Single-cell machine learning uncovers genetically anchored, cell-type specific programs of Alzheimer's disease

Univariate differential expression analysis dominates single-cell transcriptomic studies of Alzheimer's disease, yet marginal testing alone cannot resolve multivariate disease-associated structure or distinguish clinical from neuropathological signatures. Here we introduce TriSCOPE, a framework integrating multivariate predictive modeling, donor-aware differential expression, and pseudotime inference, applied to six Alzheimer's disease snRNA-seq datasets comprising ~4.2 million nuclei from 627 donors. Across six major brain cell types and 41 subclusters, we identify cell-type-specific predictively expressed genes that are largely non-overlapping with differentially expressed genes, revealing disease-relevant signals invisible to univariate approaches. Conditional GWAS-eQTL colocalization anchors a subset of these predictively expressed genes to inherited Alzheimer's disease risk loci, including 27 genetically supported gene-cell type pairs (among them ARL17B, JAZF1) with no significant differential expression. Integration across all three analytical branches nominates high-confidence convergent programs including astrocytic stress markers (such as GFAP, MT2A), neuronal signaling genes (like RASGEF1B, LINGO1, NRG1), and microglial and oligodendrocyte regulators (including SYTL3, SPP1, APOD). Comparison of clinical and neuropathological models reveals cell-type-specific divergence between cognitive and pathological Alzheimer's disease signatures. Key predictively expressed genes including ARL17B, LINGO1, and APOE replicate across independent cohorts, with cross-cohort generalizability confirmed across all three analytical branches of TriSCOPE.