Protocol for the generation of three-dimensional micropatterned neuroepithelial tissues using hPSCs, bioprinting, and matrix scaffolds
Bibliographic record
Abstract
Micropatterning technology that spatially guides the self-assembly of human pluripotent stem cells (hPSCs) into neural tissues offers enhanced fidelity to investigate early neurodevelopment and disease mechanisms. Here, we present a protocol to produce micropatterned and scaffolded neuroepithelial tissues (scNETs) from hPSCs, leveraging three-dimensional (3D) bioprinting to deposit extracellular matrix (ECM) droplets with defined geometries. We describe steps for bioprinting of ECM micropatterns, hPSC culture and seeding, and the subsequent neural induction process to form scNETs within 5 days. For complete details on the use and execution of this protocol, please refer to Imani Farahani et al. 1 • Using bioprinting to model human neural tube development at Carnegie stages 13–14 • Instructions for fabricating extracellular matrix micropatterns using a 3D bioprinter • Steps for generating micropatterned and scaffolded neuroepithelial tissues from hPSCs • Guidance on characterizing neural cell type differentiation and structural phenotypes Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Micropatterning technology that spatially guides the self-assembly of human pluripotent stem cells (hPSCs) into neural tissues offers enhanced fidelity to investigate early neurodevelopment and disease mechanisms. Here, we present a protocol to produce micropatterned and scaffolded neuroepithelial tissues (scNETs) from hPSCs, leveraging three-dimensional (3D) bioprinting to deposit extracellular matrix (ECM) droplets with defined geometries. We describe steps for bioprinting of ECM micropatterns, hPSC culture and seeding, and the subsequent neural induction process to form scNETs within 5 days.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".