A Novel High-Throughput Assay for Islet Respiration Reveals Uncoupling of Rodent and Human Islets
Bibliographic record
Abstract
BACKGROUND: The pancreatic beta cell is unique in its response to nutrient by increased fuel oxidation. Recent studies have demonstrated that oxygen consumption rate (OCR) may be a valuable predictor of islet quality and long term nutrient responsiveness. To date, high-throughput and user-friendly assays for islet respiration are lacking. The aim of this study was to develop such an assay and to examine bioenergetic efficiency of rodent and human islets. METHODOLOGY/PRINCIPAL FINDINGS: The XF24 respirometer platform was adapted to islets by the development of a 24-well plate specifically designed to confine islets. The islet plate generated data with low inter-well variability and enabled stable measurement of oxygen consumption for hours. The F1F0 ATP synthase blocker oligomycin was used to assess uncoupling while rotenone together with myxothiazol/antimycin was used to measure the level of non-mitochondrial respiration. The use of oligomycin in islets was validated by reversing its effect in the presence of the uncoupler FCCP. Respiratory leak averaged to 59% and 49% of basal OCR in islets from C57Bl6/J and FVB/N mice, respectively. In comparison, respiratory leak of INS-1 cells and C2C12 myotubes was measured to 38% and 23% respectively. Islets from a cohort of human donors showed a respiratory leak of 38%, significantly lower than mouse islets. CONCLUSIONS/SIGNIFICANCE: The assay for islet respiration presented here provides a novel tool that can be used to study islet mitochondrial function in a relatively high-throughput manner. The data obtained in this study shows that rodent islets are less bioenergetically efficient than human islets as well as INS1 cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".