ArticleGenes & diseases2024
Gene interactions analysis of brain spatial transcriptome for Alzheimer's disease.
Article in Genes & diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Spatial2GWAS: a database for linking spatial transcriptomic regions with GWAS traits.Nucleic acids research · 2026Article
- Transcriptomic Association of COL3A1Pain research & management · 2026Article
- Spatial transcriptomics in Alzheimer's disease: technologies, challenges and discoveries.Molecular neurodegeneration advances · 2026Review
- Review
- Article
- Identification and validation of pyroptosis-related genes in Alzheimer's disease based on multi-transcriptome and machine learning.Frontiers in aging neuroscience · 2025Article
- Fibroblast heterogeneity and FN1-mediated signaling in endometriosis revealed by single-cell and spatial transcriptomics.Frontiers in immunology · 2025Article
- Targeting Matrix Metalloproteinase-9 to Alleviate T Cell Exhaustion and Improve Sepsis Prognosis.Research (Washington, D.C.) · 2025Article
- Identification of ARHGAP9 as a Key Diagnostic Marker for Abdominal Aortic Aneurysm by Multiomics and Experimental Validation.Human mutation · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Recent studies have explored the spatial transcriptomics patterns of Alzheimer's disease (AD) brain by spatial sequencing in mouse models, enabling the identification of unique genome-wide transcriptomic features associated with different spatial regions and pathological status. However, the dynamics of gene interactions that occur during amyloid-β accumulation remain largely unknown. In this study, we performed analyses on ligand-receptor communication, transcription factor regulatory network, and spot-specific network to reveal the dependence and the dynamics of gene associations/interactions on spatial regions and pathological status with mouse and human brains. We first used a spatial transcriptomics dataset of the
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