Heterogenous impairment of α-cell function in type 2 diabetes is linked to cell maturation state
Bibliographic record
Abstract
Abstract In diabetes, glucagon secretion from pancreatic α-cells is dysregulated. We examined α-cells from human donors and mice using combined electrophysiological, transcriptomic, and computational approaches. Rising glucose suppresses α-cell exocytosis by reducing P/Q-type Ca 2+ channel activity, and this is disrupted in type 2 diabetes (T2D). Upon high-fat-feeding of mice, α-cells shift towards a ‘β-cell-like’ electrophysiologic profile in concert with an up-regulation of the β-cell Na + channel isoform Scn9a and indications of impaired α-cell identity. In human α-cells we identify links between cell membrane properties and cell surface signalling receptors, mitochondrial respiratory complex assembly, and cell maturation. Cell type classification using machine learning of electrophysiology data demonstrates a heterogenous loss of ‘electrophysiologic identity’ in α-cells from donors with T2D. Indeed, a sub-set of α-cells with impaired exocytosis is defined by an enrichment in progenitor markers suggesting important links between α-cell maturation state and dysfunction in T2D. Key findings α-cell exocytosis is suppressed by glucose-dependent inhibition of P/Q-type Ca 2+ currents Dysfunction of α-cells in type 2 diabetes is associated with a ‘β-cell-like’ electrophysiologic signature Patch-seq links maturation state, the mitochondrial respiratory chain, and cell surface receptor expression to α-cell function α-cell dysfunction occurs preferentially in cells enriched in endocrine lineage markers
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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) | 0.001 | 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".