Insulin secretion chip (InS-chip) reveals two peaks within first-phase that temporally coincide with a transition in glucose metabolism
Bibliographic record
Abstract
Abstract First-phase glucose-stimulated insulin secretion is mechanistically linked to type 2 diabetes yet the underlying metabolism driving this early stage of secretion is difficult to discern due to significant islet-to-islet variability. Here, we miniaturize a fluorescence anisotropy immunoassay onto a microfluidic device to measure C-peptide secretion from individual islets as a surrogate for insulin (InS-chip). This method measures secretion from up to four islets at a time with ∼7 s resolution while providing an optical window for real-time live cell imaging. Using the InS-chip, we reveal for the first time two glucose-dependent peaks of insulin secretion (i.e., a double peak) within the first phase (<10 min). By combining real-time secretion and live cell imaging, we show that islets transition from glycolytic to OxPhos-driven metabolism at the nadir of the peaks. Overall, these data validate the InS-chip to measure glucose-stimulated insulin secretion while revealing the first-phase secretion contains two peaks defined by a shift in glucose metabolism. Significance Statement Loss of the first phase of glucose-stimulated insulin secretion is one of the earliest signs of type 2 diabetes (T2D). Yet current strategies to measure the early dynamics are inadequate due to the need to pool secretion from multiple islets. In this study, we designed an islet-on-a-chip microfluidic device (InS-chip) to measure insulin secretion from individual islets with <7 s temporal resolution. Our design leaves an optical window for coupled live cell imaging to tease apart the metabolism underlying secretion. Our data reveal that first-phase insulin secretion is composed of two peaks that coincide with a shift in glucose metabolism.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".