Abstract
Single-cell RNA sequencing (scRNA-seq) studies of Alzheimer disease (AD) mouse models have proliferated across independent laboratories, brain regions, and platforms, yet the cross-study reproducibility of transcriptional signatures derived from such data remains underexplored. We reanalyzed six publicly available mouse brain scRNA-seq datasets (GSE140399, GSE140510, GSE143758, GSE208683, GSE214244, and GSE296027; 360,620 cells, 102 annotated clusters) using a standardized pipeline (Figure 1) comprising quality control, Leiden clustering, confidence-tiered marker-based cell-type annotation, sample-level pseudobulk differential expression, cross-dataset consistency analysis, and pathway enrichment. Pseudobulk differential expression, performed on 32 samples (16 AD, 16 Control) from five of the six datasets, identified 301 significant genes (FDR-adjusted p -43
and 2.1 × 10
-29
respectively), phagosome and osteoclast differentiation (KEGG), and interferon alpha/beta signaling. Of the 301 significant genes, 235 (78%) showed the same direction of effect across all datasets in which they were tested. The sixth dataset, GSE296027, was excluded from differential expression analysis due to a gene-identifier corruption artifact introduced during pseudobulk aggregation (detailed in Methods and Limitations) but contributed valid cell-type composition and microglial subclustering data, independently recovering a disease-associated microglia-like state. These findings indicate that a reproducible, strongly immune/interferon-skewed transcriptional signature, rather than a balanced up/down transcriptional shift, characterizes the mouse AD models analyzed here.