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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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Related Experiment Video

Updated: Sep 14, 2025

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

A Deep Learning Approach to Assessing Cell Identity in Stem Cell-Based Embryo Models.

Nazmus Salehin¹, Martin Proks¹, Joshua M Brickman²

¹Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW), Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark.

Methods in Molecular Biology (Clifton, N.J.)

|July 21, 2025

Summary

This summary is machine-generated.

Deep learning models now assess how accurately embryonic stem cell (ESC) derivatives mirror early human development. This new resource helps researchers evaluate in vitro models for developmental biology research.

Keywords:

Classification Integration Interpretability Variational Autoencoders scRNA-seq

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Last Updated: Sep 14, 2025

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Published on: April 21, 2022

Area of Science:

Developmental Biology
Stem Cell Biology
Computational Biology

Background:

Embryoid bodies (EBs) derived from embryonic stem cells (ESCs) are used in 3D differentiation to mimic early human development.
The fidelity of in vitro-derived cells to actual embryonic cells remains a key question.

Purpose of the Study:

To develop and present a deep learning (DL) framework for assessing the developmental accuracy of in vitro stem cell models.
To provide researchers with tools to classify and evaluate cell types generated in vitro.

Main Methods:

Leveraged single-cell RNA sequencing (scRNA-seq) data from early human development.
Employed scvi-tools for data integration and cell type classification.
Developed a DL model to assign cell identities and reliability scores (entropy) to in vitro cells.

Main Results:

Successfully integrated diverse scRNA-seq datasets to create a comprehensive model of early human development.
The DL tool accurately classifies in vitro cell types and quantifies classification confidence.
Publicly available DL models and protocols are provided for interrogating stem cell-derived embryo models.

Conclusions:

The developed DL tools offer a robust method for evaluating the developmental recapitulation by stem cell-based embryo models.
This resource enhances the ability to assess the fidelity of in vitro systems in developmental studies.
Facilitates more accurate interpretation of phenotypes and cell types derived from in vitro differentiation protocols.