Phenotype Scoring of Population Scale Single-Cell Data Dissects Alzheimer’s Disease Complexity

The complexity of Alzheimer’s disease (AD) manifests in diverse clinical phenotypes, including cognitive impairment and neuropsychiatric symptoms (NPSs). However, the etiology of these phenotypes remains elusive. To address this, the PsychAD project generated a population-level single-nucleus RNA-seq dataset comprising over 6 million nuclei from the prefrontal cortex of 1,494 individual brains, covering a variety of AD-related phenotypes that capture cognitive impairment, severity of pathological lesions, and the presence of NPSs. Leveraging this dataset, we developed a deep learning framework, called Phenotype Associated Single Cell encoder (PASCode), to score single-cell phenotype associations, and identified ∼1.5 million phenotype associate cells (PACs). We compared PACs within 27 distinct brain cell subclasses and prioritized cell subpopulations and their expressed genes across various AD phenotypes, including the upregulation of a reactive astrocyte subtype with neuroprotective function in AD resilient donors. Additionally, we identified PACs that link multiple phenotypes, including a subpopulation of protoplasmic astrocytes that alter their gene expression and regulation in AD donors with depression. Uncovering the cellular and molecular mechanisms underlying diverse AD phenotypes has the potential to provide valuable insights towards the identification of novel diagnostic markers and therapeutic targets. All identified PACs, along with cell type and gene expression information, are summarized into an AD-phenotypic single-cell atlas for the research community.