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

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Abstract
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.
Funding Information
  • National Institute on Aging (R01AG074372)
  • National Institute of Environmental Health Sciences (R01ES032470)
  • National Institute of Diabetes and Digestive and Kidney Diseases (R01DK137993)

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