Cell Population Effects in a Mouse Tauopathy Model Identified by Single Cell Sequencing
Bibliographic record
Abstract
Abstract Neurodegenerative disorders are complex multifactorial diseases that have poorly understood selective vulnerabilities among discrete cell populations. We performed single cell RNA sequencing of whole hippocampi from the rTg4510 mouse tauopathy model, which expresses a P301L MAPT mutation at two time points—before and after the onset of pathology. One population of neurons showed a robust size reduction in both the young and the old transgenic animals. Differential expression of genes expressed in this group of neurons suggested an enrichment in granule cell neurons. We identified genes that characterize this population of neurons using Pareto optimization of the specificity and precision of gene pairs for the population of interest. The resulting optimal marker genes were overwhelmingly associated with neuronal projections and their expression was enriched in the dentate gyrus suggesting that the rTg4510 mouse is a good model for Pick’s disease. This observation suggested that the tau mutation affects the population of neurons associated with neuronal projections even before overt tau inclusions can be observed. Out of the optimal pairs of genes identified as markers of the population of neurons of interest, we selected Purkinje cell protein 4 ( Pcp4+ ) and Syntaxin binding protein 6 ( Stxbp6+ ) for experimental validation. Single-molecule RNA fluorescence in situ hybridization confirmed preferential expression of these markers and localized them to the dentate gyrus.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".