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T Cell Activation and Clonal Selection

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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.
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Updated: Jun 7, 2025

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

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

Published on: November 19, 2018

Assaying and classifying T cell function by cell morphology.

Xin Wang¹, Stacey M Fernandes², Jennifer R Brown²

¹Department of Biomedical Engineering, Columbia University, New York, NY.

Biomedinformatics

|November 11, 2024

Summary

This summary is machine-generated.

Cell morphology, specifically T cell spreading on elastic surfaces, can indicate immune cell function. This method distinguishes healthy T cells from those in Chronic Lymphocytic Leukemia (CLL) patients, aiding immunotherapy.

Keywords:

CLL T cell mechanosensing T cell morphology immunotherapy intrinsic state machine learning primary human T cells rapid measurement surrounding environment

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Last Updated: Jun 7, 2025

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Published on: March 6, 2009

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Published on: June 4, 2019

Area of Science:

Immunology
Cell Biology
Biophysics

Background:

Individual immune cell function variability presents challenges for cellular immunotherapies.
T cell function assessment is crucial for effective treatment strategies.

Purpose of the Study:

To investigate T cell morphology as a quantifiable indicator of T cell function.
To differentiate T cells from healthy donors and Chronic Lymphocytic Leukemia (CLL) patients based on morphology.
To explore the influence of substrate elasticity on T cell behavior.

Main Methods:

Quantified T cell short-term spreading on elastic surfaces using 11 morphological parameters.
Analyzed morphological variations between T cells from healthy donors and CLL patients.
Assessed T cell responses to substrates with varying elastic moduli.
Employed machine learning algorithms (Decision Tree, Random Forest) to classify T cells based on combined morphological features.

Main Results:

Identified distinct morphological features differentiating T cells from healthy and CLL donors.
Observed differences in T cell spreading responses correlated with substrate elastic modulus.
Machine learning models effectively distinguished between healthy and CLL T cells using morphological data.

Conclusions:

T cell morphology on elastic surfaces serves as a reliable indicator of T cell function.
This morphometric approach can differentiate T cells from healthy and CLL individuals.
Further development could enable a rapid T cell function assay to guide cellular immunotherapy.