Spatial Multi-Omics in Alzheimer's Disease: A Multi-Dimensional Approach to Understanding Pathology and Progression.
Yixiao MaWenting ShiYahong DongYingjie SunQi-Guan JinPublished in: Current issues in molecular biology (2024)
Alzheimer's Disease (AD) presents a complex neuropathological landscape characterized by hallmark amyloid plaques and neurofibrillary tangles, leading to progressive cognitive decline. Despite extensive research, the molecular intricacies contributing to AD pathogenesis are inadequately understood. While single-cell omics technology holds great promise for application in AD, particularly in deciphering the understanding of different cell types and analyzing rare cell types and transcriptomic expression changes, it is unable to provide spatial distribution information, which is crucial for understanding the pathological processes of AD. In contrast, spatial multi-omics research emerges as a promising and comprehensive approach to analyzing tissue cells, potentially better suited for addressing these issues in AD. This article focuses on the latest advancements in spatial multi-omics technology and compares various techniques. Additionally, we provide an overview of current spatial omics-based research results in AD. These technologies play a crucial role in facilitating new discoveries and advancing translational AD research in the future. Despite challenges such as balancing resolution, increasing throughput, and data analysis, the application of spatial multi-omics holds immense potential in revolutionizing our understanding of human disease processes and identifying new biomarkers and therapeutic targets, thereby potentially contributing to the advancement of AD research.
Keyphrases
- single cell
- rna seq
- cognitive decline
- high throughput
- data analysis
- mild cognitive impairment
- poor prognosis
- stem cells
- endothelial cells
- healthcare
- induced apoptosis
- single molecule
- cell death
- risk assessment
- magnetic resonance imaging
- climate change
- health information
- endoplasmic reticulum stress
- cell cycle arrest
- big data