Single-cell multiome analysis supports α-to-β transdifferentiation in human pancreas
Bibliographic record
Abstract
Abstract Spontaneous transdifferentiation of pancreatic glucagon-producing alpha to insulin-secreting beta-cells has been observed in mouse but not in human islets 1 . Here, we analyzed the largest single-cell dataset of human islets to date, composed of 650,000 cells across 121 deceased organ donors, in search of transitional cell states. By integrating single-cell RNA-seq, single-nucleus ATAC-seq and single-nucleus multiome (joint RNA and ATAC profiling) datasets generated by the Human Pancreas Analysis Program (HPAP) 2,3 we identified two previously undescribed cell populations (c11 and c13 cells), which together represent transitional states between alpha- and beta-cells. Some c11 cells are insulin-positive while others are glucagon positive, but none are double-positive. C11 cells repress alpha-cell identity genes and activate beta-cell specific genes. Moreover, the transcriptomic and epigenetic profiles of c11 and c13 cells indicate a transitioning phenotype driven by lineage-specific transcription factors. Genetic lineage tracing in primary human islet cells confirmed alpha-to-beta cell transdifferentiation. C11 and c13 cells exist in all islet samples regardless of disease statuses, with type 2 diabetic samples having significantly more transitioning cells than matched non-diabetic controls. The discovery of these transitional cell types suggests a possibility for future therapy – transdifferentiating alpha-cells to beta-cell through activation of the c11 gene program.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".