42.5K

Related Experiment Videos

Last Updated: Jul 20, 2025

Tumor Transplantation for Assessing the Dynamics of Tumor-Infiltrating CD8+ T Cells in Mice
07:36

Tumor Transplantation for Assessing the Dynamics of Tumor-Infiltrating CD8+ T Cells in Mice

Published on: June 12, 2021

6.6K
Measuring Mitochondrial Function of Naïve and Effector CD8 T Cells
06:07

Measuring Mitochondrial Function of Naïve and Effector CD8 T Cells

Published on: March 28, 2025

361
Murine Superficial Lymph Node Surgery
04:36

Murine Superficial Lymph Node Surgery

Published on: May 21, 2012

42.5K

Area of Science:

  • Immunology
  • Systems Biology
  • Computational Biology

Background:

  • T cells are crucial for immune responses against infection and cancer.
  • Prolonged stimulation of CD8+ T cells due to antigen persistence leads to T cell exhaustion (TCE).
  • While functional changes in CD8+ T cell differentiation are known, the underlying gene expression dynamics of TCE are not fully understood.

Purpose of the Study:

  • To elucidate the gene expression state changes underlying T cell exhaustion (TCE).
  • To identify the sequence of transcriptional patterns that lead to the exhausted state in CD8+ T cells.
  • To develop a predictive model for evaluating strategies to counteract TCE.

Main Methods:

  • Utilized a previously published data-driven Boolean model of gene regulatory interactions.
  • Performed network analysis and computational modeling to predict gene expression states.
  • Simulated the sequence of gene expression patterns driving cell state evolution.

Main Results:

  • Identified the specific gene expression states corresponding to T cell exhaustion (TCE).
  • Revealed the temporal sequence of gene expression patterns that culminate in TCE.
  • Demonstrated the utility of the model in predicting and potentially inhibiting the exhausted state.

Conclusions:

  • A common pathway model of CD8+ T cell gene regulatory interactions provides insights into TCE.
  • Understanding the transcriptional changes is key to deciphering the evolution of cell states in TCE.
  • The developed model can inform therapeutic strategies aimed at restoring T cell function.