Dual-Reporter β-Cell-Specific Male Transgenic Rats for the Analysis of β-Cell Functional Mass and Enrichment by Flow Cytometry
Bibliographic record
Abstract
Mouse β-cell-specific reporter lines have played a key role in diabetes research. Although the rat provides several advantages, its use has lagged behind the mouse due to the relative paucity of genetic models. In this report we describe the generation and characterization of transgenic rats expressing a Renilla luciferase (RLuc)-enhanced yellow fluorescent protein (YFP) fusion under control of a 9-kb genomic fragment from the rat ins2 gene (RIP7-RLuc-YFP). Analysis of RLuc luminescence and YFP fluorescence revealed that reporter expression is restricted to β-cells in the adult rat. Physiological characteristics including body weight, fat and lean mass, fasting and fed glucose levels, glucose and insulin tolerance, and β-cell mass were similar between two RIP7-RLuc-YFP lines and wild-type littermates. Glucose-induced insulin secretion in isolated islets was indistinguishable from controls in one of the lines, whereas surprisingly, insulin secretion was defective in the second line. Consequently, subsequent studies were limited to the former line. We asked whether transgene activity was responsive to glucose as shown previously for the ins2 gene. Exposing islets ex vivo to high glucose (16.7 mM) or in vivo infusion of glucose for 24 hours increased luciferase activity in islets, whereas the fraction of YFP-positive β-cells after glucose infusion was unchanged. Finally, we showed that fluorescence-activated cell sorting of YFP-positive islet cells can be used to enrich for β-cells. Overall, this transgenic line will enable for the first time the application of both fluorescence and bioluminescence/luminescence-based approaches for the study of rat β-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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".