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Updated: Jul 7, 2025

Generation of Retinal Organoids from Healthy and Retinal Disease-Specific Human-Induced Pluripotent Stem Cells

Generation of Retinal Organoids from Healthy and Retinal Disease-Specific Human-Induced Pluripotent Stem Cells

Published on: December 9, 2022

Machine learning-based estimation of spatial gene expression pattern during ESC-derived retinal organoid development.

Yuki Fujimura¹, Itsuki Sakai², Itsuki Shioka²

¹Division of Information Science, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan.

Scientific Reports

|December 20, 2023

Summary

This summary is machine-generated.

Researchers developed a non-invasive machine learning method to estimate spatial gene expression in organoids. This technique uses deep learning on images, offering a new way to study tissue development and cell composition without invasive procedures.

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Retinal Organoid Induction System for Derivation of 3D Retinal Tissues from Human Pluripotent Stem Cells

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Published on: April 12, 2021

Area of Science:

Developmental Biology
Biotechnology
Computational Biology

Background:

Organoids mimic embryonic tissues, crucial for research and regenerative medicine.
Assessing spatial gene expression is vital but current methods are invasive, requiring gene editing and immunostaining.
Non-invasive techniques are needed for accurate, quantitative analysis of organoid development.

Purpose of the Study:

To develop a non-invasive method for estimating spatial gene expression patterns in organoids using machine learning.
To apply a deep learning model to retinal organoids for assessing the expression of key developmental genes.
To enable quantitative, real-time evaluation of gene expression crucial for tissue development.

Main Methods:

A deep learning model with an encoder-decoder architecture was developed.
The model was trained on paired phase-contrast and fluorescence images of organoids.
The method was applied to mouse embryonic stem cell-derived retinal organoids, focusing on the Rax gene.

Main Results:

The machine learning model successfully estimated spatially plausible fluorescent patterns with accurate intensities.
The non-invasive method provided quantitative insights into spatial gene expression.
The study demonstrated the feasibility of using deep learning for organoid analysis.

Conclusions:

This novel non-invasive method enables quantitative estimation of spatial gene expression patterns in organoids.
The technique offers a significant advancement over current invasive methods.
It opens new possibilities for evaluating spatial gene expression across diverse biological and medical fields.