Quantitative imaging of electron transfer flavoprotein autofluorescence reveals the dynamics of lipid partitioning in living pancreatic islets
Bibliographic record
Abstract
Pancreatic islet β-cells metabolically sense nutrients to maintain blood glucose homeostasis through the regulated secretion of insulin. Long-term exposure to a mixed supply of excess glucose and fatty acids induces β-cell dysfunction and type II diabetes in a process termed glucolipotoxicity. Despite a number of documented mechanisms for glucolipotoxicity, the interplay between glucose and fatty acid oxidation in islets remains debated. Here, we develop confocal imaging of electron transfer flavoprotein (ETF) autofluorescence to reveal the dynamics of fatty acid oxidation in living pancreatic islets. This method further integrates microfluidic devices to hold the islets stationary in flow, and thus achieve ETF imaging in the β-cells with high spatial and temporal resolution. Our data first confirm that ETF autofluorescence reflects electron transport chain (ETC) activity downstream of Complex I, consistent with a response directly related to fatty acid metabolism. Together with two-photon imaging of NAD(P)H and confocal imaging of lipoamide dehydrogenase (LipDH) autofluorescence, we show that the ETC predominantly draws electrons from LipDH/NADH-dependent Complex I rather than from ETF/FADH(2)-dependent ETF:CoQ oxidoreductase (ETF-QO). Islets stimulated with palmitate also show increased ETF redox state that is dose-dependently diminished by glucose (>10 mM). Furthermore, stimulation with a glucose bolus causes a two-tier drop in the ETF redox state at ∼5 and ∼20 min, suggesting glucose metabolism immediately increases ETC activity and later decreases fatty acid oxidation. Our results demonstrate the utility of ETF imaging in characterizing fatty acid-induced redox responses with high subcellular and temporal resolution. Our results further demonstrate a dominant role of glucose metabolism over fatty acid oxidation in β-cells even when presented with a mixed nutrient condition associated with glucolipotoxicity.
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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".