Personalized Single-cell Transcriptomics Reveals Molecular Diversity in Alzheimer's Disease

Precision medicine for brain diseases faces many challenges, including understanding the heterogeneity of disease phenotypes. Such heterogeneity can be attributed to the variations in cellular and molecular mechanisms across individuals. However, personalized mechanisms remain elusive, especially at the single-cell level. To address this, the PsychAD project generated population-level single-nucleus RNA-seq data for 1,494 human brains with over 6.3 million nuclei covering diverse clinical phenotypes and neuropsychiatric symptoms (NPSs) in Alzheimer's disease (AD). Leveraging this data, we analyzed personalized single-cell functional genomics involving cell type interactions and gene regulatory networks. In particular, we developed a knowledge-guided graph neural network model to learn latent representations of functional genomics (embeddings) and quantify importance scores of cell types, genes, and their interactions for each individual. Our embeddings improved phenotype classifications and revealed potentially novel subtypes and population trajectories for AD progression, cognitive impairment, and NPSs. Our importance scores prioritized personalized functional genomic information and showed significant differences in regulatory mechanisms at cell type level across various phenotypes. Such information also allowed us to further identify subpopulation-level biological pathways, including ancestry for AD. Finally, we associated genetic variants with cell type-gene regulatory network changes across individuals, i.e., gene regulatory QTLs (grQTLs), providing novel functional genomic insights compared to existing QTLs. We further validated our results using external cohorts. Our analyses are available through iBrainMap, an open-source computational framework, and as a personalized functional genomic atlas for Alzheimer's Disease.