Characterization of an APP/tau rat model of Alzheimer’s disease by positron emission tomography and immunofluorescent labeling
Bibliographic record
Abstract
Abstract Background To better understand the etiology and pathomechanisms of Alzheimer’s disease, several transgenic animal models that overexpress human tau or human amyloid-beta (Aβ) have been developed. In the present study, we generated a novel transgenic rat model by cross-breeding amyloid precursor protein (APP) rats with tau rats. We characterized this model by performing positron emission tomography scans combined with immunofluorescent labeling and cerebrospinal fluid analyses. Methods APP/Tau rats were generated by cross-breeding male McGill-R-Thy1-APP transgenic rats with female hTau-40/P301L transgenic rats. APP/Tau double transgenic rats and non-transgenic (ntg) littermates aged 7, 13, and 21 months were subjected to dynamic [11C] PiB scan and dynamic [18F]THK-5317 scans. For regional brain analysis, a template was generated from anatomical MR images of selected animals, which was co-registered with the PET images. Regional analysis was performed by application of the simplified reference tissue model ([11C]PiB data), whereas [18F]THK-5317 data were analyzed using a 2-tissue compartment model and Logan graphical analysis. In addition, immunofluorescent labeling (tau, amyloid) and cerebrospinal fluid analyses were performed. Results [11C]PiB binding potential (BPND) and [18F]THK-5317 volume of distribution (VT) showed an increase with age in several brain regions in the APP/Tau group but not in the ntg control group. Immunohistochemical analysis of brain slices of PET-scanned animals revealed a positive correlation between Aβ labeling and [11C]PiB regional BPND. Tau staining yielded a trend towards higher levels in the cortex and hippocampus of APP/Tau rats compared with ntg littermates, but without reaching statistical significance. No correlation was found between tau immunofluorescence labeling results and the respective [18F]THK-5317 VT values. Conclusions We thoroughly characterized a novel APP/Tau rat model using combined PET imaging and immunofluorescence analysis. We observed an age-related increase in [11C]PiB and [18F]THK-5317 binding in several brain regions in the APP/Tau group but not in the ntg group. Although we were able to reveal a positive correlation between amyloid labeling and [11C]PiB regional brain uptake, we observed relatively low human tau and amyloid fibril expression levels and a somewhat unstable brain pathology which questions the utility of this animal model for further studies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".