Metabolic STAMP for deciphering GPCR-regulated insulin secretion by pancreatic β cells
Bibliographic record
Abstract
., GLP1-R) GPCRs coordinate GSIS remains unclear. In this study, we employed Metabolic-STAMP (Synchronized Temporal-Spatial Analysis via Microscopy and Phosphoproteomics) in both mouse β cells (MIN6) and primary human islets to map the dynamic signaling networks governing GSIS and to link transient phosphorylation events to their functional outcomes. We systematically interrogated GPCR-mediated phosphorylation events through selective pharmacological inhibitors, resolving signaling hierarchies and consensus patterns across multiple pathways. Our multi-modal approach uncovered key insulin-secretion-associated PTMs, linked phosphorylation targets with phenotypic organelle dynamics, and provided mechanistic insights into how GLP1-R versus FFAR4 modulates GSIS through shared and GPCR-specific phospho-signatures. We highlighted key examples of stimulus-specific regulation by high glucose alone versus GPCR stimulation, including context-specific activation of the classic ERK signaling pathway, compartmentalized PKA signaling, pathway specificity in organelle dynamics and inter-organellar contacts, and HDAC6/ATAT-mediated regulation of microtubule acetylation. Collectively, these findings provided a blueprint for deconvolving pathway specificity of β cell GPCR signaling, illuminated regulatory nodes that program insulin release, and offered new therapeutic targets to enhance β-cell function.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".