02890nas a2200349   4500000000100000000000100001008004100002260001500043653004500058653002000103653003100123100001600154700001600170700003500186700001700221700001600238700001500254700002200269700001800291700002400309700002300333700001900356700002000375700001700395700001800412700001700430245010500447856005500552300000900607520191000616022001402526       2026                            d  c2026-09-2310aComputational biology and bioinformatics10aGene expression10aNeurodegenerative Diseases1 aChenfeng He1 aAthan Z. Li1 aKalpana Hanthanan Arachchilage1 aChirag Gupta1 aXiang Huang1 aXinyu Zhao1 aCarissa L. Sirois1 aKiran Girdhar1 aGeorgios Voloudakis1 aGabriel E. Hoffman1 aJaroslav Bendl1 aJohn F. Fullard1 aDonghoon Lee1 aPanos Roussos1 aDaifeng Wang00aAI-based characterization of Alzheimer’s disease phenotypes from population-scale single-cell data  uhttps://www.nature.com/articles/s41591-025-04128-1  a1-133 aThe complexity of Alzheimer’s disease (AD) manifests in diverse clinical phenotypes, including cognitive impairment and neuropsychiatric symptoms. However, the etiology of these phenotypes remains elusive. To address this, the PsychAD project generated a population-level single-nucleus RNA sequencing dataset comprising over 6 million nuclei from the prefrontal cortex of >1,000 individual brains, covering a variety of disease phenotypes. Here, leveraging this dataset, we developed a computational framework, called Phenotype Associated Single Cell encoder (PASCode), to score single-cell phenotype associations, and identified ∼1.5 million phenotype-associated cells (PACs) from 584 donors with AD-related phenotypes. PASCode ensembles multiple statistical methods into a graph neural model for robust scoring. Comparing PACs within 27 brain cell subclasses, we prioritized cell subpopulations and their expressed genes for various AD phenotypes. For instance, we identified microglia subpopulations implicated in AD pathology; reactive astrocyte subtypes with altered neuroprotective and neurotoxic gene expression that likely confer cognitive resilience; and enhanced excitatory/inhibitory imbalance and mitochondrial dysfunction in cognitively impaired AD donors. We also identified many PACs for multiple phenotypes, including the astrocytes between AD and depression showing specific gene expression patterns such as inflammation and endoplasmic reticulum stress pathways. These prioritized subpopulations, genes and pathways potentially offer valuable insights for precision diagnostic and therapeutic development. We also validated our findings in external population-scale datasets including AD and major depressive disorder, compiled an AD-phenotypic single-cell atlas and delivered the framework as an open-source tool with pre-trained models and a web application for community use.  a1546-170X