Human Brain Cell‐Type‐Specific Aging Clocks Based on Single‐Nuclei Transcriptomics

Muralidharan and colleagues develop cell‐type‐specific transcriptomic aging clocks using single‐nucleus RNA sequencing of human post mortem prefrontal cortex samples. These clocks accurately predict age and identify distinct aging trajectories in specific brain cell types. Their method also detects accelerated aging in Alzheimer's disease and schizophrenia, offering insight into differential brain aging across health and disease. Abstract Aging is the primary risk factor for most neurodegenerative diseases, yet the cell‐type‐specific progression of brain aging remains poorly understood. Here, human cell‐type‐specific transcriptomic aging clocks are developed using high‐quality single‐nucleus RNA sequencing data from post mortem human prefrontal cortex tissue of 31 donors aged 18–94 years, encompassing 73,941 high‐quality nuclei. Distinct transcriptomic changes are observed across major cell types, including upregulation of inflammatory response genes in microglia from older samples. Aging clocks trained on each major cell type accurately predict chronological age, capture biologically relevant pathways, and remain robust in independent single‐nucleus RNA‐sequencing datasets, underscoring their broad applicability. Notably, cell‐type‐specific age acceleration is identified in individuals with Alzheimer's disease and schizophrenia, suggesting altered aging trajectories in these conditions. These findings demonstrate the feasibility of cell‐type‐specific transcriptomic clocks to measure biological aging in the human brain and highlight potential mechanisms of selective vulnerability in neurodegenerative diseases. Muralidharan and colleagues develop cell-type-specific transcriptomic aging clocks using single-nucleus RNA sequencing of human post mortem prefrontal cortex samples. These clocks accurately predict age and identify distinct aging trajectories in specific brain cell types. Their method also detects accelerated aging in Alzheimer's disease and schizophrenia, offering insight into differential brain aging across health and disease. Abstract Aging is the primary risk factor for most neurodegenerative diseases, yet the cell-type-specific progression of brain aging remains poorly understood. Here, human cell-type-specific transcriptomic aging clocks are developed using high-quality single-nucleus RNA sequencing data from post mortem human prefrontal cortex tissue of 31 donors aged 18–94 years, encompassing 73,941 high-quality nuclei. Distinct transcriptomic changes are observed across major cell types, including upregulation of inflammatory response genes in microglia from older samples. Aging clocks trained on each major cell type accurately predict chronological age, capture biologically relevant pathways, and remain robust in independent single-nucleus RNA-sequencing datasets, underscoring their broad applicability. Notably, cell-type-specific age acceleration is identified in individuals with Alzheimer's disease and schizophrenia, suggesting altered aging trajectories in these conditions. These findings demonstrate the feasibility of cell-type-specific transcriptomic clocks to measure biological aging in the human brain and highlight potential mechanisms of selective vulnerability in neurodegenerative diseases. Advanced Science, Volume 12, Issue 43, November 20, 2025.