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Embryonic Stem Cells

Embryonic Stem Cells

Embryonic stem (ES) cells were first discovered in mice in 1981 by Martin Evans. In 1998, James Thomson identified a method to isolate embryonic stem cells from humans. Human embryonic stem cells (hESCs) are obtained from 3-5 day old embryos that remain unused after an in vitro fertilization procedure.
ES cells are grown in a culture medium where they can divide indefinitely, creating ES cell lines. Under certain conditions, ES cells can differentiate, either spontaneously into a variety of...

Embryonic Stem Cells

Embryonic Stem Cells

Embryonic stem (ES) cells are undifferentiated pluripotent cells, meaning they can produce any cell type in the body. This gives them tremendous potential in science and medicine since they can generate specific cell types for use in research or to replace body cells lost due to damage or disease.

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EmbryoNet: using deep learning to link embryonic phenotypes to signaling pathways.

Daniel Čapek^1,2, Matvey Safroshkin³, Hernán Morales-Navarrete^1,2,4

¹Systems Biology of Development, University of Konstanz, Konstanz, Germany.

|May 8, 2023

Summary

This summary is machine-generated.

This study introduces EmbryoNet, an AI tool that automatically identifies and classifies developmental defects in zebrafish embryos caused by disruptions in essential signaling pathways. This advances developmental biology research and drug screening by providing an unbiased, high-precision phenotyping method.

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Area of Science:

Developmental Biology
Machine Learning
Bioinformatics

Background:

Signaling pathways are crucial for embryonic development, but classifying resulting defects is complex and lacks standardization.
Expert knowledge is currently required for accurate phenotyping, hindering large-scale analysis.

Purpose of the Study:

To develop an automated phenotyping system using machine learning to classify developmental defects in zebrafish.
To identify and classify defects caused by the loss of function in seven major vertebrate signaling pathways.

Main Methods:

Trained a deep convolutional neural network, named EmbryoNet, on over 2 million zebrafish images.
Integrated EmbryoNet with a model of time-dependent developmental trajectories for precise defect classification.
Applied automated phenotyping in high-throughput drug screening.

Main Results:

EmbryoNet accurately identifies and classifies phenotypic defects in zebrafish signaling mutants.
The system demonstrates high precision in classifying defects related to seven major signaling pathways.
Successfully resolved the mechanism of action for pharmaceutical substances in drug screens.

Conclusions:

Automated phenotyping with EmbryoNet offers a standardized and unbiased approach to studying developmental signaling pathways.
This AI tool has broad applications in developmental biology, cross-species analysis, and pharmaceutical research.
The freely available image dataset facilitates further research in automated embryo phenotyping.