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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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Updated: Jul 16, 2025

Characterizing Mutational Load and Clonal Composition of Human Blood

Characterizing Mutational Load and Clonal Composition of Human Blood

Published on: July 11, 2019

cloneRate: fast estimation of single-cell clonal dynamics using coalescent theory.

Brian Johnson¹, Yubo Shuai², Jason Schweinsberg²

¹Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, United States.

Bioinformatics (Oxford, England)

|September 12, 2023

Summary

This summary is machine-generated.

We developed a new R package, cloneRate, to accurately quantify clone growth dynamics from DNA sequencing data. This method aids in understanding cancer evolution and improving prognostics for diseases like myeloproliferative neoplasms.

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Enumeration of Neural Stem Cells Using Clonal Assays

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Last Updated: Jul 16, 2025

Characterizing Mutational Load and Clonal Composition of Human Blood

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Published on: July 11, 2019

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Enumeration of Neural Stem Cells Using Clonal Assays

Enumeration of Neural Stem Cells Using Clonal Assays

Published on: October 4, 2016

Area of Science:

Evolutionary medicine
Computational biology
Cancer genomics

Background:

Measuring in vivo evolution, particularly cancer evolution, is challenging due to experimental limitations and the dynamic nature of biological systems.
Continuous observation of clonal architecture is often impossible, and longitudinal multi-timepoint samples are scarce.
Single-cell resolution DNA sequencing data offers a way to reconstruct past evolutionary dynamics using mutational history.

Purpose of the Study:

To address the need for an accurate, fast, and user-friendly method for quantifying clone growth dynamics from DNA sequencing datasets.
To develop and validate analytical methods for estimating clone net growth rates.
To provide a publicly available tool for researchers in cancer evolution and related fields.

Main Methods:

Derived methods based on coalescent theory to estimate clone net growth rates.
Utilized reconstructed phylogenies or shared mutations for growth rate estimation.
Validated analytical methods, replacing the need for complex simulations.

Main Results:

Clones with multiple driver mutations exhibit significantly increased growth rates (median 0.94/year) compared to those with single or unknown drivers (median 0.25/year).
Higher fittest clone growth rates in myeloproliferative neoplasms correlate with shorter time to diagnosis (13.9 vs 26.4 months).
The developed methods show broad applications for improving mechanistic understanding and prognostic ability in hematopoietic data.

Conclusions:

The `cloneRate` R package provides an accessible tool for estimating clone growth dynamics.
Accurate quantification of clone growth dynamics can enhance understanding of cancer evolution.
This approach has the potential to improve prognostic capabilities for hematologic malignancies.