A global view of aging and Alzheimer’s pathogenesis-associated cell population dynamics and molecular signatures in the human and mouse brains

Abstract

Although several studies have applied single-cell approaches to explore gene expression changes in aged brains, they were limited by the relatively shallow sampling of brain cell populations, and thus may have failed to capture aspects of the molecular signatures and dynamics of rare cell types associated with aging and diseases. Here, we set out to investigate the age-dependent dynamics of transcription and chromatin accessibility across diverse brain cell types. With EasySci, an extensively improved single-cell combinatorial indexing strategy, we profiled ~1.5 million single-cell transcriptomes 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) and subtypes. Leveraging these data, we generate a global readout of age-dependent cell population dynamics with high cellular subtype resolution, providing insights into cell types that expand (e.g., rare astrocytes and vascular leptomeningeal cells in the olfactory bulb, reactive microglia and oligodendrocytes) or are depleted (e.g., neuronal progenitors, neuroblasts, committed oligodendrocyte precursors) as age progresses. Furthermore, we explored cell-type-specific responses to genetic perturbations associated with Alzheimer's disease (AD) and identify 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 analyses. Finally, we profiled a total of 118,240 single-nuclei transcriptomes from twenty-four human brain samples derived from control and AD patients, revealing highly cell-type-specific and region-specific gene expression changes associated with AD pathogenesis. Critical AD-associated gene signatures were validated in both human and mice. In summary, these data comprise a rich resource for exploring cell-type-specific dynamics and the underlying molecular mechanisms in both normal and pathological mammalian aging.

A comprehensive view of cell-type-specific temporal dynamics in human and mouse brains

“…The result is further validated through the Milo (Dann et al, 2021) analysis of chromatin accessibility profiles, where significantly decreased cellular neighborhoods exclusively overlapped with the committed oligodendrocyte precursors (COPs) ( Figure 6E , 5% FDR). This observation is in accordance with the aging-associated depletion of newly formed oligodendrocytes in our companion study (Sziraki et al, 2022) and previous reports (Givre, 2003).…”

“…To further validate the cell-type-specific dynamics in brain aging, we integrated the newborn cells recovered from TrackerSci and a global mice brain cell atlas (Sziraki et al, 2022) for sub-clustering analysis. Indeed, the integration analysis at the sub-cluster level facilitated the identification of rare progenitor cells in the global brain cell atlas, such as neuronal progenitor cells (marked by Mki67 , Top2a, and Egfr ) and committed oligodendrocyte precursors (marked by high expression of Bmp4 and Enpp6 ) ( Figure 4E ).…”

“…A digital gene expression matrix was constructed from the raw sequencing data as described in our companion study (Sziraki et al, 2022). Potential doublet cells and doublet-derived subclusters were detected using an iterative clustering strategy similar to before (Cao et al, 2020).…”

“…The copyright holder for this preprint this version posted October 5, 2022. (C) We integrated the TrackerSci dataset, including both EdU+ cells and DAPI singlets, with a largescale brain cell atlas (Sziraki et al, 2022) comprising 1,469,111 cells. For the brain cell atlas, we sampled 5,000 cells of each cell type for the integration analysis.…”

“…Of note, the cell-type-specific distribution of newborn cells was highly correlated between TrackerSci transcriptome and chromatin accessibility datasets (Spearman's correlation r = 0.92; Figure 3B) and across conditions (Figure S6). We next integrated TrackerSci datasets with a global brain cell atlas from our companion study (Sziraki et al, 2022), for which we profiled 1.5 million cells from entire mouse brains spanning three age groups and two mutants associated with AD. Briefly, we integrated EdU+ brain cells (5,715 single-cell transcriptomes from TrackerSci), 'All' brain cells (8,380 DAPI singlets from TrackerSci), and "All" brain cells from the global brain cell atlas (sampling 5,000 cells for each main cell type) into the same UMAP space.…”