ABSTRACT: Prior studies applied single-cell approaches to explore gene expression changes in aged brains; however, they were limited by relatively shallow sampling of brain cell populations, and thus failed to characterize the detailed molecular signatures and dynamics of rare cell types associated with aging and diseases. Here, we set out to investigate the age-dependent transcriptional and chromatin dynamics across diverse brain cell types. With EasySci, an extensively improved single-cell combinatorial indexing strategy, we profiled ~1.5 million single-cell transcriptomes with full gene body coverage and ~400,000 single-cell chromatin accessibility profiles across mouse brains spanning different ages, genotypes, and both sexes. With a novel computational framework designed for characterizing cellular subtypes based on the expression of both genes and exons, we identified > 300 cell subtypes and deciphered the underlying molecular programs and spatial locations of rare cell types (e.g., pinealocytes, tanycytes). Leveraging the data, we generated a global readout of age-dependent cell population dynamics with great cellular subtype resolution, providing insights into expanded cell types (e.g., rare astrocytes and vascular leptomeningeal cells in olfactory bulb, reactive microglia and oligodendrocytes) and depleted cell types (e.g., neuronal progenitors, neuroblasts, committed oligodendrocyte precursors) in development and ageing. Furthermore, we explored cell-type-specific responses to genetic perturbations associated with Alzheimer’s disease and discovered rare cell types depleted (e.g., mt-Cytb+, mt-Rnr2+ choroid plexus epithelial cells) or enriched (e.g., Col25a1+ Ndrg1+ interbrain and midbrain neurons) in both AD models. Key findings are consistent between males and females, validated across the transcriptome, chromatin accessibility, and spatial analysis. In summary, these data comprise a rich resource for exploring cell-type-specific population dynamics and the underlying molecular mechanisms in the mammalian aging process.
