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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: May 11, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Cytopathic Effect Detection and Clonal Selection using Deep Learning.

Yu Yuan^1,2, Tony Wang¹, Jordan Sims¹

¹Amgen, Inc., Thousand Oaks, 91320, CA, USA.

Pharmaceutical Research

|July 24, 2024

Summary

This summary is machine-generated.

Supervised deep learning algorithms automate cell imaging analysis for virus detection and cell line development. These AI tools achieve over 95% accuracy, offering a faster, more cost-effective solution than manual inspection.

Keywords:

Cell imaging Clone selection Convolutional neural networks Cytopathic effects Deep learning Microscopy

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Area of Science:

Biotechnology
Cell Biology
Artificial Intelligence

Background:

Microscopic cell imaging is crucial in biotechnology for analyzing cell morphology and state.
Manual inspection of cell images for applications like cytopathic effect (CPE) detection and clonality verification is time-consuming and labor-intensive.

Purpose of the Study:

To develop and evaluate supervised deep learning algorithms for automating cell detection in microscopy images.
To enhance the efficiency and accuracy of identifying cytopathic effects (CPE) and verifying clonality in cell line development.

Main Methods:

Utilized image processing techniques combined with convolutional neural networks (CNNs).
Trained and tested algorithms on expert-labeled microscopy image data.

Main Results:

Achieved promising results in both accuracy and processing speed.
Demonstrated high accuracy (over 95%) for both CPE detection and clonal selection tasks.

Conclusions:

Supervised deep learning provides an accurate and efficient method for automating cell imaging analysis in biotechnology.
The proposed algorithms offer a cost-effective solution for critical applications like virus detection and cell line development.