Integrated Single-Cell and System Network Analysis: Exploring Cellular Communication Network Complexity and Signal...

Down syndrome (DS) is a widespread chromosomal disorder primarily associated with cognitive impairment and progressive neurodegenerative changes. Clinically, age 50 years is considered a pivotal turning point in the health trajectory of individuals with DS. Before this age, they primarily face developmental challenges including significant cognitive deficits and difficulties in social interaction. However, as they age, they increasingly exhibit more severe neurodegenerative changes, including Alzheimer's disease (AD)-like cognitive decline and dementia symptoms. This study aimed to dissect intricate gene expression patterns in key neuronal cell types within the DS cerebral cortex and to examine how these patterns evolve with age. We conducted a detailed gene expression analysis of key neuronal cells, including inhibitory neurons, excitatory neurons, microglia, and oligodendrocyte progenitor cells, in individuals with DS. Additionally, the bioinformatics tool NeuronChat was employed to investigate the intercellular communication networks in the DS brain. Individuals with DS were divided into younger and older groups, with age 50 years as the boundary. Through comparative analysis, our findings indicated that aging in DS is associated with exacerbated neuronal dysfunction, decreased energy metabolism in microglia, and increased neurodegenerative traits in oligodendrocyte progenitor cells. Notably, compared to the control group, the DS brain showed increased complexity in cellular communication networks, reflecting an effort to maintain adaptability during syndrome progression. However, this increased complexity does not translate into effective signal transmission, suggesting significant disruptions in the function and structure of the neural network. This study provides a deeper understanding of cell function abnormalities and signal transmission irregularities in DS. By integrating single-cell and systemic network analyses, we revealed complex pathophysiological mechanisms, laying a foundational framework for developing new treatment methods. Our comprehensive analysis emphasizes the necessity for targeted strategies to address the multifaceted nature of DS pathogenesis and improve treatment outcomes.