Single cell and spatial characterization of the human pancreas reveals drivers of beta cell dysfunction in cystic fibrosis
Bibliographic record
Abstract
Cystic fibrosis (CF) causes severely damaged pancreas morphology and results in high rates of cystic fibrosis-related diabetes (CFRD), but the cellular pathways and regulatory programs driving CFRD pathogenesis in the pancreas are not understood. In this study, we performed single cell multiomic and spatial profiling of 2.04M cells in the pancreas from 23 non-disease and CF donors and defined regulatory programs and tissue niches of specific cell types and sub-types in CF. A high-mucin sub-type of ductal cells had relatively preserved abundance, altered localization and up-regulated stress, secretion and pro-fibrotic activity in CF, and conventional ductal cells showed evidence for transition to the high-mucin state. Increased inflammatory and fibrotic pathway activity in CF was linked to closer proximity and crosstalk between immune and stellate cells in specific niches. Beta cells had extensive genomic changes in CF, including increased stress and insulin secretion-related processes, and these changes were broadly distinct from those in type 1 and type 2 diabetes. Islets in CF preferentially localized near large adipose tissue and collagen structures, and both areas were strongly linked to beta cell loss in CF due to signaling from adipocytes, macrophages, stellate, and high-mucin ductal cells. Overall, our results reveal cellular drivers of pancreatic dysfunction in CF and offer new in-roads to preserving beta cells in CFRD.
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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".