Developing a transcriptomic atlas of Alzheimer's Disease progression in the Tg2576 mouse model using single‐cell RNA‐sequencing technology
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disorder marked by cognitive decline, ultimately leading to dementia and death. While the amyloid hypothesis has historically guided AD research, emerging evidence suggests alternative mechanisms, including vascular dysfunction. Angiogenesis, the formation of new blood vessels, is increasingly implicated in early AD pathology, yet transcriptomic insights remain limited. To address this gap, we generated the first single-cell transcriptomic profile of AD progression in a mouse model, aiming to identify novel molecular mechanisms and potential therapeutic targets. METHOD: We analyzed brain tissue from Tg2576 AD model and control mice (N = 28, both sexes) at six developmental timepoints. Single-cell RNA sequencing was performed using 10X Genomics technology, followed by pathway analysis to identify transcriptional changes associated with AD pathology. RESULT: Pathway enrichment analysis in 6- and 9-month-old AD mice revealed significant upregulation of angiogenesis (p = 3.37E-02), vasculature development (p = 3.71E-02), and amyloid-beta formation (p = 1.49E-02). CONCLUSION: These findings provide a single-cell resolution view of AD-related transcriptional changes, supporting a 'vascular angiogenesis model' of AD. Disruptions in blood-brain barrier integrity due to aberrant neoangiogenesis may drive amyloid-beta accumulation, contributing to disease progression. This study underscores the therapeutic potential of targeting vascular dysfunction in AD.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".