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Tracking cell-type-specific temporal dynamics in human and mouse brains

ABSTRACT: Progenitor cells play fundamental roles in preserving optimal organismal functions under normal, aging, and disease conditions. However, progenitor cells are incompletely characterized, especially in the brain, partly because conventional methods are restricted by inadequate throughput and resolution for deciphering cell-type-specific proliferation and differentiation dynamics in vivo. Here, we developed TrackerSci, a new technique that combines in vivo labeling of newborn cells with single-cell combinatorial indexing to profile the single-cell chromatin landscape and transcriptome of rare progenitor cells and track cellular differentiation trajectories in vivo. We applied TrackerSci to analyze the epigenetic and gene expression dynamics of newborn cells across entire mouse brains spanning three age stages and in a mouse model of Alzheimer's disease. Leveraging the dataset, we identified diverse progenitor cell types less-characterized in conventional single cell analysis, and recovered their unique epigenetic signatures. We further quantified the cell-type-specific proliferation and differentiation potentials of progenitor cells, and identified the molecular programs underlying their aging-associated changes (e.g., reduced neurogenesis/oligodendrogenesis). Finally, we expanded our analysis to study progenitor cells in the aged human brain through profiling ~800,000 single-cell transcriptomes across five anatomical regions from six aged human brains. We further explored the transcriptome signatures that are shared or divergent between human and mouse oligodendrogenesis, as well as the region-specific down-regulation of oligodendrogenesis in the human cerebellum. Together, the data provide an in-depth view of rare progenitor cells in mammalian brains. We anticipate TrackerSci will be broadly applicable to characterize cell-type-specific temporal dynamics in diverse systems.

ORGANISM(S): Mus musculus

PROVIDER: GSE212251 | GEO | 2023/09/28

REPOSITORIES: GEO

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Project description: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.

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Tracking cell-type-specific temporal dynamics in human and mouse brains

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