Single-cell analyses of human islet cells reveal de-differentiation signatures
Bibliographic record
Abstract
Abstract Human pancreatic islets containing insulin-secreting β-cells are notoriously heterogeneous in cell composition. Since β-cell failure is the root cause of diabetes, understanding this heterogeneity is of paramount importance. Recent reports have cataloged human islet transcriptome but not compared single β-cells in detail. Here, we scrutinized ex vivo human islet cells from healthy donors and show that they exhibit de-differentiation signatures. Using single-cell gene expression and immunostaining analyses, we found healthy islet cells to contain polyhormonal transcripts, and INS+ cells to express decreased levels of β-cell genes but high levels of progenitor markers. Rare cells that are doubly positive for progenitor markers/exocrine signatures, and endocrine/exocrine hormones were also present. We conclude that ex vivo human islet cells are plastic and can possibly de-/trans-differentiate across pancreatic cell fates, partly accounting for β-cell functional decline once isolated. Therefore, stabilizing β-cell identity upon isolation may improve its functionality.
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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) | 0.001 | 0.000 |
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".