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Updated: Jun 20, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
Published on: September 5, 2025
Spatial transcriptomics in Alzheimer's disease: technologies, challenges and discoveries
Christina Huan Shi1, Juan C Piña-Crespo2, Kevin Y Yip2,1,3
1Center for Data Science and Artificial Intelligence, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037 USA.
Abstract:
Alzheimer's disease (AD) is a complex neurodegenerative disorder that is associated with cognitive decline in the elderly. While β-amyloid (Aβ) plaques and neurofibrillary tau tangles have been used to define and stage AD onset in human brain, how these pathologies can affect various cell types nearby remains a subject of intense interest in the field. Recent developments in spatial transcriptomic technology have seen accelerated growth, and spatial transcriptomic platforms have been used independently or together with single cell transcriptomic methods to characterize cellular changes in the AD brain from mouse models and human. Here, we review current-era spatial transcriptomic technologies, analytical pipelines and their implementation in AD research. We summarize findings from spatial transcriptomics in AD, and discuss limitations and challenges associated with various spatial transcriptomic platforms. The pathological hallmarks for AD were described by Alois Alzheimer over a century ago; from the convergence of a century of AD research and technological advances in imaging and transcriptomics, a new era in AD has emerged. Although current-era spatial platforms feature limitations and challenges, evolution of spatial transcriptomics and its combined implementation with other data modalities promises significant strides in AD and related neurodegenerative disorders.
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