411.2: Deciphering the hematopoietic pancreatic niche during human fetal development
Bibliographic record
Abstract
Introduction: Type 1 Diabetes (T1D) is an autoimmune disorder characterized by the loss of pancreatic beta cells, causing chronic hyperglycemia. While whole pancreas and islet transplantation can restore normoglycemia, the shortage of donors and the recurrent autoimmunity present challenges for the application of this therapy. Multiple research groups have developed different protocols to generate islet cells from human pluripotent stem cells (hPSCs), but unless transplanted, these cells only recapitulate some features of their mature counterpart. This underlines the importance of the microenvironment in shaping endocrine cell maturation. While the crosstalk between pancreatic epithelium and surrounding cell types, such as mesenchymal and vascular populations, has been extensively studied, little attention has been placed on understanding whether the immune cells support pancreatic organogenesis. Additionally, during fetal development, immune cells migrate and colonize the fetal organs to establish peripheral tolerance, a process that is impaired in many autoimmune diseases, such as type 1 diabetes. Here, we investigated the phenotype and the role played by immune cells during human pancreas development. Methods: To this aim, we molecularly characterized the hematopoietic cells present in the developing pancreas by performing single nuclei RNA sequencing (snRNAseq) during the second trimester of human gestation. This analysis led to the identification of 30 individual cell clusters describing the pancreatic epithelium and its microenvironment and included 13 distinct hematopoietic cell types. To investigate whether fetal-like myeloid cells play a role during human pancreatic development, we established a human pluripotent stem cell (hPSC)-derived co-culture system to study potential interactions between pancreatic cells and myeloid cells. Specifically, by modeling Yolk Sac Hematopoiesis and pancreatic development, we generated hPSC-derived embryonic myeloid cells and pancreatic endoderm, which were bench-marked in cellular composition and molecular profiles to the fetal dataset. Results: We determined that hPSC-derived myeloid cells improved the development and viability of hPSC-derived endocrine cells when compared to standard growth conditions. Finally, we found that transplantation of myeloid-endocrine co-cultures improved graft vascularization, insulin secretion, and the frequency of hormone+ cells in the murine subcutaneous space. Conclusions: This comprehensive interrogation of the hematopoietic cells within the pancreatic niche and its applications could pave the way for novel stem cell-based transplantation modalities and tissue engineering strategies for diabetes. The authors thank the donors, RCWIH BioBank, the Lunenfeld-Tanenbaum Research Institute, and the Mount Sinai Hospital/UHN Department of Obstetrics and Gynaecology for the human specimens used in this study (https://biobank.lunenfeld.ca). This work was supported by a New Idea Grant from the Ontario Institute for Regenerative Medicine, a grant from the Howard Webster Foundation, and the Toronto General and Western Hospital Foundation. A.M. was supported by an advanced post-doctoral fellowship from the Juvenile Diabetes Research Foundation. R.S. was supported by post-doctoral fellowships from the Juvenile Diabetes Research Foundation. M.H. Atkins was supported by a Canadian Institutes of Health Research Banting and Best Doctoral Research Award.
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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 | 0.001 |
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".