Geometrical constraints dictate assembly and phenotype of human iPSC-derived motoneuronal spheroids
Bibliographic record
Abstract
BACKGROUND: Neuronal spheroids represent an easy and versatile solution to model neuronal tissue in vitro. Conventional approaches to generate spheroids lack accurate size control, scalability, and customizability. This is even more exacerbated in case of pluripotent stem cell (PSC) derived spheroids, which remain challenging to standardize. Microwell devices address these limitations, providing an optimal balance between accessibility and scalability. With the aim of optimizing culture conditions, we parametrically investigated the role of microwell geometry on the formation and maturation of iPSC-derived motor neuron precursor (MNP) spheroids. METHODS: We developed a customizable mold device using Digital Light Processing (DLP) 3D printing to fabricate agarose microwell arrays with distinct aspect ratios for culturing hiPSC-derived MNP spheroids with high reproducibility. We generated nine different pyramidal microwell array geometries for culturing size-controlled spheroids in the 40-140 μm diameter range. We then evaluated the differential expression of genes related to cell proliferation and motor-neuron differentiation as function of microwell geometry and spheroid size. RESULTS: Our results indicate that spheroid size is significantly influenced by the microwell geometry, reliably due to cell partitioning at the seeding stage. Expression of proliferation and differentiation markers, such as motor neuron and pancreas homeobox 1 (MNX1) and Islet-1 (ISL1) transcription factors, is also dependent on microwell geometry and spheroid morphological descriptors. CONCLUSION: Our approach enables the scalable production of size-controlled MNP spheroids and underscores the effect of geometrical confinement on regulating motor neuron differentiation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".