Calcium-dependent transcriptional profiles of human pancreatic islet cells reveal functional diversity in islet subpopulations
Bibliographic record
Abstract
Abstract Aims/hypothesis Pancreatic islets depend on cytosolic calcium to trigger the secretion of glucoregulatory hormones and regulate the transcription of genes important for the response to stimuli. To date, there has not been an attempt to profile calcium-regulated gene expression in all islet cell types. Our aim was to construct a large single-cell transcriptomic dataset from human islets exposed to conditions that would acutely induce or inhibit intracellular calcium signalling, while preserving biological heterogeneity. Methods We exposed intact human islets from three donors to the following conditions: (1) 2.8 mM glucose; (2) 25 mM glucose and 40 mM KCl to maximally stimulate calcium signalling; and (3) 25 mM glucose, 40 mM KCl and 5 mM EGTA (calcium chelator) to inhibit calcium signalling, for 1 hour. We sequenced 43,909 cells from all islet cell types, and further subsetted the cells to form an endocrine cell-specific dataset of 32,486 cells expressing INS , GCG , SST or PPY . We compared transcriptomes across conditions to determine the differentially expressed calcium-regulated genes in each endocrine cell type, and in each endocrine cell subcluster of alpha and beta cells. Results Based on the number of calcium-regulated genes, we found that each alpha and beta cell cluster had a different magnitude of calcium response. We also showed that a polyhormonal cluster expressing INS, GCG , and SST is defined by calcium-regulated genes specific to this cluster. Finally, we identified the gene PCDH7 from the beta cell clusters that had the highest number of calcium-regulated genes, and showed that cells expressing cell surface PCDH7 protein have enhanced glucose-stimulated insulin secretory function. Conclusions Here we use our single-cell dataset to show that human islets have cell-type-specific calcium-regulated gene expression profiles, some of them specific to subpopulations. In our dataset, we identify PCDH7 as a novel marker of beta cells having an increased number of calcium-regulated genes and enhanced insulin secretory function. Data availability A searchable and user-friendly format of the data in this study, specifically designed for rapid mining of single-cell RNA sequencing data, is available at https://lynnlab.shinyapps.io/Hislet_2023/ . The raw data files are available at NCBI Gene Expression Omnibus (GSE196715).
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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 | 0.001 |
| 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".