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Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies
Published on: March 29, 2024
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Systematic scRNA-seq screens profile neural organoid response to morphogens.
Fátima Sanchís-Calleja1, Nadezhda Azbukina1, Akanksha Jain1
1Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
Nature Methods
|December 16, 2025
Summary
This study surveys how morphogens specify human neural organoids, revealing timing, concentration, and combinations critically influence regional identity. These findings aid predicting stem cell differentiation outcomes.
Area of Science:
- Neuroscience
- Developmental Biology
- Stem Cell Biology
Background:
- Morphogens are crucial for directing cell fates during embryonic development.
- Neural organoids offer a model to study human neurodevelopment in vitro.
- A comprehensive understanding of human neuroepithelial responses to morphogens is lacking.
Purpose of the Study:
- To comprehensively survey morphogen-induced regional specification in human neural organoids.
- To investigate how morphogen timing, concentration, and combinations affect neural cell-type and regional composition.
- To compare patterning dynamics under different morphogen application methods (microfluidics vs. multi-well plates).
Main Methods:
- Multiplexed single-cell transcriptomic screening of human neural organoids.
- Application of morphogen concentration gradients using microfluidic chips.
- Application of increasing static morphogen concentrations in multi-well plates.
- Analysis of cell-type and regional composition based on transcriptomic data.
Main Results:
- Morphogen timing, concentration, and combinations significantly influence organoid cell-type and regional identity.
- The response to morphogens is dependent on the specific cell line and neural induction method used.
- Different morphogen application methods (e.g., gradients vs. static concentrations) lead to distinct patterning dynamics.
- A detailed resource of morphogen-induced neural lineage specification was generated.
Conclusions:
- This study provides a detailed resource for understanding human neural lineage specification.
- The findings enable prediction of differentiation outcomes in human stem-cell-based systems when combined with deep learning models.
- This work advances the use of neural organoids for studying developmental neurobiology and disease modeling.

