Controlled clustering enhances PDX1 and NKX6.1 expression in pancreatic endoderm cells derived from pluripotent stem cells
Bibliographic record
Abstract
Pluripotent stem cell (PSC)-derived insulin-producing cells are a promising cell source for diabetes cellular therapy. However, the efficiency of the multi-step process required to differentiate PSCs towards pancreatic beta cells is variable between cell lines, batches and even within cultures. In adherent pancreatic differentiation protocols, we observed spontaneous local clustering of cells expressing elevated nuclear expression of pancreatic endocrine transcription factors, PDX1 and NKX6.1. Since aggregation has previously been shown to promote downstream differentiation, this local clustering may contribute to the variability in differentiation efficiencies observed within and between cultures. We therefore hypothesized that controlling and directing the spontaneous clustering process would lead to more efficient and consistent induction of pancreatic endocrine fate. Micropatterning cells in adherent microwells prompted clustering, local cell density increases, and increased nuclear accumulation of PDX1 and NKX6.1. Improved differentiation profiles were associated with distinct filamentous actin architectures, suggesting a previously overlooked role for cell-driven morphogenetic changes in supporting pancreatic differentiation. This work demonstrates that confined differentiation in cell-adhesive micropatterns may provide a facile, scalable, and more reproducible manufacturing route to drive morphogenesis and produce well-differentiated pancreatic cell clusters.
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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 | 0.000 |
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".