Single Cell RNA-Seq Analysis of Regenerative Drug-Treated Human Pancreatic Islets Identifies A Cycling Alpha Cell Population As Key Beta Cell Progenitors
Bibliographic record
Abstract
Abstract Diabetes ultimately results from an inadequate number of functional, insulin-producing human beta cells. Although current attempts to replenish the remaining beta cell pool in people with diabetes are encouraging, scalability and cost limit access for the millions of people with diabetes. The small molecule DYRK1A inhibitor class of beta cell regenerative drugs, either alone or in combination with GLP1 receptor agonists or TGFβ superfamily inhibitors, are capable of inducing beta cell replication in vitro and increasing beta cell mass in vivo . Despite these advances, the precise mechanisms of action of DYRK1A inhibitors remain incompletely understood. To address the mechanisms more deeply, we performed single cell RNA sequencing on human pancreatic islets treated with a DYRK1A inhibitor, either alone, or in combination with a GLP1 receptor agonist or a TGFβ superfamily inhibitor. We identify a cluster of Cycling Alpha Cells as the cells most responsive to DYRK1A inhibition. Velocity and pseudotime lineage trajectory analyses suggest that Cycling Alpha Cells serve as the primary target cell type for of DYRK1A inhibitors, and may serve as precursor cells that transdifferentiate into functional human beta cells in response to the DYRK1A inhibition. In addition to providing a novel mechanism of action for DYRK1A inhibitors, our findings suggest that efforts to target regenerative drugs to human beta cells may be mis-directed: the proper target may be Cycling Alpha Cells.
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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 | 0.001 | 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".