ArticleComputational and structural biotechnology journal2022
Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Microphysiological models of human gastrointestinal diseases.Nature biomedical engineering · 2026Review
- Diffusion-based representation integration for foundation models improves spatial transcriptomics analysis.Bioinformatics (Oxford, England) · 2026Article
- GraphTME: graph-based framework for predicting immunotherapy response by interpreting tumour microenvironment interactions using spatial transcriptomics.NPJ systems biology and applications · 2026Article
- Spatial transcriptomics and artificial intelligence: a scoping review of emerging applications in head and neck pathology.Head and neck pathology · 2026Article
- Diffusion-based Representation Integration for Foundation Models Improves Spatial Transcriptomics Analysis.bioRxiv : the preprint server for biology · 2026Article
- stRGAT: identifying spatial domains in spatial transcriptomics via a relational graph attention network.Journal of translational medicine · 2026Article
- Artificial intelligence and machine learning-driven advancements in gastrointestinal cancer: Paving the way for precision medicine.World journal of gastroenterology · 2026Review
- MMSpa is a deep learning-based tool that enhances the identification of spatial domains in spatial transcriptomics studies.PLoS biology · 2026Article
- Relation equivariant graph neural networks to explore the mosaic-like tissue architecture of kidney diseases on spatially resolved transcriptomics.Bioinformatics (Oxford, England) · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Multimodal integration strategies for clinical application in oncology.Frontiers in pharmacology · 2025Review
- A contrastive learning approach to integrate spatial transcriptomics and histological images.Computational and structural biotechnology journal · 2024Article
- IAMSAM: image-based analysis of molecular signatures using the Segment Anything Model.Genome biology · 2024Article
- Hyperedge Representations with Hypergraph Wavelets: Applications to Spatial Transcriptomics.ArXiv · 2024Article
- Graph Fourier transform for spatial omics representation and analyses of complex organs.Nature communications · 2024Article
- Dissecting tumor microenvironment from spatially resolved transcriptomics data by heterogeneous graph learning.Nature communications · 2024Article
- A single-cell and spatial RNA-seq database for Alzheimer's disease (ssREAD).Nature communications · 2024Article
- Practical considerations for machine learning-enabled discoveries in spatial transcriptomics.GEN biotechnology · 2024Article
- A Single-cell and Spatial RNA-seq Database for Alzheimer's Disease (ssREAD).bioRxiv : the preprint server for biology · 2024Article
- A systematic analysis of deep learning in genomics and histopathology for precision oncology.BMC medical genomics · 2024Review
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19 authors.
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Abstract
Spatially resolved transcriptomics provides a new way to define spatial contexts and understand the pathogenesis of complex human diseases. Although some computational frameworks can characterize spatial context via various clustering methods, the detailed spatial architectures and functional zonation often cannot be revealed and localized due to the limited capacities of associating spatial information. We present RESEPT, a deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics. Given inputs such as gene expression or RNA velocity, RESEPT learns a three-dimensional embedding with a spatial retained graph neural network from spatial transcriptomics. The embedding is then visualized by mapping into color channels in an RGB image and segmented with a supervised convolutional neural network model. Based on a benchmark of 10x Genomics Visium spatial transcriptomics datasets on the human and mouse cortex, RESEPT infers and visualizes the tissue architecture accurately. It is noteworthy that, for the in-house AD samples, RESEPT can localize cortex layers and cell types based on pre-defined region- or cell-type-enriched genes and furthermore provide critical insights into the identification of amyloid-beta plaques in Alzheimer's disease. Interestingly, in a glioblastoma sample analysis, RESEPT distinguishes tumor-enriched, non-tumor, and regions of neuropil with infiltrating tumor cells in support of clinical and prognostic cancer applications.
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