Exploring cell type‐specific hippocampal transcriptional signatures in amyloidosis mouse models
Bibliographic record
Abstract
BACKGROUND: The growing recognition that late-onset AD (LOAD) and familial AD (fAD) follow distinct pathophysiological pathways highlights the possibility that different Alzheimer's disease (AD) subtypes could reflect varying molecular and cellular features. However, the precise role of each brain cell type on each AD subtype remains poorly understood. In this context, genetically modified animal models provide a valuable tool to explore AD genetic variability. In this study, we aimed to investigate the transcriptional signatures of cell types in the hippocampus across two fAD mouse models (APP/PS1 and 3xTg) and one LOAD mouse model (hAß-KI). METHOD: We analyzed bulk hippocampal transcriptomics data from the Gene Expression Omnibus repository and the AMP-AD Knowledge Portal. Gene expression deconvolution was performed using Population-Specific Expression Analysis (PSEA) to identify differentially expressed genes (DEGs) specific to neurons, astrocytes, microglia, oligodendrocytes, and endothelial cells (FDR-adjusted p-value < 0.05). Functional enrichment analysis (FEA) of Gene Ontology Biological Processes (GOBPs) was performed for the DEGs from each cell type using the enrichGO function in the clusterProfiler R package (v3.16.1). RESULT: We identified 5,055, 1,073, and 162 DEGs in APP/PS1, 3xTg, and hAß-KI mice, respectively, with the greatest overlap observed between the fAD models (APP/PS1 and 3xTg models) (Figure 1). Additionally, fAD models revealed higher counts of DEGs in neurons, oligodendrocytes, and microglia. Meanwhile, in hAß-KI, oligodendrocytes had the highest counts. (Figure 2). At the biological level, neurons consistently demonstrated greater enrichment of GOBPs across all three models. In contrast, microglia showed more enriched GOBPs in APP/PS1, while astrocytes demonstrated more enriched GOBPs in the 3xTg and hAß-KI models, with 37 shared GOBPs between them (Figure 3). CONCLUSION: We found that fAD models had greater transcriptional alterations than the LOAD model, possibly due to the aggressive pathology caused by fAD mutations. Additionally, neurons were consistently affected across all models, highlighting their vulnerability to AD. Overall, the three models exhibited distinct transcriptional and biological signatures, suggesting each one of them is suited to address specific aspects of the AD landscape.
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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 | 0.002 | 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 | 0.000 |
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".