411.2: Deciphering the hematopoietic pancreatic niche during human fetal development
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».