Macrophages heterogeneity and significance during human fetal pancreatic development
Bibliographic record
Abstract
SUMMARY Organogenesis is a complex process that relies on a dynamic interplay between extrinsic factors originating from the microenvironment and intrinsic factors specific to the tissue. For the endocrine cells of the islet of Langerhans, the local microenvironment consists of various cell types including pancreatic acinar and ductal cells as well as neuronal, immune, endothelial, and stromal cells. Interestingly, hematopoietic cells have been detected in human pancreas as early as 6 post-conception weeks (PCW) 1,2 , but whether they play a role during islet formation in humans remains largely unknown. To shed light on this question, we performed single nuclei RNA sequencing of the human fetal pancreas during the early weeks of the second trimester, specifically focusing on the molecular interaction between the hematopoietic niche and the pancreatic epithelium. Our analysis identified a wide range of hematopoietic cells as well as two distinct subsets of macrophages that are unique to the fetal pancreas and absent in neonatal or adult pancreatic tissues. Leveraging this discovery, we developed a co-culture system of hESC-derived endocrine-macrophage organoids to model their interaction in vitro . Remarkably, we found that macrophages promoted the differentiation and viability of developing endocrine cells in vitro and enhanced tissue engraftment in immunocompromised mice, supporting a role for these cells in future tissue engineering strategies for diabetes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".