Integrative mapping of spatial transcriptomic and amyloid pathology in Alzheimer's disease at single-cell resolution

Abstract

Alzheimers disease (AD) is a complex neurodegenerative disorder that affects millions of people worldwide. Despite decades of research, the underlying molecular and cellular changes of AD remain unresolved, especially in terms of the spatial structure of gene expression changes that correlates with pathology, e.g. amyloid beta (A-beta) plaques. Recent advances in imaging-or sequencing-based single-cell spatial transcriptomics have allowed a systematic dissection of molecular and cell architectures in the brain and other tissues. In this study, we employed the recently developed Stereo-seq technology to spatially profile the whole-genome transcriptomics in the 5xFAD mouse model and established the methodology to analyze the specific neuronal transcriptomic changes spatially correlated with amyloid pathology at single cell resolution. More specifically, we developed a pipeline for integrative image- and non-image-based cell segmentation, VoxelMorph-based non-linear alignment, and Unet-based object detection to achieve reliable transcriptomics analysis at the single-cell resolution, and investigated the spatial relationship between diverse neuronal clusters and A-beta depositions. This work has demonstrated the potential of using the Stereo-seq technology as a powerful tool to investigate AD and other complex neurological disorders.