Related Experiment Video
Updated: May 22, 2026
05:45
Generation and Downstream Analysis of Single-Cell and Single-Nuclei Transcriptomes in Brain Organoids
Published on: March 29, 2024
Application of spatial transcriptomics across organoids for a high-resolution, spatial whole-transcriptome
Maria Rosaria Nucera1,2,3, Natalie Charitakis1,2,3, Ryan F Leung1,2,3
1Stem Cell Medicine Theme, Murdoch Children's Research Institute, Parkville, VIC, Australia.
Iscience
|May 21, 2026
Summary
This study introduces stereo-seq for spatial transcriptomics in stem cell-derived organoids. It optimizes RNA capture and analysis for accurate disease modeling and tissue characterization.
Area of Science:
- Biotechnology
- Genomics
- Developmental Biology
Background:
- Stem cell-derived organoids are valuable for modeling diseases.
- Comparing organoids to in vivo tissues requires spatial transcriptomic analysis.
- Current spatial transcriptomics methods face challenges in organoid applications.
Purpose of the Study:
- To systematically profile multiple stem cell-derived organoids using spatial transcriptomics.
- To optimize and validate stereo-seq for organoid characterization.
- To develop novel analytical methods for regional analysis of organoid data.
Main Methods:
- Utilized stereo-seq, a full transcriptome spatial assay with on-chip in situ RNA capture.
- Applied the assay to diverse organoid models: brain, heart muscle, heart valve, kidney, lung, cartilage, and hematopoietic.
- Developed a bespoke analysis method for partitioning organoid samples into regions.
Main Results:
- Demonstrated the feasibility of systematic spatial transcriptomic profiling across multiple organoid types.
- Identified and addressed limitations in RNA capture efficiency compared to reference tissues.
- Successfully applied a novel regional analysis method for organoid characterization.
Conclusions:
- Stereo-seq is a powerful tool for characterizing stem cell-derived organoids.
- Optimized methods enhance RNA capture and enable detailed regional analysis.
- Findings provide a foundation for advanced organoid-based disease modeling and research.
