Identification of two β-cell subtypes by 7 independent criteria
Bibliographic record
Abstract
Summary Despite the recent explosion in surveys of cell-type heterogeneity, the mechanisms that specify and stabilize highly related cell subtypes remain poorly understood. Here, focusing initially on exploring quantitative histone mark heterogeneity, we identify two major sub-types of pancreatic β-cells (β HI and β LO ). β HI and β LO cells differ in their size, morphology, cytosolic and nuclear ultrastructure, transcriptional output, epigenomes, cell surface marker, and function. Importantly, β HI and β LO cells can be FACS separated live into CD24 + (β HI ) and CD24 - (β LO ) fractions. From an epigenetic viewpoint, β HI -cells exhibit ∼4-fold higher levels of H3K27me3, more compacted chromatin, and distinct chromatin organization that associates with a specific pattern of transcriptional output. Functionally, β HI cells have increased mitochondrial mass, activity, and insulin secretion both in vivo and ex vivo . Critically, Eed and Jmjd3 loss-of-function studies demonstrate that H3K27me3 dosage is a significant regulator of β HI / β LO cell ratio in vivo, yielding some of the first-ever specific models of β-cell sub-type distortion. β HI and β LO sub-types are conserved in humans with β HI -cells enriched in human Type-2 diabetes. These data identify two novel and fundamentally distinct β-cell subtypes and identify epigenetic dosage as a novel regulator of β-cell subtype specification and heterogeneity. Highlights Quantitative H3K27me3 heterogeneity reveals 2 common β-cell subtypes β HI and β LO cells are stably distinct by 7 independent sets of parameters H3K27me3 dosage controls β HI / β LO ratio in vivo β HI and β LO cells are conserved in humans and enriched in Type-2 diabetes
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".