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.

Here we described PerturbSci-Kinetics, a novel combinatorial indexing method for capturing three-layer single-cell readout (i.e., whole transcriptome, nascent transcriptome, sgRNA identities) across hundreds of genetic perturbations. Through PerturbSci-Kinetics profiling of pooled CRISPR screens targeting a variety of biological processes, we were able to decipher the complexity of RNA regulations at multiple levels (e.g., synthesis, processing, degradation), and revealed key regulators involved in miRNA and mitochondrial RNA processing pathways. Our technique opens up the possibility of systematically decoding the genome-wide regulatory network underlying RNA temporal dynamics at scale and cost-effectively.

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.

Defining the molecular and genetic basis for heart rate regulation and for susceptibility to atrial arrhythmias has been challenging due to the complexity of the human sinoatrial node (SAN), a tiny structure with limiting numbers of several distinct pacemaker cardiomyocyte (PC) subtypes in a spatially patterned array. Here we describe a novel method to derive large numbers of human PCs from human induced pluripotent stem cells (hiPSCs) that recapitulates key features of SAN development including differentiation into anatomically and functionally distinct PC subtypes of the SAN. Single cell (sc) RNA-sequencing, sc-ATAC-sequencing, and time course analyses of cellular differentiation were used to define epigenetic and transcriptomic signatures of cellular specialization within the SAN, and to identify transcriptional pathways important for PC differentiation. Intersection of our multi-omics datasets with existing human genome wide association studies uncovered regulatory elements with differential accessibility in different PC subtypes that harbored SNPs that associated with human heart rate regulation and susceptibility to atrial fibrillation, enabling disease gene discovery and prioritization.