Deep learning-based cell type profiles reveal signatures of Alzheimer's disease resilience and resistance.

Neurological disorders result from the complex and poorly understood contributions of many cell types, essential for uncovering mechanisms behind these disorders and identifying specific therapeutic targets. Single-nucleus technologies have advanced brain disease research, but remain limited by their low nuclear transcriptional coverage, high cost, and technical complexity. To address this, we applied a transformer-based deep learning model that restores cell type-specific investigation transcriptional programs from bulk RNA-seq, significantly outperforming previous methods. This enables large-scale and cost-effective investigation of cell type-specific transcriptomes in complex and heterogeneous phenotypes such as cognitive resilience or brain resistance to Alzheimer's disease. Our analysis identified astrocytes as the major cell mediator of Alzheimer's disease resilience across cerebral cortex regions, while excitatory neurons and oligodendrocyte progenitor cells emerged as the major cell mediators of resistance, maintaining synaptic function and preserving neuron health. Finally, we show that our approach could restore the whole tissue transcriptome, offering an unbiased framework for exploring cell-specific functions beyond single nucleus data.