Alzheimer’s disease (AD) has recently been associated with diverse cell states^{1,2,3,4,5,6,7,8,9,10,11}, yet when and how these states affect the onset of AD remains unclear. Here we used a data-driven approach to reconstruct the dynamics of the brain’s cellular environment and identified a trajectory leading to AD that is distinct from other ageing-related effects. First, we built a comprehensive cell atlas of the aged prefrontal cortex from 1.65 million single-nucleus RNA-sequencing profiles sampled from 437 older individuals, and identified specific glial and neuronal subpopulations associated with AD-related traits. Causal modelling then prioritized two distinct lipid-associated microglial subpopulations—one drives amyloid-β proteinopathy while the other mediates the effect of amyloid-β on tau proteinopathy—as well as an astrocyte subpopulation that mediates the effect of tau on cognitive decline. To model the dynamics of cellular environments, we devised the BEYOND methodology, which identified two distinct trajectories of brain ageing, each defined by coordinated progressive changes in certain cellular communities that lead to (1) AD dementia or (2) alternative brain ageing. Thus, we provide a cellular foundation for a new perspective on AD pathophysiology that informs personalized therapeutic development, targeting different cellular communities for individuals on the path to AD or to alternative brain ageing.

阿尔茨海默氏病 (AD) 最近与多种细胞状态相关^{1,2,3,4,5,6,7,8,9,10,11} ，但这些状态何时以及如何影响 AD 的发病仍不清楚。在这里，我们使用数据驱动的方法来重建大脑细胞环境的动态，并确定了导致 AD 的轨迹，该轨迹与其他衰老相关的影响不同。首先，我们根据从 437 名老年人中采集的 165 万个单核 RNA 测序图谱构建了老年前额叶皮层的综合细胞图谱，并确定了与 AD 相关特征相关的特定神经胶质和神经元亚群。然后，因果模型优先考虑了两个不同的脂质相关小胶质细胞亚群（一个驱动β淀粉样蛋白病，另一个介导β淀粉样蛋白对tau蛋白病的影响）以及介导tau蛋白对认知能力下降影响的星形胶质细胞亚群。为了对细胞环境的动态进行建模，我们设计了 BEYOND 方法，该方法确定了两种不同的大脑衰老轨迹，每种轨迹都是由某些细胞群落的协调渐进变化来定义的，这些变化会导致 (1) AD 痴呆或 (2) 替代性大脑衰老。因此，我们为 AD 病理生理学的新视角提供了细胞基础，为个性化治疗的开发提供信息，针对 AD 或替代性大脑老化的个体的不同细胞群落。