Computational pipeline for processing EasySci data

Conventional approaches are limited in capturing molecular signatures and dynamics of rare cell types associated with aging and diseases. Here, we developed EasySci, a cost-effective single-cell combinatorial indexing strategy, for investigating the age-dependent brain population dynamics. We profiled ~1.5 million single-cell transcriptomes and ~400,000 chromatin accessibility profiles across mouse brains spanning different ages, genotypes, and genders. We identified > 300 cell subtypes and deciphered their molecular features and spatial locations. With a global view of brain population dynamics, we revealed rare cell types that are expanded/depleted upon aging. Furthermore, we explored cell-type-specific responses to genetic perturbations associated with Alzheimer’s disease (AD) and identified their linked rare cell types. With additional profiling of 118,240 single-cell transcriptomes from post-mortem human brain samples, we identified cell-type-specific and region-specific transcriptome changes associated with AD pathogenesis. In summary, this study provided a rich resource for exploring cell-type-specific dynamics in normal and pathological aging.