Objective With the intensification of population aging, the incidence of aging-related neurodegenerative diseases continues to rise, however, their pathogenesis remains elusive and therapeutic options are limited. This study used bioinformatics approaches to explore brain cell-type-specific changes in gene expression during brain aging and their impacts, to provide further insights into the biological mechanisms of brain aging. Methods We analyzed single-cell sequencing datasets (GSE169606) from young and old mouse brains, including integration, quality control, normalization, conduct cell-type annotation and differential gene expression analysis to identify differentially expressed genes (DEGs) across various cell types, using the Seurat package in R software.Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for functional annotation and enrichment analyses, and interactions between DEGs were analyzed by protein-protein interaction ( PPI ) networks. The hub genes in each cell type were identified using the MCC, MNC, DMNC, and Dgree algorithms in the cyto Hubba plugin. Results A total of 13 cell types were identified through cell annotation. After comparing the aged and young groups, we focused on in-depth analyses of the DEGs screened from four major cell types: neurons,microglia, astrocytes, and endothelia. GO analysis revealed that DEGs in neurons, astrocytes, and endothelial cells were significantly enriched in biological pathways related to circadian rhythm, and KEGG analysis indicated that DEGs in microglia and endothelial cells were enriched in circadian rhythm-related signaling pathways. PPI analysis also demonstrated that the biological networks of DEGs in neurons, microglia, and endothelial cells were significantly enriched in circadian rhythm functional clustering modules. Furthermore, based on the intersection of the four algorithms, we identified core genes within these cell types and also identified specific variations in circadian rhythm genes in microglia, astrocytes, and endothelial cells. Conclusions This study employed single-cell transcriptomics technology to reveal the differential expression of genes in neurons, microglia, astrocytes, and endothelial cells during aging. The identification of hub genes in microglia, astrocytes, and endothelial cells indicated specific changes in circadian rhythm genes across these three cell types. These findings provide a foundation for further studies of the molecular mechanisms involved in brain aging and for the development of related intervention strategies.