Initial Cell Seeding Density Influences Pancreatic Endocrine Development During in vitro Differentiation of Human Embryonic Stem Cells
Bibliographic record
Abstract
Human embryonic stem cells (hESCs) have the ability to form cells derived from all three germ layers, and as such have received significant attention as a possible source for insulin-secreting pancreatic beta-cells for diabetes treatment. While considerable advances have been made in generating hESC-derived insulin-producing cells, to date in vitro-derived glucose-responsive beta-cells have remained an elusive goal. With the objective of increasing the in vitro formation of pancreatic endocrine cells, we examined the effect of varying initial cell seeding density from 1.3 x 10(4) cells/cm(2) to 5.3 x 10(4) cells/cm(2) followed by a 21-day pancreatic endocrine differentiation protocol. Low density-seeded cells were found to be biased toward the G2/M phases of the cell cycle and failed to efficiently differentiate into SOX17-CXCR4 co-positive definitive endoderm cells leaving increased numbers of OCT4 positive cells in day 4 cultures. Moderate density cultures effectively formed definitive endoderm and progressed to express PDX1 in approximately 20% of the culture. High density cultures contained approximately double the numbers of PDX1 positive pancreatic progenitor cells and also showed increased expression of MNX1, PTF1a, NGN3, ARX, and PAX4 compared to cultures seeded at moderate density. The cultures seeded at high density displayed increased formation of polyhormonal pancreatic endocrine cell populations co-expressing insulin, glucagon and somatostatin. The maturation process giving rise to these endocrine cell populations followed the expected cascade of pancreatic progenitor marker (PDX1 and MNX1) expression, followed by pancreatic endocrine specification marker expression (BRN4, PAX4, ARX, NEUROD1, NKX6.1 and NKX2.2) and then pancreatic hormone expression (insulin, glucagon and somatostatin). Taken together these data suggest that initial cell seeding density plays an important role in both germ layer specification and pancreatic progenitor commitment, which precedes pancreatic endocrine cell formation. This work highlights the need to examine standard culture variables such as seeding density when optimizing hESC differentiation protocols.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".