Berson Eloise, Perna Amalia, Bukhari Syed, Kim Yeasul, Xue Lei, Seong David, Mataraso Samson, Ghanem Marc, Chang Alan L, Montine Kathleen S, Keene C Dirk, Kasowski Maya, Aghaeepour Nima, Montine Thomas J
Department of Pathology, Stanford University, Stanford, CA 94305, USA.
Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA 94305, USA.
Brain. 2025 Aug 5. doi: 10.1093/brain/awaf285.
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.
神经系统疾病是由多种细胞类型复杂且尚不清楚的作用导致的，这对于揭示这些疾病背后的机制和确定特定的治疗靶点至关重要。单核技术推动了脑部疾病研究，但仍受限于其低核转录覆盖率、高成本和技术复杂性。为了解决这一问题，我们应用了一种基于Transformer的深度学习模型，该模型可从批量RNA测序中恢复细胞类型特异性的转录程序，显著优于先前的方法。这使得在复杂和异质性表型（如认知恢复力或大脑对阿尔茨海默病的抵抗力）中对细胞类型特异性转录组进行大规模且经济高效的研究成为可能。我们的分析确定星形胶质细胞是大脑皮质区域阿尔茨海默病恢复力的主要细胞介导者，而兴奋性神经元和少突胶质细胞前体细胞则是抵抗力的主要细胞介导者，维持突触功能并保护神经元健康。最后，我们表明我们的方法可以恢复整个组织的转录组，为探索超越单核数据的细胞特异性功能提供了一个无偏倚的框架。
