Lineage conversion of mouse fibroblasts to pancreatic α-cells
Bibliographic record
Abstract
α-cells, which synthesize glucagon, also support β-cell survival and have the capacity to transdifferentiate into β-cells. However, the role of α-cells in pathological conditions and their putative clinical applications remain elusive due in large part to the lack of mature α-cells. Here, we present a new technique to generate functional α-like cells. α-like cells (iAlpha cells) were generated from mouse fibroblasts by transduction of transcription factors, including Hhex, Foxa3, Gata4, Pdx1 and Pax4, which induce α-cell-specific gene expression and glucagon secretion in response to KCl and Arg stimulation. The cell functions in vivo and in vitro were evaluated. Lineage-specific and functional-related gene expression was tested by realtime PCR, insulin tolerance test (ITT), glucose tolerance test (GTT), Ki67 and glucagon immunohistochemistry analysis were done in iAlpha cells transplanted nude mice. iAlpha cells possess α-cell function in vitro and alter blood glucose levels in vivo. Transplantation of iAlpha cells into nude mice resulted in insulin resistance and increased β-cell proliferation. Taken together, we present a novel strategy to generate functional α-like cells for the purposes of disease modeling and regenerative medicine. Connective tissue cells can be transformed into pancreatic-like cells that could be used to study and perhaps to treat type 1 diabetes. Lijian Hui at the Institutes for Biological Sciences, Shanghai, and Yongquan Shi at the Second Military Medical University, Shanghai, with colleagues elsewhere in China and Canada, achieved the conversion using mouse fibroblast cells. Altering the activity of fibroblast cell genes generated cells with key similarities to pancreatic cells called alpha cells. The alpha cells support the activity of the beta cells that produce insulin, which is deficient in diabetes. The role of alpha cells in diabetes and their clinical potential remains unclear, partly due to difficulties in obtaining mature cells to study. If the resulting insights are applicable to humans, they may allow better understanding of alpha cell activity in health and disease.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".