Imaging Human Pancreatic Endocrinogenesis During Early Prenatal Life
Bibliographic record
Abstract
Murine pancreatic endocrinogenesis has been extensively studied, but human data remain scarce due to limited sample availability. Here, we first built a large collection of human embryonic and fetal pancreases covering the first trimester of pregnancy to explore human endocrinogenesis. Using an experimental pipeline combining in toto staining, tissue clearing, and light-sheet fluorescence microscopy, we show that insulin-, glucagon-, and somatostatin-positive cells appear simultaneously at Carnegie stage (CS) 16. This contrasts with rodents, in which glucagon-positive cells appear first, followed by insulin-positive and, finally, somatostatin-positive cells and highlights interspecies differences. We also detected bihormonal endocrine cells in 7 of 9 human pancreases between CS16 and CS18, which were no longer detected at later stages. We observed that cell distribution within human fetal islets resembles adult mouse islets, with a core of β-cells surrounded by α- and δ-cells, differing from a more complex arrangement in adult human islets. This, in connection with the small size of human fetal islets when compared with adult islets, suggests that adult human islets may form by fusion of preexisting islets, in contrast to the mouse fission model. Together, our study provides a detailed and comprehensive description of the spatiotemporal dynamics of human pancreatic endocrinogenesis. ARTICLE HIGHLIGHTS: Data on human pancreas development are limited and derived from two-dimensional staining. We overcome this using in toto staining, tissue clearing, and light-sheet imaging. We sought to understand when and where endocrine cells first emerge and how they cluster. First, endocrine cell types appear simultaneously, and early pancreases contain bihormonal cells. There are morphometric differences between fetal and adult islets. We propose a mechanism of adult islet formation by fusion: a new base to reconstitute in vitro islet neogenesis.
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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) | 0.002 | 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".